An elastic controller using Colored Petri Nets in cloud computing environment

Shahidinejad, Ali; Ghobaei‐Arani, Mostafa; Esmaeili, Leila

doi:10.1007/s10586-019-02972-8

Cited by 68 publications

(32 citation statements)

References 25 publications

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“…In addition, it guarantees quality of these services through offloading demanding tasks to the nearby servers. 6,17,[25][26][27][28] The general architecture of a typical mobile computation system is depicted in Figure 1. As it is shown, this architecture has three major layers:…”

Section: Offloading In Mecmentioning

confidence: 99%

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A review on the computation offloading approaches in mobile edge computing: A game‐theoreticperspective

2020

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In recent years, novel mobile applications such as augmented reality, virtual reality, and three-dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature, offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource-intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)-based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT-based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid-base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded. K E Y W O R D S game theory, mobile edge computing, offloading, systematic review 1 INTRODUCTION Thanks to the recent improvements in hardware and software capabilities and accordingly complex mobile applications of mobile devices, they are becoming more popular in recent years. In the presence of emerging technologies,

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Section: Offloading In Mecmentioning

confidence: 99%

“…In its turn, this can decrease transmission path between the demanding side and the providing side of such services resulting decrease of energy consumption and delay for such battery constrained user equipments. In addition, it guarantees quality of these services through offloading demanding tasks to the nearby servers …”

Section: Introductionmentioning

confidence: 99%

A review on the computation offloading approaches in mobile edge computing: A game‐theoreticperspective

2020

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“…A smart device can offload a request R i (

L_{i}^{in}, L_{i}^{out}, C_{i}, D_{i}

) where

L_{i}^{in}

is input data length,

L_{i}^{out}

is the output data length, C i is the number of required CPU cycles, and D i is the maximum delay of the request. A smart device/edge server/cloud server is eligible to execute the request if it satisfies the maximum delay of the request 36 . Execution time and consumed energy of a request in a smart device/edge server/cloud server are formulated as follows.…”

Section: Proposed Approachmentioning

confidence: 99%

Joint computation offloading and resource provisioning for edge‐cloud computing environment: A machine learning‐based approach

Shahidinejad

Ghobaei‐Arani

2020

Softw Pract Exp

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Summary In recent years, the usage of smart mobile applications to facilitate day‐to‐day activities in various domains for enhancing the quality of human life has increased widely. With rapid developments of smart mobile applications, the edge computing paradigm has emerged as a distributed computing solution to support serving these applications closer to mobile devices. Since the submitted workloads to the smart mobile applications changes over the time, decision making about offloading and edge server provisioning to handle the dynamic workloads of mobile applications is one of the challenging issues into the resource management scope. In this work, we utilized learning automata as a decision‐maker to offload the incoming dynamic workloads into the edge or cloud servers. In addition, we propose an edge server provisioning approach using long short‐term memory model to estimate the future workload and reinforcement learning technique to make an appropriate scaling decision. The simulation results obtained under real and synthetic workloads demonstrate that the proposed solution increases the CPU utilization and reduces the execution time and energy consumption, compared with the other algorithms.

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“…The slow, rapid and dual phase synthetic workloads 37,38 are applied in this simulation as shown in Figure 3. Slow workload is within 450 to 1490 IoT services range with 1004 services in average.…”

Section: Performance Evaluationmentioning

confidence: 99%

An autonomous IoT service placement methodology in fog computing

2020

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With the increase in the number of Internet of Things (IoT) devices having limited resources, an extension of the cloud‐computing paradigm has emerged so‐called fog computing, where all the fog cells are located at the edge of the network and the latency can be reduced. Meanwhile, an important challenge has attracted much attention with the definition of fog computing is service placement problem that is still at its very beginning research. It allows to deployment IoT applications on computational fog resources, with the objective of optimizing quality of service (QoS) requirements of applications while taking into account maximizing the utilization of fog resources. In this paper, an autonomous IoT service placement methodology including four phases of monitoring, analysis, decision‐making, and execution is proposed called as (MADE). First, the available resources and application services' status are monitored at run time. Next, the requested services are prioritized with respect to application services' deadline. Then, the Strength Pareto Evolutionary Algorithm II is applied to take decisions about the application services placement as a multi‐objective optimization problem. Finally, the decisions made in the previous phases are executed in a fog environment. The experiment results indicate that the proposed methodology outperforms its counterparts in terms of different performance metrics.

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An elastic controller using Colored Petri Nets in cloud computing environment

Cited by 68 publications

References 25 publications

A review on the computation offloading approaches in mobile edge computing: A game‐theoreticperspective

A review on the computation offloading approaches in mobile edge computing: A game‐theoreticperspective

Joint computation offloading and resource provisioning for edge‐cloud computing environment: A machine learning‐based approach

An autonomous IoT service placement methodology in fog computing

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