5,347 research outputs found
Online Service Migration in Edge Computing with Incomplete Information: A Deep Recurrent Actor-Critic Method
Multi-access Edge Computing (MEC) is an emerging computing paradigm that
extends cloud computing to the network edge (e.g., base stations, MEC servers)
to support resource-intensive applications on mobile devices. As a crucial
problem in MEC, service migration needs to decide where to migrate user
services for maintaining high Quality-of-Service (QoS), when users roam between
MEC servers with limited coverage and capacity. However, finding an optimal
migration policy is intractable due to the highly dynamic MEC environment and
user mobility. Many existing works make centralized migration decisions based
on complete system-level information, which can be time-consuming and suffer
from the scalability issue with the rapidly increasing number of mobile users.
To address these challenges, we propose a new learning-driven method, namely
Deep Recurrent Actor-Critic based service Migration (DRACM), which is
user-centric and can make effective online migration decisions given incomplete
system-level information. Specifically, the service migration problem is
modeled as a Partially Observable Markov Decision Process (POMDP). To solve the
POMDP, we design an encoder network that combines a Long Short-Term Memory
(LSTM) and an embedding matrix for effective extraction of hidden information.
We then propose a tailored off-policy actor-critic algorithm with a clipped
surrogate objective for efficient training. 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,
and achieves near-optimal results on various MEC scenarios
Online Service Migration in Mobile Edge with Incomplete System Information:A Deep Recurrent Actor-Critic Learning Approach
Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge to support resource-intensive applications on mobile devices. As a crucial problem in MEC, service migration needs to decide how to migrate user services for maintaining the Quality-of-Service when users roam between MEC servers with limited coverage and capacity. However, finding an optimal migration policy is intractable due to the dynamic MEC environment and user mobility. Many existing studies make centralized migration decisions based on complete system-level information, which is time-consuming and also lacks desirable scalability. To address these challenges, we propose a novel learning-driven method, which is user-centric and can make effective online migration decisions by utilizing incomplete system-level information. Specifically, the service migration problem is modeled as a Partially Observable Markov Decision Process (POMDP). To solve the POMDP, we design a new encoder network that combines a Long Short-Term Memory (LSTM) and an embedding matrix for effective extraction of hidden information, and further propose a tailored off-policy actor-critic algorithm for efficient training. The extensive experimental results based on real-world mobility traces demonstrate that this new method consistently outperforms both the heuristic and state-of-the-art learning-driven algorithms and can achieve near-optimal results on various MEC scenarios
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
Mobility-Induced Service Migration in Mobile Micro-Clouds
Mobile micro-cloud is an emerging technology in distributed computing, which
is aimed at providing seamless computing/data access to the edge of the network
when a centralized service may suffer from poor connectivity and long latency.
Different from the traditional cloud, a mobile micro-cloud is smaller and
deployed closer to users, typically attached to a cellular basestation or
wireless network access point. Due to the relatively small coverage area of
each basestation or access point, when a user moves across areas covered by
different basestations or access points which are attached to different
micro-clouds, issues of service performance and service migration become
important. In this paper, we consider such migration issues. We model the
general problem as a Markov decision process (MDP), and show that, in the
special case where the mobile user follows a one-dimensional asymmetric random
walk mobility model, the optimal policy for service migration is a threshold
policy. We obtain the analytical solution for the cost resulting from arbitrary
thresholds, and then propose an algorithm for finding the optimal thresholds.
The proposed algorithm is more efficient than standard mechanisms for solving
MDPs.Comment: in Proc. of IEEE MILCOM 2014, Oct. 201
Context-Awareness Enhances 5G Multi-Access Edge Computing Reliability
The fifth generation (5G) mobile telecommunication network is expected to
support Multi- Access Edge Computing (MEC), which intends to distribute
computation tasks and services from the central cloud to the edge clouds.
Towards ultra-responsive, ultra-reliable and ultra-low-latency MEC services,
the current mobile network security architecture should enable a more
decentralized approach for authentication and authorization processes. This
paper proposes a novel decentralized authentication architecture that supports
flexible and low-cost local authentication with the awareness of context
information of network elements such as user equipment and virtual network
functions. Based on a Markov model for backhaul link quality, as well as a
random walk mobility model with mixed mobility classes and traffic scenarios,
numerical simulations have demonstrated that the proposed approach is able to
achieve a flexible balance between the network operating cost and the MEC
reliability.Comment: Accepted by IEEE Access on Feb. 02, 201
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