An autonomous resource provisioning framework for massively multiplayer online games in cloud environment

Ghobaei‐Arani, Mostafa; Khorsand, Reihaneh; Ramezanpour, Mohammadreza

doi:10.1016/j.jnca.2019.06.002

Cited by 73 publications

(38 citation statements)

References 28 publications

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“…Cyber‐physical system is a combination of sensing, communications, computation, and cyber‐physical processes, which can prepare real‐time services . As a growing cyberspace trend, CPS provides intelligent interplay between the industrial equipment computational elements and networked systems of cyber‐physical devices that are collectively involved in treating a critical manner . It moves the computation and monitoring from a standalone system to a remotely located monitoring center.…”

Section: Preliminary Conceptsmentioning

confidence: 99%

“…18 As a growing cyberspace trend, CPS provides intelligent interplay between the industrial equipment computational elements and networked systems of cyber-physical devices that are collectively involved in treating a critical manner. 4,19 It moves the computation and monitoring from a standalone system to a remotely located monitoring center. The CPS merges smart devices, consisting of both monitoring and delivery equipment with software applications.…”

Section: Figurementioning

confidence: 99%

“…However, long‐delay links between the cloud data center and industrial equipment are very challenging problem . To deal with this issue, fog computing drives the computation resources toward the network edge . Fog computing is a model that able to send results with less latency by expanding cloud resources near …”

Section: Introductionmentioning

confidence: 99%

“…3 To deal with this issue, fog computing drives the computation resources toward the network edge. 4 Fog computing is a model that able to send results with less latency by expanding cloud resources near. 5 There are many resources in fog computing that are various with each other, so the task scheduling issue is various from the traditional procedures of scheduling.…”

Section: Introductionmentioning

confidence: 99%

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An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

136

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With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

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Section: Preliminary Conceptsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

136

View full text Add to dashboard Cite

show abstract

“…Cloud computing is a high‐performance computing environment to represent the cooperation among multiple virtual machines (VMs) via a network that provides services to the users using pay‐as‐you‐go model 1, 2 . It has emerged as the fifth utility services after water, electricity, gas, and telephony 3, 4 . Using the cloud computing, customers can access to a shared resource pool applicable on demand 5–7 .…”

Section: Introductionmentioning

confidence: 99%

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Khorsand

Ramezanpour

2020

Int J Communication

Self Cite

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The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA).Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well-known NPhard problem. To resolve this problem, this paper proposes an energy-efficient task-scheduling algorithm based on best-worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision-making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization. K E Y W O R D Sbest-worst method (BWM), cloud computing, energy consumption, multi-criteria decision making, TOPSIS method

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A dynamic fog service provisioning approach for IoT applications

Faraji-Mehmandar

Jabbehdari

Javadi

2020

Int J Communication

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SummaryInternet of Things (IoT) is an ecosystem that can improve the life quality of humans through smart services, thereby facilitating everyday tasks. Connecting to cloud and utilizing its services are now public and common, and the experts seek to find some ways to complete cloud computing to use it in IoT, which in next decades will make everything online. Fog computing, where the cloud computing expands to the edge of the network, is one way to achieve the objectives of delay reduction, immediate processing, and network congestion. Since IoT devices produce variations of workloads over time, IoT application services will experience traffic trace fluctuations. So knowing about the distribution of future workloads required to handle IoT workload while meeting the QoS constraint. As a result, in the context of fog computing, the main objective of resource management is dynamic resource provisioning such that it avoids the excess or dearth of provisioning. In the present work, we first propose a distributed computing framework for autonomic resource management in the context of fog computing. Then, we provide a customized version of a provisioning system for IoT services based on control MAPE‐k loop. The system makes use of a reinforcement learning technique as decision maker in planning phase and support vector regression technique in analysis phase. At the end, we conduct a family of simulation‐based experiments to assess the performance of our introduced system. The average delay, cost, and delay violation are decreased by 1.95%, 11%, and 5.1%, respectively, compared with existing solutions.

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An autonomous resource provisioning framework for massively multiplayer online games in cloud environment

Cited by 73 publications

References 28 publications

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

A dynamic fog service provisioning approach for IoT applications

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