989 research outputs found
System Optimisation for Multi-access Edge Computing Based on Deep Reinforcement Learning
Multi-access edge computing (MEC) is an emerging and important distributed computing paradigm that aims to extend cloud service to the network edge to reduce network traffic and service latency. Proper system optimisation and maintenance are crucial to maintaining high Quality-of-service (QoS) for end-users. However, with the increasing complexity of the architecture of MEC and mobile applications, effectively optimising MEC systems is non-trivial. Traditional optimisation methods are generally based on simplified mathematical models and fixed heuristics, which rely heavily on expert knowledge. As a consequence, when facing dynamic MEC scenarios, considerable human efforts and expertise are required to redesign the model and tune the heuristics, which is time-consuming.
This thesis aims to develop deep reinforcement learning (DRL) methods to handle system optimisation problems in MEC. Instead of developing fixed heuristic algorithms for the problems, this thesis aims to design DRL-based methods that enable systems to learn optimal solutions on their own. This research demonstrates the effectiveness of DRL-based methods on two crucial system optimisation problems: task offloading and service migration. Specifically, this thesis first investigate the dependent task offloading problem that considers the inner dependencies of tasks. This research builds a DRL-based method combining sequence-to-sequence (seq2seq) neural network to address the problem. Experiment results demonstrate that our method outperforms the existing heuristic algorithms and achieves near-optimal performance. To further enhance the learning efficiency of the DRL-based task offloading method for unseen learning tasks, this thesis then integrates meta reinforcement learning to handle the task offloading problem. Our method can adapt fast to new environments with a small number of gradient updates and samples. Finally, this thesis exploits the DRL-based solution for the service migration problem in MEC considering user mobility. This research models the service migration problem as a Partially Observable Markov Decision Process (POMDP) and propose a tailored actor-critic algorithm combining Long-short Term Memory (LSTM) to solve the POMDP. Results from extensive experiments based on real-world mobility traces demonstrate that our method consistently outperforms both the heuristic and state-of-the-art learning-driven algorithms on various MEC scenarios
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Multi-Objective Robust Workflow Offloading in Edge-to-Cloud Continuum
Workflow offloading in the edge-to-cloud continuum
copes with an extended calculation network among edge
devices and cloud platforms. With the growing significance of
edge and cloud technologies, workflow offloading among these
environments has been investigated in recent years. However,
the dynamics of offloading optimization objectives, i.e., latency,
resource utilization rate, and energy consumption among the
edge and cloud sides, have hardly been researched. Consequently,
the Quality of Service(QoS) and offloading performance also
experience uncertain deviation. In this work, we propose a
multi-objective robust offloading algorithm to address this issue,
dealing with dynamics and multi-objective optimization. The
workflow request model in this work is modeled as Directed
Acyclic Graph(DAG). An LSTM-based sequence-to-sequence
neural network learns the offloading policy. We then conduct
comprehensive implementations to validate the robustness of our
algorithm. As a result, our algorithm achieves better offloading
performance regarding each objective and faster adaptation
to newly changed environments than fine-tuned typical singleobjective
RL-based offloading methods
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