Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads

Bossche, Ruben Van den; Vanmechelen, Kurt; Broeckhove, J.

doi:10.1109/cloud.2010.58

Cited by 240 publications

(116 citation statements)

References 12 publications

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“…Closest to our work, our own [8,16,17] and related [44][45][46]5] studies of multiple scheduling policies have emphasized the inability of any single policy to perform well under a wide yet realistic variety of scientific workloads.…”

Section: Related Workmentioning

confidence: 99%

A Periodic Portfolio Scheduler for Scientific Computing in the Data Center

Deng

Verboon

Ren

et al. 2014

Job Scheduling Strategies for Parallel Processing

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Abstract. The popularity of data centers in scientific computing has led to new architectures, new workload structures, and growing customerbases. As a consequence, the selection of efficient scheduling algorithms for the data center is an increasingly costlier and more difficult challenge. To address this challenge, and contrasting previous work on scheduling for scientific workloads, we focus in this work on portfolio schedulinghere, the dynamic selection and use of a scheduling policy, depending on the current system and workload conditions, from a portfolio of multiple policies. We design a periodic portfolio scheduler for the workload of the entire data center, and equip it with a portfolio of resource provisioning and allocation policies. Through simulation based on real and synthetic workload traces, we show evidence that portfolio scheduling can automatically select the scheduling policy to match both user and data center objectives, and that portfolio scheduling can perform well in the data center, relative to its constituent policies.

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Section: Related Workmentioning

confidence: 99%

A Periodic Portfolio Scheduler for Scientific Computing in the Data Center

Deng

Verboon

Ren

et al. 2014

Job Scheduling Strategies for Parallel Processing

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“…The questions raised are what workload to outsource, to which provider, what resources to rent, when, and so on. Examples of research results in this direction are reported in [14,7]. In [7] the authors discuss how an accouting service deployed within an organisation can be used to control expenditures on public cloud resources; their accounting service relies on data downloaded from the cloud provider instead of calculating it locally.…”

Section: Related Workmentioning

confidence: 99%

Consumer Side Resource Accounting in the Cloud

Mihoob

Molina-Jiménez

Shrivastava

2011

IFIP Advances in Information and Communication Technology

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Abstract. The paper investigates the possibility of consumer side resource accounting of cloud services, meaning, whether it is possible for a consumer to independently collect all the resource usage data required for calculating billing charges for pay-per-use cloud services. The accounting models of two widely used cloud services are examined and possible sources of difficulties are identified, including causes that could lead to discrepancies between the metering data collected by the consumer and the provider. The investigation is motivated by the fact that cloud service providers perform their own measurements to collect usage data, but as yet there are no equivalent facilities of consumer-trusted metering that are commonly provided by utility service providers like gas and electricity. The paper goes on to suggest how cloud service providers can improve their accounting models to facilitate the task of consumer side resource accounting.

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“…All Colonies = Countries -Initial empires (10) In equation (10), n is a percentage of total number of countries that in the proposed algorithm is considered to be 0.1. In this paper, initial population size and number of empires are 100, 10, respectively.…”

Section: Fig 2 Countries As Resource Allocationmentioning

confidence: 99%

“…Most scheduling algorithms focus on makespan. In [5]- [10], Genetic Algorithm is used to reduce makespan of tasks allocation to resources. Yue Miao [11] by using firefly algorithm based on chaos algorithm tried to reduce the average time spent by subtasks in processing request tasks, and thus improve the efficiency of task processing and achieve a rational allocation of resources.…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation in Cloud Computing Using Imperialist Competitive Algorithm with Reliability Approach

Fayazi¹,

Noorimehr²,

Alavi³

2016

ijacsa

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Abstract-Cloud computing has become a universal trend now. So, for users, the reliability is an effective factor to use this technology. In addition, users prefer to implement and get their work done quickly. This paper takes into account these two parameters for resource allocation due to their importance In this method, the Imperialist Competitive algorithm with the addition of a cross layer of cloud architecture to reliability evaluation is used. In this cross layer, initial reliability is considered for all the resources and the implementation of their tasks and due to the success or failure of implementation, reliability of resources is increased or reduced. Reliability and makespan are used as a cost function in ICA for resource allocation. Results show that the proposed method can search the problem space in a better manner and give a better performance when compared to other methods.

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Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads

Cited by 240 publications

References 12 publications

A Periodic Portfolio Scheduler for Scientific Computing in the Data Center

A Periodic Portfolio Scheduler for Scientific Computing in the Data Center

Consumer Side Resource Accounting in the Cloud

Resource Allocation in Cloud Computing Using Imperialist Competitive Algorithm with Reliability Approach

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