Multi-objective method for divisible load scheduling in multi-level tree network

Ghanbari, Shamsollah; Othman, Мohamed; Bakar, Mohd Rizam Abu; Leong, Wah June

doi:10.1016/j.future.2015.03.015

Cited by 22 publications

(8 citation statements)

References 27 publications

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“…However, this model is deficient in determining the capabilities of nodes and, configuration details, and the complete system has no backup, thus resulting in a single point of failure. In addition, Ghanbari et al [19] and Goswami et al [20] focus on scheduling tasks while considering various constraints. This state-of the art motivates the authors of this study to conduct additional research on task scheduling and resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Task scheduling and resource allocation in cloud computing using a heuristic approach

2018

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Cloud computing is required by modern technology. Task scheduling and resource allocation are important aspects of cloud computing. This paper proposes a heuristic approach that combines the modified analytic hierarchy process (MAHP), bandwidth aware divisible scheduling (BATS) + BAR optimization, longest expected processing time preemption (LEPT), and divide-and-conquer methods to perform task scheduling and resource allocation. In this approach, each task is processed before its actual allocation to cloud resources using a MAHP process. The resources are allocated using the combined BATS + BAR optimization method, which considers the bandwidth and load of the cloud resources as constraints. In addition, the proposed system preempts resource intensive tasks using LEPT preemption. The divide-and-conquer approach improves the proposed system, as is proven experimentally through comparison with the existing BATS and improved differential evolution algorithm (IDEA) frameworks when turnaround time and response time are used as performance metrics.

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Section: Related Workmentioning

confidence: 99%

Task scheduling and resource allocation in cloud computing using a heuristic approach

2018

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“…The two main types of multi-criteria decisionmaking model are multi-objective decision-making and multi-attribute decisionmaking. A multi-objective scheduling algorithm which is suitable for scheduling in big data, e.g., cloud computing has been proposed in [22].…”

Section: Introductionmentioning

confidence: 99%

Priority-Aware Job Scheduling Algorithm in Cloud Computing: A Multi-Criteria Approach

Ghanbari

2019

AzJHPC

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Job scheduling is one of the most problematic theoretical issues in the area of cloud computing. The existing scheduling methods attempt to consider only a few criteria of scheduling without covering other sufficient criteria. Since, cloud computing faces a large scale resource for allocating to a large number of jobs, due to optimizing the users' requirements; therefore, a suitable cloud-based job scheduling method must satisfy a wide range of criteria. Besides, in cloud computing, the jobs come with different priorities. Thus, in the cloud environment, a suitable job scheduling algorithm should be able to combine several priorities. This paper proposes a new multi-criteria priority-aware job scheduling algorithm in cloud computing. Experimental results indicate that the proposed method is able to consider different criteria for scheduling.

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“…They obtained the closed‐form expression, the optimal processing time and the workload fractions for slave processors. Then the DLS theory was applied to other platforms such as star, multi‐level tree and linear networks. Different cost models and distribution algorithms for star and tree topologies were introduced and multi‐objective methods for scheduling workload on tree topology were presented .…”

Section: Introductionmentioning

confidence: 99%

“…Then the DLS theory was applied to other platforms such as star, multi‐level tree and linear networks. Different cost models and distribution algorithms for star and tree topologies were introduced and multi‐objective methods for scheduling workload on tree topology were presented . The cheating problem in scheduling was considered in the work of Ghanbari and Othman .…”

Section: Introductionmentioning

confidence: 99%

Divisible load scheduling of image processing applications on the heterogeneous star and tree networks using a new genetic algorithm

Aali

Bagherzadeh

2019

Concurrency and Computation

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The divisible load scheduling of image processing applications on the heterogeneous star and multi-level tree networks is addressed in this paper. In our platforms, processors and network links have different speeds. In addition, computation and communication overheads are considered. A new genetic algorithm for minimizing the processing time of low-level image applications using divisible load theory is introduced. The closed-form solution for the processing time, the image fractions that should be allocated to each processor, the optimum number of participating processors, and the optimal sequence for load distribution are derived. The new concept of equivalent processor in tree network is introduced and the effect of different image and kernel sizes on processing time and speed up are investigated. Finally, to indicate the efficiency of our algorithm, several numerical experiments are presented. KEYWORDS divisible load scheduling, equivalent processor, genetic algorithm, image fractions, load distribution sequence, local operation INTRODUCTIONDivisible load scheduling is a special class of data parallelization methods that can be used in those applications that are divided into any number of independent fractions. These fractions can be processed in parallel on different processors. Big dataset processing, matrix computation, signal and image processing, Hough transform, and experimental data processing are examples of these applications. Divisible Load Scheduling (DLS) has been studied extensively in the last two decades because of its simplicity and analytical tractability. 1 Image processing applications need extensive computational power that cannot be provided by one processor. 2 DLS is a good option for exploiting data parallelism in image applications 3,4 because most image processing applications are divisible in nature. There are three kinds of operations in image processing, ie, pixel operations in which each pixel can be processed independently; local operations, which are most common operators in image application, and the value of each output pixel is a function of the value of that pixel plus some neighboring pixels; and global operators that need the information of the whole image to process the value of one pixel. Pixel and local operations are good candidates for data parallelism. In this paper, we focus on local operators, as the most common operators in image applications.We consider the star and tree networks as the target platforms. In these networks, the master processor holds the entire image and the kernel.It does not participate in image processing and just partitions the workload and distributes it to other slave processors. In a star topology, slave processors begin to compute their image fractions after receiving their workload from the master processor completely. Parent nodes in tree topology have the capability of computing and communicating at the same time. They are equipped with front-end processors in our model.The objective of using DLS for data parallelism in image applicat...

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Multi-objective method for divisible load scheduling in multi-level tree network

Cited by 22 publications

References 27 publications

Task scheduling and resource allocation in cloud computing using a heuristic approach

Task scheduling and resource allocation in cloud computing using a heuristic approach

Priority-Aware Job Scheduling Algorithm in Cloud Computing: A Multi-Criteria Approach

Divisible load scheduling of image processing applications on the heterogeneous star and tree networks using a new genetic algorithm

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