A Clustering Heuristic for Multiprocessor Environments Using Computation and Communication Loads of Modules

Mishra, Pramod Kumar; Mishra, Kamal Sheel; Mishra, Abhishek

doi:10.5121/ijcsit.2010.2513

Cited by 7 publications

(8 citation statements)

References 8 publications

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“…status(alloc(l)) ← busy 49 return time EVALUATE-TIME (SIMULATOR) calculates the time taken by a given clustering (Mishra et al [15]). Line 01 initializes time to 0.…”

Section: Methodsmentioning

confidence: 99%

Task Scheduling of a Distributed Computing Software in the Presence of Faults

Mishra¹,

Tripathi²

2013

IJCA

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Performance estimation of a distributed software is a challenging problem. A distributed software runs on multiple processing nodes interconnected in some fashion. In such a situation computational load of a software is distributed onto the processing nodes of the given system. Such a system makes use of an appropriate task scheduling algorithm for obtaining a good performance. The program used in this work emulates a distributed system . An emulator gives the result like an actual system. The emulator is of a fully connected distributed system in which any two processors can directly communicate. The objective of this experiment is to identify the task scheduling algorithm that also performs well in the presence of communication fault delay occured because of network failure or computation fault delay occured because of no response from processors in a distributed system.

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“…status(alloc(l)) ← busy 49 return time EVALUATE-TIME (SIMULATOR) calculates the time taken by a given clustering (Mishra et al [15]). Line 01 initializes time to 0.…”

Section: Methodsmentioning

confidence: 99%

Task Scheduling of a Distributed Computing Software in the Presence of Faults

Mishra¹,

Tripathi²

2013

IJCA

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“…The Computation Communication Load Clustering (CCLC) algorithm schedules tasks on a fully connected parallel system. The authors define a factor Computation‐Communication‐Load (CCLoad) for tasks in the graph.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we present a concise description of some of the previously proposed clustering based task scheduling algorithms. Some well‐known previous algorithms are EZ (Edge Zeroing), LC (Linear Clustering), DSC (Dominant Sequence Clustering), GDS (Greedy Dominant Sequence), MCP (Modified Critical Path), DCP (Dynamic Critical Path), CASC (Clustering Algorithm for Synchronous Communication), CPPS (Cluster Pair Priority Scheduling), CCLC (Computation Communication Load Clustering), DCCL (Dynamic Computation Communication Load), RDCC (Randomized Dynamic Computation Communication), BDSC (Bounded Dominant Sequence Clustering), and LOCAL …”

Section: Related Workmentioning

confidence: 99%

“…The duplication-based algorithms 2,24-29 attempt to minimize the communication cost between tasks through duplication of tasks onto different processors. The clustering-based algorithms, 3,16,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] mainly applicable for an unlimited quantity of processors, are divided into 2 steps. In the initial step, massively communicating tasks of a given task graph are combined into a cluster.…”

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confidence: 99%

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An edge priority‐based clustering algorithm for multiprocessor environments

Maurya

Tripathi

2018

Concurrency and Computation

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Summary In multiprocessor environments, the scheduling algorithms play a significant role in maximizing system performance. In this paper, we propose a clustering‐based task scheduling algorithm called Edge Priority Scheduling (EPS) for multiprocessor environments. The proposed algorithm extends the idea of edge zeroing heuristic and uses the concept of edge priority to minimize the makespan of the task graph. The complexity of the EPS algorithm is O(|V||E|(|V| + |E|)), where |E| represents the number of edges and |V| denotes the number of nodes in the task graph. The experiments are performed for random task graphs and the task graphs generated from some representative real‐world applications such as Gaussian Elimination and Fast Fourier Transform. The performance of the EPS algorithm is compared with six well‐known algorithms such as EZ (Edge Zeroing), LC (Linear Clustering), CPPS (Cluster Pair Priority Scheduling), DCCL (Dynamic Computation Communication Load), RDCC (Randomized Dynamic Computation Communication), and LOCAL. The results show that the EPS algorithm outperforms the compared algorithms in terms of the normalized schedule length and speedup.

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“…: Al-Maasarani[2] (TG1 having 16 nodes); Al-Mouhamed[3] (TG2 having 17 nodes); Colin and Chretienne[7] (TG3 having 9 nodes); Chung an Ranka[6] (TG4 having 11 nodes); Wu and Gajski[35] (TG5 having 18 nodes); Yang and Gerasoulis[38] (TG6 having 7 nodes); Mishra et al[24] (TG7 having 7 nodes); Kadamuddi and Tsai[13] (TG8 having 15 nodes); Kwok and Ahmad[19] (TG9 having 9 nodes).…”

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confidence: 99%

A Randomized Scheduling Algorithm for Multiprocessor Environments Using Local Search

Mishra

2016

Parallel Process. Lett.

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The LOCAL(A, B) randomized task scheduling algorithm is proposed for fully connected multiprocessors. It combines two given task scheduling algorithms (A, and B) using local neighborhood search to give a hybrid of the two given algorithms. Objective is to show that such type of hybridization can give much better performance results in terms of parallel execution times. Two task scheduling algorithms are selected: DSC (Dominant Sequence Clustering as algorithm A), and CPPS (Cluster Pair Priority Scheduling as algorithm B) and a hybrid is created (the LOCAL(DSC, CP P S) or simply the LOCAL task scheduling algorithm). The LOCAL task scheduling algorithm has time complexity O(|V ||E|(|V | + |E|)), where V is the set of vertices, and E is the set of edges in the task graph. The LOCAL task scheduling algorithm is compared with six other algorithms: CPPS, DCCL (Dynamic Computation Communication Load ), DSC, EZ (Edge Zeroing), LC (Linear Clustering), and RDCC (Randomized Dynamic Computation Communication). Performance evaluation of the LOCAL task scheduling algorithm shows that it gives up to 80.47 % improvement of NSL (Normalized Schedule Length) over other algorithms.

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A Clustering Heuristic for Multiprocessor Environments Using Computation and Communication Loads of Modules

Cited by 7 publications

References 8 publications

Task Scheduling of a Distributed Computing Software in the Presence of Faults

Task Scheduling of a Distributed Computing Software in the Presence of Faults

An edge priority‐based clustering algorithm for multiprocessor environments

A Randomized Scheduling Algorithm for Multiprocessor Environments Using Local Search

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