A Minimum Makespan Grid Workflow Scheduling algorithm

Amalarethinam, D. I. George; Selvi, F. Kurus Malai

doi:10.1109/iccci.2012.6158777

Cited by 21 publications

(14 citation statements)

References 10 publications

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“…This is simple approach to model workflow time, but it does not allow to measure possible estimation error. Some examples can be found in [6,7,15]. The last type of assumptions exploits random nature of execution time and has a potential to give the most accurate runtime predictions.…”

Section: Execution Time In Workflow Schedulingmentioning

confidence: 99%

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Towards Better Workflow Execution Time Estimation

Chirkin

Kovalchuk

2014

IERI Procedia

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Authors highlight the importance of estimating workflow execution time in the scheduling problem and in pricing models in Clouds. Claim that better approaches need to be developed to obtain precise estimates of execution time. Describe straightforward regression for stochastic execution time estimation. Propose a combination of Chebyshev-like distribution-free inequalities and distribution-based approaches to calculate better confidence bounds on runtime based on results of statistical tests. Finally, authors explain the need of developing better algorithm to compute workflow execution time based on stochastic estimates of tasks' execution time. Explain the need of developing better workflow runtime estimation algorithm. Finally, authors present an approach to estimating workflow execution time based on stochastic estimates of tasks' execution time.

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Section: Execution Time In Workflow Schedulingmentioning

confidence: 99%

“…Scheduling problem consists of mapping tasks to nodes optimally. Often used approach to represent workflow is a directed acyclic graph (DAG) [4,6,7]. It is well-known that complexity of a general scheduling problem in DAG is NP-Complete [8], thus many heuristics exist that provide near-optimal solution to the scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Towards Better Workflow Execution Time Estimation

Chirkin

Kovalchuk

2014

IERI Procedia

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“…Some examples of scheduling systems that can use any implementations of the runtime estimation module are CLAVIRE [5], [6], a scheduling framework proposed by Ling Yuan et al [7], and CLOWDRB by Somasundaram and Govindarajan [8]. task ranking and makespans [9], [10] architecture [11], [12], [7], [8] random -Chebyshev inequalities [13], composing quantiles [14] fixed NP parallelizing models [15], [16], [17] optimization algorithms [9], [18], [19], [20] ordinal Round-Robin, greedy etc. [21], [20] -There are no papers dedicated specifically to the workflow runtime estimation problem known to us.…”

Section: Related Workmentioning

confidence: 99%

“…Several papers are difficult to be classified due to two reasons: first, some approaches stay between two classes (e.g. in [9], [15], [16], [17], [10] the execution time is calculated as a mean of random variable, but the variance of the time is not used); second, some researchers focus more on the architecture of scheduling software than on the algorithms, so one can use different estimate representations [12], [8], [7].…”

Section: Related Workmentioning

confidence: 99%

“…However, in some cases a random part of the program execution time is small enough, thus this approach is often used. One example of using fixed time is the work of M. Malawski [22] Other examples of schedulers that do not exploit the stochastic nature of the runtime can be found in [9], [18], [19], [20]. They calculate the workflow makespan; this means that they have to calculate execution time of parallel branches, but the stochastic and fixed approaches to estimate it are different.…”

Section: Related Workmentioning

confidence: 99%

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Execution Time Estimation for Workflow Scheduling

Chirkin

Belloum

Kovalchuk

et al. 2014

2014 9th Workshop on Workflows in Support of Large-Scale Science

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Estimation of the execution time is an important part of the workflow scheduling problem. The aim of this paper is to highlight common problems in estimating the workflow execution time and propose a solution that takes into account the complexity and the randomness of the workflow components and their runtime. The solution proposed in this paper addresses the problems at different levels from task to workflow, including the error measurement and the theory behind the estimation algorithm. The proposed estimation algorithm can be integrated easily into a wide class of schedulers as a separate module. We use a dual stochastic representation, characteristic / distribution functions, in order to combine tasks' estimates into the overall workflow makespan. Additionally, we propose the workflow reductions -the operations on a workflow graph that do not decrease the accuracy of the estimates, but simplify the graph structure, hence increasing the performance of the algorithm.

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A deadline aware load balancing strategy for cloud computing

Haidri¹,

Alam

Shahid

et al. 2021

Concurrency and Computation

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The load balancing (LB) may be used at different levels to reduce overhead for the decision-making process. In the past decade, cloud computing has drawn a lot of attention from both the academic and commercial communities to get demanded resources (machines, platforms, data, storage, software, and so forth) as a service on rent economically. Generally, a situation may arise when requests are not meeting their deadlines and the cloud provider wants to finish the running application in minimum time. In this article, a receiver initiated deadline aware LB strategy (RDLBS2) has been proposed which attempts the migration of incoming cloudlets to appropriate virtual machines (VMs) where the deadlines of the cloudlets are met to optimize the turnaround time by exploiting the remaining processing capacities of VMs. A simulation study has been carried out by using Cloud-Sim as a simulator. A sensitivity analysis has been presented to analyze the effects on performance parameters by varying the number of cloudlets and the number of VMs while keeping the remaining input parameters fixed. The experimental evaluation and analysis suggest that RDLBS2 performs significantly better than its peers on objective parameters almost in all cases under study.

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A Minimum Makespan Grid Workflow Scheduling algorithm

Cited by 21 publications

References 10 publications

Towards Better Workflow Execution Time Estimation

Towards Better Workflow Execution Time Estimation

Execution Time Estimation for Workflow Scheduling

A deadline aware load balancing strategy for cloud computing

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