ADAPTIVE GRID JOB SCHEDULING WITH IMPROVING HIERARCHICAL LOAD BALANCED ALGORITHM

ADAPTIVE GRID JOB SCHEDULING WITH IMPROVING

HIERARCHICAL LOAD BALANCED ALGORITHM

R. T

,

K. D

Due to the advances in human civilization, problems in science and engineering are becoming more complicated than ever before. To solve these complicated problems, grid computing becomes a popular tool. A grid environment collects, integrates, and uses heterogeneous or homogeneous resources scattered around the globe by a high-speed network. A grid environment can be classified into two types: computing grids and data grids. This paper mainly focuses on computing grids. In computing grid, job scheduling is a very important task. A good scheduling algorithm can assign jobs to resources efficiently and can balance the system load. In this paper, we propose a hierarchical framework and a job scheduling algorithm called Hierarchical Load Balanced Algorithm (HLBA) for Grid environment. In our algorithm, we use the system load as a parameter in determining a balance threshold. And the scheduler adapts the balance threshold dynamically when the system load changes. The main contributions of this paper are twofold. First, the scheduling algorithm balances the system load with an adaptive threshold and second, it minimizes the make span of jobs.

Keywords: Grid Computing, Job Scheduling, HLBA Algorithm, Load balancing. 1. INTRODUCTION

Distributed systems consist of multiple computers that communicate through computer networks. Research by defined that cluster and grid computing are the most suitable ways for establishing distributed systems [1-5]. Cluster computing environment consists of several personal computers or workstations that combined through local networks in order to develop distributed applications. However, applications are difficult to be flexible in cluster computing because they are limited to a fixed area. Grid computing is proposed to overcome this problem where various resources from different geographic area are combined in order to develop a grid computing environment.

1 _{Master of Engineering, Department of Computer Science & Engineering (PG), Sri Ramakrishna} Engineering College, Anna University of technology, Coimbatore, Tamil Nadu, India.

2 _{Assistant Professor, Department of Information Technology, Sri Ramakrishna Engineering College,} Anna University of technology, Coimbatore, Tamil Nadu, India.

Load balance is also an important issue in grid environment. The main purpose of load balance is to balance the load of each resource in order to enhance the resource utilization and increase the system throughput. For a conventional distributed system, many load balancing algorithms [15-17] have been proposed. But they may not be suitable for grid environments due to the different characteristics in grids. Numbers of load balancing algorithms have been proposed for grid environments. Some take the grid characteristics into account but do not follow changes in the system status. Others may set afixed balance thresholds for controlling the load situation of the whole grid system. Hence, they might not be suitable in a dynamic grid environment. Based on this opportunity for improvement, we propose a new framework and scheduling algorithm to balance the load of a grid system with an adaptive balance threshold while trying to minimize the make span of job execution.

Figure. 1: Grid Computing Environment

2. RELATED WORKS ON HLBA IN GRID COMPUTING ENVIRONMENT

Jobs submitted to a grid computing system need to be processed by the available resources. Best resources in term of processing speed, memory and availability status are more likely to be selected for the submitted jobs during the scheduling process. Best resources are categorized as optimal resources. In a research by, Ant Colony Optimization (ACO) has been used as an effective algorithm in solving the scheduling problem in grid computing.

ACO is inspired by a colony of ants that work together to find the shortest path between their nest and food source. Every ant will deposit a chemical substance called pheromone on the ground after they move from the nest to food sources and vice versa. Therefore, they will choose the Shortest or optimal path based on the pheromone value. The path with high pheromone value is shorter than the path with low pheromone value. This behaviour is the basis for a cooperative communication. The presence of these and other unique characteristics have made ant societies an attractive and inspiring model for building new algorithms. Workers of ant colony specialize in particular tasks. For example, the soldiers aim for protection, the scouts specialize in searching for food sources, and the queen's task is producing new ants. There are various types of ACO algorithm such as Ant Colony System (ACS), Max-Min Ant System (MMAS), Rank-Based Ant System (RAS) and Elitist Ant System (EAS).

ACO has been applied in solving many problems in scheduling such as Job Shop Problem, Open Shop Problem, Permutation Flow Shop Problem, Single Machine Total Tardiness Problem, Single Machine Total Weighted Tardiness Problem, Resource Constraints Project Scheduling Problem, Group Shop Problem and Single Machine Total Tardiness Problem with Sequence Dependent Setup Times. A recent approach of ACO researches in the use of ACO for scheduling job in grid computing. ACO algorithm has been used in grid computing because it is easily adapted to solve both static and dynamic combinatorial optimization problems and job scheduling in grid computing is an example. Balanced job assignment based on ant algorithm for computing grids called BACO was proposed.

The research aims to minimize the computation time of job executing in Taiwan UniGrid environment which focused on load balancing factors of each resource. By considering the resource status and the size of the given job, BACO algorithm chooses optimal resources to process the submitted jobs by applying the local and global pheromone update technique to balance the system load. Local pheromone update function updates the status of the selected resource after job has been assigned and the job scheduler depends on the newest information of the selected resource for the next job submission. Global pheromone update function updates the status of each resource for all jobs after the completion of the jobs. By using these two update techniques, the job scheduler will get the newest information of all resources for the next job submission.

From the experimental result, BACO is capable of balancing the entire system load BACO was only tested in Taiwan UniGrid environment. An ant colony optimization for dynamic job scheduling in grid environment was proposed by [16] which aimed to minimize the total job tardiness time. The initial pheromone value of each resource is based on expected execution time and actual execution time of each job. The process to update the pheromone value on each resource is based on local update and global

update rules as in ACS. In that study, ACO algorithm performed the best when compared to First Come First Serve, Minimal Tardiness Earliest Due Date and Minimal Tardiness Earliest Release Date techniques.

The proposed a bio-inspired adaptive job scheduling mechanism in grid computing. The purpose of this research is to minimize the execution time of the computational jobs by effectively taking advantage of the large amount of distributed resource. Various software ant agents were designed with simple functionalities. The pheromone value of each resource depends on their execution time. Resource with high execution time will receive a large number of pheromone. In this research, the comparison was also performed between the bio inspired adaptive scheduling with the random mechanism and heuristic mechanism. Experimental results showed that a bio-inspired adaptive job scheduling has good adaptability and robustness in a dynamic computational grid. The study to improved ant algorithm for job scheduling in grid computing which is based on the basic idea of ACO was proposed by [4]. The pheromone update function in this research is performed by adding encouragement, punishment coefficient and load balancing factor. The initial pheromone value of each resource is based on its status where job is assigned to the resource with the maximum pheromone value. The strength of pheromone of each resource will be updated after completion of the job. The encouragement and punishment and local balancing factor coefficient are defined by users and are used to update pheromone values of resources. If a resource completed a job successfully, more pheromone will be added by the encouragement coefficient in order to be selected for the next job execution. If a resource failed to complete a job, it will be punished by adding less pheromone value. The load of each resource is taken into account and the balancing factor is also applied to change the pheromone value of each resource.

A simple grid simulation architecture for resource management and task scheduling was proposed in [20]. This study also validated the scalability of ant algorithm. The ant algorithm for grid task scheduling is integrated into the simulation architecture and good results were obtained in terms of resource average utilization, response time and task ful fill proportion.

From the above research, ACS is the most popular variant of ACO that has been successfully used in grid computing environment to solve the scheduling problems which eventually reduce the stagnation problem. This is a fertile area of research for the improvement of grid resource management with new or enhanced ACS algorithm for job scheduling. This study proposed a new pheromone initializing process which is different from, where the consideration was only on the condition of the resource.

The scheduling process in has proposed resource with the lightest load to be assigned to new submitted job regardless of the job size. This study will consider assigning new submitted jobs to resources that are suitable based on the resource processing ability as well as the characteristics of the jobs.

Strategies of load balancing algorithms proper distribution of users’ requests, load balancing is very important in the grid environment. The load balancer receives users’ request and sends them to suitable resources according to some specific distribution logic. Generally speaking, load balancing can be classified as static, dynamic, and adaptive. In static load balancing, the decision regarding the allocating resources to requests is predefined. In dynamic balancing, the request allocation decisions are decided at run time based on the current state of the system. In adaptive balancing, the allocation parameters and polices can vary depending on the run time information about the system, such as the current state of the system, previous decisions, etc [11, 12]. In [17], the authors proposed a Dynamic Load Balancing Algorithm (DLBA) which performs an intra-cluster and intercluster load balancing. DLBA considers load index and other conventional influential parameters at each node in dynamically scheduling the tasks. Intra-cluster load balancing is performed depending on the Cluster Manager (CM). CM decides whether to start the local balancing based on the current workload of the cluster which is estimated from the nodes below it. Inter-cluster load balancing is performed when some CMs fail to balance their workload. The local balancing failure may be due to a saturation of the cluster. In this situation, the jobs of the overloaded cluster will be transferred according to the selecting strategies to another cluster which is underloaded.

In order to judge whether the cluster is overloaded or underloaded, they introduced a threshold called balanced threshold denoted as Ψ. If the load of cluster > Ψ, load balancing will be executed. Simulation results show that the proposed algorithm is feasible and improves the performance of the system. However, the value of balanced threshold is fixed and set by its cluster. Therefore, the balanced threshold may not be suitable for the dynamic characteristics in the Grid system.

3. THE PROPOSED HIERARCHICAL LOAD BALANCING ALGORITHM (HLBA)

In this section we propose a hierarchical system framework. This hierarchical framework is composed of four main components:

Portal, Information Service, Scheduler, and clusters with grid resources, as shown in Figure 2.

1. The Portal provides an interface for users to submit jobs.

2. The Information Server discovers resource nodes registered with the system, and records the information of the resource, such as CPU speed, idle CPU percentage, memory utilization, average load of each cluster, etc.

3. The job scheduler accepts the job from the portal and uses the HLBA with the information from Information Service to choose the appropriate cluster and compare its load with the system.

4. Then, it selects the resource with the strongest computing power in the cluster to execute the submitted job.

5. After the job is finished, the result and the new status of the resource will be sent back to the Information Service for another scheduling.

Figure 2: The Proposed System Framework

When the scheduler receives a job submitted by a user, it will transfer a request to the Information Service in order to obtain the necessary information such as the idle CPU percentage of each resource, average load of each cluster and average load of the system.

Then the scheduler chooses a cluster which has the fastest average computing power (ACP). The average computing power of the cluster is defined as

−

∑

i k= k k

ACP = CPU_Speed *( CUP )

n (1)

whereCPU_Speedk is theCPU MIPSof resourcek in clusteri; ;CPUk is the current

CPU utilization of the resourcek in the clusteri, expressed as a percentage, andn is the number of resources in cluster i. Hence, the time complexity of the formula is

TAcp =O(n). After the scheduler selects the cluster which has the fastest ACP, it will compare the average load of the chosen cluster with the average load of the system. The average load of the cluster is defined by the average load of each resource in cluster i. We use the weighted sum of squares method to measure the load of each resource, as Loadk,i = =

∑

where Loadk,i represents the load of each resource k in the clusteri. L1 and a1

represent the load attribute and the corresponding weighted value, whereal > 0 and

Σn

l =1a1 = 1.

In HLBA, we consider three load attributes,CPU utilization of the resource (CPUk), the memory utilization of the resource (MUk) and the utilization of network (NUk). So theLoadk,i may become:

Loadk,i = a CPU1 k2+a NU2 k2+a MU3 k2 (3)

where a1 is the weight of the load attribute CPUk, a2 is the weights of the load

attributeNUk, anda3 is the weight of load attributeMUk.

In this algorithm, the time complexity ofLoadk,i is linear time.The average load of each clusteri (ALCi) is defined as

ALCi = =

∑

where Loadk,i is the load of resource k in clusteri, and n denotes the number of resources in clusteri. The time complexity ofALCi isTALC =O(n). The average load of the system (AL) is defined as

AL= =

∑

Wherem is the number of clusters in the system. The time complexity of AL is

TAL =O(m*ALCi) =O(mn). (6)

Hence, the time complexity of HLBA is equal toT =TAL +TACP =O(mn). We set the average load of each clusteri,ALCi, to be less than the balance threshold of the system. Hence, we set a threshold called balance threshold, denoted asΨ and defined as below:

Ψ =AL +σ (7)

whereσ is the standard deviation of the load of the system and defined as below:

σ = = −

∑

wherex is the average load of the system which equalsAL;xi is the average load of each clusteri which equalsALC, andN is the number of clusters in the system.When a job is to be assigned to a cluster with the highestACPi, the load of the selected cluster will be checked first to see if it is already overloaded. If the cluster’s average load is larger than the balance threshold, the cluster is marked as overloaded and the job will seek the cluster with the next highestACP.

If a cluster is assigned a job, it has to recalculateACPi,Loadk,i,ALCi,AL, andΨ immediately. This is called a local update. Local update is necessary because it reflects the situation changes in the local cluster. It will also affect the global average values. But the average computing power and average load of other clusters are not affected. Therefore, the Computation cost is less than a global update described below. When a job is completed, the job had to release all resources and a global update will recalculate all parameters again. Overloaded clusters can become available again after a global update.

Improvement of Hierarchy Load Balancing Algorithm (iHLBA)

In this section, explained the second method called iHLBA and used the same parameters for scheduling but with different orders in HLBA. In HLBA, we first choose the cluster with highest average computing power than compare its average load with the balance threshold. There is a loop while choosing the cluster. This step may waste time and has an influence on the makespan.

In this algorithm, when scheduler receives a job and obtains necessary information from the job, we will sort clusters by their average loads. Although the time complexity of sort algorithm isO(nlgn), the time complexity of iHLBA is stillO(mn). If the average load of cluster (ALC) exceeds the balance threshold (Ψ), it means that the cluster is overloaded. We sort the clusters which are under loaded and select the cluster with the highestACP within those clusters. After selecting the suitable cluster, we select the resource with the best computing power in this cluster and assign the job. Local update and global update are also performed in iHLBA to ensure that we can get the newest status of resources.

4. EXPERIMENTAL RESULTS AND DISCUSSION

We use the simulation tool which is called GridSim[22]. GridSim provides some applications for the grid environment, such as VM (visual modeler), Grid broker and task grouping. GridSim also provides some components for users to construct the grid environment, such as resource construction component and task construction component. Users can set the parameters they need, such as PE (process element), PE List, Machine, Resource, Characteristic, Grid Resource, etc., by using the resource construction component.

In this algorithm proposed, in order to achieve the load balance between clusters, we have to calculate the load of each resource. In this approach need to define the three weighted values (a1, a2, a3) in the beginning. In the first experiment, we will compare different setting for the weight value. In the second experiment, we will compare the performance of HLBA and iHLBA and choose a better one to use in the third experiment. The last experiment we will compare the performance and system load with other algorithms.

In proposed algorithm while calculating the load of each resource, the weight values of the corresponding load attributes are considered. The loads of each resource considered are CPU utilization, network utilization, and memory utilization. The weight value corresponding to CPU utilization is a1; a2 corresponds to network utilization; anda3 corresponds to memory utilization.

The sum of the weight value is equal to 1, therefore we compare makespan (total execution time) when the value ofa1 (the weight value of CPU),a2 (the weight value of network) anda3 (the weight value of memory) area1 = 0.6,a2 = 0.3,a3 = 0.1 anda1 = 0.3,a2 = 0.6,a3 = 0.1 anda1 = 0.1,a2 = 0.3,a3 = 0.6. In this simulation, the size of a task is randomly generated between 200,000 and 400,000 MI (millions of instructions).

The computing power of resource node is randomly generated between 500 and 5000 MIPS. The user submits 40 tasks every 30 s. This experiment uses the HLBA we proposed. The detailed parameters and experiment environment are shown in Table 1.

Table 1

Simulation Parameter

Parameter value

Number of tasks 1000

Size of task(MI) 200,000-400,000

Number of nodes of a cluster 10

Computing power of 500-5000

resource nodes(MIPS)

Number of cluster 10

Size of memory(MB) 500-1000

Baud rate(bps) 500-1000

User submitted number of jobs 40

The makespan of our algorithm using each set of weighted values. According to the results, we observe that the first set (a1 = 0.6, a2 = 0.3, a3 = 0.1) achieves better performance than the other sets. We considered three load attributes in our algorithm respectively: CPU utilization, network utilization, and memory utilization. If the

weighted value of CPU utilization (a1) is too small, the job scheduler may assign a job to the resource with heavy load of CPU utilization, and thereby increase the job completiontimeNetwork utilization(a2) mainly affectsthe transfer time, especially when jobs are more complicated nowadays.

If the selected resource has lower bandwidth capacity, the transfer time will increase. While executing the job, the size of memory space may affect the number of I/O actions. If the memory utilization (a3) is too heavy, the executing time is increased. However, the technique of memory is more advanced now, and the memory utilization may make less influence in comparison to the Other attributes. Therefore, we take the first set (a1 = 0.6,a2 = 0.3,a3 = 0.1) in our algorithm for the following experiments.

In this section, compared the performance of two algorithms we proposed using same parameters. HLBA selects the suitable cluster first then check whether the ALC exceeds the balance threshold. In iHLBA, we first collect clusters whose ALC is lower than the threshold, and then select a suitable cluster. We will test two amounts of jobs, 1000 and 1500, for the two algorithms. We use the same parameters in Table 1 to compare the makespan with HLBA and iHLBA, and we will use the better one to compare with different scheduling algorithms in next experiment.

According to the results shown in Table 1, the makespan of iHLBA is better than that of HLBA. This is because HLBA may select the overloaded clusters and need to re-select another cluster. On the other hand, iHLBA sorts the cluster in the beginning to ensure that the selected cluster is underloaded. Therefore, iHLBA does not need to re-select another cluster as frequently as HLBA, and this allows for better performance. We use iHLBA to compare with some other algorithms in the next experiment. We focus on the makespan and the standard deviation of the load of clusters in this section. We compare our iHLBA with ACO algorithm [9], MFTF algorithm [8] and random selection method. Parameters of ACO we set are the same as [9]. The parameters of ACO.

The weighteda1 = 0.6, a2 = 0.3, a3 = 0.1 in this experiment. We set the size of tasks to be randomly generated between 300,000 and 500,000 MI. The computing power of each node is randomly generated between 500 and 5000 MIPS. User submits 40 tasks every 30 s detailed parameters and experimental environment. The makespan of each scheduling method and the standard deviation value with 2000 tasks we can observe that iHLBA has better performance than other algorithms. We assign jobs to the resource depending on the status of the resource. A cluster with highest average computing power means that it is the suitable cluster for the resource. The average computing power of each cluster will be recalculated according to the newest status of resources which is updated by local update and global update. Therefore, iHLBA can select the suitable resources for jobs and reduce the makespan.

In ACO algorithm, the pheromone value of resource is changed by the encouragement and punishment factor. If a resource completes the assigned job, it will be encouraged by the algorithm and the probability of that resource being chosen will increase in later job submissions. It means that the better resources have higher probability of being chosen for next job. However, it may increase the load of the better resource and decrease the overall performance.

Figure 3: Makespan of Each Scheduling Method

The standard deviation value is small, it means that the gap of load between clusters is small. Thus, we can say that the system with small standard deviation value is more balanced. iHLBA always has the smallest standard deviation value. It means the difference of load of each cluster is small. The balance threshold we set can improve the balance in the loading of clusters, and the balanced threshold is adaptive according to the newest status of resources, as updated by the global and the local update. The ACO algorithm uses the update function with user defined variables. Even though ACO takes the load of resources into account, the user defined variables have a significant influence of the update function. This may cause an error in prediction of update functions and is the reason why the standard deviation value of ACO is larger than iHLBA.

Figure 4: Standard Deviation of the Load of Clusters with Each Method 5. CONCLUSION

The proposed a hierarchical system framework using iHLBA to schedule jobs in the Grid environment. iHLBA assigns the fittest resource to the job and compares the clusters’ load with the adaptive balance threshold to balance the system load.

Local and global update rules are applied to get the most up to-date status of resources and coordinate the balanced threshold, allowing the next job to be assigned to the most suitable resource. The local update rule updates the status of the resource and cluster which are selected for the job after assigning the job, and then the job scheduler will use the new status of resources to assign the next job.

The global update rule updates the status of each resource and cluster in the grid system after a job is completed by a resource the iHLBA is capable of balancing the overall load on the system, and offers an improvement to the makespan due to its selecting the most suitable resource for a given job based on the most current system status.

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