Evolutionary task scheduling in static and dynamic environments

Zamfirache, Flavia; Zaharie, Daniela; Crăciun, Ciprian

doi:10.1109/icccyb.2010.5491336

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Various approaches related to static scheduling such as grain packing algorithm [7], Ford-Fulkerson algorithm for efficient assignment of program modules [8], and scheduling considering interference costs have been reported in [9]. In [16][17][18][19], a hybrid policy for the job scheduling and load balancing is proposed integrating static and dynamic approaches for load distribution and redistribution with the redistribution driven by the performance benefit the jobs can achieve when reallocated to a remote processor. In [16][17][18][19], a hybrid policy for the job scheduling and load balancing is proposed integrating static and dynamic approaches for load distribution and redistribution with the redistribution driven by the performance benefit the jobs can achieve when reallocated to a remote processor.…”

Section: Related Workmentioning

confidence: 99%

“…The problem related to redistribution of load of the system among its nodes is defined in [4,6,[10][11][12][13][14][15] describing the load-distributing algorithms based on SLB, RLB and diffusion-based approaches. In [16][17][18][19], a hybrid policy for the job scheduling and load balancing is proposed integrating static and dynamic approaches for load distribution and redistribution with the redistribution driven by the performance benefit the jobs can achieve when reallocated to a remote processor. It has been stated in [4,6,20] that dynamic load balancing algorithms can be decomposed in three rules.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive threshold based hybrid load balancing scheme with sender and receiver initiated approach using random information exchange

Alam

Raza

2016

Concurrency and Computation

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The primary objective of load balancing for distributed systems is to minimize the job execution time while maximizing the resource utilization. Load balancing on decentralized systems need effective information exchange policy so that with minimum amount of communication the nodes have up to date information about other nodes in the system. Periodic, event-based and on-demand information exchange are some important policies used for the same. All these approaches involve a lot of overhead and even sometime leading toward obsolete data with the nodes if there is a delay in the updation. This work presents an adaptive threshold-based hybrid load balancing scheme with sender and receiver initiated approach (HLBWSR) using random information exchange (RIE). RIE ensures that the information is exchanged in such a way that each node in the system has up-to-date state of the other nodes with much reduced communication overhead. Further, the adaptive threshold ensures that almost an average numbers of jobs are executed by all the nodes in the system. The study of the effect of the use of RIE on sender initiated, receiver initiated and hybrid of sender and receiver initiated load balancing approach establishes the superior performance of HLBWSR among its RIE-based peers. A comparative analysis of HLBWSR, with periodic information exchange strategy, modified estimated load information scheduling algorithm and load balancing on arrival reveals its effectiveness under various test conditions. allocation and execution by transferring tasks from the heavily loaded to the lightly loaded nodes with the aim of improving the performance of the system. A typical load balancing algorithm is defined by three inherent policies [4-6]: Information policyIt deals with the amount of load information that is required by the job placement decision-makers for efficient job transfer. Transfer policyThe current load of the host and the size of the job under consideration determine the conditions under which the job should be transferred from heavily loaded node to lightly loaded node. Placement policyThe processing element with the least number of jobs and closest to the sender node is the best recipient. The affinity for processing element to receive job is the deciding factor during job placement.Load balancing algorithms are broadly characterized as static and dynamic. Static algorithms make use of prior information for load balancing while dynamic load balancing algorithms use runtime system-state information to make load balancing decisions. Dynamic algorithms have the potential to outperform static algorithms as they make use of system-state information during the runtime to enhance the quality of their decisions. However, dynamic algorithms incur more overhead than their static counterparts as they have to collect, store, and analyze state information. This overhead can be overcome if reduction in the response time incurred because of the redistribution outperforms the overhead [3,4,6].According to the degree of centralization, load sch...

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An adaptive threshold based hybrid load balancing scheme with sender and receiver initiated approach using random information exchange

Alam

Raza

2016

Concurrency and Computation

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“…Evolutionary Task Scheduling [26] upon the previous scheduling stages, in static and dynamic environment. The use of heuristic in the initialization phase and specific mutation operator are two parameters which provide the beneficial result for the effective scheduling in the static environment.…”

Section: Non Deterministic Task (Dynamic) Partitioning Strategiesmentioning

confidence: 99%

Classification of Task Partitioning and Load Balancing Strategies in Distributed Parallel Computing Systems

Khan¹,

Ali²

2012

IJCA

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Distribution of the tasks amongst the various computing nodes is itself an intellectually challenging problem in the high performance distributed computing systems. To choose the appropriate strategy for the required system is difficult without the meaningful comparison of the existing task partitioning and load balancing strategies. The effectiveness of the strategy depend on the number of factors-efficiency, interconnection topology, communication mode, program structure, throughput and computing capabilities of the structure. A number of task partitioning and load balancing strategies have been proposed, each of which perform remarkable results under different circumstances. The main goal of the paper is to unravel the mystery of strategies and to classify when and where each strategy is appropriate. In this paper, taxonomy of task partitioning and load balancing is presented in an attempt to provide a common terminology and classification mechanism.

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Niched evolutionary techniques for performance, energy, and temperature optimized scheduling in multi-core systems

Sheikh

Ahmad

2015

2015 Sixth International Green and Sustainable Computing Conference (IGSC)

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Evolutionary task scheduling in static and dynamic environments

Cited by 4 publications

References 7 publications

An adaptive threshold based hybrid load balancing scheme with sender and receiver initiated approach using random information exchange

An adaptive threshold based hybrid load balancing scheme with sender and receiver initiated approach using random information exchange

Classification of Task Partitioning and Load Balancing Strategies in Distributed Parallel Computing Systems

Niched evolutionary techniques for performance, energy, and temperature optimized scheduling in multi-core systems

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