An energy-efficient cloud system with novel dynamic resource allocation methods

Yang, Chao-Tung; Hsu, Chih‐Yu; Liu, Jung-Chun; Chan, Yu‐Wei; Chen, Chien-Chih; Verma, Vinod Kumar

doi:10.1007/s11227-019-02794-w

Cited by 14 publications

(8 citation statements)

References 33 publications

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“…We use GPU passthrough technology, InfiniBand virtual, and 10 Gb Ethernet network to improve the performance of the virtual cluster. In [25], they propose two algorithms of a dynamic resource allocation strategy and the energy-saving method, to increase energy utilize consumption efficiently. In this work, we used pass-through to allow VMs to use the host's GPU resources.…”

Section: Discussionmentioning

confidence: 99%

On Construction and Performance Evaluation of a Virtual Desktop Infrastructure With GPU Accelerated

et al. 2020

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More engaging and accessible solutions that offer outstanding user experience are needed. The virtual desktop infrastructure (VDI) is the first user access part of the cloud. Therefore, the performance of the VDI has a significant influence on the network. The graphics processing unit (GPU) can accelerate performance by assigning each virtual machine a dedicated GPU for performance-boosting to improve the user experience. In this paper, we propose a high-performance VDI using an integration of a GPU on OpenStack with a PCI pass-through. It might have certain benefits over using virtualized hardware, such as lower latency, better performance, and more functionality. The evaluation and comparison of the computer processing unit (CPU) and GPU are presented among the different benchmark tools.

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Section: Discussionmentioning

confidence: 99%

On Construction and Performance Evaluation of a Virtual Desktop Infrastructure With GPU Accelerated

et al. 2020

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“…A Dynamic Resource Allocation (DRA) approach is proposed in [31] by Yang et al that saves energy utilization in CDCs. This approach consists of various steps, i.e., deployment of OpenStack-based infrastructure platform for VMs live migration, allocation of dynamic resources and energy-saving algorithms, and monitoring of power distribution to record energy consumption.…”

Section: Related Workmentioning

confidence: 99%

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

et al. 2021

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The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.

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“…In [64], [90] authors added to their method a monitor function, to achieve more reliability for resource load balancing and to reduce idle resources. [91] presented an optimization for the hardware threshold from the aspect of greenhouse gas and the impact of resource overutilization.…”

Section: Parameters In Consideration In the Systematic Survey 1) Hardware Thresholdsmentioning

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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An energy-efficient cloud system with novel dynamic resource allocation methods

Cited by 14 publications

References 33 publications

On Construction and Performance Evaluation of a Virtual Desktop Infrastructure With GPU Accelerated

On Construction and Performance Evaluation of a Virtual Desktop Infrastructure With GPU Accelerated

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

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

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