Joint Task Offloading and Service Caching for Multi-Access Edge Computing in WiFi-Cellular Heterogeneous Networks

Fan, Wenhao; Han, Junting; Su, Yi; Liu, Xun; Wu, Fan; Tang, Bihua; Liu, Yuanan

doi:10.1109/twc.2022.3178541

Cited by 19 publications

(8 citation statements)

References 27 publications

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“…Coordinated allocation of processing resources and communication between mobile devices and MEC servers is crucial to optimize system performance in heterogeneous networks. While recent efforts have been made to jointly design compute offloading and caching in MEC systems [ 1 , 6 , 7 , 8 , 11 ], the issue of edge server service utility rationalization has been neglected. Therefore, further research is required to optimize offloading and caching in heterogeneous network computing systems.…”

Section: Related Workmentioning

confidence: 99%

“…The effective control of interference and resource allocation strategies are crucial in HetNets due to the presence of co-tier and cross-tier interference. Recently, HetNets resource allocation, computational offloading, and service caching have all undergone collaborative design work, frequently employing MEC to reduce system latency, increase energy efficiency, or improve forecast accuracy (usually prediction accuracy will use computational performance evaluation metrics, such as RMSE [ 2 , 9 , 10 , 11 ]).…”

Section: Introductionmentioning

confidence: 99%

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Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

Sun

Chen

2023

Sensors

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An optimal method for resource allocation based on contract theory is proposed to improve energy utilization. In heterogeneous networks (HetNets), distributed heterogeneous network architectures are designed to balance different computing capacities, and MEC server gains are designed based on the amount of allocated computing tasks. An optimal function based on contract theory is developed to optimize the revenue gain of MEC servers while considering constraints such as service caching, computation offloading, and the number of resources allocated. As the objective function is a complex problem, it is solved utilizing equivalent transformations and variations of the reduced constraints. A greedy algorithm is applied to solve the optimal function. A comparative experiment on resource allocation is conducted, and energy utilization parameters are calculated to compare the effectiveness of the proposed algorithm and the main algorithm. The results show that the proposed incentive mechanism has a significant advantage in improving the utility of the MEC server.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

Sun

Chen

2023

Sensors

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“…In fact, mobile edge computing has emerged as a potential solution to a number of problems faced by resource-constrained mobile devices, which require additional computing power to perform essential tasks for their operation [9], [10]. Mobile edge computing solves these problems by offering a proximitybased computing and caching resource that minimizes latency, optimizes bandwidth usage, and increases user density, especially when combined with high-speed radio access such as that offered by Wi-Fi [11] and 5G networks [12]. There are also ongoing standardization proposals, such as the multiaccess edge computing (MEC) framework established by ETSI [13].…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Ground Robots: Mobile Edge Computing and mmWave Communications at Work

Baruffa,

Detti,

Rugini

et al. 2024

IEEE Open J. Commun. Soc.

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The seamless integration of multiple radio access technologies (multi-RAT) and cloud/edge resources is pivotal for advancing future networks, which seek to unify distributed and heterogeneous computing and communication resources into a cohesive continuum system, tailored for mobile applications. Many research projects and focused studies are proposing solutions in this area, the impact of which is undoubtedly increased by moving from theoretical and simulation studies to experimental validations. To this aim, this paper proposes a testbed architecture that combines contemporary communication and cloud technologies to provide microservice-based mobile applications with the ability to offload part of their tasks to cloud/edge data centers connected by multi-RAT cellular networks. The testbed leverages Kubernetes, Istio service mesh, OpenFlow, public 5G networks, and IEEE 802.11ad mmWave (60 GHz) Wi-Fi access points. The architecture is validated through a use case in which a ground robot autonomously follows a moving object by using an artificial intelligence-driven computer vision application. Computationally intensive navigation tasks are offloaded by the robot to microservice instances, which are executed on demand within cloud and edge data centers that the robot can exploit during its journey. The proposed testbed is flexible and can be reused to assess communication and cloud innovations focusing on multi-RAT cloud continuum scenarios.

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“…This convergence has resulted in an air–ground cooperative heterogeneous network (AGCHN) model [ 4 ]. The proposed model not only characterizes the complexity of heterogeneous networks in terms of homogeneous and cross-layer interference, but also explores collaborative research on computational task offloading and resource allocation for HetNet and MEC interconnections [ 5 , 6 , 7 , 8 ]. This model is strategically designed to optimize MEC throughput and spectrum resource utilization.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Data Freshness in Air-Ground Collaborative Heterogeneous Networks through Contract Theory and Generative Diffusion-Based Mobile Edge Computing

Sun,

Chen

2023

Sensors

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Mobile edge computing is critical for improving the user experience of latency-sensitive and freshness-based applications. This paper provides insights into the potential of non-orthogonal multiple access (NOMA) convergence with heterogeneous air–ground collaborative networks to improve system throughput and spectral efficiency. Coordinated resource allocation between UAVs and MEC servers, especially in the NOMA framework, is addressed as a key challenge. Under the unrealistic assumption that edge nodes contribute resources indiscriminately, we introduce a two-stage incentive mechanism. The model is based on contract theory and aims at optimizing the utility of the service provider (SP) under the constraints of individual rationality (IR) and incentive compatibility (IC) of the mobile user. The block coordinate descent method is used to refine the contract design and complemented by a generative diffusion model to improve the efficiency of searching for contracts. During the deployment process, the study emphasizes the positioning of UAVs to maximize SP effectiveness. An improved differential evolutionary algorithm is introduced to optimize the positioning of UAVs. Extensive evaluation shows our approach has excellent effectiveness and robustness in deterministic and unpredictable scenarios.

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Joint Task Offloading and Service Caching for Multi-Access Edge Computing in WiFi-Cellular Heterogeneous Networks

Cited by 19 publications

References 27 publications

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

AI-Driven Ground Robots: Mobile Edge Computing and mmWave Communications at Work

Enhancing Data Freshness in Air-Ground Collaborative Heterogeneous Networks through Contract Theory and Generative Diffusion-Based Mobile Edge Computing

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