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A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing

TLDR
In this article, the authors proposed an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment, and analyzed the performance of their approach using the CloudSim toolkit.
Abstract
Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users.

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Citations
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An overview of virtual machine placement schemes in cloud computing

TL;DR: This article provides a complete survey and analyses of the existing state of the art VM placement schemes proposed in the literature for the cloud computing and data centers and classifies them based on the type of the placement algorithm and assesses their capabilities and objectives.
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A review of task scheduling based on meta-heuristics approach in cloud computing

TL;DR: Methodical analysis of task scheduling in cloud and grid computing is presented based on swarm intelligence and bio-inspired techniques and will enable the readers to decide suitable approach for suggesting better schemes for scheduling user’s application.
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Research on strong agile response task scheduling optimization enhancement with optimal resource usage in green cloud computing

TL;DR: Experimental results indicate that the proposed algorithm can achieve the stability and efficiency of task scheduling and effectively improve the throughput of the cloud computing system.
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Green Cloud Computing: A Literature Survey

Laura-Diana Radu
- 30 Nov 2017 - 
TL;DR: In this survey, the main achievements of green cloud computing are reviewed, recent studies and developments are summarized, and environmental issues are specifically addressed.
Journal ArticleDOI

An Appraisal of Meta-Heuristic Resource Allocation Techniques for IaaS Cloud

TL;DR: This appraisal investigates the meta-heuristics resource allocation techniques for maximizing financial gains and minimizing the financial expenses of cloud users for IaaS in cloud computing environment and points out the various issues addressed.
References
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A view of cloud computing

TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.
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Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility

TL;DR: This paper defines Cloud computing and provides the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs), and provides insights on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain Service Level Agreement (SLA) oriented resource allocation.
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CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

TL;DR: The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
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Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

TL;DR: An architectural framework and principles for energy-efficient Cloud computing are defined and the proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS).
Journal ArticleDOI

Learning and optimization using the clonal selection principle

TL;DR: This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response and derives two versions of the algorithm, derived primarily to perform machine learning and pattern recognition tasks.
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