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Grid Job Scheduling - A Detailed Study

Er. Vijay Dhir1, Dr. Rattan K Datta2, Dr.Maitreyee Dutta3
  1. HOD, CSE Dept, SBBSIET, Punjab, India
  2. Advisor (Retd), Govt. Of India, New Delhi, India
  3. Associate Prof & HOD CSE Dept, NITTTR, Chandigarh, India
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Abstract

The emergence of Grid Computing has created new opportunities to support compute and/or Data intensive scientific applications, which, among other, may have large computational resource requirements. One motivation of Grid computing is to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, an efficient Grid scheduling system is an essential part of the Grid. Various Grid scheduling algorithms are discussed from different points of view, such as static vs. dynamic policies, objective functions, applications models, adaptation, QoS constraints, and strategies dealing with dynamic behaviour of resources, and so on. Based on a comprehensive understanding of the challenges and the state of the art of current research the job scheduling algorithms are compared and contrasted based on the make span, flow time, resource utilization and completion time.

Keywords

Heuristic Min-Mean Scheduling, Ant Colony Algorithm, Firefly Algorithm, Community-Aware Algorithm

INTRODUCTION

Grid computing [1] enhances computing facilities over internet. Grid computing has emerged from distributed computing where the resources of many computers in a network are used are used to solve a single problem at the same time. The difference between the conventional high performance computing such as cluster computing and grid is that grid tends to be loosely coupled, heterogeneous and geographically distributed. Though grid can be dedicated for a particular application, it can also be used for a variety of purpose. Search for Extraterrestrial Intelligence (SETI) is an example of Grid Computing Application. Job scheduling is based on the necessity of a user who has a set of jobs to execute. Since the user’s machine is not able to process the jobs either because of resource or hardware constraints, the user can use the grid system for running the job. The user submits the set of jobs to the job scheduler and the job scheduler splits the job depending on certain factors and gives it to the machines having available resources on the grid. The machines will complete the task and final result will be given to the user. In order to achieve this job scheduling strategies has to be followed. Job scheduling and resource scheduling are the two main necessities in grid computing. In job
scheduling, the job scheduler has to find the appropriate resource for the job that the user submits [2]. It has to find the bestmachine in grid to process the user job. Grid has two main schedulers such as local schedulers and grid schedulers. The local schedulers work in local computational environment and hence it is reliably, fast connection, works in uniform environment and also takes full control of the homogeneous resources [3]. Grid Schedulers also called as metaschedulers are the top level schedulers. They are responsible for orchestrating resources that are managed by differentlocal schedulers [4].
Scheduling can also be classified into static and dynamic scheduling. In static scheduling, before execution the jobs are assigned to the suitable machines and those machines will continue executing those jobs without interruption. In dynamic scheduling, the rescheduling of jobs is allowed. The jobs executing can be migrated based on the dynamic information about the workload of the resources [5].
In grid, there may be lots of resources to run a job. The main focus is to find the appropriate resource for the job that is to schedule the job. The methods for job scheduling are centralized, hierarchical and decentralized. In centralized scheduling, there will be a centralized scheduler and it is responsible for scheduling the jobs. It is very useful when all the resources have same objective. In hierarchical, therewill be central scheduler. All jobs will be submitted to the central scheduler. The central scheduler redirects the jobs to the global scheduler. In decentralized, there is no central scheduler. Distributed schedulers coordinate with each other to schedule jobs [6]. The well-known techniques for job scheduling like Min – Min, Genetic Algorithm, Simulated Annealing Algorithm [7] [8] are in existence. In this paper, heuristic Min-Mean technique, Bio inspired techniques such as firefly and ant colony algorithm, grouping based technique and community aware scheduling algorithms are discussed in the following sections. The paper has been organized as follows. In section 2, new enhanced heuristic min-mean scheduling is discussed. In section 3, firefly algorithm is explained.In section 4, ant colony algorithm is described. In section 5, adaptive grouping based job scheduling is explained. In section 6, the community aware scheduling algorithm is explained. Finally, in section 7, the conclusion and future work are presented.

HEURISTIC MIN-MEAN JOB SCHEDULINGPAGE LAYOUT

Online mode and batch mode are the two classifications of heuristic scheduling algorithms [9] [10]. In online mode, the jobs are scheduled to the resources as soon as it arrives. In batch mode, the jobs are independent, there is no order of execution and jobs are scheduled as a batch every time. Here in this paper, batch mode scheduling is followed. Achieving the minimum make span is the goal. To evaluate the mapping heuristic, the expected time to complete [ETC] model is employed. Before execution, the expected execution time of the tasks on the machine should be known, and this is contained in the ETC matrix. Consider for the task ti and the arbitrary machine mjETC[ti,mj]. This represents the expected time of the task i on the machine j. In this matrix, the row represents the expected execution time of a task on different machine and column represents the expected execution time of different tasks on the same machine[11][12][13]. Based on these characteristics, the benchmark of instances for distributed heterogeneous computing system is generated.
(i) Machine heterogeneity (low/high)
(ii) Task heterogeneity (low/high)
(iii)Consistency(Consistent/Inconsistent/Partially Consistent)
Combining these 3 bench marks, 12 combinations ofETC matrices are used to evaluate the heuristic minmean scheduling algorithm.There are 2 phases in this algorithm [14]. In the first phase, all jobs are assigned to the resources. In the second phase, mean completion time of all jobs is calculated and the jobs are allocated to the machineswhose completion time is less than the mean completion time. Machine who has maximum completion time is selected as make span.

FIREFLY ALGORITHM

The firefly algorithm is based on swarm intelligence behavior of firefly. It is a meta heuristic algorithm inspired by the social behavior of firefly. Firefly algorithm finds the global optimal solution. The mainfocus of firefly algorithm is to complete the task within a minimum make span and flow time as well to utilize the grid resource efficiently. Firefly optimization as mentioned in [15] [16] in can be described as
(i) The firefly attracts and is attracted by all other Fireflies
(ii) The brighter one attracts the less bright one
(iii) The brightness decreases with distance
(iv) The brightest firefly can move randomly
(v) The firefly particles can move randomly
There are 4 phases in firefly algorithm [17] In the phase 1, the parameters are set (initial population, fitness and attractiveness), number of available resources and list of submitted jobs are identified. In the phase 2, the brightness of each firefly is found at the source using fitness function and distance is calculated. The less bright fireflies are moved towards the brighter one. In the phase 3, the new solution is evaluated and light intensity is updated In the phase 4, the fireflies are ranked and current global best is identified. Finally, the iteration parameters are updated All these are done until the termination condition is reached. The termination condition may be number ofiteration or the fitness value or sometimes the saturation state.

ANT COLONY ALGORITHM

The ant colony algorithm for job scheduling in gridaims at submitted jobs to resources based on the processing ability of jobs as well as the characteristics of the jobs.Ant colony algorithm is the bio-inspired heuristic algorithm, which is derived from the social behavior of ants. Ants work together to find the shortest path between their nest and food source. When the ants move, each ant will deposit a chemical substance called pheromone. Using this pheromone, the shortest path is found. The same concept is used to assign jobs in grid computing. When a resource is assigning a job and completes, its pheromone value will be added each time. If a resource fails to finish a job, it will be punished by adding less pheromone value. The issue here is the stagnation, where there is a possibility of jobs being submitted to same resources having high pheromone value.In this ant colony algorithm [18], the load balancingmethod is proposed to solve the issue of stagnation.The algorithm is as follows
(i) The user will send request to process a job.
(ii) The grid resource broker will find a resource for the job.
(iii)The resource broker will select the resource based on the largest value in the pheromone value matrix.
(iv)The local pheromone update is done when a job is assigned to a resource.
(v)The global pheromone update is done when a resource completes a job.
(vi)The execution result will be sent to the user when the resource broker select a particular resourcefor a job j, jthcolumn of the Pheromone Value matrixwill be removed and jobs will be assigned to other resources. Thus the load balancing is achieved.

GROUPING BASED JOB SCHEDULING IN GRID COMPUTING

The adaptive group based job scheduling focuses ongroup based scheduling, and explains the jobs are grouped based on coarse grained jobs. The grouping is based on the resources’ processing capability in Million Instructions per Second (MIPS),bandwidth (Mb/s), and memory size (In Mb).The characteristics of resources are the basic for grouping strategy. In grid computing, 2 approaches can be used for job execution. In first approach, the user can directly search the resources for job execution using an information service. In the second approach, the user can obtain information about the current availability and capability of resources using the resource manager. The Grouping Based Job Scheduling algorithm has 2 phases [19]. In the first phase, the scheduler receives information about the resource status from the Grid Information Service (GIS), and sorts the jobs in descending order. In the second phase, the system selects jobs in First Come First Served (FCFS) order and forms different job groups. The scheduler will select resource in FCFS order after sorting them in descending order of their MIPS. The jobs are put into the job groups until the sum of resource requirement of the jobs in that group is less than or equal to amount of resource available at selected site. As soon as the job group is formed, the jobs are assigned to the corresponding resource. After execution the job groups, the result is sent to the corresponding user and the resources are available to the Grid System. The .NET framework scheduler uses the FCFS scheduling technique.

COMMUNITY – AWARE SCHEDULING ALGORITHM (CASA)

CASA is a decentralized dynamic heuristic metascheduling algorithm. In CASA, jobs can be rescheduled. In order to overcome the stagnation a probabilistic approach has been used to assign jobs sothat the jobs are evenly distributed to all other resources.CASA is a two phase algorithm [20]. The first phase is the job submission phase where each node receives the jobs that are submitted by local user. Consider a node A, it receives the job, it acts as a initiator node and requests all other nodes using the REQUEST message.The other nodes who are willing to take the job will reply through ACCEPT message. The node A will evaluate the other participating nodes using the historic data and selects the appropriate node and submits the job to it.The second phase is the dynamic rescheduling phase,the node which received the job will look for the job which has large enough waiting time and has not been selected recently in the local job queue. That job will be rescheduled to the other nodes.5 algorithms are discussed in CASA. They are:
(i) Job distribution
(ii) Job delegation request acceptance
(iii) Job assignment
(iv) Job rescheduling
(v) Job rescheduling request acceptanceBased on the above analysis of the algorithm, the algorithms are tabularized in Table 1 with advantagesand disadvantages.
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CONCLUSION

Grid computing can solve complex tasks in shorter time and utilizes the hardware efficiently. To make the grid work efficiently, best job scheduling strategies have to be employed. Job scheduling is the foremost step in grid computing where the users’ jobs are scheduled to different machines. The various strategies have been studied and classified. The advantages and disadvantages of the algorithms have been studied. The future work will be concerned with the development of the better scheduling algorithmwhich is heterogeneous and works in dynamic environment.

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