Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance

Bermejo, Belén G.; Juiz, Carlos; Guerrero, Carlos

doi:10.1007/s11227-018-2613-1

Cited by 27 publications

(16 citation statements)

References 89 publications

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“…In this section, we aim to summarize all of them. The performance and energy influencing factors in virtual machine consolidation environments are analyzed in [5]. The authors classify the last ten years of the research in virtual machine consolidation, taking into account the performance and energy influencing factors.…”

Section: Related Workmentioning

confidence: 99%

“…Virtual machine consolidation is one of the techniques to help system administrators to manage them in a flexible manner [5]. This technique is based on the reallocation of virtual machines among different physical servers (see Fig.…”

Section: Virtual Machine Consolidationmentioning

confidence: 99%

“…In [5], the authors state that there is a set of factors influencing the virtual machine consolidation overhead. Among these factors, we can find the virtual machine manager type, the virtual machine features, the virtual machine consolidation degree, the resource utilization, the resource management configuration, the workload type, the physical machine features and their utilization, and the SLA between the customer and the service provider.…”

Section: Overhead In Virtual Machine Consolidationmentioning

confidence: 99%

“…Virtual machine consolidation adds some inherent CPU overhead since multiple virtual machines have different kinds of operating systems when competing for resources; thus, the hypervisor adds some overhead. This overhead is called virtual machine interference, and it refers to the performance degradation induced by the allocation of virtual machines in the same physical host [5].…”

Section: Overhead In Virtual Machine Consolidationmentioning

confidence: 99%

See 3 more Smart Citations

Virtual machine consolidation: a systematic review of its overhead influencing factors

Bermejo

Juiz

2019

J Supercomput

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This survey is an up-to-date account of the research on virtual machine consolidation overhead. The overhead influencing factors are analyzed throughout this work. Based on these factors, we propose a categorization that classifies the most important research works on virtualization and virtual machine consolidation overhead. We have analyzed and summarized 46 selected research works from an initial set of 428, attempting to update the state of the art with the most recent papers in this field.

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Section: Related Workmentioning

confidence: 99%

Section: Virtual Machine Consolidationmentioning

confidence: 99%

Section: Overhead In Virtual Machine Consolidationmentioning

confidence: 99%

Section: Overhead In Virtual Machine Consolidationmentioning

confidence: 99%

See 2 more Smart Citations

Virtual machine consolidation: a systematic review of its overhead influencing factors

Bermejo

Juiz

2019

J Supercomput

Self Cite

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“…A lot of research has been done in this direction which deals with performance analysis of virtualized platforms and measuring energy efficiency. The work reported in [7] presented a detailed study related to virtualized systems performance-energy trade-off. They suggested that virtualization overhead, performance-energy trade-off, and virtual machine interference are some of the key areas that need more attention from the research community.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Power and Energy Efficiency in Cloud Computing

Khan¹,

Shrestha

2019

Proceedings of the 9th International Conference on Cloud Computing and Services Science

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With the exponential growth in cloud computing, the steadily increasing amount of power consumption due to the use of physical and virtual machines is becoming a serious challenge. In this context, we report a study on optimizing the power and energy efficiency of physical and virtual machines in a cloud computing environment. The energy profile of different workloads is thoroughly investigated under different configurations. This paper presents the findings from our study which provides a good understanding of how different workloads affect power and energy efficiency of both physical and virtual machines.

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Nature‐inspired resource management and dynamic rescheduling of microservices in Cloud datacenters

Joseph

Chandrasekaran

2021

Concurrency and Computation

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Distributed Cloud environments are now resorting to Cloud applications composed of heterogeneous microservices. Cloud service providers strive to provide high quality of service (QoS) and response time is one of the key QoS attributes for microservices. The dynamism of microservice ecosystems necessitates runtime adaptations and microservices rescheduling to avoid performance degradation. Existing works target rescheduling in hypervisor‐based systems, while ignoring the influence of configuration parameters of container‐based microservices. In an effort to address these challenges, this article describes a novel microservice rescheduling framework, throttling and interaction‐aware anticorrelated rescheduling for microservices, to proactively perform rescheduling activities whilst ensuring timely service responses. Based on periodic monitoring of the performance attributes, the framework schedules container migrations. Considering the exponentially large solution space, a metaheuristic approach based on multiverse optimization is developed to generate the near‐optimal mapping of microservices to the datacenter resources. Experimental results indicate that our framework provides superior performance with a reduction of up to 13.97% in the average response time, when compared with systems with no support for rescheduling.

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Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance

Cited by 27 publications

References 89 publications

Virtual machine consolidation: a systematic review of its overhead influencing factors

Virtual machine consolidation: a systematic review of its overhead influencing factors

Optimizing Power and Energy Efficiency in Cloud Computing

Nature‐inspired resource management and dynamic rescheduling of microservices in Cloud datacenters

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