Energy Efficient in Virtual Infrastructure and Green Cloud Computing: A Review

Pandi, Krisztian; Somasundaram, K.

doi:10.17485/ijst/2016/v9i11/89399

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…Due to the rapid growth of data center, increasing the amount of servers, huge load, highly demands and loss or wastage of idle power are major causes of energy and heat ineffectiveness [94]. Green computing is anticipated for optimal resource allocation and utilization, by reducing heat and energy consumption in data centers [95,96].…”

Section: Power Aware Resource Allocationmentioning

confidence: 99%

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

et al. 2016

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There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future.

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Section: Power Aware Resource Allocationmentioning

confidence: 99%

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

et al. 2016

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“…Note that the VMs for users can be manually pre-assigned to each server during offline processing by an administrator, and managed in the standby mode instead of in the shutdown mode when users are receiving the results of the statistical analysis. In addition, the networks are managed by network separation to ensure stable services [18][19][20][21][22] with a management network and a user network. User access and the management of VM states occur through the management network [23].…”

Section: Cloud Computing For a Heterogeneous Platformmentioning

confidence: 99%

Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment

Lee

Jeong

2016

Symmetry

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Abstract:A fundamental key for enterprise users is a cloud-based parameter-driven statistical service and it has become a substantial impact on companies worldwide. In this paper, we demonstrate the statistical analysis for some certain criteria that are related to data and applied to the cloud server for a comparison of results. In addition, we present a statistical analysis and cloud-based resource allocation method for a heterogeneous platform environment by performing a data and information analysis with consideration of the application workload and the server capacity, and subsequently propose a service prediction model using a polynomial regression model. In particular, our aim is to provide stable service in a given large-scale enterprise cloud computing environment. The virtual machines (VMs) for cloud-based services are assigned to each server with a special methodology to satisfy the uniform utilization distribution model. It is also implemented between users and the platform, which is a main idea of our cloud computing system. Based on the experimental results, we confirm that our prediction model can provide sufficient resources for statistical services to large-scale users while satisfying the uniform utilization distribution.

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Hybrid Elastic ARM&Cloud HPC Collaborative Platform for Generic Tasks

Petrocelli

Giusti

Naiouf

2019

Communications in Computer and Information Science

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Compute-heavy workloads are currently run on Hybrid HPC structures using x86 CPUs and GPUs from Intel, AMD, or NVidia, which have extremely high energy and financial costs. However, thanks to the incredible progress made on CPUs and GPUs based on the ARM architecture and their ubiquity in today's mobile devices, it's possible to conceive of a low-cost solution for our world's data processing needs. Every year ARM-based mobile devices become more powerful, efficient, and come in ever smaller packages with ever growing storage. At the same time, smartphones waste these capabilities at night while they're charging. This represents billions of idle devices whose processing power is not being utilized. For that reason, the objective of this paper is to evaluate and develop a hybrid, distributed, scalable, and redundant platform that allows for the utilization of these idle devices through a cloud-based administration service. The system would allow for massive improvements in terms of efficiency and cost for compute-heavy workload. During the evaluation phase, we were able to establish savings in power and cost significant enough to justify exploring it as a serious alternative to traditional computing architectures.

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Energy Efficient in Virtual Infrastructure and Green Cloud Computing: A Review

Cited by 11 publications

References 13 publications

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment

Hybrid Elastic ARM&Cloud HPC Collaborative Platform for Generic Tasks

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