Mobility-Aware and Code-Oriented Partitioning Computation Offloading in Multi-Access Edge Computing

Liu, Yaqin; Liu, Chubo; Liu, Jing; Hu, Yikun; Li, Kenli; Li, Keqin

doi:10.1007/s10723-022-09599-x

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…Tong, et al [26] proposed the Lyapunov online energy consumption optimization algorithm to solve the queue backlog and energy consumption in MEC. Further, Liu, et al [27] addressed TO in MEC considering the mobility user devices and proposed a mobility-aware and code-oriented partitioning computational offloading scheme.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

Aliyu,

Oh,

et al. 2024

IEEE Access

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The computing in the network (COIN) paradigm is a promising solution that leverages unused network resources to perform tasks to meet computation-demanding applications, such as the metaverse. In this vein, we consider the partial computation offloading problem in the metaverse for multiple subtasks in a COIN environment to minimize energy consumption and delay while dynamically adjusting the offloading policy based on the changing computational resource status. The problem is NP-hard, and we transform it into two subproblems: the task-splitting problem (TSP) on the user side and the task-offloading problem (TOP) on the COIN side. We model the TSP as an ordinal potential game and propose a decentralized algorithm to obtain its Nash equilibrium (NE). Then, we model the TOP as a Markov decision process and propose the double deep Q-network (DDQN) to solve for the optimal offloading policy. Unlike the conventional DDQN algorithm, where intelligent agents sample offloading decisions randomly within a certain probability, the COIN agent explores the NE of the TSP and the deep neural network. Finally, the simulation results reveal that the proposed model approach allows the COIN agent to update its policies and make more informed decisions, leading to improved performance over time compared to the traditional baseline.

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Section: Related Workmentioning

confidence: 99%

Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

Aliyu,

Oh,

et al. 2024

IEEE Access

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“…Offloading decisions are made based on parameters such as network bandwidth, data computation, the amount of data exchanged over the networks, etc. Many of the proposed algorithms used to make offloading decisions are aimed at improving latency and minimizing energy [8], [9], [10], [11]. In [8], the perform latency optimization in multi-user Mobile edge computation offloading (MECO) system with partial computation offloading with the design objective is to minimize the weighted-sum delay of all devices under the limited communication and computation resource constraints.…”

Section: B Machine Learning Offloadingmentioning

confidence: 99%

“…In [8], the perform latency optimization in multi-user Mobile edge computation offloading (MECO) system with partial computation offloading with the design objective is to minimize the weighted-sum delay of all devices under the limited communication and computation resource constraints. Offloading strategy is decided in [9] using convex optimization and the Lagrangian approach. The novelty of this paper lies in finding an offloading strategy for devices in motion.…”

Section: B Machine Learning Offloadingmentioning

confidence: 99%

“…Other criteria include analyzing parameters: server speed, bandwidth, server load, available memory, and data transfer rate between servers and mobile systems. The solutions to solve these problems include partitioning programs and predicting parametric variations in application behavior and execution environment using machine learning approaches are discussed in [9] and [12]. In [13], a large state space scenario such as large battery levels, data rates normalized using CNN to find offloading strategies using Q-learning.…”

Section: B Machine Learning Offloadingmentioning

confidence: 99%

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Towards An Optimal Latency-Energy Dynamic Offloading Scheme for Collaborative Cloud Networks

2023

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Growing technologies like virtualization and artificial intelligence have become more popular nowadays because they are more handy and accessible on mobile devices. But lack of resources for processing these applications at the user end and the limited energy of mobile devices are still significant hurdles. Collaborative edge and cloud computing are one of the solutions to this problem. An optimal offloading strategy is required to balance transmission latency for the cloud and limited resources at edge servers. We have proposed a multi-period deep deterministic policy gradient (MP-DDPG) algorithm to find an optimal offloading policy to the collaborative cloud network including the central cloud server, edge cloud servers, and mobile devices constrained by minimization of computation, transmission delay, and energy consumption. The novelty of this algorithm lies in partitioning the task to offload in multiple time slots and reusing cloud and edge resources in every slot, rather than taking a single offloading decision and running out of remote resources by offloading a single large task. Our results show that MP-DDPG achieves the minimum latency and energy consumption in the collaborative cloud network.

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“…The goal of such problems is to identify an approach that, at each decision stage, specifies how to allocate available resources among competing task requests to optimize the system's performance. The computation offloading requirements in the actual manufacturing workshop are closely related to the complex systems in the dynamic environment, making the traditional computation offloading method unable to adapt to the business model under the cloud manufacturing scenarios, leading to the problem of load imbalance Liu et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode

Man

Zhao

et al. 2023

Journal of Database Management

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Large-scale manufacturing enterprises have complex business processes in their production workshops, and the edge-edge collaborative business model cannot adapt to the traditional computation offloading methods, which leads to the problem of load imbalance. For this problem, a computation offloading algorithm based on edge-edge collaboration mode for large-scale factory access is proposed, called the edge and edge collaborative computation offloading (EECCO) algorithm. First, the method partitions the directed acyclic graphs (DAGs) on edge server and terminal industrial equipment, then updates the tasks using a synchronization policy based on set theory to improve the accuracy effectively, and finally achieves load balancing through processor allocation. The experimental results show that the method shortens the processing time by improving computational resource utilization and employs a heterogeneous distributed system to achieve high computing performance when processing large-scale task sets.

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Mobility-Aware and Code-Oriented Partitioning Computation Offloading in Multi-Access Edge Computing

Cited by 12 publications

References 30 publications

Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

Towards An Optimal Latency-Energy Dynamic Offloading Scheme for Collaborative Cloud Networks

Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode

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