Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method

Li, Zhihua; Yan, Chengyu; Yu, Lei; Yu, Xinrong

doi:10.1016/j.future.2017.09.075

Cited by 56 publications

(41 citation statements)

References 20 publications

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“…VM consolidation primarily includes host overload detection, VM migration selection, VM placement, and running hosts shrinking [2,13]. Due to the complexity of VM consolidation, the issue of VM consolidation [13] was divided into several sub-problems, and the task of VMs consolidation was then conducted by handling and integrating the sub-problems. When using competitive ratio analysis, this type of method was very effective in practice in terms of the viewpoints [39], even though it was unable to guarantee optimal results theoretically.…”

Section: Heuristic Vm Consolidationmentioning

confidence: 99%

“…The VM consolidation problem has been receiving significant attention in recent years. Many methods [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] have been proposed with addressing several aspects involved in VM consolidation, including host overloading detection, VM selection, VM placement, and under-loaded PMs detection. The methods [1][2][3] determine hotspots by comparing the current resource utilization measurements with the given thresholds.…”

Section: Introductionmentioning

confidence: 99%

“…Many intelligence-related VM consolidation methods [3,4,15,[17][18][19][20][21][22][23][24][25] also have been developed, but they easily trap in local optimal regime and are difficult to obtain an ideal balance between energy consumption, resource utilization and QoS. Some VM consolidation methods [10][11][12][13][14][15] use heuristic algorithms to search the optimal solution of VM placement for reducing energy consumption but they may suffer easily premature convergence, which leads to sub-optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

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Energy-efficient and quality-aware VM consolidation method

et al. 2020

Future Generation Computer Systems

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To improve resource utilization and energy efficiency, cloud datacenters use VM consolidation to consolidate VMs to less number of physical machines through VM migration. However, improper VM placement may cause frequent VM migrations and constant on-off switch on physical machines (PMs), which results in decreasing service quality and increasing energy consumption. To address this problem, in this paper, we propose an effective and efficient VM consolidation approach called EQ-VMC with the goal to optimize energy efficiency and service quality. In our approach, a discrete differential evolution algorithm is developed to search the global optimum solution of VM placement. By integrating it with a set of algorithms we propose for effective host overloading detection, VM selection and under-loaded host detection, EQ-VMC effectively reduces energy consumption and improves quality of services (QoS). Extensive simulation demonstrates its effectiveness and shows its advantage compared with previous VM consolidation methods.

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Section: Heuristic Vm Consolidationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy-efficient and quality-aware VM consolidation method

et al. 2020

Future Generation Computer Systems

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“…However, the proposed algorithm by the authors was designed for large datacenter, and did not include distributed networks of datacenters as well as not including end-to-end delay. Finally, the authors in [29] proposed an energy aware dynamic optimization approach using artificial bee colony, to find the mapping relation between physical machines and virtual machines inside a datacenter. The algorithm constrains the consolidations in a manner that limits the number of virtual machines' migrations, to minimize the negative impacts on quality of service.…”

Section: Online Scenariomentioning

confidence: 99%

Offline and online power aware resource allocation algorithms with migration and delay constraints

Hejja

Hesselbach

2019

Computer Networks

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In order to handle advanced mobile broadband services and Internet of Things (IoT), future Internet and 5G networks are expected to leverage the use of network virtualization, be much faster, have greater capacities, provide lower latencies, and significantly be power efficient than current mobile technologies. Therefore, this paper proposes three power aware algorithms for offline, online, and migration applications, solving the resource allocation problem within the frameworks of network function virtualization (NFV) environments in fractions of a second. The proposed algorithms target minimizing the total costs and power consumptions in the physical network through sufficiently allocating the least physical resources to host the demands of the virtual network services, and put into saving mode all other not utilized physical components. Simulations and evaluations of the offline algorithm compared to the state-of-art resulted on lower total costs by 32%. In addition to that, the online algorithm was tested through four different experiments, and the results argued that the overall power consumption of the physical network was highly dependent on the demands' lifetimes, and the strictness of the required end-to-end delay. Regarding migrations during online, the results concluded that the proposed algorithms would be most effective when applied for maintenance and emergency conditions.

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“…An energy‐aware and multiresource overload scheme were proposed Li et al called EC‐VMC, which is based on consolidated placement. Its main objective is to improve resource utilization of physical servers in order to reduce energy consumption.…”

Section: Multiobjective Vmp Mechanismsmentioning

confidence: 99%

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Vahed

Ghobaei‐Arani

Souri

2019

Int J Communication

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Cloud computing introduced a new paradigm in IT industry by providing ondemand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PMs) hosting different types of virtual machines (VMs). Unfortunately, the cloud data centers do not fully utilize their computing resources and cause a considerable amount of energy waste that has a great operational cost and dramatic impact on the environment. Server consolidation is one of the techniques that provide efficient use of physical resources by reducing the number of active servers. Since VM placement plays an important role in server consolidation, one of the main challenges in cloud data centers is an efficient mapping of VMs to PMs. Multiobjective VM placement is generating considerable interest among researchers and academia. This paper aims to represent a detailed review of the recent state-of-the-art multiobjective VM placement mechanisms using nature-inspired metaheuristic algorithms in cloud environments. Also, it gives special attention to the parameters and approaches used for placing VMs into PMs. In the end, we will discuss and explore further works that can be done in this area of research.KEYWORDS cloud computing, multiobjective optimization, nature-inspired metaheuristic algorithms, server consolidation, virtual machine placement | INTRODUCTIONCloud computing is an emerging paradigm in IT industry, which is mainly based on two important concepts including abstraction and virtualization. 1 As NIST 2 defined, this model provides on-demand, ubiquitous, rapid, and elastic access to shared resources, which is based on the pay-as-go model. Cloud services can appear into two distinct models: deployment models and service models. Cloud deployment models are based on where the cloud's infrastructure has deployed, and they can be classified into four categories: public clouds, private clouds, hybrid clouds, and community clouds. [3][4][5] On the other hand, the cloud service models are based on the services they provide. Generally, there are three common basic cloud services including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).

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Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method

Cited by 56 publications

References 20 publications

Energy-efficient and quality-aware VM consolidation method

Energy-efficient and quality-aware VM consolidation method

Offline and online power aware resource allocation algorithms with migration and delay constraints

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

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