Multi-objective constraint and hybrid optimisation-based VM migration in a community cloud

IF 1.1 4区 计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE IET Computers and Digital Techniques Pub Date : 2020-01-01 DOI:10.1049/iet-cdt.2018.5243
Pradeepa Parthiban, Pushpalakshmi Raman
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引用次数: 3

Abstract

The growing demand for the cloud community market towards attracting and sustaining the incoming and the available cloud users is addressed actively to meet the competitive environment. There is a good scope for improving the provider capabilities in the cloud in order to satisfy the users with attractive benefits. The study introduces an effective virtual machine (VM) migration strategy using an optimisation algorithm in such a way to facilitate the user selection of the providers based on their budgetary requirements in running their own platforms. The constraints associated with the selection of the provider include cost, revenue, and resource, which are altogether confined as an elective factor. The optimisation algorithm employed for the VM migration is referred to as Taylor series-based salp swarm algorithm (Taylor-SSA) that is the integration of the Taylor series with SSA. The evaluation of the method is progressed using three setups by varying the number of providers and users. The cost, the revenue, and the resource of the proposed method are analysed and concluded that the proposed method acquired a minimal cost, maximal resource gain and revenue.

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社区云中基于多目标约束和混合优化的虚拟机迁移
为了满足竞争环境,云社区市场对吸引和维持新用户和可用云用户的需求不断增长。有一个很好的空间来改进云中的提供商能力,以使用户满意并获得有吸引力的好处。该研究引入了一种使用优化算法的有效虚拟机(VM)迁移策略,以便于用户根据运营自己平台的预算要求选择提供商。与供应商选择相关的限制因素包括成本、收入和资源,这些都被限制为一个选择性因素。用于VM迁移的优化算法被称为基于泰勒级数的salp群算法(Taylor SSA),它是泰勒级数与SSA的集成。通过改变提供者和用户的数量,使用三种设置对该方法进行评估。对该方法的成本、收益和资源进行了分析,得出结论:该方法获得了最小的成本、最大的资源收益和收益。
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来源期刊
IET Computers and Digital Techniques
IET Computers and Digital Techniques 工程技术-计算机:理论方法
CiteScore
3.50
自引率
0.00%
发文量
12
审稿时长
>12 weeks
期刊介绍: IET Computers & Digital Techniques publishes technical papers describing recent research and development work in all aspects of digital system-on-chip design and test of electronic and embedded systems, including the development of design automation tools (methodologies, algorithms and architectures). Papers based on the problems associated with the scaling down of CMOS technology are particularly welcome. It is aimed at researchers, engineers and educators in the fields of computer and digital systems design and test. The key subject areas of interest are: Design Methods and Tools: CAD/EDA tools, hardware description languages, high-level and architectural synthesis, hardware/software co-design, platform-based design, 3D stacking and circuit design, system on-chip architectures and IP cores, embedded systems, logic synthesis, low-power design and power optimisation. Simulation, Test and Validation: electrical and timing simulation, simulation based verification, hardware/software co-simulation and validation, mixed-domain technology modelling and simulation, post-silicon validation, power analysis and estimation, interconnect modelling and signal integrity analysis, hardware trust and security, design-for-testability, embedded core testing, system-on-chip testing, on-line testing, automatic test generation and delay testing, low-power testing, reliability, fault modelling and fault tolerance. Processor and System Architectures: many-core systems, general-purpose and application specific processors, computational arithmetic for DSP applications, arithmetic and logic units, cache memories, memory management, co-processors and accelerators, systems and networks on chip, embedded cores, platforms, multiprocessors, distributed systems, communication protocols and low-power issues. Configurable Computing: embedded cores, FPGAs, rapid prototyping, adaptive computing, evolvable and statically and dynamically reconfigurable and reprogrammable systems, reconfigurable hardware. Design for variability, power and aging: design methods for variability, power and aging aware design, memories, FPGAs, IP components, 3D stacking, energy harvesting. Case Studies: emerging applications, applications in industrial designs, and design frameworks.
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