Improving offload delay using flow splitting and aggregation in edge computing

Yusuke, Ito; Koga, Hiroyuki

doi:10.1587/comex.2019gcl0009

Cited by 4 publications

(7 citation statements)

References 8 publications

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“…e equality constraint in (12) shows that the sum of the proportion of transmitted data and the proportion of local data should be equal to 1 for each ground terminal.…”

Section: Model Constraints Descriptionmentioning

confidence: 99%

“…where ω 1 and ω 2 are the weights of energy consumption and delay, respectively. In addition to the constraints from ( 9) to (12), those new constraints in (13) are the initial feasible region of the optimization variables so as to find the initial search window of the algorithm.…”

Section: Optimization Objectmentioning

confidence: 99%

“…Different Strategies. Figure 7 depicts the comparison of the total utility of strategies proposed in this paper and in References [11,12,14] with the increasing number of UAVs when there are 20 mobile terminals in the system.…”

Section: Effect Of the Number Of Uavs On Total Utility Undermentioning

confidence: 99%

“…Nevertheless, the communication delay has a great impact on the quality of service [11]. In order to reduce the influence of long distance between users and cloud servers on the quality of service, Ito and Koga proposed a flow splitting and aggregation scheme to lessen the offloading delay of cloud servers in edge computing, while the energy consumption still needs to be optimized [12]. Most of the aforementioned methods focus on the single-objective optimization, such as delay in computation task processing, energy consumption, and resource allocation.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing

Wang

2021

Journal of Robotics

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Aiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introducing the unmanned aerial vehicle (UAV) cluster. First, for the scenario of the UAV cluster serving multiple ground terminals, a collaborative task offloading model is formulated to offload the tasks to UAVs or the base station selectively. Then, an objective function and related constraints are put forward to minimize the time delay and energy consumption by analysis of those in the communication and computing process in the system while considering many factors. Then, the improved genetic algorithm is introduced to solve the optimization problem, obtaining the optimal collaborative task offloading strategy. To verify the performance of the proposed method, simulations are conducted on MATLAB. Simulation results showed that the joint utilization of UAV and MEC improves the offloading efficiency of the proposed strategy. When the number of UAVs is 12, the total utility is up to 1.83 and the task completion time does not exceed 110 ms. In this case, the task can be reasonably offloaded to UAVs or accomplished locally.

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“…e equality constraint in (12) shows that the sum of the proportion of transmitted data and the proportion of local data should be equal to 1 for each ground terminal.…”

Section: Model Constraints Descriptionmentioning

confidence: 99%

Section: Optimization Objectmentioning

confidence: 99%

Section: Effect Of the Number Of Uavs On Total Utility Undermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing

Wang

2021

Journal of Robotics

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“…In addition, the requirements of various tasks are considered, that is, assuming that different tasks may have different delay requirements, the problem is expressed as a cost minimization problem, and a heuristic algorithm is designed to solve the problem. Ito and Koga [25] proposed a stream splitting and aggregation scheme, which splits TCP connections between users on the edge server and the cloud server, and then aggregates TCP connections between the edge server and the cloud server to improve the edge calculate cloud server offload latency. Work [26] proposed an edge collaboration scheme to maximize the caching efficiency and minimize the content delivery delay from the social theory perspective.…”

Section: Related Workmentioning

confidence: 99%

Load Optimization Based on Edge Collaboration in Software Defined Ultra-Dense Networks

Yang

Zhang

2020

IEEE Access

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With the intelligence of user equipment and the popularization of emerging applications such as unmanned driving and face recognition, more and more computationally intensive and delay-sensitive tasks have been generated. As a new network paradigm, ultra-dense networks can greatly improve user access capabilities by deploying dense base stations (BSs). Edge computing can effectively guarantee the low-latency requirements of users in ultra-dense networks. However, the heterogeneity of servers, the distributed resources, and the dynamic energy consumption of mobile devices in ultra-dense networks make it extremely difficult for users to offload and load balance among servers. This paper applies the idea of software defined network to proposes an edge collaboration architecture to achieve resource sharing and efficient offloading of tasks based on the characteristics of global perception. In particular, considering the high load of the local server and the idle resources of the remote server, the best offloading strategy for users is obtained game theory. Simulation results show that the performance is improved by about 30% compared to the traditional local processing, edge offload and local edge random offload schemes. INDEX TERMS Software defined network, ultra dense network, load balancing, edge collaboration. I. INTRODUCTION

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A Novel Levy Walk-based Framework for Scheduling Power-intensive Mobile Edge Computing Tasks

Younesi,

Fazli,

Ejlali

2024

J Grid Computing

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Improving offload delay using flow splitting and aggregation in edge computing

Cited by 4 publications

References 8 publications

Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing

Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing

Load Optimization Based on Edge Collaboration in Software Defined Ultra-Dense Networks

A Novel Levy Walk-based Framework for Scheduling Power-intensive Mobile Edge Computing Tasks

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