Evolutionary offloading in an edge environment

Zakaryia, Samah A.; Ahmed, Shamim; Hussein, Mohamed K.

doi:10.1016/j.eij.2020.09.003

Cited by 21 publications

(12 citation statements)

References 28 publications

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“…The MEC architecture is a successful solution for the limited computational resources in terms of computation and energy, and thus, the performance of the oﬄoaded tasks are improved by taking advantage of the low latency and high bandwidth of the edge servers ( Yu, 2016 ; Zakaryia, Ahmed & Hussein, 2020 ). UAVs have the advantages of agility, mobility and fast deployment and can provide on-demand communication and computation services when mounted with communication equipment and MEC servers with a LoS advantage, which will produce better transmission rates with reduced energy consumption of the mobile devices.…”

Section: Related Workmentioning

confidence: 99%

“…The PSO is a stochastic, population-based, search meta-heuristic that simulates the social behavior of a swarm of birds searching for food. Each particle, an individual bird in the swarm, searches for food in the search space to identify the best food source based on the particle position, particle best found solution, and swarm best found global solution ( Zakaryia, Ahmed & Hussein, 2020 ). Each individual particle represents a possible solution in the search space of the problem under observation.…”

Section: Proposed Meta-heuristicsmentioning

confidence: 99%

“…α and β are random numbers ∈ N [0 1]. PSO is an eﬀective solution for optimization problems in the continuous domain, and a discrete version of the PSO algorithm must be adopted to suit the problem under study ( Zakaryia, Ahmed & Hussein, 2020 ). A new Discrete PSO (DPSO) is designed and presented in the following subsection.…”

Section: Proposed Meta-heuristicsmentioning

confidence: 99%

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Efficient UAV-based mobile edge computing using differential evolution and ant colony optimization

Mousa

Hussein

2022

PeerJ Computer Science

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Internet of Things (IoT) tasks are offloaded to servers located at the edge network for improving the power consumption of IoT devices and the execution times of tasks. However, deploying edge servers could be difficult or even impossible in hostile terrain or emergency areas where the network is down. Therefore, edge servers are mounted on unmanned aerial vehicles (UAVs) to support task offloading in such scenarios. However, the challenge is that the UAV has limited energy, and IoT tasks are delay-sensitive. In this paper, a UAV-based offloading strategy is proposed where first, the IoT devices are dynamically clustered considering the limited energy of UAVs, and task delays, and second, the UAV hovers over each cluster head to process the offloaded tasks. The optimization problem of dynamically determining the optimal number of clusters, specifying the member tasks of each cluster, is modeled as a mixed-integer, nonlinear constraint optimization. A discrete differential evolution (DDE) algorithm with new mutation and crossover operators is proposed for the formulated optimization problem, and compared with the particle swarm optimization (PSO) and genetic algorithm (GA) meta-heuristics. Further, the ant colony optimization (ACO) algorithm is employed to identify the shortest path over the cluster heads for the UAV to traverse. The simulation results validate the effectiveness of the proposed offloading strategy in terms of tasks delays and UAV energy consumption.

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Section: Related Workmentioning

confidence: 99%

Section: Proposed Meta-heuristicsmentioning

confidence: 99%

Section: Proposed Meta-heuristicsmentioning

confidence: 99%

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Efficient UAV-based mobile edge computing using differential evolution and ant colony optimization

Mousa

Hussein

2022

PeerJ Computer Science

Self Cite

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“…It can be easily implemented in practice. Zakaryia studied a task scheduling method using mobile edge computing (MEC) with multiple base stations (BS), each consisting of a MEC server, that guides multiple latencysensitive user equipment (UE) in computing [9,10]. Sheng et al [11] also have worked on computing offloading techniques in mobile edge computing.…”

Section: Literature Reviewmentioning

confidence: 99%

Intrinsic Profit Maximization of the Offloading Tasks for Mobile Edge Computing with Fixed Memory Capacities and Low Latency Constraints Using Ant Colony Optimization

Tunga¹,

Kar²,

Giri³

2022

MMEP

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Artificial intelligence and the Internet of Things (IoT) have resulted in more computationally demanding and time-sensitive applications. Given the limited processing power of current mobile computers, there is a need for on-demand computing resources with minimal latency. Edge computing has already made a significant contribution to mobile networks, enabling the distribution, scaling, and faster access of computational resources at network margins closer to users, especially in power-constrained mobile devices. Offloading tasks efficiently on the Mobile Edge Computing Server (MECS) is an important part of our proposed method. We propose a method of offloading multiple tasks for Mobile Edge Computing servers that require fixed memory capacities and low latency. We calculate the optimum cumulative intrinsic profit of the number of offloaded tasks efficiently using the Ant Colony Optimization (ACO) model, which is flexible and versatile in the context of real-time applications.

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“…Evolutionary algorithms are well-known for solving optimization problems. 24 They create the search space of multiple solutions where each solution consists of all feasible elements that satisfy certain criteria. The worth of each solution is evaluated against fitness function.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Meta-heuristic-based offloading task optimization in mobile edge computing

Abbas

Raza

Aadil

et al. 2021

International Journal of Distributed Sensor Networks

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With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.

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Evolutionary offloading in an edge environment

Cited by 21 publications

References 28 publications

Efficient UAV-based mobile edge computing using differential evolution and ant colony optimization

Efficient UAV-based mobile edge computing using differential evolution and ant colony optimization

Intrinsic Profit Maximization of the Offloading Tasks for Mobile Edge Computing with Fixed Memory Capacities and Low Latency Constraints Using Ant Colony Optimization

Meta-heuristic-based offloading task optimization in mobile edge computing

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