Decentralized Scheduling of Bursty Workload on Computing Grids

Zhang, Juemin; Mi, Ningfang; Tai, Jianzhe; Meleis, Waleed

doi:10.1109/icc.2011.5962547

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The model has a good convergence rate and the initial population is mostly surrounding the average value or in equivalence often Job scheduling is a fundamental issue in achieving high performance at the grid, however it's a big challenge for efficient scheduling algorithm in design and implementation. The following researches Farzi (2009), Miyagi et al (2007), Khanli et al (2010a), Lopez and Kasmir (2009), Zhang et al (2011), Dai and Wang (2005) and Khanli et al (2010b) have studied and introduced many algorithms to solve the problems of allocating resources which are unsolved problems. They try to solve the problem by quickly finding the resources and identifying resources and also how to balance workloads among all computing sites.…”

Section: Literature Reviewmentioning

confidence: 99%

Resources Centralization System for Grid Resources

Muhsem¹,

Maaita²,

Nsour³

2013

RJASET

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The increasing expansion of communications that is characterized by quality and availability led to interest on grid computing paradigm. The grid computing solves large-scale scientific problems, by providing the feature of sharing and selecting of various resources accessibility and utility. These resources solve intensive problems by increasing the computation and storage power. This study focuses on system with centralized resources for managing the grid resources. The proposed idea will create a resource list, which includes the resource history that will help the user to search for resources. The proposal resources list system will improve the resource serving by showing the most resources used and will save the time search time for the job request, by these points we will improve the quality of the user jobs execution and the quality type of the used resources.

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Section: Literature Reviewmentioning

confidence: 99%

Resources Centralization System for Grid Resources

Muhsem¹,

Maaita²,

Nsour³

2013

RJASET

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“…We refer the interested readers to [5] for the details on system design and remark that such a simulation environment can be used to simulate a cloud computing framework. In our simulation, the system consists of N computing sites, where each site runs the First-In-First-Out (FIFO) policy to schedule the assigned jobs.…”

Section: Motivationmentioning

confidence: 99%

ArA: Adaptive resource allocation for cloud computing environments under bursty workloads

Tai

Zhang

et al. 2011

30th IEEE International Performance Computing and Communications Conference

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Abstract-Cloud computing nowadays becomes quite popular among a community of cloud users by offering a variety of resources. However, burstiness in user demands often dramatically degrades the application performance. In order to satisfy peak user demands and meet Service Level Agreement (SLA), efficient resource allocation schemes are highly demanded in the cloud. However, we find that conventional load balancers unfortunately neglect cases of bursty arrivals and thus experience significant performance degradation. Motivated by this problem, we propose new burstiness-aware algorithms to balance bursty workloads across all computing sites, and thus to improve overall system performance. We present a smart load balancer, which leverages the knowledge of burstiness to predict the changes in user demands and on-the-fly shifts between the schemes that are "greedy" (i.e., always select the best site) and "random" (i.e., randomly select one) based on the predicted information. Both simulation and real experimental results show that this new load balancer can adapt quickly to the changes in user demands and thus improve performance by making a smart site selection for cloud users under both bursty and non-bursty workloads.

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Information Fusion Architecture for Variable-Load Scheduling in a Cloud-Assisted CPS

Chejerla

Madria

2016

2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC)

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Decentralized Scheduling of Bursty Workload on Computing Grids

Cited by 4 publications

References 18 publications

Resources Centralization System for Grid Resources

Resources Centralization System for Grid Resources

ArA: Adaptive resource allocation for cloud computing environments under bursty workloads

Information Fusion Architecture for Variable-Load Scheduling in a Cloud-Assisted CPS

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