dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network

• URL: http://arxiv.org/abs/2402.00874v1
• Date: Wed, 10 Jan 2024 17:21:20 GMT
• Title: dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network
• Authors: Asad Aftab and Semeen Rehman
• Abstract summary: We propose a technique to minimize the total computation and communication overhead for optimal resource utilization with joint computational offloading. Compared to baseline schemes our technique achieves a 37.03% reduction in total system costs.
• Score: 0.7645708712865565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-access-Mobile Edge Computing (MEC) is a promising solution for computationally demanding rigorous applications, that can meet 6G network service requirements. However, edge servers incur high computation costs during task processing. In this paper, we proposed a technique to minimize the total computation and communication overhead for optimal resource utilization with joint computational offloading that enables a green environment. Our optimization problem is NP-hard; thus, we proposed a decentralized Reinforcement Learning (dRL) approach where we eliminate the problem of dimensionality and over-estimation of the value functions. Compared to baseline schemes our technique achieves a 37.03% reduction in total system costs.

Related papers

• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• PECCO: A Profit and Cost-oriented Computation Offloading Scheme in Edge-Cloud Environment with Improved Moth-flame Optimisation [22.673319784715172]
  Edge-cloud computation offloading is a promising solution to relieve the burden on cloud centres. We propose an improved Moth-flame optimiser PECCO-MFI which addresses some deficiencies of the original Moth-flame Optimiser.
  arXiv  Detail & Related papers  (2022-08-09T23:26:42Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing [9.598394554018164]
  This paper addresses the problem of minimizing both, the network delay, and the number of edge servers to provide a MEC design with minimum cost. We propose a novel RL framework with an efficient representation and modeling of the state space, action space and the penalty function in the design of the underlying Markov Decision Process (MDP) for solving our problem.
  arXiv  Detail & Related papers  (2022-02-21T03:04:50Z)
• Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT [106.83952081124195]
  Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. We propose an adaptive ADMM (asI-ADMM) algorithm and apply it to decentralized RL with edge-computing-empowered IIoT networks. Experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.
  arXiv  Detail & Related papers  (2021-06-30T16:49:07Z)
• Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning [12.845204986571053]
  In this work, we study the problem of energy-efficient offloading enabled by edge computing. In the considered scenario, multiple users simultaneously compete for radio and edge computing resources. The proposed solution also allows to increase the network's energy efficiency compared to a benchmark approach.
  arXiv  Detail & Related papers  (2021-03-31T11:02:29Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond [7.953533529450216]
  5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous capabilities of the end devices, edge servers, and the cloud. In multi user wireless networks, diverse application requirements and the possibility of various radio access modes for communication among devices make it challenging to design an optimal computation offloading scheme. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information.
  arXiv  Detail & Related papers  (2020-11-17T05:51:03Z)
• A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks [108.57859531628264]
  A joint task, spectrum, and transmit power allocation problem is investigated for a wireless network. The proposed algorithm can reduce the number of iterations needed for convergence and the maximal delay among all users by up to 18% and 11.1% compared to the standard Q-learning algorithm.
  arXiv  Detail & Related papers  (2020-07-20T13:46:42Z)
• Computation Offloading in Multi-Access Edge Computing Networks: A Multi-Task Learning Approach [7.203439085947118]
  Multi-access edge computing (MEC) has already shown the potential in enabling mobile devices to bear the computation-intensive applications by offloading some tasks to a nearby access point (AP) integrated with a MEC server (MES) However, due to the varying network conditions and limited computation resources of the MES, the offloading decisions taken by a mobile device and the computational resources allocated by the MES may not be efficiently achieved with the lowest cost. We propose a dynamic offloading framework for the MEC network, in which the uplink non-orthogonal multiple access (NOMA) is used to enable multiple devices to upload their
  arXiv  Detail & Related papers  (2020-06-29T15:11:10Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.