A Comprehensive Evaluation of Scheduling Methods of Virtual Machine Migration for Energy Conservation

Li, Dancheng; Wang, Wei; Li, Quanzuo; Cheng, Jingde

doi:10.1109/jsyst.2015.2436930

Cited by 5 publications

(6 citation statements)

References 20 publications

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“…In a data center, energy consumption can be evaluated by comprehensive scheduling of VM migrations. This suggests a new set of evaluation methods [19] that were constructed according to some new metrics for different VM Migration Scheduling for various viewpoints of data center scale and with different workload types.…”

Section: Related Workmentioning

confidence: 99%

VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment

2022

KSII TIIS

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With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

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Section: Related Workmentioning

confidence: 99%

VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment

2022

KSII TIIS

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“…However, the simulator is hard to adapt to different scheduling algorithms, and need to be changed every time. Li et al [5] extend CloudSim to present a method to evaluate scheduling algorithms. However, some metrics such as availability, reliability are not considered in their work.…”

Section: Related Workmentioning

confidence: 99%

“…Success VM migration should cause a minimum service downtime only and transfer the entire VM to the destination host. The process consists of the following three steps [5]: i) determining of the time to migrate VMs. The first step is mainly used to determine which servers can be consolidated to save energy.…”

Section: Introductionmentioning

confidence: 99%

“…It might also be time consuming, as the simulation experiments need to be conducted many times. Besides, some metrics such as availability or reliability are commonly not taken into consideration by this approach [5], [9]. In contrast, an adaptive evaluation method using stochastic model may be an attractive alternative due to its lower cost and shorter processing time.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Evaluation of Virtual Machine Placement and Migration Scheduling Algorithms Using Stochastic Petri Nets

Ying

Wang

et al. 2019

IEEE Access

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More and more mobile applications rely on the combination of both mobile and cloud computing technology to bring out their full potential. The cloud is usually used for providing additional computing resources that cannot be handled efficiently by the mobile devices. Cloud usage, however, results in several challenges related to the management of virtualized resources. A large number of scheduling algorithms are proposed to balance between performance and cost of data center. Due to huge cost and time consuming of measure-based and simulation method, this paper proposes an adaptive method to evaluate scheduling algorithms. In this method, the virtual machine placement and migration process are modeled by using Stochastic Reward Nets. Different scheduling methods are described as reward functions to perform the adaptive evaluation. Two types of performance metrics are also discussed: one is about quality of service, such as system availability, mean waiting time, and mean service time, and the other is the cost of runtime, such as energy consumption and cost of migration. Compared to a simulation method, the analysis model in this paper only modifies the reward function for different scheduling algorithms and does not need to reconstruct the process. The numeric results suggest that it also has a good accuracy and can quantify the influence of scheduling algorithms on both quality of service and cost of runtime. INDEX TERMS Adaptive evaluation, virtual machine placement and migration, scheduling algorithms, stochastic reward net.

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“…Pinkston et al [13] and Wang et al [16] introduced an evaluation method to evaluate the reconfiguration scheme. A comprehensive evaluation method was used for monitoring energy management systems [17], improving hotel service quality [18], scheduling virtual machine migration [19] and selecting the most preferred service [20]. In this paper, we propose a comprehensive evaluation method considering both subjective and objective evaluations to calculate the probability of selecting the optimal reconfiguration scheme.…”

Section: Introductionmentioning

confidence: 99%

Operation Chain Model of Reconfigurable Printing Manufacturing System Based on Stochastic Process Algebra

Zhou

Luo

et al. 2019

IEEE Access

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When printing orders change, the reconfigurable printing manufacturing system (RPMS) needs to be reconfigured, which produces many feasible reconfiguration schemes. To find the optimal reconfiguration scheme and verify its integrity, an operation chain model (OCM) of an RPMS is proposed to describe the operating process of an RPMS. First, a comprehensive evaluation algorithm considering both subjective and objective evaluation probabilities is designed by analyzing the characteristics of the printing production process. Then, the syntax of stochastic process algebra is conservatively extended to meet the operation chain's specifications. The syntax and transition rules of the OCM are defined and the semantic model of the operation chain is constructed. Finally, we carried out experiments on the RPMS of a printing enterprise and provide the experimental results. The results show that the optimal printing scheme that is selected by the OCM is in line with expectations. In addition, the measurement result I CP = true shows that the optimal reconfigurable printing scheme process has good integrity.INDEX TERMS Reconfigurable printing manufacturing system, comprehensive evaluation algorithm, stochastic process algebra, operation chain model, integrity measurement.

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A Comprehensive Evaluation of Scheduling Methods of Virtual Machine Migration for Energy Conservation

Cited by 5 publications

References 20 publications

VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment

VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment

Adaptive Evaluation of Virtual Machine Placement and Migration Scheduling Algorithms Using Stochastic Petri Nets

Operation Chain Model of Reconfigurable Printing Manufacturing System Based on Stochastic Process Algebra

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