A multi-objective ant colony system algorithm for virtual machine placement in cloud computing
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TLDR
The proposed multi-objective ant colony system algorithm to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption is proposed.About:
This article is published in Journal of Computer and System Sciences.The article was published on 2013-12-01 and is currently open access. It has received 602 citations till now. The article focuses on the topics: Ant colony optimization algorithms & Virtual machine.read more
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Journal ArticleDOI
A Survey on Mobile Edge Computing: The Communication Perspective
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management is provided in this paper, where a set of issues, challenges, and future research directions for MEC are discussed.
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A Survey on Mobile Edge Computing: The Communication Perspective
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management and recent standardization efforts on MEC are introduced.
Journal ArticleDOI
Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches
TL;DR: Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources, then paints a landscape of the scheduling problem and solutions, and a comprehensive survey of state-of-the-art approaches is presented systematically.
Journal ArticleDOI
An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing
TL;DR: The results show that the OEMACS generally outperforms conventional heuristic and other evolutionary-based approaches, especially on VMP with bottleneck resource characteristics, and offers significant savings of energy and more efficient use of different resources.
Proceedings ArticleDOI
Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks
Jie Xu,Lixing Chen,Pan Zhou +2 more
TL;DR: In this paper, the authors investigated the problem of dynamic service caching in MEC-enabled dense cellular networks and proposed an efficient online algorithm, called OREO, which jointly optimizes service caching and task offloading to address service heterogeneity, unknown system dynamics, spatial demand coupling and decentralized coordination.
References
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Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Journal ArticleDOI
Ant system: optimization by a colony of cooperating agents
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Journal ArticleDOI
Ant colony system: a cooperative learning approach to the traveling salesman problem
TL;DR: The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
Journal ArticleDOI
Cloud computing: state-of-the-art and research challenges
Qi Zhang,Lu Cheng,Raouf Boutaba +2 more
TL;DR: A survey of cloud computing is presented, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges to provide a better understanding of the design challenges of cloud Computing and identify important research directions in this increasingly important area.
Proceedings ArticleDOI
Power provisioning for a warehouse-sized computer
TL;DR: This paper presents the aggregate power usage characteristics of large collections of servers for different classes of applications over a period of approximately six months, and uses the modelling framework to estimate the potential of power management schemes to reduce peak power and energy usage.
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