Object storage workflow optimization leveraging storage area network value adds

A mechanism is provided for optimizing object storage workflow. A category of a computational algorithm received from a user of a client device is identified, the category identifying a set of storage area network (SAN) features that are optimal for executing the computational algorithm. Features associated with a plurality of nodes in a plurality of infrastructures in an object storage architecture are searched for at least one node that has the set of features identified by the category of the computational algorithm. Responsive to identifying a node that has the set of features identified by the category of the computational algorithm, a determination is made as to whether resources associated with the node are immediately available. Responsive to the resources associated with the node being immediately available, the computational algorithm is issued to the node for execution.

BACKGROUND

The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for object storage workflow optimization leveraging storage area network virtualization value adds.

Traditionally, object storage is used for backup, archival, data mining, searching, analytics, and the like.FIG. 1depicts an example of a traditional object storage architecture. Traditional object storage architecture100comprises two diverse infrastructures102and112that are accessible by client devices120and122via load balancer124. Each of infrastructures102and112further comprise two node groups. The first node groups104and114comprise proxy nodes104a-104nand114a-114nthat are used for distributed load handling/request handling from client devices120and122into the storage namespace. The second node groups106and116, i.e. the storage namespace, comprises storage nodes106a-106nand116a-116nthat are responsible for writing to the disks or storage subsystems and, in this illustrative architecture, purely serves as a storage unit repository. However, in order to analyze or extract any meaningful information from raw data retrieved from the storage nodes106a-106nand116a-116nin second node groups106and116, the data must be sent back to client120and122or to an additional client126or compute node128for analysis.

With the evolution of embedded compute infrastructures with built-in object storage architecture, computation utilizing the data stored in these compute infrastructures is offloaded to storage units instead of using a traditional client device for computation purposes.FIG. 2depicts an example of an embedded compute engine in an object storage (Storlet) architecture. As with the architecture shown inFIG. 1, storlet architecture200ofFIG. 2comprises two diverse infrastructures202and212that are accessible by client devices220and222via load balancer224. Each of infrastructures202and212further comprise two node groups. The first node groups204and214comprise proxy nodes204a-204nand214a-214nthat are used for distributed load handling/request handling from client devices220and222into the storage namespace. The second node groups206and216, i.e. the storage namespace, comprises storage nodes206a-206nand216a-216nthat are responsible for writing to the disks or storage subsystems.

However, in addition to the common infrastructure, storlet architecture200also comprises software engines208and218as shown within second node groups206and216, respectively. In an alternative embodiment, software engines208and218may reside within first node groups204and214. Utilizing software engines208and218, any computation or analysis required by client device220or222may be implemented by software engine208or218. However, a user of client devices220and222has to frame computational algorithm to perform the computation or analysis and has to deploy or pass the computational algorithm to software engine208or218at the time of the original request. Then software engine208or218sends the results of the computation back to the requesting user of client device220or222. Therefore, storlet architecture200differs from the traditional object storage architecture100ofFIG. 1in that, storlet architecture200does not require any additional client or compute node to perform computation or analysis of the data. That is, second node groups206and216act as compute nodes and return any results back to the user.

SUMMARY

In one illustrative embodiment, a method, in a data processing system, is provided for optimizing object storage workflow. The illustrative embodiment identifies a category of a computational algorithm received from a user of a client device. In the illustrative embodiment, the category identifies a set of storage area network (SAN) features that are optimal for executing the computational algorithm. In the illustrative embodiment, the set of SAN features comprise one or more of thin provisioning, tiering, compression, deduplication, Flash acceleration support, high/low speed disk rpm support, Active/Active replication, or encryption. The illustrative embodiment searches features associated with a plurality of nodes in a plurality of infrastructures in an object storage architecture for at least one node that has the set of features identified by the category of the computational algorithm. The illustrative embodiment determines whether resources associated with the node are immediately available in response to identifying a node that has the set of features identified by the category of the computational algorithm. The illustrative embodiment issues the computational algorithm to the node for execution in response to the resources associated with the node being immediately available. In the illustrative embodiment, execution of the computational algorithm returns results to the user via the client device.

DETAILED DESCRIPTION

As discussed previously, a storlet (embedded compute engine in an object storage) architecture comprises a software engine present within the nodes, the nodes being a storage node or a proxy node. When an end user wants the storlet architecture to perform a computation, the end user has to frame a computational algorithm and deploy or pass the computational algorithm to the embedded software engine as a normal object PUT operation. The storlet architecture does not require any additional client or compute node to perform analysis of the data. That is, in the storlet architecture, the storage nodes/proxy nodes themselves act as compute node and return computational results back to the end user. The storlet architecture also uses virtual machines, such as Linux™ containers, Docker, ZeroVM, or the like, deployed on the storage nodes/proxy nodes to perform the computation tasks.

Currently, datacenters comprise a plurality of servers that are coupled to shared pools of object storage devices via a storage area network (SAN) that is a dedicated high-speed network. A SAN moves storage resources off a common user network and reorganizes them into an independent, high-performance network. This allows each server to access the shared pools of object storage devices as if the shared storage were a storage drive directly attached to the server. When a host wants to access a storage device on the SAN, the host sends out a block based access request for the storage device. Value additions provided by SAN storage include, but are not limited to, thin provisioning, tiering, compression, deduplication, Flash acceleration support, high/low speed disk rpm support, Active/Active replication, encryption, or the like.

The storlet engine deployed within these object storage units which helps in preparing the hardware resources computation ready typically comprises a virtualization unit (may be Linux™ containers, Docker, ZeroVM, or the like) and few middleware's (software units) that helps decide the computation operation to be performed by the virtualization unit based on the user deployed computational algorithm. The signal flow graph corresponding to storlet engine is as follows:user deploys computational algorithm (PUT operation)parse for syntax errorsdetermine the computation operation (may be arithmetic or any specialized operation such as txt to pdf, editing jpg, or the like)determine the node to be used for instantiating virtualization unitpass the computation operation to virtualization unitvirtualization unit pulls/reads/writes data based on the steps defined in the computational algorithmreturn results back to the user with code (success/failure).

The traditional storlet engine treats each node participating in the object storage cluster equally (irrespective of their type, model, operating system flavor, version, virtualization technology, etc.) and identifies the nodes unique using network address (IP address). With this kind of model, the storlet engine execution steps of: determining the node to be used for instantiating virtualization unit and passing the computation operation to virtualization unit, is achieved in two ways:1. Virtualization unit may be instantiated on the node that comprises maximum data required for fulfilling that particular computational algorithm.2. Virtualization unit may be instantiated on the node that comprises maximum available hardware resources.

Therefore, these scaled-out object storage units are built using commodity hardware mostly reusing the existing infrastructure. In a large enterprise environment, raw storage is primarily derived from a plurality of SAN appliances, either single or multi-vendor appliances, maintained as a pool and supplied to compute units depending upon their necessity. Without taking into consideration the features of the object storage units, such as hardware accelerators, disk speed, tiering, compression, deduplication, optimization efficiency, or the like, scheduling based on the above described two scheduling techniques proves to be less efficient in the case of a multi-SAN storage environment.

For example, in a scenario where in the storage servers used to build an object storage system are powered by volumes derived from multi-vendor SAN units (nodes 1, 2, and 3 being powered by storage derived from Hewlett-Packard® (HP®) SAN unit, nodes 4, 5, and 6 being powered by International Business Machines Corporation (IBM®) SAN unit, and nodes 7, 8, and 9 are powered by Hitachi® SAN unit, these SAN storage units contain different built-in features which helps the SAN storage units process faster in case of certain workloads. For example, HP® SAN volumes contain special hardware accelerators which provides faster processing of compression workloads, IBM® SAN volumes contain special ASIC (Application Specific Integrated Chip) which provides interaction with FLASH disks and provides faster processing of arithmetic workloads, Hitachi® SAN volumes contain special software which provides for faster image processing. In this scenario, assume an end user has deployed a computational algorithm that is identified as an arithmetic operation and both the IBM® and HP® storage units reported a similar resource availability. As current storlet engines treat all nodes as equal, the storlet engine receiving the computational algorithm may assign this computational algorithm to a node powered by HP storage unit for handling, which in turn results in poor performance in terms of increased time for processing and increased load on storage unit. That is, if the features of the SAN storage units were taken into consideration, the storlet engine would have selected the node powered by IBM storage unit for this workload which could deliver better results than any other nodes because the IBM® SAN volumes units contain special ASIC (Application Specific Integrated Chip) which provides interaction with FLASH disks and provides faster processing of arithmetic workloads.

The core reason for the above mentioned problem is lack of framework and middleware's which helps storlet engine (embedded compute infrastructure within object storage) to understand the underlying storage features and select the nodes based on the workloads (computational algorithm input) which can be accelerated using a particular SAN platform.

In order to perform object storage workflow optimization that leverages underlying storage features or value adds built into the SAN storage devices, such as thin provisioning, tiering, compression, deduplication, Flash acceleration support, high/low speed disk rpm support, Active/Active replication, encryption, or the like, the illustrative embodiments storlet scheduler mechanism installs a daemon on each infrastructure participating in the object storage cluster. This daemon collects the SAN storage features powering the storage nodes as well as the role executed by each node in the SAN storage (i.e. proxy or storage). Each daemon exports the collected information to the storlet scheduler mechanism. Using the collected storage information, for each node, the storlet scheduler mechanism identifies the underlying storage features along with the role served by the node (i.e. proxy or storage). For example, the information collected for a first node may reveal the SAN capabilities of: thin provisioning, tiering, compression, deduplication, and proxy node. As another example, the information collected for a second node may reveal the SAN capabilities of: replication, encryption, a disk speed of 7200 rpm, and storage node.

Responsive to receiving a deployed computational algorithm from an end user, the storlet scheduler mechanism parses the computational algorithm to identify the operations required within the computational algorithm. Utilizing the identified operations from the computational algorithm and a set of predefined rules for operations, the storlet scheduler mechanism identifies a class of each operation such as encryption, seismic processing, mobile/code render operations, compress and store, or the like. With the class of the operation identified, the storlet scheduler mechanism determines a category of the computational algorithm based on the identified class from a pre-programmed table of categories and value adds. Examples of category classification of deployed computational algorithm by the proposed middleware may include:Computation operations (Encrypt an object)→Encryption categoryComputation operations (Mobile development)→Developer categoryComputation operations (Seismic data processing)→Arithmetic categoryComputation operations (Compress and Store)→Direct Memory category

For each category within the pre-programmed table of categories and value adds, the value adds associated with each category are preferred SAN features, such as encryption, Flash acceleration, deduplication, compression, or the like. Utilizing the identified category and value adds associated with the computational algorithm, the storlet scheduler mechanism searches the storage features associated with each node for features that best match the value adds associated with the identified category of the computational algorithm. Responsive to determining a best match node, the storlet scheduler mechanism schedules the computational algorithm to be executed on the best match node.

Thus, the mechanisms of the illustrative embodiments provide for a storlet scheduler mechanism that improves computation performance and reduces workload on the object storage units in a multi-vendor commodity SAN powered object storage environment by performing specific workflow changes in the embedded compute engine according to SAN features, such as thin provisioning, tiering, compression, deduplication, Flash acceleration support, high/low speed disk rpm support, Active/Active replication, encryption, or the like.

FIG. 3depicts a pictorial representation of an example distributed data processing system in which aspects of the illustrative embodiments may be implemented. Distributed data processing system300may include a network of computers in which aspects of the illustrative embodiments may be implemented. The distributed data processing system300contains at least one network302, which is the medium used to provide communication links between various devices and computers connected together within distributed data processing system300. The network302may include connections, such as wire, wireless communication links, or fiber optic cables.

In the depicted example, server304and server306are connected to network302along with storage unit308and storage unit309. In addition, clients310,312, and314are also connected to network302. These clients310,312, and314may be, for example, personal computers, network computers, or the like. In the depicted example, server304provides data, such as boot files, operating system images, and applications to the clients310,312, and314. Clients310,312, and314are clients to server304in the depicted example. Distributed data processing system300may include additional servers, clients, and other devices not shown.

As shown inFIG. 3, one or more of the computing devices, e.g., storage unit308and storage unit309, may be specifically configured to implement a storlet scheduler mechanism. The configuring of the computing device may comprise the providing of application specific hardware, firmware, or the like to facilitate the performance of the operations and generation of the outputs described herein with regard to the illustrative embodiments. The configuring of the computing device may also, or alternatively, comprise the providing of software applications stored in one or more storage devices and loaded into memory of a computing device, such as server104, for causing one or more hardware processors of the computing device to execute the software applications that configure the processors to perform the operations and generate the outputs described herein with regard to the illustrative embodiments. Moreover, any combination of application specific hardware, firmware, software applications executed on hardware, or the like, may be used without departing from the spirit and scope of the illustrative embodiments.

It should be appreciated that once the computing device is configured in one of these ways, the computing device becomes a specialized computing device specifically configured to implement the mechanisms of the illustrative embodiments and is not a general-purpose computing device. Moreover, as described hereafter, the implementation of the mechanisms of the illustrative embodiments improves the functionality of the computing device and provides a useful and concrete result that facilitates improving computation performance and reducing workload on object storage units in a multi-vendor commodity SAN powered object storage environment by performing specific workflow changes in the embedded compute engine according to SAN storage value additions.

As noted above, the mechanisms of the illustrative embodiments utilize specifically configured computing devices, or data processing systems, to perform the operations for improving computation performance and reducing workload on object storage units in a multi-vendor commodity SAN powered object storage environment by performing specific workflow changes in the embedded compute engine according to SAN storage value additions. These computing devices, or data processing systems, may comprise various hardware elements that are specifically configured, either through hardware configuration, software configuration, or a combination of hardware and software configuration, to implement one or more of the systems/subsystems described herein.FIG. 4is a block diagram of an example data processing system in which aspects of the illustrative embodiments may be implemented. Data processing system400is an example of a computer, such as server304, storage unit308, and client310inFIG. 3, in which computer usable code or instructions implementing the processes for illustrative embodiments of the present invention may be located.

In the depicted example, data processing system400employs a hub architecture including north bridge and memory controller hub (NB/MCH)402and south bridge and input/output (I/O) controller hub (SB/ICH)404. Processing unit406, main memory408, and graphics processor410are connected to NB/MCH402. Graphics processor410may be connected to NB/MCH402through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter412connects to SB/ICH404. Audio adapter416, keyboard and mouse adapter420, modem422, read only memory (ROM)424, hard disk drive (HDD)426, CD-ROM drive430, universal serial bus (USB) ports and other communication ports432, and PCI/PCIe devices434connect to SB/ICH404through bus438and bus440. PCI/PCIe devices may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not. ROM424may be, for example, a flash basic input/output system (BIOS).

An operating system runs on processing unit406. The operating system coordinates and provides control of various components within the data processing system400inFIG. 4. As a client, the operating system may be a commercially available operating system such as Microsoft® Windows 7®. An object-oriented programming system, such as the Java™ programming system, may run in conjunction with the operating system and provides calls to the operating system from Java™ programs or applications executing on data processing system400.

As a server, data processing system400may be, for example, an IBM eServer™ System p® computer system, Power™ processor based computer system, or the like, running the Advanced Interactive Executive (AIX®) operating system or the LINUX® operating system. Data processing system400may be a symmetric multiprocessor (SMP) system including a plurality of processors in processing unit406. Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as HDD426, and may be loaded into main memory408for execution by processing unit406. The processes for illustrative embodiments of the present invention may be performed by processing unit406using computer usable program code, which may be located in a memory such as, for example, main memory408, ROM424, or in one or more peripheral devices426and430, for example.

A bus system, such as bus438or bus440as shown inFIG. 4, may be comprised of one or more buses. Of course, the bus system may be implemented using any type of communication fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. A communication unit, such as modem422or network adapter412ofFIG. 4, may include one or more devices used to transmit and receive data. A memory may be, for example, main memory408, ROM424, or a cache such as found in NB/MCH402inFIG. 4.

As mentioned above, in some illustrative embodiments the mechanisms of the illustrative embodiments may be implemented as application specific hardware, firmware, or the like, application software stored in a storage device, such as HDD426and loaded into memory, such as main memory408, for execution by one or more hardware processors, such as processing unit406, or the like. As such, the computing device shown inFIG. 4becomes specifically configured to implement the mechanisms of the illustrative embodiments and specifically configured to perform the operations and generate the outputs described hereafter with regard to a storlet scheduler mechanism that improves computation performance and reduces workload on the object storage units in a multi-vendor commodity SAN powered object storage environment by performing specific workflow changes in the embedded compute engine according to SAN storage value additions.

FIG. 5depicts a functional block diagram of a storlet scheduler mechanism that improves computation performance and reduces workload on the storage area network (SAN) powered object storage units in accordance with one illustrative embodiment. Object storage architecture500comprises a plurality of diverse infrastructures502a-502nthat are accessible by client devices504a-504nvia load balancer506. Each of infrastructures502a-502ncomprises a set of node groups508and510. First node group508is proxy nodes that are used for distributed load handling/request handling from client devices504a-504ninto the storage namespace. Second node group510is storage nodes, i.e. the storage namespace, that are responsible for writing to the disks or storage subsystems. Further, each of the infrastructures502a-502ncomprise software engine512. As discussed previously, embedded compute engine512performs any computation or analysis required by client devices504a-504n. A user of client devices504a-504nhas to frame the computational algorithm to perform the computation or analysis and has to deploy or pass the computational algorithm to embedded compute engine512at the time the request is submitted. Embedded compute engine512then sends the results of the computation back to the requesting user of client device504a-504n.

In accordance with the illustrative embodiments, in order to perform object storage workflow optimization that leverages underlying SAN storage value adds, object storage architecture500also comprises storlet scheduler mechanism514. In operation, storlet scheduler mechanism514initially installs daemon516on each infrastructure participating in the object storage cluster. Daemon516collects SAN storage feature information, such as thin provisioning, tiering, compression, deduplication, Flash acceleration support, high/low speed disk rpm support, Active/Active replication, encryption, or the like, powering of the infrastructure on which the daemon516is installed as well as the role executed by each node (i.e. proxy or storage) on the infrastructure. Each daemon516exports the collected information from the node to storlet scheduler mechanism514. Using the collected storage information, for each node, storlet scheduler mechanism514identifies the underlying storage features (value add features) along with the role served by the node (i.e. proxy or storage), which is then stored in storage518as node features520. For example, the information collected from infrastructure502areveals the SAN features of: thin provisioning, tiering, and compression. The information from node502breveals the SAN features of: deduplication and Flash acceleration. The information from node502creveals the SAN features of: Active/Active replication, encryption, and high-speed disk. The information from node502dreveals the SAN features of: thin provisioning, tiering, and compression. The information from node502nreveals the SAN features of: deduplication and Flash acceleration. Again, each infrastructure may contain both proxy nodes and storage nodes. Therefore, for each node on the infrastructure, storlet scheduler mechanism514stores the operating system type, operating system version, virtualization technology, and hardware associated with that node. As an example, if infrastructure502acomprises two proxy nodes and two storage nodes, storlet scheduler mechanism514would store the following in node features520:IN:502a, Node 1: Proxy, thin provisioning, tiering, and compression.IN:502a, Node 2: Proxy, thin provisioning, tiering, and compression.IN:502a, Node 3: Store, thin provisioning, tiering, and compression.IN:502a, Node 4: Store, thin provisioning, tiering, and compression.

With the value add features of each node collected and stored, storlet scheduler mechanism514waits for a computational algorithm from a user of one of client devices504a-504n. Responsive to receiving a computational algorithm from a user, storlet scheduler mechanism514determines whether the computational algorithm has an identified category. If not, then storlet scheduler mechanism514parses the computational algorithm to identify the operations required within the computational algorithm. Utilizing the identified operations from the computational algorithm and a set of predefined rules for operations, storlet scheduler mechanism514identifies a class of each operation such as encryption, seismic processing, mobile/code render operations, compress and store, or the like. With the class of the operation identified, storlet scheduler mechanism514determines a category of the computational algorithm based on the identified class from a pre-programmed table of categories and value adds. Examples of category classification of a deployed computational algorithm by the proposed middleware may include:Computation operations (Encrypt an object)→Encryption categoryComputation operations (Mobile development)→Developer categoryComputation operations (Seismic data processing)→Arithmetic categoryComputation operations (Compress and Store)→Direct Memory category

For each category within the pre-programmed table of categories and value adds, the value adds associated with each category are an optimal set of SAN features, such as preferred SAN features of encryption, Flash acceleration, deduplication, compression, or the like. Utilizing the identified category and value adds associated with the computational algorithm, storlet scheduler mechanism514searches the node features520associated with each node for features that match the value adds associated with the identified category of the computational algorithm. The result of the search may result in a ranked list of nodes indicating a percentage that each node matches the value adds associated with the identified category of the computational algorithm.

Responsive to determining one or more exact matches of the value adds associated with the identified category of the computational algorithm and a node from the node features520, storlet scheduler mechanism514randomly selects a node from one of the one or more exact matches and determines whether resources associated with the node are immediately available. If the resources are immediately available, then storlet scheduler mechanism514issues the computational algorithm to the node for execution. If the resources are not immediately available, then storlet scheduler mechanism514selects another of the one or more exact matches and determines whether resources associated with the node are immediately available and repeats the process until all nodes have been checked. If none of the one or more exact match nodes has resources immediately available, storlet scheduler mechanism514continues to check each node until one of the nodes has resources available, at which time storlet scheduler mechanism514issues the computational algorithm to the node for execution.

Responsive to determining only one exact match of the value adds associated with the identified category of the computational algorithm and a node from the node features520, storlet scheduler mechanism514determines whether resources associated with the node are immediately available. If the resources are immediately available, then storlet scheduler mechanism514issues the computational algorithm to the node for execution. If the resources are not immediately available, then storlet scheduler mechanism514waits for a predetermined time period for the resources to become available. If the resources become available within the predetermined time period, storlet scheduler mechanism514issues the computational algorithm to the node for execution.

If the resources fail to become available within the predetermined time period, then storlet scheduler mechanism514may select a next node in the ranked list and determine whether resources associated with the node are immediately available. If the resources are immediately available, then storlet scheduler mechanism514issues the computational algorithm to the node for execution. If the resources are not immediately available, then storlet scheduler mechanism514waits for a predetermined time period for the resources to become available. If the resources become available within the predetermined time period, storlet scheduler mechanism514issues the computational algorithm to the node for execution. Storlet scheduler mechanism514repeats the process for each node in the ranked list until the computational algorithm is issued to a node for execution.

FIG. 6depicts a flowchart of the operation performed by a storlet scheduler mechanism in optimizing object storage workflow that leverages underlying SAN feature value adds in accordance with an illustrative embodiment. As the operation begins, the storlet scheduler mechanism receives a computational algorithm from a user of one of a set of client devices (step602). Responsive to receiving a computational algorithm from a user, the storlet scheduler mechanism determines whether the computational algorithm has an identified category (step604). If at step604the computational algorithm fails to have an identified category, the storlet scheduler mechanism parses the computational algorithm to identify the operations required within the computational algorithm (step606). Utilizing the identified operations from the computational algorithm and a set of predefined rules for operations, the storlet scheduler mechanism identifies a class of each operation (step608) such as encryption, seismic processing, mobile/code render operations, compress and store, or the like. The storlet scheduler mechanism then determines a category of the computational algorithm based on the identified class from a pre-programmed table of categories and value adds (step610). For each category within the pre-programmed table of categories and value adds, the value adds associated with each category are an optimal set of SAN features, such as preferred SAN features of encryption, Flash acceleration, deduplication, compression, or the like.

From step610or if at step604the computational algorithm has an identified category, the storlet scheduler mechanism utilizes the identified category and value adds associated with the computational algorithm to search node features associated with each node for features that match the value adds associated with the identified category of the computational algorithm (step612). The node features associated with each node may be gathered by the storlet scheduler mechanism receiving and storing SAN feature information powering each infrastructure participating in the object storage cluster as well as the role executed by each node on the infrastructure (i.e. proxy or storage). The information is collected by individual daemons installed on each infrastructure. Each daemon exports the collected information from the node to the storlet scheduler mechanism. Using the collected storage information, for each node, the storlet scheduler mechanism identifies the underlying SAN features (value add features) along with the role served by the node (i.e. proxy or storage). Therefore, for each node on the infrastructure, the storlet scheduler mechanism stores associated SAN features, such as thin provisioning, tiering, compression, deduplication, Flash acceleration support, high/low speed disk rpm support, Active/Active replication, encryption, or the like. The result of the search may result in a ranked list of nodes indicating a percentage that each node matches the value adds associated with the identified category of the computational algorithm.

The storlet scheduler mechanism then determines whether there are any exact matches of the value adds associated with the identified category of the computational algorithm one or more nodes from the node features (step614). If at step614there are one or more exact matches, then the storlet scheduler mechanism determines whether there is more than one exact match (step616). If at step616there is more than one exact match, the storlet scheduler mechanism randomly selects a node from one of the more than one exact match (step618) and determines whether resources associated with the node are immediately available (step620). If at step620the resources associated with the node are immediately available, then the storlet scheduler mechanism issues the computational algorithm to the node for execution (step622) with the operation terminating thereafter. If at step620the resources are not immediately available, the operation returns to step618to select another node from then more than one exact matches. This process is repeated for instances where there is more than one exact match due to performing the computational algorithm on an exact match being preferred.

If at step616there is only one exact match, the storlet scheduler mechanism determines whether resources associated with the node are immediately available (step624). If at step624the resources are immediately available, then the operation proceeds to step622. If at step624the resources are not immediately available, then the storlet scheduler mechanism waits for a predetermined time period for the resources to become available (step626). If at step626the resources become available within the predetermined time period, the storlet scheduler mechanism issues the computational algorithm to the node for execution (step622). If at step626the resources fail to become available within the predetermined time period or if at step614there are no exact matches, then the storlet scheduler mechanism selects a first/next best match node in the ranked list (step628) and proceeds/returns to step624. The storlet scheduler mechanism repeats the process for each node in the ranked list until the computational algorithm is issued to a node for execution. If the end of the list is reached, the storlet scheduler mechanism returns to the top of the list.

Thus, the illustrative embodiments provide mechanisms for improving computation performance and reducing workload on the object storage units in a multi-vendor commodity object storage environment by performing specific workflow changes in the embedded compute engine according to SAN feature value additions such as thin provisioning, tiering, compression, deduplication, Flash acceleration support, high/low speed disk rpm support, Active/Active replication, encryption, or the like.