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AoI-Aware User Service Satisfaction Enhancement in Digital Twin-Empowered Edge Computing

IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第2期32卷 1677-1690页

作者： Li, Jing Guo, Song Liang, Weifa Wang, Jianping Chen, Quan Xu, Zichuan Xu, Wenzheng Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Guangdong Univ Technol Sch Comp Guangzhou 510006 Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Peoples R China

The emerging digital twin technique enhances the network management efficiency and provides comprehensive insights on network performance, through mapping physical objects to their digital twins. The user satisfaction on digital twin-enabled service relies on the freshness of digital twin data, which is measured by the Age of Information (AoI). Due to long service delays, the use of the remote cloud for delay-sensitive service provisioning faces serious challenges. Mobile Edge Computing (MEC), as an ideal paradigm for delay-sensitive services, is able to realize real-time data communication between physical objects and their digital twins at the network edge. However, the mobility of physical objects and dynamics of user query arrivals make seamless service provisioning in MEC become challenging. In this paper, we investigate dynamic digital twin placements for improving user service satisfaction in MEC environments, by introducing a novel metric to measure user service satisfaction based on the AoI concept and formulating two user service satisfaction enhancement problems: the static and dynamic utility maximization problems under static and dynamic digital twin placement schemes. To this end, we first formulate an Integer Linear Programming (ILP) solution to the static utility maximization problem when the problem size is small;otherwise, we propose a performance-guaranteed approximation algorithm. We then propose an online algorithm with a provable competitive ratio for the dynamic utility maximization problem, by considering dynamic user query services. Finally, we evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform the comparison baseline algorithms, improving the algorithm performance by at least 10.7\%, compared to the baseline algorithms.

关键词： Digital twins Heuristic algorithms approximation algorithms Delays Prediction algorithms Cloud computing Real-time systems Digital twin mobile edge computing age of information approximation and online algorithms digital twin placement

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Age-Aware Data Selection and Aggregator Placement for Timely Federated Continual Learning in Mobile Edge Computing

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IEEE TRANSACTIONS ON COMPUTERS 2024年第2期73卷 466-480页

作者： Xu, Zichuan Wang, Lin Liang, Weifa Xia, Qiufen Xu, Wenzheng Zhou, Pan Rana, Omer F. Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaonin Dalian 116620 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Sichuan Univ Dept Comp Network & Commun Chengdu 610207 Sichuan Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Hubei Peoples R China Cardiff Univ Phys Sci & Engn Coll Cardiff CF10 3AT Wales

Federated continual learning (FCL) is emerging as a key technology for time-sensitive applications in highly adaptive environments including autonomous driving and industrial digital twin. Each FCL trains machine learning models using newly-generated datasets as soon as possible, to obtain a highly accurate machine learning model for new event predictions. The age of data, defined as the time difference between the generation time of a dataset and the current time, is widely adopted as a key criterion to evaluate both timeline and quality of training. In this paper, we study the problem of age-aware FCL in a mobile edge computing (MEC) network. We not only investigate optimization techniques that optimize the data selection and aggregator placement for FCL but also implement a real system as a prototype for age-aware FCL. Specifically, we first propose an approximation algorithm with a provable approximation ratio for the age-aware data selection and aggregator placement problem for FCL with a single request. In real application scenarios, there are usually multiple FCL requests that require to train models, and delays in the MEC network are usually uncertain. We then study the problem of age-aware data selection and aggregator placement problem for FCL with uncertain delays and multiple requests, by devising an online learning algorithm with a bounded regret based on contextual bandits. We finally implement a prototype for FCL in an MEC network, with various heterogeneous user equipments (UEs) and cloudlets with different computing capabilities in the network. Experiment results show that the performance of the proposed algorithms outperform existing studies, by achieving 47% lower age of data and 12% higher model accuracy.

关键词： Mobile edge computing federated continual learning data selection and aggregator placement approximation and online algorithms

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Efficient algorithms for Service Chaining in NFV-Enabled Satellite Edge Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 5677-5694页

作者： Xia, Qiufen Wang, Guijie Xu, Zichuan Liang, Weifa Xu, Zhou Dalian Univ Technol Int Sch Informat Sci & Engn Dalian 116024 Liaoning Peoples R China Key Lab Ubiquitous Network & Serv Software Liaonin Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Liaoning Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Satellite-terrestrial networks are emerging as the next-generation networking paradigm for Beyond-5 G (B5G) and 6 G networks. Meanwhile, Mobile Edge Computing (MEC) is envisioned as the key technology to provide network services within the proximity of users, by deploying computing resource in ground locations that are close to users. With the fast deployment of Low-Earth-Oribt (LEO) satellites, a new paradigm of MEC is emerging by enabling LEO satellites serving as edge servers in lower orbits that are close to ground users. In this way, the ground users can be further served by LEO satellites in lower orbits instead of conventional high-orbit satellites. Also, since LEO satellites provide shorter paths from users to services, the performance is enhanced compared with ground MEC networks. In this paper, we aim to enable low-latency network services in a Satellite Edge Computing (SEC) network that integrates the MEC and satellite-terrestrial networks. In particular, we consider that each network service is composed of a sequence of Virtualized Network Functions (VNFs), where the traffic of user requests has to be processed by the VNFs in a service chain in the specified order before reaching its destination. To this end, we first formulate a delay-aware service chaining problem in an SEC network to minimize the average delay of implementing a user request, by jointly placing VNFs to LEO satellites in the SEC network and routing the traffic of each user request from its source to destination. We then devise an approximation algorithm with an approximation ratio for the problem in an SEC network with a single user request, by devising a novel concept of chaining orbit and auxiliary graph construction technique. We also design an online algorithm for the online delay-aware service chaining problem in an SEC network, if user requests arrive into the system without the knowledge of their arrivals and the network delays are uncertain. We finally evaluate the performance o

关键词： Satellites Low earth orbit satellites Delays Orbits Internet of Things approximation algorithms Low latency communication approximation and online algorithms mobile edge computing network function chaining satellite edge network

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Throughput Maximization of Delay-Aware DNN Inference in Edge Computing by Exploring DNN Model Partitioning and Inference Parallelism

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第5期22卷 3017-3030页

作者： Li, Jing Liang, Weifa Li, Yuchen Xu, Zichuan Jia, Xiaohua Guo, Song Australian Natl Univ Sch Comp Canberra ACT 0200 Australia City Univ Hong Kong Dept Comp Sci 83 Tat Chee Ave Hong Kong Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Liaoning Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to overwhelming explosions of mobile applications, by offloading compute-intensive tasks to MEC networks for processing. The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. To accelerate the processing of the DNN inference of a user request in an MEC network, the DNN inference model usually can be partitioned into two connected parts: one part is processed in the local IoT device of the request, and another part is processed in a cloudlet (edge server) in the MEC network. Also, the DNN inference can be further accelerated by allocating multiple threads of the cloudlet to which the request is assigned. In this paper, we study a novel delay-aware DNN inference throughput maximization problem with the aim to maximize the number of delay-aware DNN service requests admitted, by accelerating each DNN inference through jointly exploring DNN partitioning and multi-thread execution parallelism. Specifically, we consider the problem under both offline and online request arrival settings: a set of DNN inference requests is given in advance, and a sequence of DNN inference requests arrives one by one without the knowledge of future arrivals, respectively. We first show that the defined problems are NP-hard. We then devise a novel constant approximation algorithm for the problem under the offline setting. We also propose an online algorithm with a provable competitive ratio for the problem under the online setting. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising

关键词： Inference algorithms Delays Partitioning algorithms Computational modeling Task analysis approximation algorithms Parallel processing Mobile edge computing (MEC) DNN model inference provisioning throughput maximization Intelligent IoT devices approximation and online algorithms delay-aware DNN inference DNN partitioning inference parallelism computing and bandwidth resource allocation and optimization algorithm design and analysis

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Mobility-Aware and Delay-Sensitive Service Provisioning in Mobile Edge-Cloud Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2022年第1期21卷 196-210页

作者： Ma, Yu Liang, Weifa Li, Jing Jia, Xiaohua Guo, Song Australian Natl Univ Res Sch Comp Sci Canberra ACT 2601 Australia City Univ Hong Kong Dept Comp Sci Kowloon 83 Tat Chee Ave Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

Mobile edge computing (MEC) has emerged as a promising technology to push the cloud frontier to the network edge, provisioning network services in proximity of mobile users. Serving users at edge clouds can reduce service latency, lower operational cost, and improve network resource availability. Along with the MEC technology, network function virtualization (NFV) is another promising technique that implements various network service functions as pieces of software in cloudlets (servers or clusters of servers). Providing virtualized network service for mobile users can improve user service experience, simplify network service deployment, and ease network resource management. However, mobile users move in networks arbitrarily, and different users usually request different services with different resource demands and delay requirements. It thus poses a great challenge to providing reliable and seamless virtualized network services for mobile users in an MEC network while meeting their individual delay requirements, subject to resource capacities on the network. In this paper, we focus on the provisioning of virtualized network function services for mobile users in MEC that takes into account user mobility and service delay requirements. We first formulate two novel optimization problems of user service request admissions with the aims to maximize the accumulative network utility and accumulative network throughput for a given time horizon, respectively. We then devise a constant approximation algorithm for the utility maximization problem. We also develop an online algorithm for the accumulative throughput maximization problem. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.

关键词： Cloud computing approximation algorithms Delays Mobile computing Mobile handsets Prediction algorithms Servers Mobile Edge computing network function virtualization VNF instance deployment virtualized service provisioning approximation and online algorithms delay-sensitive request admission utility gain maximization user mobility cloudlets or edge-clouds resource allocations and provisioning in MEC optimization problems

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Energy-Aware Inference Offloading for DNN-Driven Applications in Mobile Edge Clouds

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第4期32卷 799-814页

作者： Xu, Zichuan Zhao, Liqian Liang, Weifa Rana, Omer F. Zhou, Pan Xia, Qiufen Xu, Wenzheng Wu, Guowei Dalian Univ Technol Sch Software Dalian 116024 Peoples R China Key Lab Ubiquitous Network & Serv Software Dalian 116024 Liaoning Peoples R China Australian Natl Univ Res Sch Comp Sci Canberra ACT 2601 Australia Cardiff Univ Cardiff CF10 3AT Wales Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Hubei Engn Res Ctr Big Data Secur Wuhan 430074 Peoples R China Dalian Univ Technol Int Sch Informat Sci & Engn Dalian 116024 Peoples R China Sch Sichuan Univ Chengdu 610017 Peoples R China

With increasing focus on Artificial Intelligence (AI) applications, Deep Neural Networks (DNNs) have been successfully used in a number of application areas. As the number of layers and neurons in DNNs increases rapidly, significant computational resources are needed to execute a learned DNN model. This ever-increasing resource demand of DNNs is currently met by large-scale data centers with state-of-the-art GPUs. However, increasing availability of mobile edge computing and 5G technologies provide new possibilities for DNN-driven AI applications, especially where these application make use of data sets that are distributed in different locations. One fundamental process of a DNN-driven application in mobile edge clouds is the adoption of "inferencing" - the process of executing a pre-trained DNN based on newly generated image and video data from mobile devices. We investigate offloading DNN inference requests in a 5G-enabled mobile edge cloud (MEC), with the aim to admit as many inference requests as possible. We propose exact and approximate solutions to the problem of inference offloading in MECs. We also consider dynamic task offloading for inference requests, and devise an online algorithm that can be adapted in real time. The proposed algorithms are evaluated through large-scale simulations and using a real world test-bed implementation. The experimental results demonstrate that the empirical performance of the proposed algorithms outperform their theoretical counterparts and other similar heuristics reported in literature.

关键词： Artificial intelligence Cloud computing 5G mobile communication Base stations Task analysis Mobile handsets Heuristic algorithms Inference offloading mobile edge clouds approximation and online algorithms

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Affinity-Aware VNF Placement in Mobile Edge Clouds via Leveraging GPUs

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IEEE TRANSACTIONS ON COMPUTERS 2021年第12期70卷 2234-2248页

作者： Xu, Zichuan Zhang, Zhiheng Lui, John C. S. Liang, Weifa Xia, Qiufen Zhou, Pan Xu, Wenzheng Wu, Guowei Dalian Univ Technol Sch Software Dalian 116621 Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116621 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Australian Natl Univ Res Sch Comp Sci Canberra ACT 2601 Australia Dalian Univ Technol Int Sch Informat Sci & Engn Dalian 116621 Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Hubei Engn Res Ctr Big Data Secur Wuhan 430074 Peoples R China Sichuan Univ Dept Comp Network & Commun Chengdu 610000 Sichuan Peoples R China

Mobile edge computing becomes a promising technology to mitigate the latency of various cloud services. In addition, network function virtualization (NFV) has been shown a great potential in reducing the operational cost of cloud services while enhancing the flexibility of virtual network function deployments, by implementing dedicated hardware network functions as pieces of software in generic servers. Recently, the GPU acceleration has been investigated to speed up flow processing in virtual network functions (VNFs), by leveraging the parallelism of GPUs. VNFs that need accelerations prefer to stay at cloudlets (locations) equipped with GPUs. However, little attention has been paid for the VNF placement that takes into account GPU-affinity in cloudlets of mobile edge clouds. In this paper, we consider the affinity-aware throughput maximization problem in a mobile edge cloud via leveraging the parallelism on GPUs for user requests with VNF requirements. We consider two types of affinities in the VNF placement: The soft-affinity that allows VNFs to be executed by either CPUs or GPUs in cloudlets;and the hard-affinity that only allows VNFs to be placed to the GPUs of a specified set of cloudlets. We formulate two corresponding VNF placement problems in a mobile edge cloud. Specifically, we first propose an exact solution to the soft-affinity throughput maximization problem by formulating an Integer Linear Program (ILP). We then propose an efficient algorithm for the problem, by proposing a randomized algorithm with a provable approximation ratio for the hard-affinity-aware throughput maximization problem and extending the proposed approximation algorithm to the soft-affinity throughput maximization problem. Furthermore, assuming that user requests arrive into the mobile edge cloud one by one without the knowledge of future arrivals, we devise an online algorithm with a good competitive ratio for this dynamic hard-affinity-aware throughput maximization problem. Finall

关键词： Cloud computing Graphics processing units Throughput approximation algorithms Heuristic algorithms Acceleration Switches VNF placement GPU affinity mobile edge clouds approximation and online algorithms

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Scheduling to maximize participation

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THEORETICAL COMPUTER SCIENCE 2008年第2-3期402卷 142-155页

作者： Caragiannis, Ioannis Kaklamanis, Christos Kanellopoulos, Panagiotis Papaioannou, Evi Univ Patras Res Acad Comp Technol Inst Rion 26500 Greece Univ Patras Dept Comp Engn & Informat Rion 26500 Greece

We study a problem of scheduling client requests to servers. Each client has a particular latency requirement at each server and may choose either to be assigned to some server in order to get serviced provided that her latency requirement is met, or not to participate in the assignment at all. From a global perspective, in order to optimize the performance of such a system, one would aim to maximize the number of clients that participate in the assignment. However, clients may behave selfishly in the sense that, each of them simply aims to participate in an assignment and get serviced by some server where her latency requirement is met with no regard to overall system performance. We model this selfish behavior as a strategic game, show how to compute pure Nash equilibria efficiently, and assess the impact of selfishness on system performance. We also show that the problem of optimizing performance is computationally hard to solve, even in a coordinated way, and present efficient approximation and online algorithms. (C) 2008 Elsevier B.V. All rights reserved.

关键词： scheduling Nash equilibria approximation and online algorithms competitive analysis

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Scheduling to maximize participation

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2nd Symposium on Trustworthy Global Computing

ISBN: (纸本)9783540753339

关键词： scheduling Nash equilibria approximation and online algorithms competitive analysis

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