An adaptive resource management scheme in cloud computing

Huang, Chenn‐Jung; Guan, Chih-Tai; Chen, Heng-Ming; Wang, Yu-Wu; Chang, Shun-Chih; Li, Ching-Yu; Weng, Chuan-Hsiang

doi:10.1016/j.engappai.2012.10.004

Cited by 72 publications

(33 citation statements)

References 10 publications

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“…Huang et al [10] introduce an adaptive resource orchestration scheme in order to find a balance between performance, cost and energy consumption while maintaining SLAs. Their technique aims at a sub-optimal allocation scheme by predicting resource needs and planning resource redistribution.…”

Section: Related Workmentioning

confidence: 99%

Infrastructure Aware Scientific Workflows and Infrastructure Aware Workflow Managers in Science Gateways

et al. 2016

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Abstract-The workflow interoperability problem was successfully solved by the SHIWA project if the workflows to be integrated were running in the same grid infrastructure. However, in the more generic case when the workflows were running in different infrastructures the problem has not been solved yet. In the current paper we show a solution for this problem by introducing a new type of workflow called infrastructure-aware workflow. These are scientific workflows extended with new node types that enable the on-the-fly creation and destruction of the required infrastructures in the clouds. The paper shows the semantics of these new types of nodes and workflows and also how they can solve the workflow interoperability problem. The paper also describes how these new type of workflows can be implemented by a new service called Occopus, and how this service can be integrated with the existing SHIWA Simulation Platform services like the WS-PGRADE/gUSE portal to provide the required functionalities of solving the workflow interoperability problem.

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Section: Related Workmentioning

confidence: 99%

Infrastructure Aware Scientific Workflows and Infrastructure Aware Workflow Managers in Science Gateways

et al. 2016

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“…To deal with this issue, while driving green cloud computing, data center resources must be designated in an energy-efficient manner. Specifically, Cloud assets must not be exclusively designated to fulfill the QoS requirements determined by the clients using Service Level Agreements (SLAs), but also to diminish power consumption [17][18][19][20].…”

Section: Requirements For Green Cloud Computingmentioning

confidence: 99%

EH-GC: An Efficient and Secure Architecture of Energy Harvesting Green Cloud Infrastructure

et al. 2017

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Nowadays, the high power consumption of data centers is the biggest challenge to making cloud computing greener. Many researchers are still seeking effective solutions to reduce or harvest the energy produced at data centers. To address this challenge, we propose a green cloud infrastructure which provides security and efficiency based on energy harvesting (EH-GC). The EH-GC is basically focused on harvesting the heat energy produced by data centers in the Infrastructure-as-a-Service (IaaS) infrastructure. A pyroelectric material is used to generate the electric current from heat using the Olsen cycle. In order to achieve efficient green cloud computing, the architecture utilizes a genetic algorithm for proper virtual machine allocation, taking into consideration less Service Level Agreement (SLA) violations. The architecture utilizes Multivariate Correlation Analysis (MCA) correlation analysis based on a triangular map area generation to detect Denial of Service (DoS) attacks in the data center layer of the IaaS. Finally, the experimental analysis is explained based on the energy parameter, which proves that our model is efficient and secure, and that it efficiently reuses the energy emitted from the data center.

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“…Computing technology is developing towards generalization, large scale data centers have to be built for satisfying the society's increasing requirement to cloud computing however its operation has to be maintained with a great deal of energy [1]. It becomes an urgent issue that how to increase resource utilization rate and reduce energy consumption under the premise of ensuring high performance of cloud computing system and providing generalized service.…”

Section: Introductionmentioning

confidence: 99%

“…By estimation, China Data Center consumed 7 million KWh power in 2011 which takes up 5% of China's total power consumption. When 1 KWh power is consumed, 0.555kg CO2 will be produced, which will result in unprecedented huge pressure on energy supply and environment [1].…”

Section: Introductionmentioning

confidence: 99%

Research on Resource Allocation Algorithm Based on Energy Optimization and Immune Algorithm in Cloud Computing Environment

Dai¹,

OuYang²,

Ding³

2014

TOAUTOCJ

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This paper will explore the key factors affecting the resource allocation performance; reveal the essential relationship among CPU dynamic power consumption, resources computing capacity, resources supply voltage strategy, the amount of resources, supply voltage, and the frequency of CPU; and solve the huge energy consumption problem and the poor performance of load balancing in the process of resource allocation. The issues include: design and analysis of the key technology of resource allocation in cloud computing environment; combined with time span, load balancing and energy optimization, design of multi-objective optimization model for resource allocation and heuristic resource allocation algorithm. This research has important significance for further study on resource allocation and energy optimization in cloud computing environment.

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An adaptive resource management scheme in cloud computing

Cited by 72 publications

References 10 publications

Infrastructure Aware Scientific Workflows and Infrastructure Aware Workflow Managers in Science Gateways

Infrastructure Aware Scientific Workflows and Infrastructure Aware Workflow Managers in Science Gateways

EH-GC: An Efficient and Secure Architecture of Energy Harvesting Green Cloud Infrastructure

Research on Resource Allocation Algorithm Based on Energy Optimization and Immune Algorithm in Cloud Computing Environment

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