e-journal

Self-Adaptive Learning PSO-Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud

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Public clouds provide Infrastructure as a Service (IaaS) to users who do not own sufficient compute resources. IaaS achieves the economy of scale by multiplexing, and therefore faces the challenge of scheduling tasks to meet the peak demand while preserving Quality-of-Service (QoS). Previous studies proposed proactive machine purchasing or cloud federation to resolve this problem. However, the former is not economic and the latter for now is hardly feasible in practice. In this paper, we propose a
resource allocation framework in which an IaaS provider can outsource its tasks to External Clouds (ECs) when its own resources are not sufficient to meet the demand. This architecture does not
require any formal inter-cloud agreement that is necessary for the cloud federation. The key issue is how to allocate users’ tasks to maximize the profit of IaaS provider while guaranteeing QoS. This
problem is formulated as an integer programming (IP) model, and solved by a self-adaptive learning particle swarm optimization (SLPSO)-based scheduling approach. In SLPSO, four updating strategies are used to adaptively update the velocity of each particle to ensure its diversity and robustness. Experiments show that, SLPSO can improve a cloud provider’s profit by 0.25%–11.56% compared with standard PSO; and by 2.37%–16.71% for problems of nontrivial size compared with CPLEX under reasonable
computation time.
Note to Practitioners—IaaS has become an important paradigm in cloud computing and there are many commercial offerings such as Amazon EC2, IBM Smart Cloud Enterprise, and Microsoft Azure. IaaS providers are facing a big challenge on how to schedule their resources to meet the demand and at the same time guarantee the QoS promised. We propose a framework allowing an IaaS provider to outsource its workloads to external clouds when its own resources are not sufficient. A SLPSO is used to effectively schedule inter-cloud resources. This framework is more practical compared with cloud federation because it does
not need any business agreement to be reached in advance. The scheduling approach is able to find the optimal or suboptimal allocation scheme of internal and external resources to maximize the profit of IaaS providers, and can greatly improve the quality of scheduling solution.
Index Terms—Hybrid cloud, infrastructure as a service (IaaS) cloud, particle swarm optimization (PSO), task scheduling.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 11, NO. 2, APRIL 2014

Bahasa

English

ISBN/ISSN

1545-5955

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 11, NO. 2, APRIL 2014

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

Lampiran Berkas

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