Guaranteed quality of service in cloud computing environments

Systems, methods, apparatuses, and computer-readable media for guaranteed quality of service (QoS) in cloud computing environments. A workload related to an immutable log describing a transaction may be received. A determination is made based on the immutable log that a first compute node stores at least one data element to process the transaction. Utilization levels of computing resources of the first compute node may be determined. Utilization levels of links connecting the first compute node to the fabric may be determined. A determination may be made, based on the utilization levels, that processing the workload on the first compute node satisfies one or more QoS parameters specified in a service level agreement (SLA). The workload may be scheduled for processing on the first compute node based on the determination that processing the workload on the first compute node satisfies the one or more QoS parameters specified in the SLA.

TECHNICAL FIELD

Embodiments herein generally relate to cloud computing, and more specifically, to providing guaranteed quality of service in cloud computing environments.

BACKGROUND

Cloud computing services generally provide computing resources to a plurality of different customers. One challenge to cloud computing providers is the ability to ensure that Quality of Service (QoS) offered to customers meets guaranteed levels of QoS (e.g., in a service level agreement). Often, the vast amount of data flowing through the cloud computing environment poses significant challenges to delivering QoS.

SUMMARY

Embodiments disclosed herein provide systems, methods, articles of manufacture, and computer-readable media for guaranteed quality of service (QoS) in cloud computing environments. A workload comprising an immutable log describing a transaction may be received. A determination is made based on the immutable log that a first compute node stores at least one data element to process the transaction. Utilization levels of computing resources of the first compute node may be determined. Utilization levels of links connecting the first compute node to the fabric may be determined. A determination may be made, based on the utilization levels, that processing the workload on the first compute node satisfies one or more QoS parameters specified in a service level agreement (SLA). The workload may be scheduled for processing on the first compute node based on the determination that processing the workload on the first compute node satisfies the one or more QoS parameters specified in the SLA.

DETAILED DESCRIPTION

Embodiments disclosed herein provide techniques to support predictable quality of service (QoS) guarantees in cloud computing platforms. Massive amounts of data may be stored in a mass storage system using a hierarchy of caches to provide predictable performance. Performance may be defined using service level agreements (SLAs) which provide for a predictable amount of jitter. Embodiments disclosed herein may provide predictable performance using a non-blocking matrix switch that enables the transfer of large amounts of data with guaranteed jitter and predictability (as may be defined in the SLAs).

Programs may operate against the data, which may be stored using a flat model that is broken into small workloads that can be divided between the various hardware compute nodes. Because of the massive amount of data that can be stored, ongoing and/or recurring calculations, such as generating monthly financial statements, can be calculated based on the totality of data rather than persisting intermediary reports (e.g., using October end-of-month statements as the starting point for November operations). Some calculations may be made with limited accuracy, such that a user can request an answer within a short period of time (e.g., one second), at a certain accuracy (e.g., 99%), or both at a very high cost.

Generally, a workload to be processed may be received by the cloud computing platform which includes a plurality of hardware compute nodes communicably coupled via a fabric. The workload may be related to an immutable log for one or more transactions. A scheduler may determine where to place the workload for processing based on one or more heuristics that will meet the parameters defined by the SLA. For example, if a first compute node stores data needed to process the workload, the scheduler may determine to place the workload on the first compute node. As another example, if the first compute node is not suitable to accept the workload (e.g., because the computing resources of the first compute node are being utilized to process other workloads), the scheduler may place the workload on a second compute node that is proximate to the first compute node, thereby facilitating faster access to the needed data that is stored on the first compute node. As yet another example, if the communications link between the first and second compute nodes is saturated (and/or used at a level that exceeds a utilization threshold), the scheduler may place the workload on a third compute node that is proximate to the first compute node, where the communications link between the first and third compute nodes is not overutilized. Doing so allows the workload to be processed in a manner which satisfies the guarantees specified in the SLA.

FIG.1is a block diagram that provides an illustration of the hardware components of a data transmission network100, according to embodiments of the present technology. Data transmission network100is a specialized computer system that may be used for processing large amounts of data where a large number of computer processing cycles are required.

Data transmission network100may also include computing environment114. Computing environment114may be a specialized computer or other machine that processes the data received within the data transmission network100. Data transmission network100also includes one or more network devices102. Network devices102may include client devices that are capable of communicating with computing environment114. For example, network devices102may send data to the computing environment114to be processed, may send signals to the computing environment114to control different aspects of the computing environment or the data it is processing, among other reasons. Network devices102may interact with the computing environment114through a number of ways, such as, for example, over one or more networks108. As shown inFIG.1, computing environment114may include one or more other systems. For example, computing environment114may include a database system118and/or a communications grid120.

In other embodiments, network devices102may provide a large amount of data, either all at once or streaming over a period of time to the computing environment114via networks108. For example, network devices102may include network computers, sensors, databases, or other devices that may transmit or otherwise provide data to computing environment114. For example, network devices102may include local area network devices, such as routers, hubs, switches, or other computer networking devices. These devices may provide a variety of stored or generated data, such as network data or data specific to the network devices themselves. Network devices102may also include sensors that monitor their environment or other devices to collect data regarding that environment or those devices, and such network devices102may provide data they collect over time. Network devices102may also include devices within the internet of things (IoT), such as devices within a home automation network. Some of these devices may be referred to as edge devices, and may involve edge computing circuitry. Data may be transmitted by network devices directly to computing environment114or to network-attached data stores, such as network-attached data stores110for storage so that the data may be retrieved later by the computing environment114or other portions of data transmission network100.

Data transmission network100may also include one or more network-attached data stores110. Network-attached data stores110are used to store data to be processed by the computing environment114as well as any intermediate or final data generated by the computing system in non-volatile memory. However, in certain embodiments, the configuration of the computing environment114allows its operations to be performed such that intermediate and final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory (e.g., disk). This can be useful in certain situations, such as when the computing environment114receives ad hoc queries from a user and when responses, which are generated by processing large amounts of data, need to be generated on-the-fly. In this non-limiting situation, the computing environment114may be configured to retain the processed information within memory so that responses can be generated for the user at different levels of detail as well as allow a user to interactively query against this information.

Network-attached data stores110may store a variety of different types of data organized in a variety of different ways and from a variety of different sources. For example, network-attached data storage may include storage other than primary storage located within computing environment114that is directly accessible by processors located therein. Network-attached data storage may include secondary, tertiary or auxiliary storage, such as large hard drives, servers, virtual memory, among other types. Storage devices may include portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing data. A machine-readable storage medium or computer-readable storage medium may include a non-transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals. Examples of a non-transitory medium may include, for example, a magnetic disk or tape, optical storage media such as compact disk or digital versatile disk, flash memory, memory or memory devices. A computer-program product may include code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, among others. Furthermore, the data stores may hold a variety of different types of data. For example, network-attached data stores110may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales).

The unstructured data may be presented to the computing environment114in different forms such as a flat file or a conglomerate of data records, and may have data values and accompanying time stamps. The computing environment114may be used to analyze the unstructured data in a variety of ways to determine the best way to structure (e.g., hierarchically) that data, such that the structured data is tailored to a type of further analysis that a user wishes to perform on the data. For example, after being processed, the unstructured time stamped data may be aggregated by time (e.g., into daily time period units) to generate time series data and/or structured hierarchically according to one or more dimensions (e.g., parameters, attributes, and/or variables). For example, data may be stored in a hierarchical data structure, such as a ROLAP OR MOLAP database, or may be stored in another tabular form, such as in a flat-hierarchy form.

Data transmission network100may also include one or more server farms106. Computing environment114may route select communications or data to the one or more sever farms106or one or more servers within the server farms. Server farms106can be configured to provide information in a predetermined manner. For example, server farms106may access data to transmit in response to a communication. Server farms106may be separately housed from each other device within data transmission network100, such as computing environment114, and/or may be part of a device or system.

Server farms106may host a variety of different types of data processing as part of data transmission network100. Server farms106may receive a variety of different data from network devices, from computing environment114, from cloud network116, or from other sources. The data may have been obtained or collected from one or more sensors, as inputs from a control database, or may have been received as inputs from an external system or device. Server farms106may assist in processing the data by turning raw data into processed data based on one or more rules implemented by the server farms. For example, sensor data may be analyzed to determine changes in an environment over time or in real-time.

Data transmission network100may also include one or more cloud networks116. Cloud network116may include a cloud infrastructure system that provides cloud services. In certain embodiments, services provided by the cloud network116may include a host of services that are made available to users of the cloud infrastructure system on demand. Cloud network116shown inFIG.1as being connected to computing environment114(and therefore having computing environment114as its client or user), but cloud network116may be connected to or utilized by any of the devices inFIG.1. Services provided by the cloud network can dynamically scale to meet the needs of its users. The cloud network116may comprise one or more computers, servers, and/or systems. In some embodiments, the computers, servers, and/or systems that make up the cloud network116are different from the user's own on-premises computers, servers, and/or systems. For example, the cloud network116may host an application, and a user may, via a communication network such as the Internet, on demand, order and use the application.

While each device, server and system inFIG.1is shown as a single device, it will be appreciated that multiple devices may instead be used. For example, a set of network devices can be used to transmit various communications from a single user, or remote server140may include a server stack. As another example, data may be processed as part of computing environment114.

Each communication within data transmission network100(e.g., between client devices, between a device and connection management system150, between servers106and computing environment114or between a server and a device) may occur over one or more networks108. Networks108may include one or more of a variety of different types of networks, including a wireless network, a wired network, or a combination of a wired and wireless network. Examples of suitable networks include the Internet, a personal area network, a local area network (LAN), a wide area network (WAN), or a wireless local area network (WLAN). A wireless network may include a wireless interface or combination of wireless interfaces. As an example, a network in the one or more networks108may include a short-range communication channel, such as a Bluetooth or a Bluetooth Low Energy channel. A wired network may include a wired interface. The wired and/or wireless networks may be implemented using routers, access points, bridges, gateways, or the like, to connect devices in the network108, as will be further described with respect toFIG.2. The one or more networks108can be incorporated entirely within or can include an intranet, an extranet, or a combination thereof. In one embodiment, communications between two or more systems and/or devices can be achieved by a secure communications protocol, such as secure sockets layer (SSL) or transport layer security (TLS). In addition, data and/or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things (e.g., machines, devices, phones, sensors) can be connected to networks and the data from these things can be collected and processed within the things and/or external to the things. For example, the IoT can include sensors in many different devices, and high value analytics can be applied to identify hidden relationships and drive increased efficiencies. This can apply to both big data analytics and real-time analytics. This will be described further below with respect toFIG.2.

As noted, computing environment114may include a communications grid120and a transmission network database system118. Communications grid120may be a grid-based computing system for processing large amounts of data. The transmission network database system118may be for managing, storing, and retrieving large amounts of data that are distributed to and stored in the one or more network-attached data stores110or other data stores that reside at different locations within the transmission network database system118. The compute nodes in the grid-based computing system120and the transmission network database system118may share the same processor hardware, such as processors that are located within computing environment114.

FIG.2illustrates an example network including an example set of devices communicating with each other over an exchange system and via a network, according to embodiments of the present technology. As noted, each communication within data transmission network100may occur over one or more networks. System200includes a network device204configured to communicate with a variety of types of client devices, for example client devices230, over a variety of types of communication channels.

As shown inFIG.2, network device204can transmit a communication over a network (e.g., a cellular network via a base station210). The communication can be routed to another network device, such as network devices205-209, via base station210. The communication can also be routed to computing environment214via base station210. For example, network device204may collect data either from its surrounding environment or from other network devices (such as network devices205-209) and transmit that data to computing environment214.

Although network devices204-209are shown inFIG.2as a mobile phone, laptop computer, tablet computer, temperature sensor, motion sensor, and audio sensor respectively, the network devices may be or include sensors that are sensitive to detecting aspects of their environment. For example, the network devices may include sensors such as water sensors, power sensors, electrical current sensors, chemical sensors, optical sensors, pressure sensors, geographic or position sensors (e.g., GPS), velocity sensors, acceleration sensors, flow rate sensors, among others. Examples of characteristics that may be sensed include force, torque, load, strain, position, temperature, air pressure, fluid flow, chemical properties, resistance, electromagnetic fields, radiation, irradiance, proximity, acoustics, moisture, distance, speed, vibrations, acceleration, electrical potential, electrical current, among others. The sensors may be mounted to various components used as part of a variety of different types of systems (e.g., a financial operation). The network devices may detect and record data related to the environment that it monitors, and transmit that data to computing environment214.

In another example, another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a home automation or similar automated network in a different environment, such as an office space, school, public space, sports venue, or a variety of other locations. Network devices in such an automated network may include network devices that allow a user to access, control, and/or configure various home appliances located within the user's home (e.g., a television, radio, light, fan, humidifier, sensor, microwave, iron, and/or the like), or outside of the user's home (e.g., exterior motion sensors, exterior lighting, garage door openers, sprinkler systems, or the like). For example, network device202may include a home automation switch that may be coupled with a home appliance. In another embodiment, a network device can allow a user to access, control, and/or configure devices, such as office-related devices (e.g., copy machine, printer, or fax machine), audio and/or video related devices (e.g., a receiver, a speaker, a projector, a DVD player, or a television), media-playback devices (e.g., a compact disc player, a CD player, or the like), computing devices (e.g., a home computer, a laptop computer, a tablet, a personal digital assistant (PDA), a computing device, or a wearable device), lighting devices (e.g., a lamp or recessed lighting), devices associated with a security system, devices associated with an alarm system, devices that can be operated in an automobile (e.g., radio devices, navigation devices), and/or the like. Data may be collected from such various sensors in raw form, or data may be processed by the sensors to create parameters or other data either developed by the sensors based on the raw data or assigned to the system by a client or other controlling device.

In another example, another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a power or energy grid. A variety of different network devices may be included in an energy grid, such as various devices within one or more power plants, energy farms (e.g., wind farm, solar farm, among others) energy storage facilities, factories, homes and businesses of consumers, among others. One or more of such devices may include one or more sensors that detect energy gain or loss, electrical input or output or loss, and a variety of other efficiencies. These sensors may collect data to inform users of how the energy grid, and individual devices within the grid, may be functioning and how they may be made more efficient.

Network device sensors may also perform processing on data it collects before transmitting the data to the computing environment214, or before deciding whether to transmit data to the computing environment214. For example, network devices may determine whether data collected meets certain rules, for example by comparing data or values computed from the data and comparing that data to one or more thresholds. The network device may use this data and/or comparisons to determine if the data should be transmitted to the computing environment214for further use or processing.

Computing environment214may include machines220and240. Although computing environment214is shown inFIG.2as having two machines,220and240, computing environment214may have only one machine or may have more than two machines. The machines that make up computing environment214may include specialized computers, servers, or other machines that are configured to individually and/or collectively process large amounts of data. The computing environment214may also include storage devices that include one or more databases of structured data, such as data organized in one or more hierarchies, or unstructured data. The databases may communicate with the processing devices within computing environment214to distribute data to them. Since network devices may transmit data to computing environment214, that data may be received by the computing environment214and subsequently stored within those storage devices. Data used by computing environment214may also be stored in data stores235, which may also be a part of or connected to computing environment214.

Computing environment214can communicate with various devices via one or more routers225or other inter-network or intra-network connection components. For example, computing environment214may communicate with devices230via one or more routers225. Computing environment214may collect, analyze and/or store data from or pertaining to communications, client device operations, client rules, and/or user-associated actions stored at one or more data stores235. Such data may influence communication routing to the devices within computing environment214, how data is stored or processed within computing environment214, among other actions.

Notably, various other devices can further be used to influence communication routing and/or processing between devices within computing environment214and with devices outside of computing environment214. For example, as shown inFIG.2, computing environment214may include a machine240, such as a web server. Thus, computing environment214can retrieve data of interest, such as client information (e.g., product information, client rules, etc.), technical product details, news, current or predicted weather, and so on.

In addition to computing environment214collecting data (e.g., as received from network devices, such as sensors, and client devices or other sources) to be processed as part of a big data analytics project, it may also receive data in real time as part of a streaming analytics environment. As noted, data may be collected using a variety of sources as communicated via different kinds of networks or locally. Such data may be received on a real-time streaming basis. For example, network devices may receive data periodically from network device sensors as the sensors continuously sense, monitor and track changes in their environments. Devices within computing environment214may also perform pre-analysis on data it receives to determine if the data received should be processed as part of an ongoing project. The data received and collected by computing environment214, no matter what the source or method or timing of receipt, may be processed over a period of time for a client to determine results data based on the client's needs and rules.

FIG.3illustrates a conceptual overview of a system300that may generally be representative of a distributed cloud-based computing system or another type of computing network in that one or more techniques described herein may be implemented according to various embodiments. As shown inFIG.3, system300may generally include computing resources (CRs)302-y, where y is any positive integer, to compute information and data. The computing resources302may include resources of multiple types, such as—for example—processors, co-processors, fully-programmable gate arrays (FPGAs), memory, networking equipment, circuit boards, storage, and other computing equipment. The embodiments are not limited to these examples.

The computing resources302may be included as part of a computer, such as a server, server farm, blade server, a server sled, or any other type of server or computing device, and may be within one or more racks304. In embodiments, the racks304may be part of one or more data centers308and may be coupled with each other via various networking equipment. For example, the racks304within a data center308may be coupled with each via a fabric303. The fabric303may include a combination of electrical and/or optical signaling media, and high bandwidth interconnects, such as Gigabit Ethernet, 10 Gigabit Ethernet, 100 Gigabit Ethernet, InfiniBand, Peripheral Component Interconnect (PCI) Express (PCIe), and so forth. Further, the fabric303may include switching infrastructure, such as switches, routers, gateways, and so forth. The fabric303is configured such that any rack304may send signals to (and receive signals from) each other racks304within a data center308to communicate data and information. In embodiments, the fabric303may be coupled with networking infrastructure305such that it enables communication of signals between racks of one data center308with racks304of another data center308to communicate data and information. For example,FIG.3illustrates racks304of data center308-1coupled with racks304of data center308-2via fabrics303, networking infrastructure305, and the cloud-based infrastructure307. The cloud-based infrastructure307illustratively includes a cloud controller309. Although depicted as a component of the cloud-based infrastructure307, the fabrics303, racks304, networking infrastructure305, and data centers308may each include an instance of the controller309. The controller309may comprise software, hardware, and/or a combination of software and hardware.

In embodiments, the networking infrastructure305includes networking equipment, such as routers, firewalls, switches, gateways, cabling, and so forth to communicate data and information between a data center308and with the cloud-based infrastructure307and another data center308. For example, the networking infrastructure305may include edge access routers, edge access switches, and edge firewalls capable of communicating with core routers, core switches, and core firewalls of the cloud-based infrastructure307. The core networking equipment of the cloud-based infrastructure307may couple with edge networking equipment of another data center308to enable communication between data centers308. Note that embodiments are not limited in this manner, and the networking infrastructure305and/or cloud-based infrastructure307may include other networking equipment, servers, relays, interconnects, and so forth to enable communication between a data center308and other data centers308.

In one example, the system300may be a distributed cloud-based computing system to provide a financial service platform. The system300may process data and information, such as financial data and financial information, to provide financial services, for example. The financial services include, but are not limited to, investment and asset management services, active equity management services, active quantitative equity services, cash fund services, alternatives services, currency management services, index investing services, electronic trading services, multi-asset services, investment research services, investment trading services, accounting services, custody services, fund administration services, outsourcing services, performance measurement services, portfolio analysis services, data analytics services, investment analytics services, benchmark/indices/indicator services, D-as-a-Service (DaaS) services, and so forth. Embodiments are not limited to these examples. To provide these financial services, the controller309may dynamically pool or compose a plurality of the computing resources302together within a data center308and/or among data centers308in the cloud via the cloud-based infrastructure307. In one example, computing resources302of data center308-1may be composed with computing resources302of data center308-2to process data and information, e.g., a workload, to provide a financial service. Once the workload completes, the controller309may decompose the composed computing resources302and make the computing resources302available to process another workload. Note that in embodiments, the system300may enable multiple instances of pooled or composed computing resources302to provide data and information processing in parallel and embodiments are not limited in this manner.

In embodiments, system300may be coupled with one or more other systems, such as investment trading systems, banking systems, regulatory systems, risk management systems, performance systems, accounting system, data warehouse systems, financial institution system, and so forth. These other systems may be coupled with system300via networking, such as the networking infrastructure305and the cloud-based infrastructure. Embodiments are not limited in this manner.

The controller309may be used to provide access to resources in the system300. For example, the controller309may control access to the fabrics303, racks304, networking infrastructure305, and data centers308. Furthermore, when a workload is received for processing in the system300, the controller309may select one or more computing resources302to process the workload. For example, a workload may be defined by one or more entries in an immutable log. The immutable log may store data that are related to one or more transactions to be processed by the system300. For example, the immutable log may specify an account identifier of an account, an asset (e.g., a stock, ETF, mutual fund, etc.) identifier subject to the transaction, a transaction type (e.g., buy, sell, etc.), and any other data describing the transaction. To process the transaction, the system300may generally need to determine the current price of the asset, process the transaction according to the determined price, update the account balances, and update the position holdings of the account in the asset.

The system300may process transactions according to one or more service level agreements (SLAs). The SLAs may define quality of service (QoS) parameters that must be fulfilled by the system300when processing transactions. For example, the QoS parameters may specify guaranteed latency, jitter, bandwidth, transaction processing times, and the like. More generally, however, because the system300processes large numbers of transactions for a large number of clients, the system300must attempt to process each transaction as quickly as possible. Therefore, the controller309is configured to schedule received workloads in the system300in a manner that satisfies the QoS guarantees in the SLAs.

In some embodiments, the controller309may consider the CRs302as an n by m matrix of compute nodes, where n and m are any positive integer. For example, each compute node (physical and/or virtual) may be considered a point in the matrix, and the connections between each point in the matrix correspond to a network link in the fabric303. Doing so allows the controller309to consider the matrix when scheduling workloads for processing in the system300such that the processing conforms with the QoS guarantees in the SLA for a given client.

Generally, to schedule workloads, the controller309may consider a plurality of heuristics. For example, the controller309may consider utilization levels of the computing resources302. For example, for a given compute node and/or virtual machine executing on a compute node, the controller309may determine processor (CPU) utilization, memory utilization, storage utilization, network I/O utilization, and utilization of any other resource of the compute node. Generally, the controller309may select CRs302that have the lowest levels of utilization. Furthermore, the controller309may consider the utilization of the network links of the fabric303connected to each CR302. For example, the controller309may consider the bandwidth utilization, throughput, latency, jitter, and any other aspect of the links of the fabric303. More generally, the controller309may select nodes having the links with the lowest levels of utilization. Furthermore, the controller309may analyze the workload to determine what data stored by the system300is needed to process the transaction. For example, CRs302-13may store the current price of a stock that is specified in an immutable log for the transaction, while CRs302-1may store stale price data of the stock (e.g., last month's price of the stock). Therefore, the controller309may determine to place the workload on or near CRs302-13to provide faster access to the pricing data needed to process the trade. Similarly, the controller309may determine that the workload need not be placed near CRs302-1, as CRs302-1include pricing data that will not be used to process the transaction. Further still, the controller309may determine which CRs302have the greatest number of links to the needed data. Doing so may ensure that alternate routes to the data exist if one or more other links become saturated.

Therefore, to schedule a workload in compliance with the QoS guarantees, the controller309may determine which CRs302have the lowest levels of resource utilization, which CRs302were least recently used, which CRs302have network links have the lowest levels of utilization, which CRs302store data that is most frequently accessed to process transactions, which CRs302include and/or are located nearest to the data required to process the transaction, and/or which CRs302have the most links in the fabric303to access the data required to process the transaction. Furthermore, the controller309may consider the impact that processing the workload will have on the system300. For example, if placing the workload on CRs302-5would saturate the links to CRs302-5in the fabric303, the controller309may refrain from placing the workload on CRs302-5.

In some embodiments, the controller309may determine whether the utilization of the CRs302and/or the links in the fabric303exceed a respective threshold. For example, if the current and/or estimated use of the processors of a compute node is 80% and a processor use threshold is 75%, the controller309may determine to forego deploying a workload (and/or a portion thereof) to the compute node. As another example, if 70% of the memory of a compute node is currently utilized (and/or estimated to be utilized while processing the workload), and the memory use threshold is 60%, the controller309may forego deploying a workload (and/or a portion thereof) to the compute node. As another example, a compute node may have 10 network links in the fabric303to the data needed to process a transaction. If deploying the workload to the compute node would saturate all 10 links, the controller309may determine to forego deploying a workload (and/or a portion thereof) to the compute node. Instead, the controller309may determine to deploy the workload to a compute node that has more links to the needed data and/or links that will not be saturated (and/or utilized beyond a threshold utilization level) by processing the workload. As another example, if a network switch of the compute node is utilized beyond a threshold utilization, the controller309may determine to forego deploying a workload to the compute node. If, however, the network switch is not utilized beyond the threshold, the controller309may deploy the workload (and/or a portion thereof) to the compute node. As another example, the controller309may estimate an amount of time required to process the workload on the CRs302in light of the resource and/or fabric utilizations and determine whether the estimated time exceeds a guaranteed processing time in the SLA. If the estimated time to process the workload does not exceed the guaranteed processing time specified in the SLA, the controller309may deploy the workload (and/or a portion thereof) to the CRs302.

In some embodiments, the controller309may maintain a log310describing each received workload and/or transaction. The log310may include entries specifying where each transaction is deployed for processing, what data the transaction accessed, and the results of processing each transaction (e.g., whether the amount of time required to process the transaction satisfied and/or violated the QoS guarantees). The log310may comprise a model which may process a transaction to produce a generate distribution specifying how the processing of the transaction will access data and/or communicate data within the system300. For example, an ETF may comprise a plurality of stocks. The log310may specify that processing trades of the ETF may require access to data describing each stock in the ETF. Therefore, the controller309may determine to generate a grouping of the CRs302(e.g., one or more compute nodes) to process the ETF transaction. The grouping of CRs302may generally include the shortest number of paths in the fabric303to the data needed to process the transaction (e.g., by selecting CRs302that are nearest to the data describing each stock in the ETF). However, the grouping of CRs302may be defined by a radius that ensures the links in the fabric303will not become oversaturated when processing the ETF transaction.

In some embodiments, the controller309may compute a score for each of the CRs302and select one or more of the CRs302having the highest score to process a workload (or a portion thereof). The score may generally reflect the most suitable CRs302for processing the workload in light of the QoS requirements. For example, each score may be compared to threshold. If the score computed for a given CR302does not exceed the threshold, the controller309may determine to not deploy the workload (or a portion thereof) to the CR302. If the score exceeds the threshold, the controller309may deploy the workload (or a portion thereof) to the CR302. The controller309may use any suitable function to compute a score for the workload, where the function considers one or more of the current and forecast utilization of the CRs309, the current and forecast utilization of the links of the fabric303to each CR309, the distance (e.g., a number of network hops) of each CR309to the data needed to the workload, and the like.

In some embodiments, the controller309may break up the workload into smaller workloads (or subunits) and schedule each subunit of the workload on one or more CRs302to ensure that each subunit is processed in a manner that satisfies the QoS guarantees. The controller309may generally schedule each subunit according to the heuristics described above to ensure that each subunit is scheduled for processing according to the QoS guarantees. Furthermore, in some embodiments, the controller309may consider a grouping of CRs302when determining to deploy a workload. In such embodiments, the controller309considers whether the current and/or planned utilization of the grouped CRs302exceeds a threshold when deploying workloads. Similarly, the controller309may consider the number of links in the fabric303of the grouped CRs302as well as the use of the links in the fabric303when determining to deploy a workload to the grouped CRs302.

FIG.4depicts a block diagram describing exemplary logic and data flows through a computing architecture.

Data402may be received in a data storage404. The data402may include discrete units of data and/or one or more data streams (e.g., communication channels that repeatedly provide one or more data units at a given rate). The data402may include, for example, data relating to an individual user (e.g., a financial client), aggregate data (e.g., reflecting conditions in a market, such as a financial market), cancelations of previously-received data, corrections of previously-received data, etc. In some cases, cancelations and/or corrections may be received that cancels or corrects other data items that have not yet been received (e.g., due to the order in which the data was transmitted or batched, network conditions, data losses, etc.).

The data storage404may include hardware, software, or a combination of hardware and software suitable for storing data. The data storage404may include one or more data structures, such as the log310or a database. The data structures may be configured to store and organize the data, and/or to facilitate retrieval of the data. In some embodiments, the entries in the log310may be organized chronologically (e.g., in the order in which the data402was received by the data storage404, in a time-stamp order of the data402, etc.). In some embodiments, the log310is a persistent and/or immutable log which allows individual data records to be written, but not to be directly deleted or changed. In some embodiments, the immutable log310specifies one or more transactions for processing. The controller309may schedule the transactions specified in the immutable log310for processing in compliance with QoS guarantees as described above.

In some embodiments, the data402stored in the data storage404may be subjected to one or more filters408. The filters408may include data governance filters which, for example, match one or more rules against the data402and selectively pass the data402to other components in the architecture.

The data storage404and/or the filter(s)408may provide information to a machine learning model410, such as an artificial neural network (ANN). The underlying model410may be configured to learn associations from patterns in the data402, to predict future trends based on historical data observations, and to provide insights into why the data402appears the way that it does.

A library414of microservices412-imay make use the data (e.g., the raw data stored in the data storage404, the filtered data as presented by the filters408, information output from the machine learning model410, or various combinations of these types of data). Each microservice412-imay represent an atomic computing unit configured to perform a defined task (e.g., computing a value for a financial variable for certain subsets of the data402). The microservices412-imay be used individually, or variously combined into macroservices416-i. The macroservices416-imay represent more complex operations in which the outputs of various microservices412-iare combined or otherwise used to perform a specified task.

For instance, one macroservice416-1may use the outputs of various microservices412-ito generate a report418(such as a financial report, disclosure form, etc.). In another example, an entity (such as a financial regulator) may issue a request420via a macroservice416-2, and the microservice416-2may perform various operations to comply with the request (e.g., calling on another macroservice416-1to generate a report responsive to the request420, correcting data402in the data storage404, etc.). In some embodiments, macroservices416-imay be combined to form other macroservices416-i.

The microservices412-iand/or the macroservices416-imay be exposed to a third party (e.g., by use of an application programming interface, or “API”). In some cases, a single entity may provide the microservices412-iand the macroservices416-i. In other cases, one entity may provide the library414of microservices412-i, and another entity may use the microservices414to generate their own customized macroservices416-i.

The machine learning model410may be generated and/or refined via a machine learning process, such as the one depicted in the flow chart ofFIG.4. Machine learning is a branch of artificial intelligence that relates to mathematical models that can learn from, categorize, and make predictions about data. Such mathematical models, which can be referred to as machine-learning models, can classify input data among two or more classes; cluster input data among two or more groups; predict a result based on input data; identify patterns or trends in input data; identify a distribution of input data in a space; or any combination of these. Examples of machine-learning models can include (i) neural networks; (ii) decision trees, such as classification trees and regression trees; (iii) classifiers, such as Naïve bias classifiers, logistic regression classifiers, ridge regression classifiers, random forest classifiers, least absolute shrinkage and selector (LASSO) classifiers, and support vector machines; (iv) clusterers, such as k-means clusterers, mean-shift clusterers, and spectral clusterers; (v) factorizers, such as factorization machines, principal component analyzers and kernel principal component analyzers; and (vi) ensembles or other combinations of machine-learning models. In some examples, neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks, convolutional neural networks, radial basis function (RBF) neural networks, echo state neural networks, long short-term memory neural networks, bi-directional recurrent neural networks, gated neural networks, hierarchical recurrent neural networks, stochastic neural networks, modular neural networks, spiking neural networks, dynamic neural networks, cascading neural networks, neuro-fuzzy neural networks, or any combination of these.

Different machine-learning models410may be used interchangeably to perform a task. Examples of tasks that can be performed at least partially using machine-learning models include various types of scoring; workload placement; transaction analysis; bioinformatics; cheminformatics; software engineering; fraud detection; customer segmentation; generating online recommendations; adaptive websites; determining customer lifetime value; search engines; placing advertisements in real time or near real time; classifying DNA sequences; affective computing; performing natural language processing and understanding; object recognition and computer vision; robotic locomotion; playing games; optimization and metaheuristics; detecting network intrusions; medical diagnosis and monitoring; or predicting when an asset, such as a machine, will need maintenance.

Machine-learning models can be constructed through an at least partially automated (e.g., with little or no human involvement) process called training. During training, input data can be iteratively supplied to a machine-learning model to enable the machine-learning model to identify patterns related to the input data or to identify relationships between the input data and output data. With training, the machine-learning model can be transformed from an untrained state to a trained state. Input data can be split into one or more training sets and one or more validation sets, and the training process may be repeated multiple times. The splitting may follow a k-fold cross-validation rule, a leave-one-out-rule, a leave-p-out rule, or a holdout rule. An overview of training and using a machine-learning model is described below with respect to the flow chart ofFIG.5.

In block502, training data is received. In some examples, the training data is received from a remote database or a local database, constructed from various subsets of data, or input by a user. The training data can be used in its raw form for training a machine-learning model or pre-processed into another form, which can then be used for training the machine-learning model. For example, the raw form of the training data can be smoothed, truncated, aggregated, clustered, or otherwise manipulated into another form, which can then be used for training the machine-learning model. In one example, the training data comprises a transaction log maintained by the controller309that describes each of a plurality of transactions scheduled for processing by the controller309.

In block504, a machine-learning model is trained using the training data. The machine-learning model can be trained in a supervised, unsupervised, or semi-supervised manner. In supervised training, each input in the training data is correlated to a desired output. This desired output may be a scalar, a vector, or a different type of data structure such as text or an image. This may enable the machine-learning model to learn a mapping between the inputs and desired outputs. In unsupervised training, the training data includes inputs, but not desired outputs, so that the machine-learning model has to find structure in the inputs on its own. In semi-supervised training, only some of the inputs in the training data are correlated to desired outputs.

In block506, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database. The evaluation dataset can include inputs correlated to desired outputs. The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine-learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy. Otherwise, the machine-learning model may have a low degree of accuracy. The 90% number is an example only. A realistic and desirable accuracy percentage is dependent on the problem and the data.

In some examples, if the machine-learning model has an inadequate degree of accuracy for a particular task, the process can return to block504, where the machine-learning model can be further trained using additional training data or otherwise modified to improve accuracy. If the machine-learning model has an adequate degree of accuracy for the particular task, the process can continue to block508.

In block508, new data is received. In some examples, the new data is received from a remote database or a local database, constructed from various subsets of data, or input by a user. The new data may be unknown to the machine-learning model. For example, the machine-learning model may not have previously processed or analyzed the new data. The new data may comprise a new transaction for scheduling by the controller309.

In block510, the trained machine-learning model is used to analyze the new data and provide a result. For example, the new data, such as the workload (and/or the immutable log describing the transactions of the workload) can be provided as input to the trained machine-learning model. The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these. For example, the trained model may output one or more CRs302to process one or more portions of the workload. The controller309may then schedule the one or more portions of the workload on the CRs302outputted by the model.

In block512, the result is post-processed. For example, the result can be added to, multiplied with, or otherwise combined with other data as part of a workload. As another example, the result can be transformed from a first format, such as a time series format, into another format, such as a count series format. Any number and combination of operations can be performed on the result during post-processing.

A more specific example of a machine-learning model is the neural network600shown inFIG.6. The neural network600is represented as multiple layers of interconnected neurons, such as neuron608, that can exchange data between one another. The layers include an input layer602for receiving input data, a hidden layer604, and an output layer606for providing a result. The hidden layer604is referred to as hidden because it may not be directly observable or have its input directly accessible during the normal functioning of the neural network600. Although the neural network600is shown as having a specific number of layers and neurons for exemplary purposes, the neural network600can have any number and combination of layers, and each layer can have any number and combination of neurons.

The neurons and connections between the neurons can have numeric weights, which can be tuned during training. For example, training data can be provided to the input layer602of the neural network600, and the neural network600can use the training data to tune one or more numeric weights of the neural network600.

In some examples, the neural network600can be trained using backpropagation. Backpropagation can include determining a gradient of a particular numeric weight based on a difference between an actual output of the neural network600and a desired output of the neural network600. Based on the gradient, one or more numeric weights of the neural network600can be updated to reduce the difference, thereby increasing the accuracy of the neural network600. This process can be repeated multiple times to train the neural network600. For example, this process can be repeated hundreds or thousands of times to train the neural network600.

In some examples, the neural network600is a feed-forward neural network. In a feed-forward neural network, every neuron only propagates an output value to a subsequent layer of the neural network600. For example, data may only move one direction (forward) from one neuron to the next neuron in a feed-forward neural network.

In other examples, the neural network600is a recurrent neural network. A recurrent neural network can include one or more feedback loops, allowing data to propagate in both forward and backward through the neural network600. This can allow for information to persist within the recurrent neural network. For example, a recurrent neural network can determine an output based at least partially on information that the recurrent neural network has seen before, giving the recurrent neural network the ability to use previous input to inform the output.

In some examples, the neural network600operates by receiving a vector of numbers from one layer; transforming the vector of numbers into a new vector of numbers using a matrix of numeric weights, a nonlinearity, or both; and providing the new vector of numbers to a subsequent layer of the neural network600. Each subsequent layer of the neural network600can repeat this process until the neural network600outputs a final result at the output layer606. For example, the neural network600can receive a vector of numbers as an input at the input layer602. The neural network600can multiply the vector of numbers by a matrix of numeric weights to determine a weighted vector. The matrix of numeric weights can be tuned during the training of the neural network600. The neural network600can transform the weighted vector using a nonlinearity, such as a sigmoid tangent or the hyperbolic tangent. In some examples, the nonlinearity can include a rectified linear unit, which can be expressed using the following equation:
y=max(x,0)  Equation 1

In Equation 1, y is the output and x is an input value from the weighted vector. The transformed output can be supplied to a subsequent layer, such as the hidden layer604, of the neural network600. The subsequent layer of the neural network600can receive the transformed output, multiply the transformed output by a matrix of numeric weights and a nonlinearity, and provide the result to yet another layer of the neural network600. This process continues until the neural network600outputs a final result at the output layer606.

Other examples of the present disclosure may include any number and combination of machine-learning models having any number and combination of characteristics. The machine-learning model(s) can be trained in a supervised, semi-supervised, or unsupervised manner, or any combination of these. The machine-learning model(s) can be implemented using a single computing device or multiple computing devices, such as the communications system discussed herein.

Implementing some examples of the present disclosure at least in part by using machine-learning models can reduce the total number of processing iterations, time, memory, electrical power, or any combination of these consumed by a computing device when analyzing data. For example, a neural network may more readily identify patterns in data than other approaches. This may enable the neural network to analyze the data using fewer processing cycles and less memory than other approaches, while obtaining a similar or greater level of accuracy.

The methods, systems, and functionality described herein may be embodied as instructions on a computer readable medium or as part of a computing architecture.FIG.7illustrates an embodiment of an exemplary computing architecture700suitable for implementing various embodiments described herein. In one embodiment, the computing architecture700may comprise or be implemented as part of an electronic device, such as a computer701. The embodiments are not limited in this context.

As shown inFIG.7, the computing architecture700comprises a processing unit702, a system memory704and a chipset706. The processing unit702can be any of various commercially available processors, including without limitation an AMD® Athlon®, Duron® and Opteron® processors; ARM® application, embedded and secure processors; IBM® and Motorola® DragonBall® and PowerPC® processors; IBM and Sony® Cell processors; Intel® Celeron®, Core (2) Duo®, Core i9™, Core m3™, vPro™, Itanium®, Pentium®, Xeon®, and XScale® processors; and similar processors. Dual microprocessors, multi-core processors, and other multi-processor architectures may also be employed as the processing unit702.

In some embodiments, the processing unit702couples with the chipset706via a highspeed serial link703and couples with the system memory704via a highspeed serial link705. In other embodiments, the processing unit702may couple with the chipset706and possibly other processor units via a system bus and may couple with the system memory704via the chipset706. In further embodiments, the processing unit702and the chipset may reside in a System-On-Chip (SoC) package.

The chipset706provides an interface for system components including, but not limited to, the system memory704to the processing unit702. The chipset706may couple with any of several types of bus structure that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. Interface adapters722,724,726,728,740,752, etc., may connect to the chipset706via a slot architecture. Example slot architectures may include without limitation Accelerated Graphics Port (AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA), Micro Channel Architecture (MCA), NuBus, Peripheral Component Interconnect (Extended) (PCI(X)), PCI Express, Personal Computer Memory Card International Association (PCMCIA), and the like.

The computing architecture700may include various types of computer-readable storage media in the form of one or more lower speed memory units, including an internal (or external) hard disk drive (HDD)712, a magnetic floppy disk drive (FDD)714to read from or write to a removable magnetic disk716, and an optical disk drive718to read from or write to a removable optical disk720(e.g., a CD-ROM or DVD). The HDD712, FDD714and optical disk drive720can be connected to the system bus706by an HDD interface722, an FDD interface724and an optical drive interface726, respectively. The HDD interface722for external drive implementations can include at least one or both of Universal Serial Bus (USB) and IEEE 694 interface technologies.

The drives and associated computer-readable media provide volatile and/or nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For example, a number of program modules can be stored in the drives and memory units708,712, including an operating system728, one or more application programs730, other program modules732, and program data734. In one embodiment, the one or more application programs730, other program modules732, and program data734can include, for example, the various applications and/or components described herein, such as the controller309and the immutable log310.

A user may enter commands and information into the computer701through one or more wire/wireless input devices, for example, a keyboard736and a pointing device, such as a mouse738. Other input devices may include microphones, infra-red (IR) remote controls, radio-frequency (RF) remote controls, game pads, stylus pens, card readers, dongles, finger print readers, gloves, graphics tablets, joysticks, keyboards, retina readers, touch screens (e.g., capacitive, resistive, etc.), trackballs, trackpads, sensors, styluses, and the like. These and other input devices are often connected to the processing unit702through an input device interface740that is coupled to the chipset706, but can be connected by other interfaces such as a parallel port, IEEE 694 serial port, a game port, a USB port, an IR interface, and so forth.

A monitor742or other type of display device is also connected to the chipset706via an interface, such as a video adaptor728. The monitor742may be internal or external to the computer701. In addition to the monitor742, a computer typically includes other peripheral output devices, such as speakers, printers, and so forth.

The computer701may operate in a networked environment using logical connections via wire and/or wireless communications to one or more remote computers, such as a remote computer744. The remote computer744can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many of or all the elements described relative to the computer701, although, for purposes of brevity, only a memory/storage device746is illustrated. The logical connections depicted include wire/wireless connectivity to a local area network (LAN)748and/or larger networks, for example, a wide area network (WAN)750. Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all which may connect to a global communications network, for example, the Internet.

When used in a LAN networking environment, the computer701is connected to the LAN748through a wire and/or wireless communication network interface or adaptor752. The adaptor752can facilitate wire and/or wireless communications to the LAN748, which may also include a wireless access point disposed thereon for communicating with the wireless functionality of the adaptor752.

When used in a WAN networking environment, the computer701can include a modem754, or is connected to a communications server on the WAN750, or has other means for establishing communications over the WAN750, such as by way of the Internet. The modem754, which can be internal or external and a wire and/or wireless device, connects to the chipset706via the input device interface740. In a networked environment, program modules depicted relative to the computer701, or portions thereof, can be stored in the remote memory/storage device746. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used.

Some aspects may utilize the Internet of Things (IoT), where things (e.g., machines, devices, phones, sensors) can be connected to networks and the data from these things can be collected and processed within the things and/or external to the things. For example, with the IoT, sensors may be deployed in many different devices, and high-value analytics can be applied to identify hidden relationships and drive increased efficiencies. This can apply to both Big Data analytics and realtime (streaming) analytics.

Some systems may use Hadoop®, an open-source framework for storing and analyzing big data in a distributed computing environment. Apache™ Hadoop® is an open-source software framework for distributed computing. For example, some grid systems may be implemented as a multi-node Hadoop® cluster, as understood by a person of skill in the art. Some systems may use cloud computing, which can enable ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.

FIG.8illustrates an example of a system800that may generally be representative of a distributed cloud-based computing system or another type of computing network in that one or more techniques described herein may be implemented according to various embodiments. As shown inFIG.8, system800may generally be a rack-based system including of a number of racks804-n, where n may be any positive integer. Each of the racks804may be configured to house computing resources802to process data and information. Moreover, the racks804may be coupled with each via a fabric803, which may be similar to or the same as fabric303as similarly discussed above. The racks804may be located within the same data center and other data centers coupled via the fabric803and cloud-based networking infrastructure. The fabric803may include a combination of electrical and/or optical signaling media, and high bandwidth interconnects, such as Gigabit Ethernet, 10 Gigabit Ethernet, 100 Gigabit Ethernet, InfiniBand, Peripheral Component Interconnect (PCI) Express (PCIe), PCIe 1.0, PCIe 2.0, PCIe 3.0, PCIe 4.0, PCIe 5.0, and so forth. In one example, the fabric803may include networking hardware to support communication of data and information in accordance with the PCIe 4.0 and provides 16 gigatransfers per second (GT/s) bit rate. As will be discussed in more detail below, these high data rates enables computing resources between the racks803and across the cloud to be pooled together to provide processing and memory capabilities. Although not pictured, each rack804may include instances of the controller309and/or the log310. More generally, the system800may include instances of the controller309and/or the log310to schedule workloads for processing on the racks804-1and/or any component thereof.

In embodiments, a rack804includes computing resources802, which may include processing resources822and memory resources820. The processing resources822include one or more processors850having processing circuitry to process information and data. The one or more processors850may be a single core processor or multi-core processor. In some embodiments, the one or more processors850may each be a multi-chip package (MCP), a system on chip (SoC) package, and so forth including other circuitry and components, such as memory840, a memory controller842, and one or more interfaces844. Embodiments are not limited in this manner. In one embodiment, the computing resources802are representative of the computing resources302of the system300, and the controller309may schedule workloads for processing in the system800as described above.

In embodiments, the memory840is a local memory, e.g., coupled and/or on the same die or package as the one or more processors850. In one example, the memory840is cache memory and stores information and data for processor cores of the processors850. More specifically, the memory840may store copies of data that is frequently used by a processor core stored in “main” memory, such as memory830of the same rack804or memory830of a different rack804. In embodiments, the memory840local to a processor core, e.g., on the same die or package, may be relatively small in size compared to “main” memory. For example, memory830may be 4, 8, 16, kilobytes (KB) or megabytes (MB) in size, while “main” memory may be on the order of gigabytes (GB) in size, e.g., 2, 4, 8, 16 GBs.

In embodiments, the memory830and memory840may be configured as a hierarchy of one or more cache levels (L1, L2, L3, etc.). Memory840may be on the same die or package as the processor850and may be part of higher level cache (L1, or L2). Memory830, which may be within the same rack804but not on the die with the processing cores, may be lower level cache, e.g., L3 cache. While memory830of a different rack804then the processor850utilizing it, may be used as even lower level cache, e.g., L4 or L5 cache. In these embodiments, the higher level cache may store information and data that is accessed more frequently than information and data store in a low-level cache. Moreover, the information and data may be moved among the different levels of memory840and830based on a change in use and/or access. For example, as information and data are used more frequently, it may be moved/copied from a lower level cache, e.g., L3, L4, or L5, to a higher level cache, e.g., L1 or L2. Similarly, as information and data stored in lower level cache is used less frequently, it may be copied or moved to a higher level cache.

In one example, the first rack804-1may include processing resources822-1having one or more processors850-1and local memory840-1, which may be part of the same die or package as processors850-1, and may be an L1 or L2 cache for the processor850-1. Further, memory830-1of the same rack804-1may be considered or configured a lower level cache for the processor850-1, e.g., L3. The processor850-1may also use memory830-n, where n may be any positive integer other than 1 in this example, as even lower level cache, L4 or L5 cache.

In embodiments, the processing resources822may include a memory controller842or a digital circuit to manage the flow of data between memory840, memory830, and processors850. The memory controller842may be part of the same die or package as the processors850or integrated on another chip. The memory controller842may control read and writes to memory840, which may be local or on the same die as the memory controller842. In some embodiments, the memory controller842may be coupled with the memory resources820via one or more interfaces834and844. The memory controller842may operate in conjunction with memory controller832of the memory resources820. The memory controllers832and842may operate in conjunction with each other to perform read/write operations to store data and information in memory830, for example. In embodiments, the information, data, and memory requests may be communicated between the memory controllers832and842via the interfaces844and834. In some embodiments, the interfaces844and834may be part of the fabric803coupling the processing resources822and the memory resources820within the same rack804. The interfaces844and834may be optical and/or electrical interfaces and enable high-speed communication between the computing resources802, e.g., utilizing PCIe 4. However, Embodiments are not limited in this manner.

In some embodiments, the computing resources802, including the processing resources822and the memory resources820, of one rack804may be coupled with and utilize computing resources802of another rack804via one or more switches850, which may be part of the fabric803. In embodiments, the switches850may be electrical and/or optically coupled with each other and enable communication via packet switching. In embodiments, the switches802include circuitry to extend the PCIe fabric from within a rack804to one or more other racks804and computing resources802therein. More specifically, a switch850may include circuitry and logic such that computing resources802of one rack804can share input/output (I/O) and memory functionality with computing resources802of another rack804utilizing single root I/O virtualization (SR-IOV) or multifunction virtualization. Embodiments are not limited in this manner.

Like system300, system800may enable the combination of computing resources802to process a workload, job, and/or task. For example, the controller309may receive a request to process one or more workloads, such as those related to performing financial calculations, determine the computing resources802required to perform the one or more workloads as described above, and combine the computing resources802to perform the one or more workloads, e.g. generate composed computing resources802. In embodiments, the controller309may determine the number of computing resources802to combine based on requirements, such as a service level agreement (SLA), or another prioritizing scheme. As mentioned, the computing resources may be combined within a rack804, across racks804within the same data center, and across racks804in different data centers via the fabric803and cloud-based infrastructure.

FIG.9illustrates another example of a system900, which may be similar to or the same as system800, and/or any other system discussed herein.FIG.9illustrates a number of computing resources, including memory resources920and processing resources922, coupled with each via a plurality of switches950-g, where g may be any positive integer. In embodiments, the computing resources may be coupled via fabric and the cloud-base infrastructure. In the illustrated example, one or more of the computing resources may be located within the same data center, while one or more other computing resources may be located in a different data center.

As discussed, the processing resources922may include one or more processors, processing packages, and processing cores, and the memory resources920may include one or more memory chips or banks of memory to store information and data for the system900. The system900may include any number of processing resources922-mand memory resources920-p, where m and p may be any positive integers (same or different). These computing resources may be pooled and grouped together to perform one or more workloads. The system900includes the controller309, which may be one or more servers and devices capable of coordinate various aspects of the system900including receiving workloads, determining computing resources for the workloads, causing the resources to perform work on the workloads, grouping computing resources together to perform the work on the workloads, notifying a user and/or user system that the workloads completed, and other coordinating tasks. For example, the controller309may coordinate and cause processing of multiple workloads at time, which may include determine priority levels for the workloads and so forth. In embodiments, the controller309may operating in accordance with one or more SLAs and or user configurations. For example, an SLA and/or user setting may specify which workloads and/or which types of workloads have higher priorities than other types of workloads. Embodiments are not limited in this manner.

In the illustrated example ofFIG.9, the controller309has generated grouped resources931that includes processors from processing resources922-1and memory from memory resources920-1. In embodiments, the grouped resources931may represent a composed node, and the computing resources may be utilized to perform one or more workloads. Note that although the box around the grouped resources931includes a portion of switch950-1, the traffic including memory read/write requests, data, and information, communicated between the processing resources922-1and memory resources920-1may pass through switch950-1and/or any other of the switches950-gbased on the networking configuration. Further, in embodiments, the processing resources922-1and memory resources920-1may be incorporated in the same rack, and the traffic may pass through switch950-1. In other instances, the processing resources922-1and memory resources920-1may be incorporated into different racks, and the traffic may pass through switch950-1and/or any other switches950-g. As similarly discussed above, the switches950, the fabric, and the cloud-based infrastructure907support high band width communications, e.g., PCIe 4 operating at approximately 16GT/s or 8 GB/s total for 4 lanes).

FIG.10illustrates another example of a system1000, which may be similar to or the same as system300, system800, system900, and/or any other system discussed herein.FIG.10illustrates a number of computing resources, including memory resources1020and processing resources1022, coupled with each via a plurality of switches1050-g, where g may be any positive integer. In embodiments, the computing resources may be coupled via fabric and the cloud-base infrastructure1007. In the illustrated example, one or more of the computing resources may be located within the same data center, while one or more other computing resources may be located in a different data center.

In the illustrated example, system1000may be the same as system1000, however, may be in a different configuration. In this example, the controller309has generated grouped resources1031that includes processors from processing resources1022-1, a memory from memory resources1020-1, and memory from memory resources1020-2. In embodiments, the grouped resources1031may represent a composed node, and the computing resources may be utilized to perform one or more workloads, such as processing transactions defined by one or more records (or entries) in an immutable log310. As similarly discussed, traffic communicated between the computing resources of the grouped resources1031may be communicated through one or more switches, including switch1050-1and switch1050-2. However, embodiments are not limited in this manner, and the traffic may be communicated to other switches1050-g(and networking equipment). In this example, the controller309may receive a request to perform one or more workloads to process data and information and based on a priority level, generated the grouped resources1031. Moreover, embodiments are not limited to these examples. One or more computing resources may grouped to generate a composed node from any one of a plurality of racks that may be part of one or more data centers and coupled via the cloud-based infrastructure1007, for example.

FIG.11illustrates an example of a processing flow1100that may be representative of some or all the operations executed by one or more embodiments described herein. For example, the processing flow1100may illustrate operations performed by the controller309to schedule one or more workloads for processing on a cloud-based distributed system. However, embodiments are not limited in this manner, and one or more other components may perform operations to enable and support the operations discussed in this processing flow1100.

At block1102, the processing flow1100includes receiving a request to process or one or more workloads by a cloud-based computing system. The request may include information and data used to perform the workload, such as financial information, and may be received from one or more other systems. In embodiments, the request may indicate processing that needs to be done on the information and data to generate a result. For example, the workload may be defined by an immutable log specifying details of a transaction to be processed (e.g., the purchase and/or sale of a stock).

At block1104, the processing flow1100includes the controller309determining one or more computing resources to be utilized to process the workload. The one or more resources may include processing resources, such as processors and/or processing cores, and memory resources, such as memory. In embodiments, the controller may determine which resources to process based on one or more criteria including, a priority level for the workload, computing resources available, location of computing resources, processing/memory capabilities of the computing resources, processing requirements for the workload, an SLA associated with the requester of the workload, and so forth. For example and in some embodiments, the controller may determine one or more computing resources to process the workload based on the computing resources being within the same data center. In other instances, the controller may determine group computing resources that are located within different data centers. Embodiments are not limited to this example. As stated, the controller309may consider the utilization levels of the computing resources (e.g., compute nodes), the utilization levels of the fabric links to each computing resource, the number of links from the computing resource to data needed to process the transaction, and/or the log310when selecting one or more computing resources to process the workload.

At block1106, the controller309may allocate and group the one or more computing resources to process the workload. For example, the controller309may identify and provide information to the computing resources (and controlling software) to allocate the resources for the workload. In one example, the computing resources may be controlled by an operating system and the entire computing resource may be allocated to process the workload. In another example, the computing resources may be part of a virtual environment and may be controlled via a virtual machine monitor, such as a hypervisor, that operates virtual machines in the virtual environment. In another example, the computing resources may be controlled by a docker engine, and the workload may be processed in a container operating. In both the virtual machines and docker system, the computing resources may be shared among a plurality of workloads. Embodiments are not limited in this manner. The controller309may generally allocate and group resources based on the utilization levels of the grouped computing resources, the utilization levels of the fabric links to each group of computing resources, the number of links from the group of computing resources to data needed to process the transaction, and/or the log310.

At block1108, the processing flow1100includes processing and/or causing the workload to be processed by the computing resources. For example, the transaction defined by the immutable log may be processed. The computing resources may communicate between each other via a fabric and/or cloud-based networking infrastructure which includes one or more high speed interconnects, such PCIe-4. As previously discussed, these high-speed interconnects enable the computing resources to be located within the same data center and among other data centers while maintaining high-speed connectivity between themselves. For example, a processing resource may be coupled with a memory resource in a different data center and still be able to utilize the memory resource as “main” memory, cache memory, and/or in a memory hierarchy as previously discussed.

In embodiments, the processing1100includes determining that the workload is complete, e.g., done being processed at block1110. For example, the computing resources and/or controlling software may send a notification to the controller indicating that the workload has completed. At block1112, the controller309may release the computing resource, e.g., make them available to process other workloads. Further and at block1114, the controller may notify the requesting system that the workload is complete including providing any results for the workload.

FIGS.12A-12Bdepict embodiments of a database system1200andFIGS.12C-12Ddepicts embodiments of data structures in the database system1200. In particular,FIG.12Aillustrates a layer diagram of a database system1200that interacts with computing node(s)1210. The computing node(s)1210may be local or remotely located computers, servers, workstations, or the like such as the computer700illustrated inFIG.7. In many embodiments, the computing node(s)1210obtain and forward raw event data to the database system1200or consume data, derived data, queried data, summarized data, reports, and/or the like from the database system1200.

The database system1200may comprise a combination of hardware and code to receive and store raw event data as log object(s)1260in a persistent log and to derive or compute derived data based on the raw event data to store in database object(s)1250in a persistent database. In many embodiments, the database system1200captures a representation of the financial environment within which financial calculations and decisions are made and permanently stores that representation, at least for a period of time, to facilitate derivation and/or summarization of financial data. The summarization and/or derivation adds business intelligence to the data to form financial information to provide to authorized users or consumers in the form of reports, tables, lists, SQL databases, graph databases, relational databases, and/or any other data structure. The database system1200may maintain persistent records of derivations and summarizations in the form of database objects1250. The derivations include, for instance, computations, corrections, and/or cancelations of the raw event data in the log object(s)1260. The derived data may include, for instance, trades, settlements, and holdings such as stock holdings, stock trades, stock buys, stock sells, mutual fund holdings, mutual fund trades, mutual fund buys, mutual fund sells, commodity holdings, commodity trades, commodity buys, commodity sells, net asset values, and/or the like. The database system1200may include immutable log objects, such as the log objects1260, describing transactions scheduled for processing by the controller309.

In many embodiments, the database system1200may maintain not only persistent records of the computations, summarizations, corrections, and/or cancelations but also persistent records of the logic or code to perform the computations, summarizations, corrections, and/or cancelations such that the database system1200or another system can perform such derivations on the raw event data included in the log object(s)s1260to recreate the data derived from such computations, summarizations, corrections, and/or cancelations. The logic or code may comprise, for instance, the logic to perform the derivations, code in the host environment (e.g. software environment) that affect the logic to perform the derivations, code of a virtual machine within which the database system1200performs derivations, and/or the like.

The database system1200may comprise service layer(s)1220, translation layer(s)1230, and physical layer(s)1240. The service layer(s)1220may comprise producer(s)1222and consumer(s)1224. The producer(s)1222may comprise one or more application programming interfaces (APIs) that receive, index, encrypt, and store raw event data from the computing node(s)1210as log object(s)1260in a persistent log. The consumer(s)1224may query data and derived data on behalf of the computing node(s)1210and present the results of the query in a format requested by the computing node(s)1210or a format optimized for usage by the computing node(s)1210. In other words, the service layer(s)1220may provide an interface to access the data in the database object(s)1250and/or the log object(s)1260in any data structure. For example, a consumer may request a report via a comma-separated values file format, an online analytical processing (OLAP) format or an online transactional processing (OLTP) format. The producer(s)1222and consumer(s)1224may each comprise one or more of the microservices and/or macroservices such as the microservices412-1through412-N and the macroservices416-1through416-N illustrated inFIG.4to perform the services provided to the computing node(s)1210. One or more microservices and/or macroservices operating on behalf of the consumer(s)1224may generate the data structure and populate fields of the data structure with pointers to the corresponding raw event data and derived data prior to transmitting the data structure to the computing node(s)1210.

In some embodiments, the service layer(s)1220may perform services such as estimating a trade settlement, estimating a net asset value, estimating a stock holding, estimating a stock value, estimating a mutual fund value, estimating a commodity value, and/or the like. In further embodiments, the service layer(s)1220may perform services such as estimating a trade settlement, estimating a net asset value, estimating a stock holding, estimating a stock value, estimating a mutual fund value, estimating a commodity value, and/or the like as of a specified time and within a specific time frame.

In many embodiments, in addition to limiting access to data to authorized users or processes, the database system1200may affinitize data to one or more geographical locations and/or anti-affinitize the data to one or more geographical locations. In other words, some financial data should or must remain within certain geographical areas and should not or must not enter other geographical areas. A service layer(s)1220service may provide at least one mechanism to enforcing geographical locations on data in the financial information provide to the computing node(s)1210.

The translation layer(s)1230may include one or more layers to facilitate the generation of representations of the data in multiple formats or any format for which an API can generate by provision of predetermined indices and indices that the translation layer1230generates on-the-fly. For instance, the raw event data in the log object(s)1260may include a unique index or pointer that uniquely identifies the location of the log object(s)1260in the persistent log. Similarly, the database object(s) may include a unique index or pointer that uniquely identifies the location of the database object(s)1250in the persistent database. Furthermore, the database object(s)1250may include predetermined sets of indices to support common or frequently-requested queries or derivations. The translation layer(s)1230may generate indices during execution of less frequently requested queries or derivations.

In one embodiment, generation of indices to raw event data may comprise generation of indices to a current holding of stock shares, a buy of stock shares, a sell of stock shares, a correction of raw event data such as trade data or settlement data, a cancelation of raw event data such as trade data or settlement data, and/or the like. In another embodiment, generation of indices to raw event data may comprise generation of indices to timestamps to indicate that time at which the transaction settled, a price of a share of the stock at the time of settlement of the transaction, an entity that placed the order, an entity that fulfilled the order, the number of shares of the stock that transferred at the settlement, the funds transferred at settlement, the entity that transferred the funds, the entity that received the funds, and/or the like.

The physical layer(s)1240may support the random-access of data such as raw event data in the log object(s)1260and derived data in the data object(s)1250to support the translation(s) layer1230and services layer(s)1220services. In some embodiments, the physical layer(s)1240provide “byte access”. In other words, the physical layer(s)1240provide access at a level of granularity of a byte or 8 bits to increase the efficiency of access to the data without necessarily implementing queuing or caching schemes to reduce inefficiencies involved with levels of granularity that are greater than a byte. In many of the embodiments, the physical layer(s)1240maintains a list of the log object(s)1260and a list of the database object(s)1250. In some embodiments, the list of database object(s)1250reside in a relational database such as a database with an Apache Kafka architecture.

FIG.12Billustrates an embodiment of a system1201such as the physical layer(s)1240illustrated inFIG.12A. The system1201may provide random access to large stores of data including the database object(s)1250and the log object(s)1260to the hosts1211-1through1211-N via switch1202. The hosts1211-1through1211-N may perform the services layer(s)1220and translation layer(s)1230operations. The switch1202may comprise one or more switches such as an Avago/PLX PEX3090 family switch to provide random-access to the drives1212-1through1212-N for the hosts1211-1through1211-N via a fabric such as PCIe (Peripheral Component Interconnect Express). In one embodiment, the switch1202can support up to fifty hosts1211-1through1211-N. In other embodiments, the fabric may include Ethernet, other conductor-based buses or optical buses such as Fiber Channel, Infiniband, Omni-path, and/or the like.

In many embodiments, the database system1200executing on the hosts1211-1through1211-N, is optimized for a low granularity of random access, such as byte access, to data residing on the drives1212-1through1212-N. For example, at a byte level of granularity, the database system1200may, in many instances, access the data and only the data of interest since the byte is a common level of granularity with which many computer systems operate. In contrast, hard disk drive may have a level of granularity of on block, which is 4 kilobytes (KB). As a result, if the database system1200stores data on a hard disk drive and requires access to one byte of data, the hard disk drive will read and return 4 KB. If the database system1200requires another byte of data, the database system1200may have to read another 4 KB of data to obtain the one byte.

The switch1202may provide random access to the drives1212-1through1212-N by virtualizing routing between the hosts1211-1through1211-N and the drives1212-1through1212-N. Bus architectures such as PCIe are designed to interconnect one host with one device. The switch1202may implement connections such as Tunneled Window Connections (TWCs) that allow multiple hosts1211-1through1211-N to communicate with multiple drives1212-1through1212-N via a PCIe bus. In other words, the switch1202may offer multi-route input/output (I/O) virtualization (MRIOV) to facilitate direct access by any host1211-1through1211-N to the content on any drive1212-1through1212-N. For instance, the switch1202may intercept an incoming packet and emulate another device to hide the host or drive status and make the host or drive look like a target device so the hosts1211-1through1211-N may each directly access any of the drives1212-1through1212-N.

In several embodiments, the drives1212-1through1212-N may comprise 3D XPoint® NVMe (non-volatile memory) solid state drives (SSDs) that offer byte level access. In combination with the MRIOV or similar arrangement, the bank of drives1212-1through1212-N can be as accessible as memory such as DDR dynamic random-access memory and 3D XPoint® NVMe (non-volatile memory). In other embodiments, the drives1212-1through1212-N may comprise other SSDs, flash drives, optical drives, hard drives, or the like.

FIG.12Cillustrates an embodiment of a log object1271such as the log object(s)1260inFIG.12Afor a persistent log such as the log310. The log object1271may persist raw event data. In many embodiments, the raw event data receives an order1272that identifies the order in which the raw data arrives at the log in relation to other raw event data that arrives at the log. The log object1271encompasses the order1272and the raw event data as data1273. In several embodiments, a cryptographic hash such as SHA-1 encrypts the content of the log object1271.

The data1273may include a timestamp to identify a time of the occurrence of the event, a hash of the raw event data to uniquely identify the raw event data, a context to describe the event, and possibly other data. For example, an event may involve the purchase of shares of a stock. The raw event data may include a timestamp to indicate that time at which the transaction settled, a price of a share of the stock at the time of settlement of the transaction, an entity that placed the order, an entity that fulfilled the order, the number of shares of the stock that transferred at the settlement, the funds transferred at settlement, the entity that transferred the funds, the entity that received the funds, and/or the like. The hash of the raw event data may operate as a pointer to uniquely identify the log object1271for the purposes of generation of report, databases, and/or the like by the translation layer(s)1230and the services layer(s) such as the translation layer(s)12030and the services layer(s)1220depicted inFIG.12A.

FIG.12Dillustrates an embodiment of a database objects1280such as the database object(s)1250inFIG.12Afor a persistent database. The database objects1280may persist derived data such as computed data, summarized data, corrected data, and canceled data. The database objects1280may comprise a first database1281, or child table, coupled with a second database1282, or parent table, via a relation such as a foreign key relation. In the context of relational databases, a foreign key is a field (or collection of fields) in one table that uniquely identifies a row of another table or the same table. In other words, the foreign key is defined in a second table, but it refers to the primary key or a unique key in the first table. For example, a table called Corrections and Cancelations has a primary key called correction_id. Another table called Correction and Cancelation Details has a foreign key which references correction_id to uniquely identify the relationship between both tables.

The table containing the foreign key is called the child table, and the table containing the candidate key is called the referenced or parent table. In database relational modeling and implementation, a unique key is a set of zero or more attributes, the value(s) of which are guaranteed to be unique for each tuple (row) in a relation. Note that the database system1200inFIG.12Acan implement the database objects1280in other database structures and are not limited to a relational database.

In some embodiments, the number of instances of the first database1281may differ and may be less than the number of instances of the second database1282. The first database may include an original creator identifier (ID) (Kid)1283, a timestamp1284, queries1285, and possibly other fields. The original creator ID1283may refer to the creator of an original record that resides in a log object such as the log object1271in the persistent log or immutable log. The timestamp1284may be a pointer to a timestamp in the log object that refers to the time of creation of the raw event data and the queries1285may include one or more columns that include indices or pointers for data in the log object or other database objects1280. For example, the log object may include a raw data event that describes a current holding of stock shares, a buy of stock shares, a sell of stock shares, a correction of raw event data, a cancelation of raw event data, and/or the like. The database objects1280may create a corrections table to identify corrections in the persistent log and perform the corrections in the database objects1280. Thereafter, the reports or other queries related to the stock holdings can find the corrections data in the database object1280and avoid having to perform the incorrect or canceled trades included in the log.

The second database1282may provide details related to raw event data in a log such as a correction associated with the raw event data or derived data based on the raw event data. The second database1282may include a deleter ID (Did)1290, a corrector ID (Cid)1291, data (1392), and possibly other fields. The deleter ID1290may include an index for an event that deletes or cancels an event in the raw event data of the log. The corrector ID1291may include an index for the event that changes or corrects the raw event data of the log or adds derived data related to the raw event data and the data1292may include data that is the corrected data or derived data associated with the log object. Many embodiments may include corrections and cancelations that result from, e.g., the differences in time between a trade and settlement of that trade.

FIG.13illustrates an embodiment1300of a corrections and cancelations table in a database object such as the database objects1280depicted inFIG.12Dand how the table changes over a period of time from time1through time6. The database object1310describes the first database1381that includes a relation1305such as a foreign key relation to a derived database object1320at time1such as the second database1282illustrated inFIG.12D. The derived database1320may maintain corrections and cancelations for a specific stock of a specific fund such as a mutual fund. The corrections and cancelations table1320may track the cancelations and corrections for this stock and fund because the process of trading stock by this fund may involve corrections and cancelations over periods of time that have an impact on one or more financial aspects of the fund such as tax liabilities, net asset values (NAVs), and/or the like.

At time1, the derived database object1320may include a first-row entry1312that indicates that the trade is still valid because the deleter id (Did) is set to infinity, the creator ID (Cid) is an event number100, the timestamp (Ts) of the event is 1 and the trade (V) is a buy of 10 shares of the stock. At the time2, a second-row entry1322is added to the derived database object1320to indicate that the trade is still valid because the deleter id (Did) is set to infinity, the creator ID (Cid) is an event number110, the timestamp (Ts) of the event is 2 and the trade (V) is a buy of 20 shares of the stock.

Event120performs a correction at time3. In particular, at the time3, the first-row entry1332is modified in the derived database object1320to indicate that the trade is not valid as of the event120because the deleter id (Did) is changed from infinity to the event120. A third-row entry1334is added to the derived database object1320to indicate that the trade is still valid because the deleter id (Did) is set to infinity, the creator ID (Cid) is the event number120that is the same event that canceled the trade in the first-row entry1332, the timestamp (Ts) of the event is 1, and the trade (V) is a buy of 8 shares of the stock. Note that setting the timestamp (Ts) to 1 indicates that the added trade replaces the original trade, which was canceled, at the time associated with Ts equal to 1.

Time4illustrates another embodiment of a cancelation and correction. The event130cancels the trade from the event120by amending the deleter ID in the third row entry1342to include the event number130and by adding a fourth row entry1344to the derived database object1320to indicate that the trade is still valid because the deleter id (Did) is set to infinity, the creator ID (Cid) is an event number130, the timestamp (Ts) of the event is 1 and the trade (V) is a buy of 5 shares of the stock. Again, setting the Ts equal to 1 indicates that this trade is the replacement trade for the trade that previously occurred at the time Ts equal to 1.

Time5illustrates an embodiment of to reintroduce a succession of canceled events and to cancel those events while introducing a new event to replace the event at the time of Ts equal to 1 via cancelation of the fourth row and addition of the fifth, sixth, and seventh rows1352. The event140cancels the fourth row by inclusion of the event number140in the delete ID field. The event140also adds the fifth and sixth rows but also deletes these rows by inclusion of the event number140in the delete ID fields. In particular, the fifth row reintroduces and cancels the buy of 10 shares at the time Ts equal to 1 and the sixth row reintroduces and cancels the buy of 8 shares at the time Ts equal to 1. The event adds the trade at the seventh row with the indication that the trade is still valid because the deleter id (Did) is set to infinity, the creator ID (Cid) is an event number140, the timestamp (Ts) of the event is 1, and the trade (V) is a buy of 7 shares of the stock.

Time6illustrates an embodiment of an undelete function in the derived database object1320at the fourth, fifth, sixth, and seventh rows1362. At the fourth row, the event150undeletes the trade by event130by changing the delete ID in the fourth row from event140to infinity. The event150also cancels or reconfirms the deletion of the trades in the fifth and sixth rows by changing the delete IDs in the fifth and sixth rows from event140to150. Then, at the seventh row in the derived database object1320at time6, the event150changes the delete ID from infinity to event150to cancel the trade of buying 7 shares of the stock.

In several embodiments, database objects such as the derived database object1320may include ladders to describe a jump from, e.g., time1to time6, to avoid repetitions of calculations involved with making corrections and calculations over a period of time. If, for instance, a consumer such the consumer(s)1224inFIG.12Adoes not need to know the detail of the corrections that occurred between time1and time6, the ladder may provide a record that describes indicates a ladder from time1to time6and indicates the resulting buy of shares for that time period. In such embodiments, the ladder may only be valid for calculations or reports that begin on or after the time6because the corrections and calculations could have affected values of other trades or other financial information during the period of time between times1and6.

FIG.14illustrates an example of a processing flow1400that may be representative of some or all the operations executed by one or more embodiments described herein. For example, the processing flow1400may illustrate operations performed by the controller309to schedule one or more workloads for processing on a cloud-based distributed system. However, embodiments are not limited in this manner, and one or more other components may perform operations to enable and support the operations discussed in this processing flow1400.

As shown, at block1402, the controller309may analyze an immutable log310for a transaction. As stated, one or more entries of the immutable log310may specify an account identifier, asset identifier (e.g., a stock ticker), a transaction type, and any other parameter for the transaction. For example, the immutable log310may specify to purchase a specified amount of a stock. At block1404, the controller309may determine one or more data elements required to process the transaction. For example, the controller309may determine that information describing the account and/or the stock is needed to process the transaction. At block1310, the controller309may determine the locations of the data elements determined at block1404. For example, the account information may be stored on a first compute node, while the current price of the stock is located on a second compute node.

At block1408, the controller309may identify one or more compute nodes proximate to the determined locations of the data. For example, the controller309may identify the first compute node, the second compute node, and a plurality of other compute nodes within a predefined distance (e.g., a number of network hops) of the first and/or second compute nodes. At block1410, the controller309may determine the utilization levels of each resource (e.g., CPU, RAM, storage I/O, network I/O, etc.) of the compute nodes identified at block1408. The controller309may further estimate an amount of time the compute node may require to process the workload (and/or a portion thereof). At block1412, the controller309may determine the utilization levels of each network link to the fabric302for the compute nodes identified at block1408. The controller309may further determine other attributes of each network link, such as latency, jitter, etc. At block1414, the controller309may compute a score for each node identified at block1408. For example, using the log310, the controller309may compute a score reflecting the suitability of each node identified at block1408to process the workload in accordance with the QoS parameters of the SLA for the requesting client.

At block1416, the controller309selects one or more nodes identified at block1408that satisfy the QoS parameters. For example, the controller309may select a predefined number of nodes having the highest scores computed at block1414. In addition and/or alternatively, the controller309may select the nodes having the closest proximity to the data needed to process the transaction. In addition and/or alternatively, the controller309may select the nodes having the lowest resource utilization levels. In addition and/or alternatively, the controller309may select the nodes having the lowest network link utilization levels. In addition and/or alternatively, the controller309may select the nodes having the greatest number of network links to the needed data. In addition and/or alternatively, the controller309may select the least recently used nodes. In addition and/or alternatively, the controller309may select the compute nodes determined to process the workload (and/or one or more portions thereof) in the least amount of time (and/or within amounts of time specified in the SLA). Once selected, the controller309may deploy the workload (and/or one or more portions thereof) to each node selected by the controller309to process the workload.