Joint Shareability and Interference for Multiple Edge Application Deployment in Mobile-Edge Computing Environment

Zhao, Lu; Tan, Wenan; Li, Bo; He, Qiang; Huang, Li; Yong, Sun; Xu, Lida; Yang, Yun

doi:10.1109/jiot.2021.3088493

Cited by 29 publications

(9 citation statements)

References 46 publications

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“…This scheme takes autonomous cycle management on the edge platform as the center and achieves the goal of dynamic resource scheduling of multitenant services. To solve the problem of multiservice deployment in the MEC network environment, Lu Zhao et al [19] described how to minimize the utilization of edge resources while meeting the quality of service required by edge users. They used a heuristic algorithm based on priority to realize the edge deployment mode of distributed control and maximize the overall service quality of users under the condition of comprehensive consideration of service sharing and communication interference.…”

Section: Research On Edge Service Deploymentmentioning

confidence: 99%

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Song¹,

Sun²,

Yu³

et al. 2022

Preprint

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In recent years, edge computing, as an important pillar for future networks, has been developed rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed and save energy. An efficient and feasible task offloading scheme can not only greatly improve the quality of experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the industrial Internet of Things (IIoT), computing networks and so on. To achieve these goals, this paper proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on the optimization of the energy utilization ratio (EUR) and the processing latency. In the pre-implementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service's popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on the task priority. The task priority is generated by the corresponding service popularity and task offloading direction. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.

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Section: Research On Edge Service Deploymentmentioning

confidence: 99%

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Song¹,

Sun²,

Yu³

et al. 2022

Preprint

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“…), are shared between their assigned users. However, different from conventional cloud-centric computing models, where the resources of the data centers are assumed to be practically in inite, the computing capacity of the edge servers is constrained by inite sizes [14]. Let 𝐶 𝑗 denote the capacity limit in regards to resource availability of 𝑠 𝑗 ∈ 𝑆, beyond which its assigned users begin to experience losses in frame quality and increased interaction delay that can cause VR vertigo.…”

Section: Edge Provisioning Modelmentioning

confidence: 99%

“…Notwithstanding the criticality of the issue in player churn [13], discovering optimal solutions is not straightforward when considering the coverage range of the BSs in combination with the bounded computing capacity of the servers. The latter, in edge computing (contrary to its cloud counterpart), is characterized by limited available resources that need to be elastically provisioned and shared between the users, in order to maximize their Quality of Service (QoS), and in turn their perceived QoE [14].…”

Section: Introductionmentioning

confidence: 99%

Towards fairness and QoE-based edge allocation for multiplayer virtual reality applications in edge computing

Tsipis¹,

Komianos²,

Oikonomou³

et al. 2022

ITU J-FET

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Edge computing has emerged as the next big thing in distributed computing, by extending the cloud paradigm and offering efficient ways to engage with latency-intolerant applications, such as Virtual Reality (VR) multiplayer games. In edge computing, the service providers can benefit from existing cellular infrastructure to deploy services on edge servers that reside in close proximity to the users. Given the limited available budget for edge resource investment, one fundamental problem that manifests is the discovery of a prudent edge allocation strategy, that will efficiently prescribe which users are assigned to which edge servers, in order to tackle application-specific requirements, like minimizing system deployment costs. In this paper, considering the frequent interactions and view inconsistencies occurring among multiple users immersed in the same VR game, we address the problem from the users' perspective, focusing on improving their edge admission rate, resource provisioning and overall fairness, in order to subsequently maximize the average Quality of Experience (QoE). We call this the "Fairness and QoE-Based Edge Allocation" (FQEA) problem, formally formulating its properties and theoretically proving its complexity. However, discovering optimal solutions to the NP-hard FQEA in large-scale VR scenarios is challenging. Hence, we propose FQEA-H, a heuristic algorithm to generate allocation strategies in reasonable time. Comprehensive simulations, conducted on a real-world topological trace, demonstrate how FQEA-H can tackle the problem effectively, generally outperforming both the baseline and state-of-the-art alternatives.

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“…Nevertheless, limited by the computing resources and energy of UAVs, the multi-source information fusion computing tasks cannot be adequately performed at UVA. Therefore, edge computing technology [7][8][9][10] will be used to perform reasonable computing task offloading for these computing tasks, thereby overcoming the above shortcomings and providing an additional benefit of reducing task processing latency. There are many works on UAV-assisted MEC systems.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficiency Computation Offloading Strategy in UAV Aided V2X Network With Integrated Sensing and Communication

Liu

Liang

Luo

et al. 2022

IEEE Open J. Commun. Soc.

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Integrated Sensing and Communications (ISAC) technology can jointly design radio sensing and communication functionalities, which enable 6G Ere to have the ability to "see" the physical world rather than communication-only. Benefitting from ISAC, vehicular-to-everything (V2X) networks may efficiently complete high-precision traffic environment perception. Furthermore, with the assistance of flexibly deployed Unmanned Aerial Vehicles (UAVs), the V2X networks overcome the limited sensing range of sensors equipped on a vehicle and guarantee safe driving. This paper proposes an energy-efficient computation offloading strategy for multiple sensor data fusion in UAV Aided V2X Network supported by Integrated Sensing and Communication. Firstly, a vehicle-UAV cooperative perception architecture is proposed to perceive a wide range of traffic environments. Secondly, we introduce a computation offloading strategy jointly considering offloading decisions and dynamic computing resource allocation. Finally, a successive convex approximation (SCA) algorithm transforms a non-convex formulation problem into a tractable convex approximation problem. The simulation results show that the strategy proposed in this paper reduces the UAV energy consumption and data fusion task processing delay.

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Joint Shareability and Interference for Multiple Edge Application Deployment in Mobile-Edge Computing Environment

Cited by 29 publications

References 46 publications

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Towards fairness and QoE-based edge allocation for multiplayer virtual reality applications in edge computing

Energy-Efficiency Computation Offloading Strategy in UAV Aided V2X Network With Integrated Sensing and Communication

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