Kullback-Leibler distance criterion consolidation in cloud

Rahmani, Somayeh; Khajehvand, Vahid; Torabian, Mohsen

doi:10.1016/j.jnca.2020.102789

Cited by 5 publications

(6 citation statements)

References 49 publications

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“…Table 6 illustrate the generated random dataset specification. Multiple articles such as 18,51,52 have exploited the random workload for the evaluation of their approaches. The proposed framework, which is represented in Figure 5, was executed 20 times for each scenario.…”

Section: Simulation and Evaluation Resultsmentioning

confidence: 99%

Confidence interval‐based overload avoidance algorithm for virtual machine placement

et al. 2022

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Virtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) and their consolidation through live migration. Excessive migrations and a lack of required VMs are two critical factors in QoS degradation. The current consolidation approaches impose an intensive time complexity and cannot be used in large data centers with hundreds of hosts. This article proposes a framework for dynamic consolidation divided into a QoS‐aware algorithm for overload avoidance and a power‐aware algorithm for VM placement. To compute a safe zone criterion for any VM, relations were suggested by applying an interval estimate with a confidence level. By employing this criterion, the offered algorithm could guarantee the quality of service (QoS), particularly for specific VMs, while avoiding overhead. The VM placement algorithm is developed based on the maximum utilization of active hosts. It provides the capability to control the number of active hosts for the data center manager. The simulation results with real workloads revealed that the proposed framework could decline the amount of service level agreement violations by 78% and the number of migrations by 74%, and energy consumption by up to 13% in comparison with the best results of the benchmark algorithms. Hence, the application of this framework upgrades the QoS of data centers and declines their energy costs.

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Section: Simulation and Evaluation Resultsmentioning

confidence: 99%

Confidence interval‐based overload avoidance algorithm for virtual machine placement

et al. 2022

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“…They also considered the energy consumption as one of the main factors for migration of VMs in this process. Rahmani et al [42] suggested a burstiness-aware VM consolidation approach to improve the efficiency in data centers. The approach utilize of the static thresholds for identifying the source hosts.…”

Section: Shaw and Singhmentioning

confidence: 99%

“…Table 1 contains a comparison of the related literature and underlines the characteristics of the current research. As it is shown, there are few studies such as [7,17,34,42], which, detection of underloaded and overloaded hosts is not sensitive to the workload explosions. Detection of these hosts, while considering the bursts, plays a key role in energy saving and performance improvement in the cloud computing systems.…”

Section: Shaw and Singhmentioning

confidence: 99%

DBSHD: Dynamic Burst-aware Source Host Detection in the Cloud

Rahmani

Khajehvand

Torabian

2022

Preprint

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One of the essential phases during the Virtual Machine (VM) consolidation is to detect the source (underloaded and overloaded) hosts. Bursts with a sudden and transient increase in the workload have an adverse effect on this process. Also, burst-aware methods for detecting the overloaded and underloaded hosts play a key role in achieving performance and energy efficiency tradeoffs in the cloud computing systems. In this study, we deal with these methods as an open issue in the resource management process. We present a dynamic burst-aware method for detecting the overloaded and underloaded hosts. The principal idea in our proposed method is to keep the weighted average load of the hosts within a dynamic range. The thresholds and weights (impact factors) are obtained from the analysis of the historical data related to hosts. Furthermore, we have used the CloudSim simulator and PlanetLab real-world dataset in order to measure the performance of our proposed method. The simulation results verify the superiority of the proposed technique in achieving performance and energy efficiency tradeoffs, compared to the benchmark methods.

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“…Also, U a Δt+1 (S i ) is the actual CPU efficiency in the next time interval for S i . The value of U p Δt+1 (S i ) is calculated using Equation (8).…”

Section: Monitoring and Analysis Phasementioning

confidence: 99%

“…The reduction in energy consumption can be achieved by turning off idle physical machines or switching them to low mode power consumption. 8 Beloglazov and Buyy 9 have divided the virtual machine consolidation process into the following four parts:…”

Section: Introductionmentioning

confidence: 99%

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

2021

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In recent years, cloud computing has become more popular because of advancements in virtualization technology. By increasing the number of servers in cloud computing environment, cloud data centers have expanded and consumed much energy. Virtual machine consolidation is a solution for energy management in cloud environment. On the other hand, by increasing resource utilization in virtual machine consolidation, service level agreement assurance is difficult to obtain. Two main challenges in virtual machine consolidation are timely detection of overloaded servers and proper immigrant virtual machine selection from detected servers. In this paper, a new model is proposed based on MAPE-k loop for autonomous virtual machine selection. The presented model uses a proposed ensemble prediction algorithm in the analysis phase. Also, in the planning phase, a new multi-heuristics algorithm with flexible weights using learning automata is proposed. The effectiveness of the proposed model is evaluated by CloudSim simulator under real workload as compared with well-known algorithms in this domain. The experimental results indicate that, the proposed approach has averagely improved the balance between service level agreement violations, energy and migration counts by 47.39% compared to other methods.

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Kullback-Leibler distance criterion consolidation in cloud

Cited by 5 publications

References 49 publications

Confidence interval‐based overload avoidance algorithm for virtual machine placement

Confidence interval‐based overload avoidance algorithm for virtual machine placement

DBSHD: Dynamic Burst-aware Source Host Detection in the Cloud

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

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