Enhancing energy efficiency in resource allocation for real-time cloud services

Bagheri, Zahra; Zamanifar, Kamran

doi:10.1109/istel.2014.7000793

Cited by 9 publications

(13 citation statements)

References 11 publications

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“…The proposed framework considers both server and communication network's energy consumptions in order to provide the complete solution. Efficient energy resources allocation model for real time services in the cloud environment is presented in [22]. There are several VMs and hosts that have been applied in order to minimize energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Adaptive Energy-Scheduling Algorithm for Virtualized Cloud Computing

B¹,

Krishnappa²,

Rajan³

et al. 2019

IJDPS

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Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies, which causes the degradation of performance. In this paper, we propose the Real Time Adaptive Energy-Scheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud Computing-Virtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.

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Section: Related Workmentioning

confidence: 99%

Real-Time Adaptive Energy-Scheduling Algorithm for Virtualized Cloud Computing

B¹,

Krishnappa²,

Rajan³

et al. 2019

IJDPS

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“…O resultado ultrapassa 29 (quantidade dos artigos selecionados para esta RSL) porque 22 artigos respondem pelo menos duas das questões de pesquisa e 7 respondem apenas uma questão de pesquisa. Com base nestes dados, pode-se concluir que a maioria dos artigos apresentam respostas satisfatórias para esta busca, considerando que muitas propostas de trabalhos futuros foram apresentadas e podem ser vistas [19], EP_04 [20], EP_05 [21], EP_06 [22], EP_10 [23], EP_11 [24], EP_12 [25], EP_13 [26], EP_16 [27], EP_17 [40], EP_18 [28], EP_19 [29], EP_20 [30], EP_26 [18], EP_27 [31], EP_28 [32] QP2 EP_02 [19], EP_20 [30], EP_26 [18] (RNF) EP_01 [33], EP_03 [34], EP_07 [35], EP_08 [36], EP_09 [37], EP_14 [38], EP_15 [39], EP_17 [40], EP_21 [41], EP_22 [42], EP_23 [43], EP_24 [44], EP_25 [45], EP_29 [46] (Sust.) QP3 EP_01 [33], EP_04 [20], EP_05 [21], EP_07 [35], EP_09 [37], EP_10 [23], EP_11 [24], EP_12 [25], EP_13 [26], EP_14 [38], EP_17…”

Section: Figura 2: Seleção Dos Artigosunclassified

Requisitos não Funcionais e Sustentabilidade para Computação em Nuvem: uma Revisão Sistemática da Literatura

Camboim

Alencar

2018

Anais Do WER18 - Workshop Em Engenharia De Requisitos

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Resumo A computação em nuvem continua a se disseminar no mundo web, possibilitando o uso de aplicações, plataformas e infraestruturas através de conexões remotas. Requisitos não funcionais de grande importância para computação em nuvem devem considerar desempenho e sustentabilidade, de modo a impactar diretamente nos custos e na qualidade dos serviços prestados, considerando ainda evitar (maiores) danos à saúde ambiental. Uma das grandes dificuldades enfrentadas é a falta de padrões na adoção desta tecnologia, o que implica na maneira de melhor configurar infraestruturas, levando em conta a diversidade de requisitos essenciais que devem ser atendidos para garantias de entrega e fornecimento dos serviços. Este trabalho apresenta quais requisitos não funcionais e parâmetros de sustentabilidade necessitam de especial atenção no processo de migração para computação em nuvem. Os dados são apresentados como resultados de uma revisão sistemática da literatura.

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“…The author in [4], proposed optimum dynamic VS Placement policy using CPU consumption, but they have not considered the network topology for VM placement. The authors in [9], discussed VM migration using the maximum processing power (MPP) and random selection (RS) approach AH, the solution proposed by [9] preserved same firewall rule for VM to the destination server after migration. The authors in [10], have proposed Hierarchical Decentralized Dynamic VS Consolidation Framework, where they discussed how the global controller does take decision for VM migration.…”

Section: Related Workmentioning

confidence: 99%

Decision Making Framework for Decentralized Virtual Machine Placement in Cloud Computing

Rathod¹,

Reddy²

2018

IJET

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In distributed cloud environment hosts are configured with Local Resource Monitors (LRM). This LRM monitors the underlying hosts' resource usage, runs independently and balances the underling host's load by migrating Virtual Machine (VM) instance. For the dynamic environment, each hosts has varying resource requirement, hosts load cannot remain constant. LRM at each host takes decision for VM migration considering static threshold on its own and other hosts current CPU utilization. This result in chances of getting selected same host for VM placement by multiple hosts to reduce resource usage of underlying hosts. The decision making at each server causes the problem of same host identification by multiple hosts during VM placement and consumes extra CPU power and network bandwidth consumption towards each server. This paper addresses the above said issue by proposing decentralized decision making framework for cloud considering hybrid Peer to Peer (P2P) network topology. Proposed solution results avoiding above said issues and balances the load across servers in DC.

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Enhancing energy efficiency in resource allocation for real-time cloud services

Cited by 9 publications

References 11 publications

Real-Time Adaptive Energy-Scheduling Algorithm for Virtualized Cloud Computing

Real-Time Adaptive Energy-Scheduling Algorithm for Virtualized Cloud Computing

Requisitos não Funcionais e Sustentabilidade para Computação em Nuvem: uma Revisão Sistemática da Literatura

Decision Making Framework for Decentralized Virtual Machine Placement in Cloud Computing

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