DYNAMIC AGENT DEPLOYMENT IN A DATA PROCESSING SYSTEM

A method of selecting an agent node for deploying an agent includes identifying a plurality of computing nodes in a distributed computing network that are configured to execute computing jobs, selecting an agent node from among a plurality of agent nodes for deploying an agent within the distributed computing network, wherein the agent controls processing of the computing jobs on at least one of the plurality of computing nodes, and wherein the agent node is selected in response to an anticipated workload on the computing nodes and network path lengths of the agent nodes to the computing nodes, and deploying the agent onto the selected agent node to control processing of at least one the computing jobs on the plurality of computing nodes.

BACKGROUND

The present disclosure relates to data processing systems, and in particular, to the scheduling of jobs in data processing systems.

Data processing systems utilize scheduling engines to schedule execution of computer processes, or “jobs.” Scheduling the execution of computer processes is often referred to as job management, which may involve scheduling a computer process to occur at one designated time, repeatedly at periodic times, or according to other time schedules. Numerous scheduling engines exist today, such as Unicenter CA-7, Unicenter CA-Scheduler, and Unicenter CA-Job track available from Computer Associates.

In a distributed computing environment that includes many different data processing devices, such as a multi-server cluster, job scheduling is an important task. Distributed computing environment typically include software that allocates computing tasks across a group of computing devices, enabling large workloads to be processed in parallel.

Cloud computing/storage environments have become a popular choice for implementing data processing systems. In a cloud computing/storage environment, a cloud provider hosts hardware and related items and provides systems and computational power as a service to a customer, such as a business organization.

Cloud computing/storage environments may support virtual machines (VM), which are emulations of physical machines implemented in software, hardware, or combination of both software and hardware. In a cloud computing environment, jobs may be delegated to virtual machines. Virtual machine resources may be scheduled in a similar manner as physical machine resources. Thus, a distributed computing environment may include a number of network nodes that include physical machines, virtual machines, or a collection of both physical and virtual machines.

Entities to which tasks, or jobs, are assigned by a scheduler are generally referred to as “agents,” computing node and may reside on physical machines and/or virtual machines. An agent can execute jobs locally (i.e., on the same computing node on which the agent is hosted) or remotely (i.e., on a different computing node on which the agent is hosted). Network nodes that can host agents are referred to as “agent nodes.” Network nodes that can execute jobs on behalf of an agent are referred to herein as “computing nodes.” A network node can be both a computing node and an agent node. That is, a network node can both host an agent and execute jobs on behalf of an agent.

SUMMARY

Some embodiments provide methods of selecting agent nodes for deploying agents. The methods may be performed on a computing device. A method according to some embodiments includes identifying a plurality of computing nodes in a distributed computing network that are configured to execute computing jobs, selecting an agent node from among a plurality of agent nodes for deploying an agent within the distributed computing network, wherein the agent controls processing of the computing jobs on at least one of the plurality of computing nodes, and wherein the agent node is selected in response to an anticipated workload on the computing nodes and network path lengths of the agent nodes to the computing nodes, and deploying the agent onto the selected agent node to control processing of at least one the computing jobs on the plurality of computing nodes.

Selecting the agent node may include selecting an agent node that maximizes a value function.

The value function may take into account network path lengths between the agent nodes and the computing nodes and the number of computing jobs to be executed at the computing nodes.

The value function may include: VF=Σ(Wm)(Sma)(Dma), where Wm is the workload at an mth computing node, Dma is a scale factor that depends on a network path length between the mth computing node and an ath agent node, and Sma is a scale factor indicating that an agent deployed on the ath agent node is executing computing jobs on the mth computing node.

The network path length may be based on a number of network hops through intervening network forwarding nodes between the mth computing node and the ath agent node and/or a communication latency between the mth computing node and the ath agent node.

The value of Dma may be equal to one if the ath agent node is the agent node closest to the mth computing node based on the network path length between the mth computing node and the ath agent node, and may be equal to zero if the ath agent node is not the agent node closest to the mth computing node based on the network path length between the mth computing node and the ath agent node.

The value of Sma may be equal to one if the ath agent node is executing computing jobs on the mth computing node, and may be equal to zero if the ath agent node is not executing computing jobs on the mth computing node.

The workload at the mth computing node, Wm, may corresponds to a number of computing jobs to be executed on the mth computing node.

The value of Dma may be equal to an average network path length from all agent nodes to the mth computing node divided by a network path length from the ath agent node to the mth computing node.

The method may further include relocating the agent from the selected agent node to a second agent node in response to changes in workloads on the first and second computing nodes and network path lengths of the selected agent node and the second agent node to the first and second computing nodes.

Other methods, devices, and computers according to embodiments of the present disclosure will be or become apparent to one with skill in the art upon review of the following drawings and detailed description. It is intended that all such methods, mobile devices, and computers be included within this description, be within the scope of the present inventive subject matter, and be protected by the accompanying claims.

DETAILED DESCRIPTION

As discussed above, in a distributed computing environment, an agent, or scheduler, may assign various tasks to one or more computing nodes. Computing nodes, whether they are implemented in physical or virtual machines, have a finite capacity for handling assigned workloads based on the amount of resources, such as processor capacity, memory, bandwidth, etc., available to the computing node. Moreover, the resources available to a virtual server may change dynamically, as the resources may be shared by other virtual machines hosted on the same physical server as the agent.

Conventionally, if a scheduler assigns a job to a computing node and the computing node does not have capacity to perform the job, either the job will be queued until the computing node has capacity or the workload agent will return an error in response to the assignment. In either case, execution of the job is delayed, and the scheduler may incur additional overhead associated with the job.

Some embodiments described herein are based on the realization that, in many network environments in which an agent may be deployed, the efficiency with which the agent can schedule and manage the performance of jobs by computing nodes may be affected by the proximity within the network of the agent to the computing nodes that are being managed by the agent. In this context, “deploy” means that an agent is installed and activated on a computing node. By way of non-limiting example, an agent may be deployed by moving the agent from an existing node to a new node, by installing a new agent onto a new or existing node, or by activating a previously installed agent on an existing node. Accordingly, embodiments of the inventive concepts provide systems/methods that dynamically deploy agents at locations within a network that enhances the efficiency with which the agents can schedule jobs for execution on servers.

FIG. 1is a block diagram of a distributed data processing network in which systems/methods according to embodiments of the inventive concepts may be employed. Referring toFIG. 1, a plurality of agent nodes130A-130D are provided. The agent nodes130A-130D may be generally referred to as agent nodes130. The agent nodes130may be physical devices, such as servers that have processors and associated resources, such as memory, storage, communication interfaces, etc., or virtual machines that have virtual resources assigned by a virtual hypervisor. The agent nodes communicate over a communications network200, which may be a private network, such as a local area network (LAN) or wide area network (WAN), or a public network, such as the Internet. The communications network200may use a communications protocol, such as TCP/IP, in which each network node is assigned a unique network address, or IP address.

One or more of the agent nodes130may host one or more agents120, which are software applications configured to control execution of jobs assigned by a scheduler100. In the distributed computing environment illustrated inFIG. 1, jobs are requested by client applications110. A job request may be sent by a client application110to the scheduler100. The scheduler100in turn distributes the job to one of the available agents120based on one or more parameters.

An agent120, such as agent120A, may receive a job assignment from the scheduler100, cause the job to be executed, and return a result of the job to the scheduler100, or alternatively to the client application110that requested the job. In other embodiments, the client application110may submit the job to a job manager (not shown), that may split the job into a plurality of sub-tasks, and request the scheduler100to assign each of the individual sub-tasks to one or more agents120for completion.

FIG. 2is a block diagram of a scheduler100according to some embodiments showing components of the scheduler100in more detail. The scheduler100includes various modules that communicate with one another to perform the workload scheduling function. For example, the scheduler100includes a job scheduler module102, a task queue105, a database108, a broker module104, and a data collection module106. It will be appreciated that the scheduler100may be implemented on a single physical or virtual machine, or its functionality may be distributed over multiple physical or virtual machines. Moreover, the database108may be located in the scheduler100or may be accessible to the scheduler100over a communication interface.

Client applications110submit job requests to the scheduler100. The job requests are forwarded to the job scheduler module102for processing. The job scheduler module102uses the task queue to keep track of the assignment and status of jobs. The scheduler100transmits job information to agents120for processing, and may also store information about jobs in the database108.

Information about the status of computing nodes, such as the available workload capacity of computing nodes, is collected by the broker module104and stored in the database108, which is accessible to both the job scheduler102and the broker module104. The data collection module106may collect information about events relating to jobs and metrics provided by agents and stores such information in the database108. The job-related events may include job status information (queued, pending, processing, completed, etc.) and/or information about the agents, such as whether an agent is available for scheduling, is being taken offline, etc.

According to some embodiments, the broker module104provides routing map information directly to the agents120. The routing map information may be provided along with job information or independent from job information to allow a computing node to forward jobs to other computing nodes as needed.

FIG. 3is a block diagram illustrating a distributed data processing network environment300in which agents can be deployed according to embodiments of the inventive concepts. In particular,FIG. 3illustrates a distributed data processing network environment300including a plurality of computing nodes125that can execute jobs.FIG. 3also illustrates a plurality of agent nodes130on which agents120can be deployed. That is, in the diagram ofFIG. 3, circles represent computing nodes125, and stars represent agent nodes130on which agents120can be or have been deployed. Shaded stars represent agent nodes130on which agents120have been deployed, while un-shaded stars represent agent nodes130on which agents120have not yet been deployed, but could be. As can be seen inFIG. 3, agents and computing nodes can in some cases reside on the same computing node125.

For example, agent120A is deployed on computing node125A which also acts as agent node130A, while agent120B is deployed on agent node130B. Agent node130C is configured to host an agent, but does not currently host an agent.

Lines between adjacent network nodes, such as computing nodes125and agent nodes130, represent network effective network path lengths between adjacent computing nodes. Other network nodes, such as routers, gateways, or other computing nodes, may be present in network shown inFIG. 3but are not illustrated for ease of understanding.

Agents120are deployed onto agent nodes130by the network management server50shown inFIG. 1. According to some embodiments, the network management server50determines where in the distributed data processing network environment300to deploy agents. As described in more detail below, the network management server50may choose locations within the distributed data processing network environment300at which to deploy agents based on the evaluation of a value function and/or a cost function that may take into account factors, such as the effective network path lengths between agent nodes130and computing nodes125, the number of agents that are being deployed, and/or the actual workloads at the computing nodes125.

For example, in some embodiments, when the network management server50determines that it is necessary to deploy a new agent120in the distributed data processing network environment300, the network management server50may select an agent node130for deployment of the agent120that maximizes a value function. The value function may take into account network path lengths between the agent nodes and the computing nodes and the number of computing jobs to be executed at the computing nodes.

The value function may have the form:

where Wm is the workload at an mth computing node, Dma is a scale factor that depends on a network path length between the mth computing node and an ath agent node, and Sma is a scale factor indicating that an agent deployed on the ath agent node is executing computing jobs on the mth computing node.

The network path length may be based on an effective path length between the mth computing node and the ath agent node. For example, the network path length may be based on a number of network hops through intervening network forwarding nodes between the mth computing node and the ath agent node and/or a communication latency between the mth computing node and the ath agent node.

The value of Dma may be equal to one if the ath agent node is the agent node closest to the mth computing node based on the network path length between the mth computing node and the ath agent node, and may be equal to zero if the ath agent node is not the agent node closest to the mth computing node based on the network path length between the mth computing node and the ath agent node.

The value of Sma may be equal to one if the ath agent node is executing (or will execute) computing jobs on the mth computing node, and may be equal to zero if the ath agent node is not/will not be executing computing jobs on the mth computing node.

The workload at the mth computing node, Wm, may corresponds to a number of computing jobs to be executed on the mth computing node.

The value of Dma may be equal to an average network path length from all agent nodes to the mth computing node divided by a network path length from the ath agent node to the mth computing node.

According to further embodiments, an agent may be relocated from a first agent node to a second agent node in response to changes in workloads on the computing nodes and network path lengths of the first agent node and the second agent node to the computing nodes.

FIG. 4is a flowchart illustrating operations of systems/methods in accordance with some embodiments of the inventive concepts. Referring toFIG. 4, operations commence when a network management server50determines at block402that an agent should be deployed onto an agent node (or re-deployed from one agent node to another agent node) in a distributed data processing network. The operations then identify a plurality of network nodes within the distributed data processing network environment that are capable of hosting agents, i.e., that are capable of acting as an agent node (Block404). Next, the operations then identify one or more computing nodes within the distributed data processing network that are configured to execute computing jobs under the direction and control of the agents (Block406). The operations may further determine current and/or anticipated workloads of the computing nodes.

The operations then select an agent node within the distributed computing network for deploying the agent based on at least the anticipated workloads on the computing nodes and the network path lengths of the agent nodes to the computing nodes (Block408). The network path length of an agent node to a computing node may be determined, for example, by an effective path length between the agent node and the computing node.

Once the agent node has been selected, the agent may be deployed onto the agent node (Block410). An agent may be deployed onto an agent node by, for example, sending an instruction from the network management server50to the agent node instructing the agent node to load and initialize a specified agent module in the agent node.

FIG. 5is a block diagram illustrating an example deployment of an agent according to embodiments of the inventive concepts. In particular, in the example shown inFIG. 5, a network management server50is determining whether to deploy an agent onto a first agent node230A or a second agent node230B within a distributed computing network500. The distributed computing network500includes four computing nodes225A to225D, which are executing the number of jobs illustrated inFIG. 5. In particular, the first computing node225A will execute five jobs, the second computing node225B will execute six jobs, the third computing node225C will execute four jobs, and the fourth computing node225D will execute nine jobs for the agent.

The agent nodes230A,230B and the computing nodes225A to225D communicate over communication links240-1to240-8. Effective path lengths of the communication links240-1to240-8between agent nodes230A,230B and the computing nodes225A to225D are represented by the lengths of the lines connecting the respective nodes. Accordingly, the first agent node230A has a shorter network path to the first computing node225A than it does to the third and fourth computing nodes225C,225D.

In some embodiments, the effective path length, Dma, of a communication length between two of the nodes may be set as a latency (or reciprocal latency), or normalized latency (or reciprocal latency), of communications between the mth computing node and the ath agent node. In other embodiments, the value of Dma may be chosen as the bandwidth, or a normalized bandwidth, of the communication link between the mth computing node and the ath agent node. In still other embodiments, the value of Dma may represent the number of network “hops” between two nodes, or the number of intervening nodes in a shortest communication path between two nodes.

According to some embodiments, the network management server50may choose to deploy the agent onto whichever of the first agent node230A or the second agent node230B maximizes the value function given in Equation [1] above. In this case, for the first agent node230A, the value function evaluates as follows, assuming that Dma is 1 when the ath agent node is the closest agent node to the mth computing node, and zero otherwise:

Likewise, for the second agent node230B, the value function evaluates as follows:

Thus, in this example, the network management server50will deploy the agent at the second agent node220B.

As another example, assume that normalized bandwidths of the communication links240-1to240-2are as shown in the following table:

In this case, the value function evaluates as follows for the first agent node220A:

Likewise, for the second agent node230B, the value function evaluates as follows:

In this example, the agent would be deployed at the first agent node230A.

As noted above, in other embodiments a cost function may be employed, and the agent may be deployed at the agent node that minimizes the cost function.

FIG. 6is a block diagram of a device that can be configured to operate as the network management server50according to some embodiments of the inventive concepts. The network management server50includes a processor800, a memory810, and a network interface which may include a radio access transceiver826and/or a wired network interface824(e.g., Ethernet interface). The radio access transceiver826can include, but is not limited to, a LTE or other cellular transceiver, WLAN transceiver (IEEE 802.11), WiMax transceiver, or other radio communication transceiver via a radio access network.

The processor800may include one or more data processing circuits, such as a general purpose and/or special purpose processor (e.g., microprocessor and/or digital signal processor) that may be collocated or distributed across one or more networks. The processor800is configured to execute computer program code in the memory810, described below as a non-transitory computer readable medium, to perform at least some of the operations described herein. The server50may further include a user input interface820(e.g., touch screen, keyboard, keypad, etc.) and a display device822.

The memory810includes computer readable code that configures the scheduler100to implement the job scheduler module102, the broker module104, and the data collection module106. In particular, the memory810includes deployment code812that configures the network management server50to deploy agents according to the methods described above.

Further Definitions and Embodiments