Green-Aware Virtual Machine Migration Strategy in Sustainable Cloud Computing Environments

Wang, Xiaoying; Zhang, Guojing; Yang, Mengqin; Zhang, Lei

doi:10.5772/67350

Cited by 4 publications

(11 citation statements)

References 25 publications

(23 reference statements)

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“…Virtualization technology is an important part of sustainable cloud datacenters to support energy-efficient VM migration, VM elasticity, VM load balancing, VM consolidation, VM fault tolerance and VM scheduling [88]. Operational costs can be reduced by using VM scheduling to manage cloud resources using efficient dynamic provisioning of resources [102].…”

Section: Virtualizationmentioning

confidence: 99%

“…The followings are the important issues with VM migration in geographically distributed datacenters [88] [99] [105] [106]:…”

Section: Storage Migrationmentioning

confidence: 99%

Section: Virtualizationmentioning

confidence: 99%

“…Further, a statistical searching approach is used to find out the destination of migration and post-copy technique is used in GVM. Wang et al [88] proposed an Extended version of GVM (E-GVM) by replacing grid energy with renewable energy to make datacenters sustainable, which further reduces power cost and carbon emissions. Further, VM consolidation-based resource allocation is improved using IT-enabled virtualization [100], which measures the variation of energy requirements during pre-copy and postcopy of data from one server to another.…”

Section: Related Studiesmentioning

confidence: 99%

“…• E-GVM [88] • FoS: IT-enabled virtualization • SVMS [158] • FoS: CPU utilization • EVMFT [161] • FoS: Core Mapping • DCVME [163] • FoS: Docker Container Management…”

mentioning

confidence: 99%

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A Taxonomy and Future Directions for Sustainable Cloud Computing

2018

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The cloud computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT) based applications the usage of cloud services is increasing exponentially. The next generation of cloud computing must be energy-efficient and sustainable to fulfil the end-user requirements which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The usage of large number of cloud datacenters increases cost as well as carbon footprints, which further effects the sustainability of cloud services. In this paper, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. Further, the current research on sustainable cloud computing is organized into several categories: application design, sustainability metrics, capacity planning, energy management, virtualization, thermal-aware scheduling, cooling management, renewable energy and waste heat utilization. The existing techniques have been compared and categorized based on the common characteristics and properties. A conceptual model for sustainable cloud computing has been proposed along with discussion on future research directions.

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Section: Virtualizationmentioning

confidence: 99%

“…The followings are the important issues with VM migration in geographically distributed datacenters [88] [99] [105] [106]:…”

Section: Storage Migrationmentioning

confidence: 99%

Section: Virtualizationmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

“…• E-GVM [88] • FoS: IT-enabled virtualization • SVMS [158] • FoS: CPU utilization • EVMFT [161] • FoS: Core Mapping • DCVME [163] • FoS: Docker Container Management…”

mentioning

confidence: 99%

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A Taxonomy and Future Directions for Sustainable Cloud Computing

2018

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CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers

Zhao,

Zhou,

2024

IEEE Trans. Sustain. Comput.

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The rapid growth and widespread adoption of cloud computing have led to significant electricity costs and environmental impacts. Traditional approaches that rely on static utilization thresholds are ineffective in dynamic cloud environments, and simply consolidating virtual machines (VMs) to minimize energy costs does not necessarily result in the lowest carbon footprints. In this paper, a deep reinforcement learning (DRL) based framework called CFWS is proposed to enhance the energy efficiency of renewable energy sources (RES) supplied data centers (DCs). CFWS incorporates an adaptive thresholds adjustment method TCN-MAD by evaluating the predicted probability of a physical machine (PM) being overloaded to prevent unnecessary VM migrations and mitigate service level agreement (SLA) violations due to imbalanced workload distribution. Additionally, CFWS introduces a novel action space in the DRL algorithm by representing VM migrations among geo-distributed cloud data centers as flattened indices to accelerate its execution efficiency. Simulation results demonstrate that CFWS can achieve a superior optimization of energy costs and carbon footprints, saving 5.67% to 13.22% brown energy with maximized RES utilization. Furthermore, CFWS reduces VM migrations by up to 86.53% and maintains the lowest SLA violations within suboptimal execution time in comparison to the state-of-art algorithms.

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