Task Graph offloading via Deep Reinforcement Learning in Mobile Edge Computing

• URL: http://arxiv.org/abs/2309.10569v4
• Date: Thu, 21 Mar 2024 07:12:06 GMT
• Title: Task Graph offloading via Deep Reinforcement Learning in Mobile Edge Computing
• Authors: Jiagang Liu, Yun Mi, Xinyu Zhang, Xiaocui Li,
• Abstract summary: This paper investigates the task graph offloading in MEC, considering the time-varying capabilities of edge computing devices. To adapt to environmental changes, we model the task graph scheduling for computation offloading as a Markov Decision Process. Then, we design a deep reinforcement learning algorithm (SATA-DRL) to learn the task scheduling strategy from the interaction with the environment.
• Score: 6.872434270841794
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Various mobile applications that comprise dependent tasks are gaining widespread popularity and are increasingly complex. These applications often have low-latency requirements, resulting in a significant surge in demand for computing resources. With the emergence of mobile edge computing (MEC), it becomes the most significant issue to offload the application tasks onto small-scale devices deployed at the edge of the mobile network for obtaining a high-quality user experience. However, since the environment of MEC is dynamic, most existing works focusing on task graph offloading, which rely heavily on expert knowledge or accurate analytical models, fail to fully adapt to such environmental changes, resulting in the reduction of user experience. This paper investigates the task graph offloading in MEC, considering the time-varying computation capabilities of edge computing devices. To adapt to environmental changes, we model the task graph scheduling for computation offloading as a Markov Decision Process (MDP). Then, we design a deep reinforcement learning algorithm (SATA-DRL) to learn the task scheduling strategy from the interaction with the environment, to improve user experience. Extensive simulations validate that SATA-DRL is superior to existing strategies in terms of reducing average makespan and deadline violation.

Related papers

• Resource Allocation for Stable LLM Training in Mobile Edge Computing [11.366306689957353]
  This paper explores a collaborative training framework that integrates mobile users with edge servers to optimize resource allocation. We formulate a multi-objective optimization problem to minimize the total energy consumption and delay during training. We also address the common issue of instability in model performance by incorporating stability enhancements into our objective function.
  arXiv  Detail & Related papers  (2024-09-30T12:36:27Z)
• Structural Knowledge-Driven Meta-Learning for Task Offloading in Vehicular Networks with Integrated Communications, Sensing and Computing [21.50450449083369]
  Task offloading is a potential solution to satisfy the strict requirements of latencysensitive vehicular applications due to the limited onboard computing resources. We propose a creative structural knowledge-driven meta-learning (SKDML) method, involving both the model-based AM algorithm and neural networks.
  arXiv  Detail & Related papers  (2024-02-25T03:31:59Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing [11.403989519949173]
  This work focuses on the timeliness of computational-intensive updates, measured by Age-ofInformation (AoI) We study how to jointly optimize the task updating and offloading policies for AoI with fractional form. Experimental results show that our proposed algorithms reduce the average AoI by up to 57.6% compared with several non-fractional benchmarks.
  arXiv  Detail & Related papers  (2023-12-16T11:13:40Z)
• Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach [58.911515417156174]
  We propose a new definition of Age of Information (AoI) and, based on the redefined AoI, we formulate an online AoI problem for MEC systems. We introduce Post-Decision States (PDSs) to exploit the partial knowledge of the system's dynamics. We also combine PDSs with deep RL to further improve the algorithm's applicability, scalability, and robustness.
  arXiv  Detail & Related papers  (2023-12-01T01:30:49Z)
• 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)
• Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
  Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
  arXiv  Detail & Related papers  (2023-08-27T03:55:28Z)
• MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems [12.215537834860699]
  Recently proposed scheduling methods leverage the low response times of edge computing platforms to optimize application Quality of Service (QoS) We propose MCDS: Monte Carlo Learning using Deep Surrogate Models to efficiently schedule workflow applications in mobile edge-cloud computing systems.
  arXiv  Detail & Related papers  (2021-12-14T10:00:01Z)
• 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)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Online Service Migration in Edge Computing with Incomplete Information: A Deep Recurrent Actor-Critic Method [18.891775769665102]
  Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge. Service migration needs to decide where to migrate user services for maintaining high Quality-of-Service (QoS) We propose a new learning-driven method, namely Deep Recurrent ActorCritic based service Migration (DRACM), which is usercentric and can make effective online migration decisions.
  arXiv  Detail & Related papers  (2020-12-16T00:16:24Z)

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.