A Study on a Resource Allocation Algorithm for On-demand Data Center Services

Adachi, M.; Hiraoka, Tatsuo; Komatsu, Nobuyoshi

doi:10.1109/icact.2008.4493765

Cited by 6 publications

(3 citation statements)

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“…An area assisted methodology is projected with the help of a protocol that supports the situation data to scale back repetitive transmissions of data [18]. Work was done in [19], [20], authors focus on demand allocation of resources which used prediction algorithm for forecasting of resources which are going to be used. In the research work of [21] authors proposed a system that manages complexity and changes consequently in the IoT environment.…”

Section: Figure 1: the Six -A Connectivity Of Iotmentioning

confidence: 99%

An Energy Efficient Architecture of IoT based on Service Oriented Architecture (SOA)

Maurya

Mukherjee²

2019

IJCAI

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In the present scenario, IoT is a platform where everyday gadgets are becoming smarter, each day processing will become shrewd, and every day verbal exchange is becoming informative. Even as the IoT is still searching for its basic form, its result has already started in making exquisite strides as a universal solution media for the associated situation. The study which focused on architecture always paves the conformation of associated discipline. The shortage of overall architectural abilities is at this time resisting the researchers to get a way of the scope of procedures based on energy efficient IoT. In this endeavor Service Oriented Architecture (SOA) based IoT architecture and algorithm have been proposed to address energy efficiency in IoT environment. Experimental result has been shown to lay bare the effectiveness of the proposed approach.Povzetek: V tem rokopisu je bila predlagana energetsko učinkovita arhitektura interneta stvari na osnovi storitveno usmerjene arhitekture (SOA) z algoritmom.

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Section: Figure 1: the Six -A Connectivity Of Iotmentioning

confidence: 99%

An Energy Efficient Architecture of IoT based on Service Oriented Architecture (SOA)

Maurya

Mukherjee²

2019

IJCAI

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“…Workload allocation schemes for example, consolidate traffic on fewer network devices, allowing portions of unused capacity to be switched off to reduce management costs. In addition, advancements have also been made in the design of management strategies (e.g., in [5]). From these examples, it therefore becomes obvious that the research community has provided a range of hardware and software solutions to respond to energy efficiency challenges associated with cloud operation.…”

Section: Introductionmentioning

confidence: 99%

An Energy Aware Network Management Approach Using Server Profiling in 'Green' Clouds

Peoples

Parr

McClean

et al. 2012

2012 Second Symposium on Network Cloud Computing and Applications

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Clouds and data centres are significant consumers of power. There are however, opportunities for optimising carbon cost here as resource redundancy is provisioned extensively. Data centre resources, and subsequently clouds which support them, are traditionally organised into tiers; switch-off activity when managing redundant resources therefore occurs in an approach which exploits cost advantages associated with closing down entire network portions. We suggest however, an alternative approach to optimise cloud operation while maintaining application QoS: Simulation experiments identify that network operation can be optimised by selecting servers which process traffic at a rate that more closely matches the packet arrival rate, and resources which provision excessive capacity additional to that required may be powered off for improved efficiency. This recognises that there is a point in server speed at which performance is optimised, and operation which is greater than or less than this rate will not achieve optimisation. A series of policies have been defined in this work for integration into cloud management procedures; performance results from their implementation and evaluation in simulation show improved efficiency by selecting servers based on these relationships.

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“…The demand model proposed by Adachi [42] is more applicable to the data center industry because it combines the workloads and resources of data centers into resource pools, then provides those resources based on priority or predicted algorithm use. This technique satisfies the demand by different processes retaining the optimization values set for achieving energy utilization.…”

Section: Related Workmentioning

confidence: 99%

Power Usage Effectiveness Metrics to Measure Efficiency and Performance of Data Centers

Uddin

Alsaqour

Shah

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:Intensifying computation demand from enterprises has driven the growth of large, multifaceted data centers to manage current Internet, financial, commercial, and business applications. A data center comprises thousands of servers and other equipment that require substantial amounts of power to operate. This condition results in numerous challenges for the data center industry, such as massive energy consumption, underutilization of installed equipment, emission of greenhouse gases, and effect on global warming. This paper highlights the significance of identifying metrics to determine the performance and efficiency of a data center, which can help such a facility achieve operational cost savings through proper implementation of performance-measuring metrics. This paper discusses the implementation of Power Usage Effectiveness metrics in a tier-level data center in Pakistan. The results show that the overall performance value of the facility is 3.3, which indicates poor and inefficient operations.

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A Study on a Resource Allocation Algorithm for On-demand Data Center Services

Cited by 6 publications

References 0 publications

An Energy Efficient Architecture of IoT based on Service Oriented Architecture (SOA)

An Energy Efficient Architecture of IoT based on Service Oriented Architecture (SOA)

An Energy Aware Network Management Approach Using Server Profiling in 'Green' Clouds

Power Usage Effectiveness Metrics to Measure Efficiency and Performance of Data Centers

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