Workload management with data access awareness using an ordered list of hosts in a computing cluster

Embodiments for workload management with data access awareness by ordering hosts for scheduling workloads in a computing cluster. In response to receiving an input workload for scheduling by a workload manager, a set of inputs is retrieved from a storage system by a data requirements evaluator module. The data requirements evaluator module generates a list of cluster hosts ranked for performing the input workload according to data access considerations.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates in general to computing systems, and more particularly to, various embodiments for workload management and scheduling within and/or between distributed computing components.

Description of the Related Art

In today's society, computer systems are commonplace. Computer systems may be found in the workplace, at home, or at school. As computer systems become increasingly relied upon, convenient, and portable, the Internet has grown exponentially. Now, more than ever before, individuals and businesses rely upon distributed systems (commonly referred to as “the cloud”) to provide computing services and store information and data. As wide strides in technological advancement relating to computing devices have been accomplished, there is an ever-growing demand for growth and development within the back end supporting systems that provide computing services and store data content.

SUMMARY OF THE INVENTION

A computing cluster, referred to as cluster for short, is a type of computer system which completes computing jobs by means of multiple collaborative computers (also known as computing resources such as software and/or hardware resources) which are connected together. These computing resources which are in a same management domain have a unified management policy and provide services to users as a whole. A single computer in a cluster system is usually called a host or a computing node.

The cluster system has many advantages. For example, the cluster system, when working in a load-balance manner, can achieve a higher efficiency through performing a same work by multiple computers. The cluster system may also work in a high availability manner. Once a server, that is acting as a master server of a group of servers, fails, another server of the group of servers can assume the role of the master server and provide services in substitute of the master server, thereby exhibiting a high fault-tolerance.

When scheduling a job, corresponding computing resources are allocated to the job to be processed. This process is referred as job scheduling in a cluster environment. The job scheduling is actually a process for mapping jobs to corresponding resources for execution based on characteristics of the jobs and resources according to scheduling policies.

In cluster computing, the efficiency of these jobs (i.e., workloads) that access and process data depends significantly on the distance, in terms of data access and networking latencies, between the cluster hosts processing the workloads and the cluster hosts storing the data accessed and processed by the workloads. The lower the distance (with regard to lower data access and networking latencies) for accessing data, the higher the efficiency of the workloads.

The objective of the present disclosure is to reduce the latency of accessing data by workloads, by placing workloads close to their data. Specifically, the challenge being addressed in the current disclosure is determining how to combine workload-related knowledge (typically coming from workload management systems) with data storage-related knowledge (typically coming from storage systems) in an efficient and automatic way, to place workloads close to their underlying data and therefore increase the efficiency of the workloads and the computing system as a whole.

Accordingly, and to improve upon the art, various embodiments are disclosed herein for workload management with data access awareness in a computing cluster by a processor. In one embodiment, by way of example only, a method comprises configuring a workload manager within the computing cluster to include a data requirements evaluator module and a scheduler module; and in response to receiving an input workload for scheduling by the workload manager: (i) retrieving, by the data requirements evaluator module, a set of inputs from a storage system, wherein the inputs each include at least one of: (a) data locality proportions for a set of files associated with the input workload, the data locality proportions specifying a respective proportion of a total data size of the set of files associated with the input workload stored on each of a plurality of cluster hosts of the computing cluster, and (b) data access costs specified for each pair of cluster hosts in the computing cluster, wherein the data access costs are computed for an entirety of the computing cluster; and (ii) generating, by the data requirements evaluator module, a list of the cluster hosts ranked for running the input workload according to data access considerations associated with the set of inputs.

In addition to the foregoing exemplary embodiment, various other system and computer program product embodiments are provided and supply related advantages. The foregoing summary has been provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

DETAILED DESCRIPTION OF THE DRAWINGS

As previously mentioned, in cluster computing, the efficiency of workloads that access and process data depends significantly on the distance, in terms of data access and networking latencies, between the cluster hosts processing the workloads and the cluster hosts storing the data accessed and processed by the workloads. The lower the distance (with regard to lower data access and networking latencies) for accessing data, the higher the efficiency of the workloads.

The objective of the present disclosure is to reduce the latency of accessing data by workloads by placing workloads close to their data. Specifically, the challenge being addressed in the current disclosure is determining how to combine workload-related knowledge (typically coming from workload management systems) with data storage-related knowledge (typically coming from storage systems) in an efficient and automatic way, to place workloads close to their underlying data and therefore increase the efficiency of the workloads and the computing system as a whole.

Some known scheduling techniques exist which primitively attempt to address some of these challenges. For example, a rack-aware scheduling mechanism in current art exists for MapReduce workloads with the objective of scheduling map tasks at or close to hosts storing the required input data for the tasks. Under this mechanism, the topology of the network is submitted using a user-defined topology script representing the mapping between hosts in the cluster and network groups. The topology is represented as a tree, grouping hosts into racks and racks into data centers. Using this topology, network distances (latency) are determined between hosts. Accordingly, when scheduling a workload, resources are attempted to be allocated from hosts closest to the input data required for the given workload.

Also existing is a data-aware scheduling mechanism using interfaces for service instances and an external plug in. Utilizing this mechanism, user-defined expressions containing data attributes are presented, and logic is inputted by the user for assigning a cost value of accessing a file for a service instance. In this mechanism, the cost values are, again, calculated by logic that is defined and implemented by the user, use file granularity, and provide no specification of how data locality, data distribution, data attributes and network costs are to be used in the calculation. In other words, all logic under this mechanism depends on unspecified user implementation.

The challenge with existing methods is that these mechanisms rely on user implemented logic and procedures for providing data access costs. Examples of this include the network topology script in rack-aware scheduling and the logic for generating file access cost per service instance in data-aware scheduling. Moreover, these methods use data access costs on file granularity, and it is left up to the user logic to determine how to aggregate this file granularity to workload granularity. The data access costs are also considered to be static, as they are user-entered, and methods such as the rack-aware scheduling use a qualitative measurement of access costs. Furthermore, existing methods typically support specific types of workloads and specific types of I/O patterns for workloads, and are not generic to support the wide range of possible workload types and I/O patterns.

Accordingly, the techniques and algorithms considered herein overcome the limitations of existing methods and provide a more efficient and generic solution with regard to workload scheduling and data locality in clustered computing. These techniques include combining workload-related knowledge, retrieved from a workload management system, with data storage-related knowledge, retrieved from a data storage management system, to produce optimized placement of workloads close to their data. In some embodiments, the relevant workload-related knowledge may include (a) data to be accessed by the workloads; (b) hosts with available compute resources in the cluster; and (c) networking costs between hosts in the cluster. Commensurately, the relevant data storage related knowledge may comprise (a) at what physical location the data accessed by the workloads is stored in the cluster; (b) hosts with available storage resources in the cluster; and (c) data access costs within and across hosts in the cluster.

The considered architecture comprises a cluster of hosts consisting of one or a plurality of hosts that are interconnected with a network and are coordinated to work together. Coordination between the cluster hosts is maintained by at least (a) a workload management system that schedules, controls, and monitors workloads running on hosts in the cluster; and (b) a data storage management system that stores and provides access to data from hosts in the cluster.

In some embodiments within the considered architecture, the data storage management system may store data using at least one of the following methods: (a) data may be stored on local storage devices, where each local storage device is attached to one of the plurality of hosts in the cluster; (b) data may be stored on a shared storage device that is accessible from the hosts in the cluster; and (c) data may be stored on a combination of local and shared storage devices. Moreover, data to be accessed by a given workload may be specified as a list of data files or data objects that is/are associated with and expected to be accessed by the given workload. In various embodiments, locations in the cluster of data accessed by the workloads may be specified for both local storage and shared storage where: (a) shared storage devices have a corresponding host name; (b) local storage devices are attached to hosts which are assigned with host names; and (c) within a shared or local storage device, a location is specified using a storage identification (ID). Based on this, the combination of host name and storage ID hence provides a global storage location in a cluster for both types of storage devices.

Data access costs within and across hosts in the cluster are calculated based on a combination of networking latencies between hosts and storage device access latencies within each host. For instance, the inventive concepts herein disclose several new efficient algorithms, including: (a) an algorithm for scheduling workloads with data access awareness in a cluster of hosts, where the algorithm considers different I/O patterns of workloads for calculating optimized scheduling; (b) an algorithm for calculating an ordered list of preferred hosts for scheduling workloads with data access awareness in the cluster of hosts based on data locality information and data access costs; and (c) an algorithm for aggregating locality information for a large set of files in the cluster of hosts by selecting an efficient subset of files for calculating approximations of the locality aggregations, and defining efficient triggers for updating the approximations of the locality aggregations.

Using the novel algorithms considered herein, limitations of the aforementioned existing methods are overcome while providing a more efficient and generic solution, as (a) all required elements are calculated automatically without requiring user implemented logic, where the automatic calculations include data locality, network costs, overall data access costs, and the optimal workload placement information; (b) data access costs per workload are computed automatically and utilized across the cluster; (c) these data access costs are automatically and dynamically updated when changes occur in the cluster and in the workloads; (d) quantitative measurement of data access costs are utilized (rather than qualitative); (e) various types of workloads are considered and supported; and (f) various types of I/O patterns for workloads are considered and supported (i.e., I/O of existing data, I/O of new data, and workloads which are not I/O intensive). These concepts will be further described in detail, following.

Turning now toFIG. 1, a schematic pictorial illustration of a data processing storage system20is shown, in accordance with a disclosed embodiment of the invention. The particular system shown inFIG. 1is presented to facilitate an explanation of the invention. However, as the skilled artisan will appreciate, the invention can be practiced using other computing environments, such as other storage systems with diverse architectures and capabilities.

Storage system20receives, from one or more host computers22, Input/Output (I/O) requests, which are commands to read or write data at logical addresses on logical volumes. Any number of host computers22are coupled to storage system20by any means known in the art, for example, using a network. Herein, by way of example, host computers22and storage system20are assumed to be coupled by a Storage Area Network (SAN)26incorporating data connections24and Host Bus Adapters (HBAs)28. The logical addresses specify a range of data blocks within a logical volume, each block herein being assumed by way of example to contain 512 bytes. For example, a 10 KB data record used in a data processing application on a given host computer22would require 20 blocks, which the given host computer might specify as being stored at a logical address comprising blocks 1,000 through 1,019 of a logical volume. Storage system20may operate in, or as, a SAN system.

Storage system20comprises a clustered storage controller34coupled between SAN26and a private network46using data connections30and44, respectively, and incorporating adapters32and42, again respectively. In some configurations, adapters32and42may comprise host SAN adapters (HSAs). Clustered storage controller34implements clusters of storage modules36, each of which includes an interface38(in communication between adapters32and42), and a cache40. Each storage module36is responsible for a number of storage devices50by way of a data connections48as shown.

As described previously, each storage module36further comprises a given cache40. However, it will be appreciated that the number of caches40used in storage system20and in conjunction with clustered storage controller34may be any convenient number. While all caches40in storage system20may operate in substantially the same manner and comprise substantially similar elements, this is not a requirement. Each of the caches40may be approximately equal in size and is assumed to be coupled, by way of example, in a one-to-one correspondence with a set of physical storage devices50, which may comprise disks. In one embodiment, physical storage devices may comprise such disks. Those skilled in the art will be able to adapt the description herein to caches of different sizes.

Each set of storage devices50comprises multiple slow and/or fast access time mass storage devices, herein below assumed to be multiple hard disks.FIG. 1shows caches40coupled to respective sets of storage devices50. In some configurations, the sets of storage devices50comprise one or more hard disks, which can have different performance characteristics. In response to an I/O command, a given cache40, by way of example, may read or write data at addressable physical locations of a given storage device50. In the embodiment shown inFIG. 1, caches40are able to exercise certain control functions over storage devices50. These control functions may alternatively be realized by hardware devices such as disk controllers (not shown), which are linked to caches40.

Each storage module36is operative to monitor its state, including the states of associated caches40, and to transmit configuration information to other components of storage system20for example, configuration changes that result in blocking intervals, or limit the rate at which I/O requests for the sets of physical storage are accepted.

Routing of commands and data from HBAs28to clustered storage controller34and to each cache40may be performed over a network and/or a switch. Herein, by way of example, HBAs28may be coupled to storage modules36by at least one switch (not shown) of SAN26, which can be of any known type having a digital cross-connect function. Additionally, or alternatively, HBAs28may be coupled to storage modules36.

In some embodiments, data having contiguous logical addresses can be distributed among modules36, and within the storage devices in each of the modules. Alternatively, the data can be distributed using other algorithms, e.g., byte or block interleaving. In general, this increases bandwidth, for instance, by allowing a volume in a SAN or a file in network attached storage to be read from or written to more than one given storage device50at a time. However, this technique requires coordination among the various storage devices, and in practice may require complex provisions for any failure of the storage devices, and a strategy for dealing with error checking information, e.g., a technique for storing parity information relating to distributed data. Indeed, when logical unit partitions are distributed in sufficiently small granularity, data associated with a single logical unit may span all of the storage devices50.

While not explicitly shown for purposes of illustrative simplicity, the skilled artisan will appreciate that in some embodiments, clustered storage controller34may be adapted for implementation in conjunction with certain hardware, such as a rack mount system, a midplane, and/or a backplane. Indeed, private network46in one embodiment may be implemented using a backplane. Additional hardware such as the aforementioned switches, processors, controllers, memory devices, and the like may also be incorporated into clustered storage controller34and elsewhere within storage system20, again as the skilled artisan will appreciate. Further, a variety of software components, operating systems, firmware, and the like may be integrated into one storage system20.

FIG. 2is a schematic pictorial illustration of facility60configured to perform host computer monitoring, in accordance with an embodiment of the present invention. In the description herein, host computers22, storage controllers34and their respective components may be differentiated by appending a letter to the identifying numeral, so that facility60comprises a first host computer22A (also referred to herein as a primary host computer) coupled to a clustered storage controller34A via a SAN26A, and a second host computer22B (also referred to herein as a secondary host computer) coupled to a clustered storage controller34B via a SAN26B. In the configuration shown inFIG. 2storage controllers34A and34B are coupled via a facility SAN62. In other embodiments, as will be described herein, the first host computer22A may be directly connected to the clustered storage controller34B, and the second host computer22B may be directly connected to the clustered storage controller34A via a SAN similar to SAN62, a virtualized networking connection, or any other computer implemented medium.

Host computer22A comprises a processor64A, a memory66A, and an adapter68A. Adapter68A is coupled to SAN26A via a data connection24A.

As described supra, module36A is coupled to storage devices50A via data connections48A, and comprises adapters32A and42A, a cache40A, and an interface38A. Module36A also comprises a processor70A and a memory72A. As explained in detail hereinbelow, processor70A is configured to establish metrics74that indicate a connectivity status of host computer22A, and store the metrics to memory72A. In some embodiments, processor70A may store metrics74to storage devices50A.

Host computer22B comprises a processor64B, a memory66B, and an adapter68B. Adapter68B is coupled to SAN26B via a data connection24B.

As described supra, module36B is coupled to storage devices50B via data connections48B, and comprises adapters32B and42B, a cache40B, and an interface38B. Module36B also comprises a processor70B and a memory72B.

Processors64A,64B,70A and70B typically comprise general-purpose computers, which are programmed in software to carry out the functions described herein. The software may be downloaded to host computers22A and22B and modules36A and36B in electronic form, over a network, for example, or it may be provided on non-transitory tangible media, such as optical, magnetic or electronic memory media. Alternatively, some or all of the functions of the processors may be carried out by dedicated or programmable digital hardware components, or using a combination of hardware and software elements.

Examples of adapters32A,32B,42A,42B,68A and68B, include switched fabric adapters such as Fibre Channel (FC) adapters, Internet Small Computer System Interface (iSCSI) adapters, Fibre Channel over Ethernet (FCoE) adapters and Infiniband™ adapters.

While the configuration shown inFIG. 2shows storage host computers22A and22B coupled to storage controllers34A and34B via SANs26A and26B, other configurations are to be considered within the spirit and scope of the present invention. For example, host computers22A and22B can be coupled to a single storage controller34via a single SAN26.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes and storage systems (e.g. storage system20).

Referring now toFIG. 3, illustrative cloud computing environment52is depicted. As shown, cloud computing environment52comprises one or more storage systems20and cloud computing nodes with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone54A, desktop computer54B, laptop computer54C, and/or automobile computer system54N may communicate. Storage systems20and the cloud nodes may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment52to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices54A-N shown inFIG. 3are intended to be illustrative only and that storage systems20, cloud computing nodes and cloud computing environment52can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Referring now toFIG. 4, a set of functional abstraction layers provided by cloud computing environment52(FIG. 3) is shown. It should be understood in advance that the components, layers, and functions shown inFIG. 4are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

Hardware and software layer80includes hardware and software components. Examples of hardware components include: mainframes81; RISC (Reduced Instruction Set Computer) architecture based servers82; servers83; blade servers84; storage devices85; and networks and networking components86. In some embodiments, software components include network application server software87and database software88.

Virtualization layer90provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers91; virtual storage92; virtual networks93, including virtual private networks; virtual applications and operating systems94; and virtual clients95.

Workloads layer110provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation111; software development and lifecycle management112; virtual classroom education delivery113; data analytics processing114; transaction processing115; and, in the context of the illustrated embodiments of the present invention, various workload and job scheduling functions116. One of ordinary skill in the art will appreciate that the workload and job scheduling functions116may also work in conjunction with other portions of the various abstractions layers, such as those in hardware and software80, virtualization90, management100, and other workloads110(such as data analytics processing114, for example) to accomplish the various purposes of the illustrated embodiments of the present invention.

As aforementioned, the resultant goal of the mechanisms described herein is to generate a scheduling of a given workload to cluster hosts optimized with data access awareness, such that the workload is performed within the cluster hosts with which the data required to be accessed by the workload is most optimally available or attainable relative to other cluster hosts. To realize this goal, an architecture500for workload management and scheduling in a computing cluster is presented inFIG. 5.

The architecture500includes the storage system20as previously described which is in communication with a workload manager502having multiple modules contained therein, including at least a data requirements evaluator module506and a scheduler module510. It should be noted that, as one of ordinary skill in the art would appreciate, the multiple modules described in architecture500(i.e., the data requirements evaluator module506and scheduler module510) may be each comprised of computer-executable code portions or may be comprised of one or more physical hardware module(s) within the distributed computing environment, to accomplish the functionality presented herein. Moreover, the workload manager502may include further, additional modules than those instantly disclosed.

In various embodiments, the data requirements evaluator module506receives at least three types of input (referenced as blocks504) from the storage system20and from other modules in the workload manager502, as will be described. The data requirements evaluator module506then generates a list of cluster hosts ranked for running the given workload according to data access considerations associated with the received inputs from the respective modules.

A first input504received by the data requirements evaluator506may include data locality proportions generated for a set of files associated with a given workload. This data locality proportions input specifies, for the set of files associated with the given workload, the proportion of the total data of the set of files that is stored on each of the hosts in the cluster. In other words, the data locality proportions input indicates which proportion of the total data of the set of files is stored on differing respective hosts storing the underlying data.

A second input504received by the data requirements evaluator506may include data access costs specified for each pair of hosts in the cluster. This data access costs input specifies, for each host in the cluster, the cost (e.g., with regard to latency and other considerations) of accessing data stored on any other host in the cluster.

In addition to the aforementioned inputs, further inputs504may be received by the data requirements evaluator module506specified herewith, including: (a) an indication as to whether the current workload is intensive in I/O of new data or intensive in I/O of existing data. This indication input can typically be retrieved from other modules in the workload manager502that track certain information on workload attributes, and/or from the storage system20which may track information on workload I/O patterns; (b) an availability of compute resources in the cluster. This input can typically be retrieved from other modules in the workload manager502that track compute resources availability in the cluster; and (c) an availability of storage resources (free storage space) in the cluster. This input can typically be retrieved from the storage system20. Given the inputs specified previously, the data requirements evaluator module506then uses the information associated with each input to generate the list of cluster hosts ranked for running the given workload according to the data access considerations508.

In various embodiments, the scheduler module510receives, from the data requirements evaluator module506, the list of cluster hosts ranked for running the given workload according to the data access considerations, and subsequently generates as output512a scheduling of the given workload to certain cluster hosts, where the output scheduling is optimized with data access awareness.

In various embodiments, the storage system20stores the underlying data required to perform the given workload, provides access to this data, and provides the aforementioned inputs to the various modules in the workload manager502.

Data Requirements Evaluator Algorithm

FIG. 6illustrates a flowchart diagram illustrating an exemplary method/algorithm600for evaluating data requirements of workloads in the computing cluster, in accordance with aspects of the present invention. More specifically, the method600specifies the considered algorithm applied by the data requirements evaluator module506to generate the list of cluster hosts ranked for running the given workload according to the data access considerations. The algorithm of the data requirements evaluator module506handles at least three exemplary scenarios specified herewith.

The method600may be performed in accordance with the present invention in any of the environments depicted inFIGS. 1-4, among others, in various embodiments. Of course, more or less operations than those specifically described inFIG. 6may be included in method600, as would be understood by one of skill in the art upon reading the present descriptions.

In a first scenario, the workload is intensive in I/O of existing data. That is, the workload is determined to be intensive in utilizing existing data stored in, for example, the storage system20. Thus, beginning at block602and provided that the given workload is indeed intensive in I/O at block604, and further upon determining that the workload is intensive in I/O of the existing data in block606, the algorithm600generates an ordered list of preferred hosts according to data locality and data access costs information in block610. The method600then ends in block614.

In a second scenario, the workload is intensive in I/O of new data. That is, the workload is determined to be intensive in creating new data to be stored in, for example, the storage system20. Thus, returning to block604where it is determined that the given workload is indeed intensive in I/O, and further upon determining that the workload is intensive in creating new data in block606, the algorithm600generates an ordered list of preferred hosts according to available storage in block608. Specifically, the more available storage space associated with a host when compared to other hosts in the cluster, the higher the ranking of this host with the more available storage space is in the generated list when compared with the other hosts of the list. The method600then ends in block614.

In the third scenario, the workload is determined not to be intensive in I/O. Thus, returning to block604where it is determined that the workload is not intensive in I/O, the algorithm600generates an empty list of preferred hosts, to indicate that there are no preferred hosts based on data access considerations in block612. The method600then ends in block614.

Generating an Ordered List of Preferred Hosts According to Data Locality and Data Access Costs Information

Following, the considered algorithm for generating the ordered list of preferred hosts according to the data locality and data access costs information is specified. The algorithm is applied by the data requirements evaluator module506to handle the scenario of workloads that are determined to be intensive in I/O of existing data. The algorithm receives, as input, data locality proportions and data access costs (both inputs discussed in further detail in the following), and generates, as output, an ordered list of preferred hosts that is specifically optimized for such workloads intensive in I/O when utilizing existing data stored in the storage system20.

Input: Data Locality Proportions

In some embodiments, a first input received by the algorithm of the data requirements evaluator module506is the data locality proportions generated for the set of files associated with the given workload. This data locality proportions input specifies, for the set of files associated with the given workload, the proportion of the total data of the set of files that is stored on each of the cluster hosts. To generate this input, locality information for each file (i.e., the proportion of the data of each file stored on each of the cluster hosts) is aggregated to the level of the set of files associated with the workload.

In this embodiment, for each host in the cluster a value is calculated, ranging from 0 (indicating that no data of the set of files is stored on the host) to 1 (indicating that all the data of the set of files is stored on the host). It should be noted that it is possible for a section of data to be stored on multiple hosts in the cluster. Further, it should be noted that the reference hereinafter of D[Hi] denotes the proportion of the total size of the set of files associated with the workload that is stored on host Hi (as will be further described within the second and third inputs, following).

FIG. 7illustrates a block diagram of an example of the data locality proportions input700for a given set of files of a workload within the computing cluster. This example of the input700shows three hosts in the cluster, namely Host1(block702), Host2(block704), and Host3(block706). The total size of a set of files associated with a given workload is shown as an aggregated bar that includes a demarked bar and a lined bar, where the aggregated bar represents 100% of the total size of the set of files. The proportion of the total size of the set of files associated with the given workload that is stored on each host (referenced as a percentage) is therefore shown as a lined bar under each demarked bar, illustrated for Host1(702) or D[H1] having 40% of the total data of the set of files or a value of 0.4; for Host2(704) as D[H2] having 20% of the total data of the set of files or a value of 0.2; and for Host3(706) as D[H3] having 80% of the total data of the set of files or a value of 0.8. Note in this example that, as referenced above, the total percentage (40%+20%+80%) of all the hosts equals a percentage greater than 100%, as a section of the data of the set of files may be stored within multiple hosts in the cluster.

Input: Data Access Costs

In some embodiments, a second input received by the algorithm of the data requirements evaluator module506is the data access costs specified for each pair of hosts in the cluster. This information is calculated for the entire cluster, or in other words, data access costs are evaluated for each pair of hosts in the entire cluster. For a current cluster topology, this calculated information is static, however upon determining a topology change in the cluster has taken place (e.g., addition/removal of hosts to the cluster), the data access costs information for the new cluster topology is updated within the data requirements evaluator module506.

As aforementioned, the data access costs information is calculated for each pair of hosts in the cluster and may be represented in a matrix, where the matrix notation may be:
C[Hi, Hj]=Cost of accessing data stored in hostHjfrom hostHi
The value range for each cell in the matrix may range from 0 to 1, where 0=local host access, and 1=a maximum network access cost (e.g., a maximum network access cost beyond a predetermined latency threshold). In various embodiments, the data access costs may be calculated automatically using existing functionality that runs on each host by performing I/O to each of the other hosts in the cluster, measuring a latency of the I/O, and computing statistical metrics thereof. When a new host joins the cluster, this functionality should run on the new host in addition to each of the existing hosts in the cluster to measure the latency to the new host. Moreover, a further option may comprise tracking ongoing data related networking between the cluster hosts and inferring data access costs based on this information.

The following table 100 shows an example of data access costs for 3 hosts in a cluster using the value range previously specified. In this example, hosts 2 and 3 are closer to each other (i.e., having a value range less than 1), and host 1 is farther especially from host 3 (i.e., having a value range of 1).

TABLE 100Hosts123100.5120.500.2310.20
Calculating an Ordered List of Preferred Hosts

In various embodiments and given the aforementioned two inputs, the algorithm of the data requirements evaluator module506then generates the ordered list of preferred hosts for running (executing) the given workload. The ordering of the hosts is generated based on, for each host Hi, the computation of an expected cost for data access for a workload running on the host Hiaccording to the following proposed formula:

The prescribed formula enables the computation of an expected cost for data access for a workload running on host Hi, denoted as EC[Hi], by multiplying the expected cost of retrieving data from a host other than host Hiwith the probability of this event (i.e., the probability of retrieving data from the host other than host Hi). It is additionally assumed, as specified previously, that the cost of data access on host Hi(i.e., the local host) is zero in terms of network latency.

As noted in the given formula, the probability of retrieving data from a host other than host Hiis given by the proportion of the total size of the files associated with the given workload which is not stored on host Hi. This element may be computed based on the data locality proportions input previously described. Further, the expected cost of retrieving data from a host other than host Hiis given by summarizing, over all hosts in the cluster other than host Hi, the cost of retrieving data from a host Hjby a workload running on host Himultiplied by the probability of retrieving data from host Hjby a workload running on host Hi. A proposed method for calculating this probability is specified in the following formula:

In this formula for computing the probability, the probability of retrieving data from host Hjby a workload running on host Hiis calculated by dividing the proportion of the data portion that is stored on host Hjwith the total data proportions stored on all hosts in the cluster other than host Hi. To produce an output ordered list of preferred hosts that is optimized for workloads intensive in I/O of existing data, the hosts are ordered based on an ascending order of their calculated EC[Hi] values. Namely, the lower the EC[Hi] value of a host Hi, the higher is the preference for host Hito be selected for running the given workload.

Workload Manager Algorithm

In some embodiments, the scheduler module510of the workload manager502receives a resource allocation request for performing the given (input) workload, accompanied by the ordered list of preferred hosts ranked for running the given workload according to data access considerations, computed by the data requirements evaluator module506.

The scheduler module510then attempts to allocate compute resources from the preferred hosts according to the resource allocation request associated with the given workload and the ordered list of preferred hosts (provided the list is not empty), to satisfy the allocation request. The output of the scheduler module510is a scheduling of the given workload to cluster hosts, where the scheduling is optimized with data access awareness, and the given workload is performed using the allocated compute resources within these hosts of the cluster.

Aggregating File Level Locality Information to the Level of the Set of Files

In various embodiments, an algorithm for aggregating the locality information from a file level to the level of a set of files is performed in accordance with the following.

(1) First, a set of storage size counters is reset, where each counter is assigned to a host in the cluster. Further, an overall storage size counter is additionally reset for the cluster. These counters specify the data size of the proportion of data stored on each host of the cluster. (2) All the files in the set are subsequently scanned, and for each file: (a) the locality information of the current file is retrieved (i.e., the data size of the file stored on each of the cluster hosts). This information is typically obtained from the storage system20; (b) the locality information of the current file is added to the storage size counters of the hosts; and (c) the total size of the current file is added to an overall storage size counter. (3) The proportion of the storage size counter of each host from the overall storage size counter is then computed.

In cases where a set of files associated with a given workload is large, it may be prohibitive in terms of performance to query the locality information of each individual file of the set of files. For such cases of a large set of files (i.e., a set of files where the number of files in the set is over a predetermined threshold), the following optimizations are considered. One optimization may include computing and maintaining approximations of the locality information for the set of files. For example, the approximations may be based on querying a subset of the files from the set of files, where the subset of files can be any combination of the following criteria: (a) the K largest files of the set of files; (b) the L files characterized with the highest I/O access; and/or (c) the M files having the most recent I/O access.

In this example, where K, L, and M are predetermined values being smaller than the total number of files in the set of files. The values of K, L, and M should be selected such that the typical cost and/or time for querying the locality information of the resulting number of files will be no larger than an acceptable threshold. Since the metrics of size, I/O access patterns and access recency of files are dynamic over time, these metrics may be calculated as statistical values for a recent window of time.

In some embodiments, a combination of the described criteria may be computed, for example, by calculating for each file a weighted aggregated metric based on the file's metrics for each of the criteria. The considered technique therefore selects a subset of files from the set of files based on the given criteria, queries the locality information for the files in the subset of files, aggregates this information, and updates the approximations of the locality information for the full set of files based on this information. The approximations may additionally be maintained and associated with a type (or template) of a workload, rather than a specific instance of a workload that is submitted for execution.

To further reduce the overhead of querying the locality information, further proposed is a method for defining triggers for updating the approximations of the locality information for the set of files. Defining triggers for updating the approximations of the locality information may be based on any combination of the following criteria, such that the triggers may include: (a) a registration of a type (or template) of a workload; (b) a request to deploy an instance of a workload type to run in the cluster; and/or (c) the detection of an elapsed time period from the previous update of the approximations exceeds a specified threshold. An example trigger may therefore comprise a request to deploy an instance of a workload type, where the request is submitted at a time whose difference from the latest update time is not smaller than the specified threshold.

FIG. 8illustrates a flowchart diagram illustrating an exemplary method of the algorithm for computing data locality information associated with the given workload in the computing cluster, illustrating the aforementioned concepts. The method800may be performed in accordance with the present invention in any of the environments depicted inFIGS. 1-4, among others, in various embodiments. Of course, more or less operations than those specifically described inFIG. 8may be included in method800, as would be understood by one of skill in the art upon reading the present descriptions.

The method800begins in block802by determining whether a trigger (based on the criteria specified previously) for updating data locality approximations has occurred (block804). If no trigger has been detected, the method800proceeds to wait for a trigger in block812and returns to block804. If, at block804, a trigger has been detected, a subset of files from within the set of files associated with the workload is selected for updating the data locality approximations in block806. Locality information for these files within the subset of files is queried, and this locality information is aggregated from the file level to the level of the subset of files in block808. Finally, the data locality approximations are updated within the data requirements evaluator module506in block810using the aggregated locality information for the subset of files, and the method800proceeds to wait for another triggering event in block812. Of note and as previously specified, the data locality approximations may be associated with a type (or template) of a workload, rather than a specific instance of a workload that is submitted for execution, as referenced in block810A.

FIG. 9illustrates an additional flowchart diagram illustrating an exemplary method for workload management with data access awareness in the computing cluster, by which aspects of the present invention may be implemented. The method900may be performed in accordance with the present invention in any of the environments depicted inFIGS. 1-4, among others, in various embodiments. Of course, more or less operations than those specifically described inFIG. 9may be included in method900, as would be understood by one of skill in the art upon reading the present descriptions.

The method900begins in block902by configuring a workload manager within the computing cluster to include a data requirements evaluator module and a scheduler module, as in block904. In response to receiving an input workload for scheduling by the workload manager: the data requirements evaluator module retrieves a set of inputs from a storage system, wherein the inputs each include at least one or more of (a) data locality proportions for a set of files associated with the input workload, the data locality proportions specifying a respective proportion of a total data size of the set of files associated with the input workload stored on each of a plurality of cluster hosts of the computing cluster, and (b) data access costs specified for each pair of cluster hosts in the computing cluster, wherein the data access costs are computed for an entirety of the computing cluster, as in block906. The data requirements evaluator module subsequently generates a list of cluster hosts ranked for running the input workload according to data access considerations in block908. The method900ends in block910.