Resource Allocation in Cloud Computing Using the Uncertainty Principle of Game Theory

Pillai, Parvathy Sudhir; Rao, Shrisha

doi:10.1109/jsyst.2014.2314861

Cited by 121 publications

(35 citation statements)

References 27 publications

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“…Game theory [34] is concerned with strategic decision making among two or more players. It has been used by various authors for game-based learning [35], [36], for industrial decision making [37]- [39], in cloud computing environment [40], [41], and many others. A little work is dedicated to the use of game theory in IoT such as for studying tradeoff between energy consumption and bandwidth [42] and implementing security in eHealth systems [43].…”

Section: Related Workmentioning

confidence: 99%

A Game Theoretic Approach for an IoT-Based Automated Employee Performance Evaluation

Kaur

Sood

2017

IEEE Systems Journal

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In the present scenario, performance evaluation of employees in industries is done manually, in which there are ample chances of biases. It is observed that manual employee evaluation systems can be efficiently eliminated by using ubiquitous sensing capabilities of Internet of things (IoT) devices to monitor industrial employees. However, none of the authors have used IoT data for automating performance evaluation systems of employees. Hence, this paper proposes a game theoretic approach for an IoT-based employee performance evaluation in industry. The system infers useful results about the performance of employees by mining data collected by the sensory nodes using the MapReduce model. The information hence obtained is then used to draw automated decisions for employees using game theory. The system is analyzed both experimentally and mathematically. The experimental evaluation compares the proposed system with other techniques of data mining and decision making. The results depict that the proposed system evaluates the performance of employees efficiently and shows a performance improvement over other techniques. The mathematical evaluation shows that correct evaluation of employees by the system effectively motivates employees in favor of the industry. Thus, the proposed system effectively and efficiently automates the employee evaluation system and decision-making process in the industry.

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Section: Related Workmentioning

confidence: 99%

A Game Theoretic Approach for an IoT-Based Automated Employee Performance Evaluation

Kaur

Sood

2017

IEEE Systems Journal

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“…With the help of simulations, the CPU module is useful for IaaS administrators to correct usage of VM and proficiently notice the resources with reservation parameters and SLA. To avoid underutilization of resources, Pillai and Rao [128] expose the usage of the uncertainty standards of game theory to model association development between machines in cloud. The benefit of the proposed method avoids the complexities of integer programming by explaining the optimization issue of coalition formation.…”

Section: Utilization 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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“…Energy-aware resource allocation methods have been proposed in References [12] and [13].Reference [14] has proposed a formalism based on the Event-B language for specifying Cloud resource allocation policies in business process models and analyzed its correctness according to user requirements and resource capabilities. Reference [15] has proposed a resource allocation mechanism for machines on the cloud, based on the principles of coalition formation and the uncertainty principle of game theory. Reference [16] has proposed dynamic resource allocation with the combinational auction model.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Congestion Control Methods for Joint Multiple Resource Allocation in Cloud Computing Environments

ichiKuribayashi¹

2017

IJCNC

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Resource Allocation in Cloud Computing Using the Uncertainty Principle of Game Theory

Cited by 121 publications

References 27 publications

A Game Theoretic Approach for an IoT-Based Automated Employee Performance Evaluation

A Game Theoretic Approach for an IoT-Based Automated Employee Performance Evaluation

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

Evaluation of Congestion Control Methods for Joint Multiple Resource Allocation in Cloud Computing Environments

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