The Internet of Things (IoT) technology provisions unprecedented opportunities to evolve the interconnection among human beings. However, the latency brought by unstable wireless networks and computation failures caus...
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The Internet of Things (IoT) technology provisions unprecedented opportunities to evolve the interconnection among human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated Mobile Edge Computing (MEC) with remote clouds is a promising platform to enable delay-sensitive service provisioning for IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices. Thus, computation-intensive and sensing data from IoT devices can be offloaded to the MEC network immediately for processing, and the service response latency can be significantly reduced. In this paper, we first formulate two novel optimization problems for delay-sensitive IoT applications, i.e., the total utility maximization problems under both static and dynamic offloadingtask request settings, with the aim to maximize the accumulative user satisfaction on the use of the services provided by the MEC, and show the NP-hardness of the defined problems. We then devise efficient approximation and online algorithms with provable performance guarantees for the problems in a special case where the bandwidth capacity constraint is negligible. We also develop efficient heuristic algorithms for the problems with the bandwidth capacity constraint. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising in reducing service delays and enhancing user satisfaction, and the proposed algorithms outperform their counterparts by at least 10.8 percent.
Fog Computing is an architecture that provides computing, storage, control and networking capacities for realizing Internet of Thing applications. Fog computing enhances the QoS Applications sensitive to delay, enable...
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ISBN:
(纸本)9781665409520
Fog Computing is an architecture that provides computing, storage, control and networking capacities for realizing Internet of Thing applications. Fog computing enhances the QoS Applications sensitive to delay, enable them to use fog computing's low latency instead of the large cloud latency. tasks in different IoT applications should be correctly dispersed through fog nodes, improving service quality and reaction time. Many research papers have been published to investigate either latency or power consumption improvement. However, this new research area still further research. This article work seeks to examine how IoT services are placed and processing data in a Fog system with low latency and low power consumption in optimal way. The meta heuristics Particle Swarm Optimization (PSO) algorithm is suggested for the proposed approach to manage network resources (latency and power consumption). For testing purposes, the well-known "iFogSim" simulator is used to setup an experiment and to build a case study network in fog layer based on virtual reality EEG game. The simulation results for the suggested experiment show that the PSO algorithm has better performance than competitive approaches such as First Come First Serve (FCFS) and Greedy Knapsack -based scheduling (GKS) algorithms. The simulated results that we get when applying PSO optimizer in power and latency outperforms other algorithms. The result of latency is (in FCFS =1.39ms, in GKS =1.23ms, in PSO=1.12ms) and the results of power consumption is (in FCFS =1.63mj, in GKS =1.13mj, in PSO=1.09mj).
The current thinking concerning computations required by Internet of Things (IoT) applications is shifting toward fog computing instead of cloud computing, thereby achieving most of the required computations at the ne...
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The current thinking concerning computations required by Internet of Things (IoT) applications is shifting toward fog computing instead of cloud computing, thereby achieving most of the required computations at the network edge of the IoT devices. Fog computing can thus improve the quality of service of delay-sensitive applications by allowing such applications to take advantage of the low latency provided by fog computing rather than the high latency of the cloud. Therefore, tasks in various IoT applications must be effectively distributed over the fog nodes to improve the quality of service, specifically the task response time. In this paper, two nature-inspired meta-heuristic schedulers, namely ant colony optimization (ACO) and particle swarm optimization (PSO), are used to propose two different scheduling algorithms to effectively load balance IoT tasks over the fog nodes under communication cost and response time considerations. The experimental results of the proposed algorithms are compared with those of the round robin (RR) algorithm. The evaluations show that the proposed ACO-based scheduler achieves an improvement in the response times of IoT applications compared to the proposed PSO-based and RR algorithms and effectively load balances the fog nodes.
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