HMGOWM: A Hybrid Decision Mechanism for Automating Migration of Virtual Machines

Cao, Ronghui; Li, Kenli; Li, Keqin

doi:10.1109/tsc.2018.2873694

Cited by 15 publications

(8 citation statements)

References 21 publications

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“…Trabalhos associados ao gerenciamento de migrações podem ser reduzidos a dois grupos: gerência intra-DC e inter-DC. Dentro dessa classificação destacam-se iniciativas para atender objetivos específicos de gerenciamento e para a criação de arquiteturas para o controle da migração [Cao et al 2018]. Os trabalhos relevantes de cada classificação são discutidos nessa Seção.…”

Section: Trabalhos Relacionadosunclassified

Uma arquitetura para a Gerência de Migração de Máquinas Virtuais

Matos¹,

Maziero²

2020

Anais Do XVIII Workshop Em Clouds E Aplicações (WCGA 2020)

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Provedores de Infraestrutura (Cloud Provider - CP) fornecem recursos físicos para a provisão de Serviços Baseados em Nuvem (Cloud Service - CS) que são coordenados por um Provedor de CSs (Cloud Service Provider - CSP). Para garantir a elasticidade de CSs, a migração de VMs tem sido adotada no gerenciamento dinâmico de recursos. Contudo, o dinamismo que as cargas de trabalho (workload) impõe às VMs tem sido um obstáculo para as Plataformas de Gerência de Nuvens (Cloud Management Platforms - CMP). Propor soluções nesse cenário é desafiador, considerando a diversidade de abordagens de integração de serviços dos CMPs. Neste cenário, migrações tendem a ser mal planejadas, aumentando o risco de falhas e atrasos. Este trabalho propõe uma arquitetura que possibilita a integração das diferentes fontes de informação, contribuindo para a automatização e o gerenciamento das migrações de VMs. A proposta foi comparada com uma solução que não emprega recursos de gerenciamento, em um cenário de migrações entre Datacenters (DCs). A avaliação mostra a efetividade da abordagem proposta, diminuindo a quantidade de migrações desnecessárias e a frequência de sobrecargas pontuais.

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Section: Trabalhos Relacionadosunclassified

Uma arquitetura para a Gerência de Migração de Máquinas Virtuais

Matos¹,

Maziero²

2020

Anais Do XVIII Workshop Em Clouds E Aplicações (WCGA 2020)

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“…[61] classified the server resources based on resource ranking, while [63] scheduled the VMs based on the availability of the server resources where they achieved an optimal number of migrations. On the reliability side, the VM overhead has optimized as in [67], [73], [78], [79]. In [81] the network traffic was optimized by auto-scaling that integrates both application auto-scaling and dynamic VM allocation when they reduced the communication latency among VM2VM.…”

Section: ) Migration Overheadmentioning

confidence: 99%

“…By coming up with a novel heuristics-based energy aware model that achieves more stability for the data-centre and reduces the number of VMs to be migrated. [67] presented a mechanism for automating VMs migration by means of hybrid decision-making that decreases the migration probability with increase in the access probability while minimizing the downtime experience of users and boosting the PMs' balancing, thus increasing the users' satisfaction.…”

Section: ) Reliabilitymentioning

confidence: 99%

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

Ala’anzy

Othman

2019

IEEE Access

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The data-center is considered the heart of cloud computing. Recently, the growing demand for cloud computing services has caused a growing load on data centers. In terms of system behavior and workload, patterns of cloud computing are very dynamic; and that might serve to imbalance the load among data center resources. Eventually, some data-center resources could come to be over-loaded/under-loaded, which leads to an increase in energy consumption in addition to decreased functioning and wastage of resources. Just considering energy-efficiency (that can be attained efficiently by consolidate the servers) may not be enough for real applications because it may cause problems such as unbalanced load for each Physical Machine (PM). Therefore, this paper surveys published load balancing algorithms that achieved by server consolidation via a meta-analysis. Load balancing with server consolidation enriches the exploitation of resource utilization and can enhance Quality of Service (QoS) metrics, since data-centers and their applications are increasing exponentially. This meta-study, reviews the literature on load balancing and server consolidation and presents a ready reference taxonomy on the most efficient algorithms that achieve load balancing and server consolidation. This work attempts to present a taxonomy with a new classification for load balancing and server consolidation, such as migration overhead, hardware threshold, network traffic, and reliability.

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“…Moreover, VMP optimization can prolong the stability of the datacenter before the reallocation of VMs becomes an urgent issue [12,13].VMP optimization is an NP-hard combinatorial problem. The problem can be addressed with diverse, conflicting objectives [14,15]. The VMP problem has been solved for several objectives, i.e., a linear programming problem (LP) is used to minimize the cost of hosting VMs in PMs [16] and heuristic data to consolidate VMs on a minimal number of PMs [17].Evolutionary computation algorithms have been used to reduce the power consumption and increase the resource utilization, i.e., genetic algorithm (GA).…”

mentioning

confidence: 99%

“…VMP optimization is an NP-hard combinatorial problem. The problem can be addressed with diverse, conflicting objectives [14,15]. The VMP problem has been solved for several objectives, i.e., a linear programming problem (LP) is used to minimize the cost of hosting VMs in PMs [16] and heuristic data to consolidate VMs on a minimal number of PMs [17].…”

mentioning

confidence: 99%

Optimal Virtual Machine Placement Based on Grey Wolf Optimization

et al. 2019

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Virtual machine placement (VMP) optimization is a crucial task in the field of cloud computing. VMP optimization has a substantial impact on the energy efficiency of data centers, as it reduces the number of active physical servers, thereby reducing the power consumption. In this paper, a computational intelligence technique is applied to address the problem of VMP optimization. The problem is formulated as a minimization problem in which the objective is to reduce the number of active hosts and the power consumption. Based on the promising performance of the grey wolf optimization (GWO) technique for combinatorial problems, GWO-VMP is proposed. We propose transforming the VMP optimization problem into binary and discrete problems via two algorithms. The proposed method effectively minimizes the number of active servers that are used to host the virtual machines (VMs). We evaluated the proposed method on various VM sizes in the CloudSIM environment of homogeneous and heterogeneous servers. The experimental results demonstrate the efficiency of the proposed method in reducing energy consumption and the more efficient use of CPU and memory resources.

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HMGOWM: A Hybrid Decision Mechanism for Automating Migration of Virtual Machines

Cited by 15 publications

References 21 publications

Uma arquitetura para a Gerência de Migração de Máquinas Virtuais

Uma arquitetura para a Gerência de Migração de Máquinas Virtuais

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

Optimal Virtual Machine Placement Based on Grey Wolf Optimization

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