RALBA: a computation-aware load balancing scheduler for cloud computing

Hussain, Altaf; Aleem, Muhammad; Khan, Abid; Iqbal, Muhammad Azhar; Islam, Muhammad Arshad

doi:10.1007/s10586-018-2414-6

Cited by 47 publications

(109 citation statements)

References 32 publications

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“…For the experimental consideration, the numbers of tasks are taken from 25 to 250. The above result shows that the throughput of the proposed SVR-MTLB technique is considerably improved than the other methods DLBS [1] and RALBA [2]. The reported results are plotted in the two-dimensional graph as shown below.…”

Section: A Performance Analysis Of Throughputmentioning

confidence: 82%

“…Experimental results of proposed SVR-MTLB technique and existing DLBS [1] and RALBA [2] are discussed in this section with different performance metrics such as throughput, makespan, migration time and response time. The comparison results of three methods and their experimental results are explained using table values and graphical representation.…”

Section: Resultsmentioning

confidence: 99%

“…The designed DLBS technique significantly minimizes the transmission delay but it failed to consider the makespan minimization. A Resource-Aware Load Balancing Algorithm (RALBA) was designed in [2] to balance the workload based on resource capabilities of a virtual machine. The algorithm increased the throughput as well as minimized the makespan but the performance of task migration was not improved.…”

Section: Related Workmentioning

confidence: 99%

“…The above columns are considered for efficient load balancing among the multiple virtual machines using big data in the cloud. Experimental results and discussion of proposed SVR-MTLB technique and existing DLBS [1] and RALBA [2] are described with various performance metrics such as throughput, makespan, migration time and response time with the number of tasks (i.e. user requested tasks).…”

Section: Figure 3 Support Vector Regressionmentioning

confidence: 99%

“…The reported results are plotted in the two-dimensional graph as shown below. Figure 4 illustrates the experimental results of throughput based on the number of user tasks using three methods namely SVR-MTLB technique, DLBS [1] and RALBA [2]. The numbers of user tasks are taken as input in "x" direction and the corresponding throughput results are obtained at "y" direction.…”

Section: A Performance Analysis Of Throughputmentioning

confidence: 99%

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Support Vector Regression based Mapreduce Throttled Load Balancer for Data Centers

Devi *,

Chezian

2019

IJITEE

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The process of analyzing big data and other valuable information is a significant process in the cloud. Since big data processing utilizes a large number of resources for completing certain tasks. Therefore, the incoming tasks are allocated with better utilization of resources to minimize the workload across the server in the cloud. The conventional load balancing technique failed to balance the load effectively among data centers and dynamic QoS requirements of big data application. In order to improve the load balancing with maximum throughput and minimum makespan, Support Vector Regression based MapReduce Throttled Load Balancing (SVR-MTLB) technique is introduced. Initially, a large number of cloud user requests (data/file) are sent to the cloud server from different locations. After collecting the cloud user request, the SVR-MTLB technique balances the workload of the virtual machine with the help of support vector regression. The load balancer uses the index table for maintaining the virtual machines. Then, map function performs the regression analysis using optimal hyperplane and provides three resource status of the virtual machine namely overloaded, less loaded and balanced load. After finding the less loaded VM, the load balancer sends the ID of the virtual machine to the data center controller. The controller performs migration of the task from an overloaded VM to a less loaded VM at run time. This in turn assists to minimize the response time. Experimental evaluation is carried out on the factors such as throughput, makespan, migration time and response time with respect to a number of tasks. The experimental results reported that the proposed SVR-MTLB technique obtains high throughput with minimum response time, makespan as well as migration time than the state -of -the -art methods.

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Section: A Performance Analysis Of Throughputmentioning

confidence: 82%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Figure 3 Support Vector Regressionmentioning

confidence: 99%

Section: A Performance Analysis Of Throughputmentioning

confidence: 99%

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Support Vector Regression based Mapreduce Throttled Load Balancer for Data Centers

Devi *,

Chezian

2019

IJITEE

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RALB‐HC: A resource‐aware load balancer for heterogeneous cluster

Ahmed

Aleem

Khalid

et al. 2019

Concurrency and Computation

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Summary In the heterogeneous computing environment, programmers map the applications either on CPUs or GPUs. However, this default mapping process does not produce improved results, particularly on the heterogeneous clusters. If one resource of the cluster is more compute capable, then most of the scheduling schemes favor that powerful device. In this scenario, the scheduling schemes overload the powerful resources while making all other compute resources remain under utilized. This load imbalance problem results in higher energy consumption and increased execution time. In this research, a novel Resource‐Aware Load Balancer for the Heterogeneous Cluster (RALB‐HC) is proposed that distributes workload based on resources computing capabilities and applications computing needs. The RALB‐HC uses supervised machine learning approach to classify applications using the static code‐features. The RALB‐HC framework comprises of two phases: (1) job mapping based on the availability of the resources and (2) the resource‐aware load balancing to achieve the higher resource utilization ratio. The experimental results on a large set of real‐world and synthetic workloads show that the RALB‐HC reduces execution time by 31.61%, increased resource utilization ratio by 67.8% and improved throughout 147.35% as compared to baseline scheduling schemes.

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