Energy-efficient and QoS-aware model based resource consolidation in cloud data centers

Li, Hongjian; Zhu, Guofeng; Zhao, Yanan; Yu, Dongmei; Tian, Wenhong

doi:10.1007/s10586-017-0893-5

Cited by 33 publications

(17 citation statements)

References 32 publications

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“…Li et al in their previous study have proposed a model to balance energy consumption and QoS in data centers. For this purpose, a utility function is defined, and the goal is to minimize this function.…”

Section: Related Workmentioning

confidence: 99%

Burst‐aware virtual machine migration for improving performance in the cloud

Rahmani

Khajehvand

2020

Int J Communication

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Burst is a common pattern in the user's requirements, which suddenly increases the workload of virtual machines (VMs) and reduces the performance and energy efficiency of cloud computing systems (CCS). Virtualization technology with the ability to migrate VMs attempts to solve this problem. By migration, VMs can be dynamically consolidated to the users' requests. Burst temporarily increases the workload. Ignoring this issue will lead to incorrect decisions regarding the migration of VMs. It increases the number of migrations and Service Level Agreement Violations (SLAVs) due to overload. This may cause waste of resources, increase in energy consumption, and misplaced VMs. Therefore, a burst-aware method for these issues is proposed in this paper. The method consists of two algorithms: one for determining the migration time and the other for the placement of VMs. We use the PlanetLab real dataset and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding performance compared to benchmark methods.

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Section: Related Workmentioning

confidence: 99%

Burst‐aware virtual machine migration for improving performance in the cloud

Rahmani

Khajehvand

2020

Int J Communication

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“…Similar to [26], Particle Swarm Optimization (PSO) based algorithm was proposed by Li et al [27] for VMs consolidation that also utilizes static thresholds for detecting under-utilization and over-utilization hosts. Another PSO based VM consolidation model is proposed by Li et al [28]. The proposed model incorporates Euclidean distance of resource and degree of user satisfaction along with traditional energy consumption and quality of service related traits.…”

Section: Related Workmentioning

confidence: 99%

“…Dynamic consolidation helps in optimizing resource utilization in the cloud data center [29]. Meta-heuristic based methods [22]- [28] have occasionally shown better results, but they tend to slow down the optimization processes with the exceeding number of VMs involved as the search space grows quite significantly. Some works [25]- [27] tried to limit the number of VMs for reducing search space by taking a static value of threshold of utilization.…”

Section: Related Workmentioning

confidence: 99%

Utilization Driven Model for Server Consolidation in Cloud Data Centers

2020

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The application of cloud computing has diversified with the adoption of Internet of Things (IoTs) and edge computing. However, it has increased the uncertainty of workload demand; thus, the efficient utilization of cloud computing resources become more challenging. Traditionally, dynamic consolidation of workload inside cloud data centers relies on identifying overload and under-load hosts using either static or dynamic threshold value. In this paper, we propose a Utilization Driven Model (UDM) model to estimate the number of under-utilized and over-utilized processing machines through percentile ranks of low and high utilization from mean value of resource utilization of hosts and the value of mean absolute deviation of resource demand. UDM swiftly reacts to any change in workload demand and adapts the system to the current demand of resource utilization. The UDM approach not only impacts the energy consumption and quality of service but also increases the elastic nature of cloud by robustly managing the sudden changes in workload. Experiment results show that UDM is an efficient server consolidation technique, improving 30% energy, 40% quality of service compared to contemporary techniques. Thus, the UDM is more robust to support stochastic resource demand compared to traditional techniques. INDEX TERMS Server consolidation, cloud computing, efficient resource utilization, cloud data centers, dynamic workload consolidation.

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“…[61] classified the server resources based on resource ranking, while [63] scheduled the VMs based on the availability of the server resources where they achieved an optimal number of migrations. On the reliability side, the VM overhead has optimized as in [67], [73], [78], [79]. In [81] the network traffic was optimized by auto-scaling that integrates both application auto-scaling and dynamic VM allocation when they reduced the communication latency among VM2VM.…”

Section: ) Migration Overheadmentioning

confidence: 99%

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

Ala’anzy

Othman

2019

IEEE Access

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The data-center is considered the heart of cloud computing. Recently, the growing demand for cloud computing services has caused a growing load on data centers. In terms of system behavior and workload, patterns of cloud computing are very dynamic; and that might serve to imbalance the load among data center resources. Eventually, some data-center resources could come to be over-loaded/under-loaded, which leads to an increase in energy consumption in addition to decreased functioning and wastage of resources. Just considering energy-efficiency (that can be attained efficiently by consolidate the servers) may not be enough for real applications because it may cause problems such as unbalanced load for each Physical Machine (PM). Therefore, this paper surveys published load balancing algorithms that achieved by server consolidation via a meta-analysis. Load balancing with server consolidation enriches the exploitation of resource utilization and can enhance Quality of Service (QoS) metrics, since data-centers and their applications are increasing exponentially. This meta-study, reviews the literature on load balancing and server consolidation and presents a ready reference taxonomy on the most efficient algorithms that achieve load balancing and server consolidation. This work attempts to present a taxonomy with a new classification for load balancing and server consolidation, such as migration overhead, hardware threshold, network traffic, and reliability.

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Energy-efficient and QoS-aware model based resource consolidation in cloud data centers

Cited by 33 publications

References 32 publications

Burst‐aware virtual machine migration for improving performance in the cloud

Burst‐aware virtual machine migration for improving performance in the cloud

Utilization Driven Model for Server Consolidation in Cloud Data Centers

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

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