Journal ArticleDOI
BGM-BLA: A New Algorithm for Dynamic Migration of Virtual Machines in Cloud Computing
TLDR
The proposed BGM-BLA algorithm performs relatively well in terms of the Pareto sets obtained and computational time in comparison with two optimization algorithms, i.e., Non-dominated Sorting Genetic Algorithm (NSGA-II) and binary graph matching-based common-coding algorithm.Abstract:
Cloud computing is getting more prevalent and finding a way to reduce the cost of cloud computing platform through the migration of virtual machines (VM) is a concerned issue. In this paper, the problem of dynamic migration of VMs (DM-VM) in the cloud computing platform (or simply the cloud) is investigated. A triple-objective optimization model for DM-VM is established, which takes energy consumption, communication between VMs, and migration cost into account under the situation that the platform works normally. The DM-VM problem is divided into two parts: (i) forming VMs into groups, and (ii) determining the best way to place the groups into certain physical nodes. A binary graph matching-based bucket-code learning algorithm (BGM-BLA) is designed for solving the DM-VM problem. In BGM-BLA, bucket-coding and learning is employed for finding the optimal solutions, and binary graph matching is used for evaluating the candidate solutions. The computational results demonstrate that the proposed BGM-BLA algorithm performs relatively well in terms of the Pareto sets obtained and computational time in comparison with two optimization algorithms, i.e., Non-dominated Sorting Genetic Algorithm (NSGA-II) and binary graph matching-based common-coding algorithm.read more
Citations
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Journal ArticleDOI
SDMSim: A manufacturing service supply–demand matching simulator under cloud environment
TL;DR: In this article, a manufacturing service SDM simulator (SDMSim) is proposed to realize the effective and intelligent supply-demand matching (SDM) of various manufacturing resources and capabilities (MR&C) in the form of service.
Journal ArticleDOI
Manufacturing Service Management in Cloud Manufacturing: Overview and Future Research Directions
TL;DR: In this paper, the authors provide a brief overview of cloud manufacturing and then focus on the problem of manufacturing service management (MSM) from the service lifecycle perspective, and summarize the existing works and technologies on MSM in cloud manufacturing.
Journal ArticleDOI
A Survey of Virtual Machine Placement Techniques in a Cloud Data Center
Zoha Usmani,Shailendra Singh +1 more
TL;DR: A comprehensive study of the state-of-the-art VM placement and consolidation techniques used in green cloud which focus on improving the energy efficiency is presented, revealing pitfalls and suggesting improvisation methods along this direction.
Journal ArticleDOI
Energy and Migration Cost-Aware Dynamic Virtual Machine Consolidation in Heterogeneous Cloud Datacenters
TL;DR: A consolidation score function is designed for an overall evaluation on the basis of a migration cost estimation method and an upper bound estimation method for maximal saved power, and an improved grouping genetic algorithm (IGGA) based on them is proposed.
Journal ArticleDOI
Advanced manufacturing systems: supply–demand matching of manufacturing resource based on complex networks and Internet of Things
TL;DR: A new method for manufacturing resource supply–demand matching based on complex networks and Internet of Things (IoT) is proposed, and a four-layered architecture for implementing this method is designed.
References
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A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Journal ArticleDOI
Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II
Hui Li,Qingfu Zhang +1 more
TL;DR: The experimental results indicate that MOEA/D could significantly outperform NSGA-II on these test instances, and suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated PS shapes.
Journal ArticleDOI
The cost of a cloud: research problems in data center networks
TL;DR: This work examines the costs of cloud service data centers today and proposes (1) joint optimization of network and data center resources, and (2) new systems and mechanisms for geo-distributing state.
Journal ArticleDOI
Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers
Anton Beloglazov,Rajkumar Buyya +1 more
TL;DR: A competitive analysis is conducted and competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems are proved, and novel adaptive heuristics for dynamic consolidation of VMs are proposed based on an analysis of historical data from the resource usage by VMs.
Proceedings ArticleDOI
Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement
TL;DR: This paper designs a two-tier approximate algorithm that efficiently solves the VM placement problem for very large problem sizes and shows a significant performance improvement compared to existing general methods that do not take advantage of traffic patterns and data center network characteristics.