Dynamic Load Balancing and Pricing in Grid Computing with Communication Delay

Qin, Zheng; Tham, Chen-Khong; Veeravalli, Bharadwaj

doi:10.1007/s10723-007-9093-5

Cited by 24 publications

(10 citation statements)

References 16 publications

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“…In literature, the researchers have proposed several loadbalancing strategies in Grid environments [6,7,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

“…The approach circumvents the scalability problem by using an ordinal distributed learning strategy, and realizes multi-agent coordination based on an information sharing mechanism with limited communication. Zheng et al [26] addressed the problem of determining which group an arriving job should be allocated to and how its load can be distributed among computers in the group to optimize the performance. The algorithms guarantee finding a load distribution over computers in a group that leads to the minimum response time or computational cost.…”

Section: Related Workmentioning

confidence: 99%

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An improved load-balancing mechanism based on deadline failure recovery on GridSim

Patel

Tripathy

2015

Engineering with Computers

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failure of the Gridlets. Extensive simulation experiments are conducted to quantify the performance of the proposed load-balancing strategy on the GridSim platform. Experiments have shown that the proposed system can considerably improve Grid performance in terms of total execution time, percentage gain in execution time, average response time, resubmitted time and throughput. The proposed loadbalancing technique gives 7 % better performance than EGDC in case of constant number of resources, whereas in case of constant number of Gridlets, it gives 11 % better performance than EGDC.

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“…In literature, the researchers have proposed several loadbalancing strategies in Grid environments [6,7,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An improved load-balancing mechanism based on deadline failure recovery on GridSim

Patel

Tripathy

2015

Engineering with Computers

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show abstract

“…Zheng [27], Mills and Dabrowski [16] and Penmatsa [20] also consider alternative rules for resource allocation. The methods by Zheng [27] and Penmatsa and Chronopoulos [20] follow a game-theoretic approach, typically based on pricing assumptions, stochastic arrivals, and an M/M/1 queue model for a computer [27]. Mills and Dabrowski [16] provide simulations of economicsbased mechanisms for grid computing that provide better system welfare.…”

Section: Related Workmentioning

confidence: 99%

Minimizing the Average Completion Time for Concurrent Grid Applications

Villela

2009

J Grid Computing

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When multiple grid applications are executed on a common grid computing infrastructure, the policy of resource allocation impacts the time to complete these applications. In this paper, we formulate an analytical model that permits us to compare different allocation policies. We show that a uniform allocation policy penalizes large jobs (i.e., the work required for an application), whereas a linear allocation of resources penalizes small jobs. In particular, we study an allocation policy that aims at minimizing the average job completion time. We show that such policy can reduce the average completion time by as much as 50% of the completion time required for uniform or linear allocation policies. Using such policy is also fair to applications because it does not penalize small jobs or large jobs as other policies (such as uniform or linear) do.

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“…So, the need to adapt the initial machine performance estimation is justified. Dynamic load balancing approach considers, for task scheduling, the current processor load [7,9,12].…”

Section: Introductionmentioning

confidence: 99%

New Hybrid Task Scheduling Algorithm with Fuzzy Logic Controller in Grid Computing

Hajoui¹,

Bouattane²,

Youssfi³

et al. 2018

ijacsa

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Abstract-Distributedheterogeneous architecture is extensively applied to a diversity of large scale research projects conducive to solve complex computational problems. Mentioned distributed systems consist of multiple heterogenous linked processing units used to handle the continuous arrival jobs. The tasks scheduling problem is concerned with resource allocation strategies to assign jobs to available computing resources. The load balancing of linked resources becomes a main issue to select in each task schedule the adequate computing resource. Our proposal consists of combining Q-learning with ACO (Ant Colony Optimization) to solve the tasks allocation dilemma. In our proposed Fuzzy Hybrid Framework, Fuzzy ants are used to calculate at each scheduling operation, the novel reward values whereas Q-learning is used to select the suitable Worker Machine. The simulation findings confirmed the efficiency of the proposed framework due to the significant decrease of the makespan.

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Dynamic Load Balancing and Pricing in Grid Computing with Communication Delay

Cited by 24 publications

References 16 publications

An improved load-balancing mechanism based on deadline failure recovery on GridSim

An improved load-balancing mechanism based on deadline failure recovery on GridSim

Minimizing the Average Completion Time for Concurrent Grid Applications

New Hybrid Task Scheduling Algorithm with Fuzzy Logic Controller in Grid Computing

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