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IEEE/IFIP Network Operations and Management Symposium

作者： Sharma, Rahul Chan, Chien Aun Leckie, Christopher Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia Univ Melbourne Dept Elect & Elect Engn Melbourne Vic Australia

ISBN: (纸本)9781665406017

multi-access edge computing (MEC) provides high network bandwidth and ultra-low latency response time but also inherits the security vulnerabilities of cloud computing to Distributed Denial of Service (DDoS) attacks. To guard MEC nodes from such attacks, previous work has proposed the use of Collaborative Intrusion Detection Systems (CIDS) as a prime candidate for attack detection. Our previous work looked at Centralised and Distributed CIDS architectures and identified some shortcomings in them. In this paper, we address these shortcomings by presenting a purely edge-based novel Hybrid CIDS architecture, and its improvement using an exponential threshold mechanism. We assess the performance of our novel architectures through experimentation on a real-world worm dataset based on detection accuracy, bandwidth utilisation, and CPU and memory usage, and compare against benchmark performance results for the Centralised and the Distributed CIDS architectures proposed previously. Following this, we present the performance metrics of our novel architectures during node failure events and outline their resilience to such failures.

关键词： 5G multi-access edge computing Intrusion Detection Cybersecurity DHT

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Preference-Aware Computation Offloading for IoT in multi-access edge computing Using Probabilistic Model Checking 19th

Preference-Aware Computation Offloading for IoT in Multi-acc...

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19th International Conference on Quantitative Evaluation Systems (QEST)

作者： Ray, Kaustabha Banerjee, Ansuman Indian Stat Inst Adv Comp & Microelect Unit Kolkata India

ISBN: (纸本)9783031163364;9783031163357

A key cornerstone of multi-access edge computing (MEC) is an offloading policy utilized to determine whether to execute computation tasks on IoT devices or to offload the tasks to MEC servers for processing. In this work, we propose a Probabilistic Model Checking based offloading policy catering to device user preferences. We model the interactions between the various components of the MEC environment using a Turn-Based Stochastic multi-Player Game (SMG). We present experiments on practical scenarios on data gathered from a test-bed setup with benchmark applications to show the benefits of an adaptive preferenceaware approach over conventional approaches in MEC offloading.

关键词： multi-access edge computing Probabilistic Model Checking

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A Socio-Temporal Cache Prefetching Policy for the multi-access edge computing Architecture 14

A Socio-Temporal Cache Prefetching Policy for the Multi-acce...

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IEEE Latin-American Conference on Communications (LATINCOM)

作者： Marques de Oliveira, Cleomar Marcio Moraes, Igor Monteiro Albuquerque, Celio de Menezes Lima, Jose Jeronimo Univ Fed Fluminense Inst Comp Lab MidiaCom Niteroi RJ Brazil Univ Vale Rio dos Sinos Programa Posgrad Adm Porto Alegre RS Brazil

ISBN: (纸本)9781665482257

In this paper, we propose a cache prefetching policy for the multi-access edge computing architecture (MEC) that considers the temporal and social behaviors of mobile users. The key point of our policy is to prefetch new contents based on the popularity of their categories and not on the popularity of each content individually. Our goal is to increase the Quality of Experience (QoE) of mobile users by increasing the cache hit ratio. Simulation results show that HASSAN provides a hit ratio up to 65% for a cache size of 16 MB.

关键词： Cache Hierarchy Caching multi-access edge computing Prefetching Quality of Experience Wireless access Networks

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Deep reinforcement learning based controller placement and optimal edge selection in SDN-based multi-access edge computing environments

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JOURNAL OF PARALLEL AND DISTRIBUTED computing 2024年 193卷

作者： Li, Chunlin Liu, Jun Ma, Ning Zhang, Qingzhe Zhong, Zhengwei Jiang, Lincheng Jia, Guolei Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430063 Peoples R China Xly Salvationdata Informat Secur Technol Co Ltd Data Recovery Key Lab Sichuan Prov Neijiang 641000 Peoples R China Natl Key Lab Special Vehicle Design & Mfg Integrat Baotou 014030 Peoples R China Natl Univ Def Technol Coll Adv Interdisciplinary Studies Changsha 410073 Hunan Peoples R China

multi-access edge computing (MEC) can provide computility close to the clients to decrease response time and enhance Quality of Service (QoS). However, the complex wireless network consists of various network hardware facilities with different communication protocols and Application Programming Interface (API), which result in the MEC system's high running costs and low running efficiency. To this end, Software-defined networking (SDN) is applied to MEC, which can support access to massive network devices and provide flexible and efficient management. The reasonable SDN controller scheme is crucial to enhance the performance of SDN-assisted MEC. At First, we used the Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM) model to predict the network traffic to calculate the load. Then, the optimization objective is formulated by ensuring the load balance and minimizing the system cost. Finally, the Deep Reinforcement Learning (DRL) algorithm is used to obtain the optimal value. Based on the controller placement algorithm ensuring the load balancing, the dynamical edge selection method based on the Channel State Information (CSI) is proposed to optimize the task offloading, and according to CSI, the strategy of task queue execution is designed. Then, the task offloading problem is modeled by using queuing theory. Finally, dynamical edge selection based on Lyapunov's optimization is introduced to get the model solution. In the experiment studies, the assessment method evaluated the performance of two sets of baseline algorithms, including SAPKM, the PSO, the K-means, the LADMA, the LATA, and the OAOP. Compared to the baseline algorithms, the proposed algorithms can effectively reduce the average communication delay and total system energy consumption and improve the utilization of the SDN controller.

关键词： multi-access edge computing SDN Controller placing edge selection

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Online optimal service caching for multi-access edge computing: A constrained multi-Armed Bandit optimization approach

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COMPUTER NETWORKS 2024年 246卷

作者： Chu, Weibo Zhang, Xiaoyan Jia, Xinming Lui, John C. S. Wang, Zhiyong Northwestern Polytech Univ Xian Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

In order to fully exploit the power of multi -access edge computing, services need to be cached at the network edge in an adaptive and responsive way to accommodate the high system dynamics and uncertainty. In this paper, we study the online service caching problem in MEC, with the goal to minimize users' perceived latency while at the same time, ensure the rate of tasks processed by the edge server is no less than a preset threshold. We model the problem with a Constrained stochastic multi -Armed Bandit formulation, and propose a simpl e \/ yet effective online caching algorithm called Constrained Confidence Bound (CCB). CCB achieves O ( T ln T ) bounds on both regret and violation of the constraint, and is able to achieve a good balance between them. We further consider the scenario when there is cost (i.e., delay) due to service switches, and propose two service switch -aware caching algorithms - Explore -First (EF) and Successive Elimination -based (SE) caching, 2 1 together with a novel sam pling s cheme. We prove that EF achieves O ( T \/ 3 (ln T ) 3 ) bound on regret and violation, whereas SE achieves O ( T ln T ) and converges significantly faster. Lastly, we conduct extensive simulations to evaluate our algorithms and results demonstrate their superior performance over baselines.

关键词： multi-access edge computing Service selection Service caching Constrained multi-armed bandit Online algorithm

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Dynamic Air-Ground Collaboration for multi-access edge computing

Dynamic Air-Ground Collaboration for Multi-Access Edge Compu...

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IEEE International Conference on Communications (ICC)

作者： Wang, Shaoyu Yang Huang Clerckx, Bruno Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing Peoples R China Imperial Coll London Dept Elect & Elect Engn London England

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

Unmanned aerial vehicles (UAVs) are expected to improve the quality of services for the fifth-generation (5G) and beyond networks. Nevertheless, in the context of multi-access edge computing (MEC), a key pillar for meeting 5G key performance indicators, state-of-the-art design suffers from difficulty in matching time/spatial-varying communication/computation demands with distributed resources in highly dynamic air-ground integrated networks. To handle this issue, this paper proposes a joint online trajectory planning and offloading scheduling scheme based on reinforcement learning (RL), such that a UAV and base stations in a multi-cell network can dynamically and collaboratively offer edge computing services. We formulate the decision-making on trajectory planning/offloading scheduling as mutually embedded Markov decision processes, so as to avoid exponentially increasing joint state/action spaces and non-cooperative decision-making. In order to learn the policies for decision-making, novel RL algorithms are proposed based on deep Q-network and the kernel method, respectively. It is shown that the learned policy is able to make the joint trajectory planning and offloading scheduling adaptive to dynamic computation demands. Benefiting from this, the air-ground collaborative MEC can significantly outperform the terrestrial-only MEC in terms of the average backlog of newly produced computational task bits.

关键词： Unmanned aerial vehicle multi-access edge computing computation offloading reinforcement learning

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Traffic Data Scheduling of Frequent Application Sets for Task Offloading in multi-access edge computing

Traffic Data Scheduling of Frequent Application Sets for Tas...

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IEEE International Conference on Communications (ICC)

作者： Hu, Yifeng Qiu, Tie Chi, Jiancheng Yang, Xuan Wang, Zimu Li, Wenguang Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China State Grid Elect Commerce Co Ltd Beijing Peoples R China State Grid Ecommerce Technol Co Ltd Tianjin Peoples R China

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

Frequently task offloading in the multi-access edge computing (MEC) system takes up a lot of network resources, which leads to serious network congestion problems. To deal with such problems, applying data pricing strategies in task offloading to schedule traffic data is perceived as a promising solution for Internet service providers (ISPs). However, the traditional application-oriented data pricing strategies do not consider user satisfaction of the task executing process, resulting in lower ISP profit. In this paper, a novel scheme named Appset Usage Patterns-Aware task offloading scheme (AUPA) is proposed to alleviate the tension between traffic data supply and user satisfaction. We mine the sequential patterns in frequent application sets to extract temporal association rules. Then these association rules are used to design a smart data pricing strategy to guide the task offloading decision. Finally, we formulate the ISP's profit maximization problem as a nonlinear programming (NLP) problem based on partial offloading, and we simulate the scheduling processuse using the Stackelberg game model. The performance of our solution is evaluated in terms of ISP's profit, consumers' surplus, capacity utilization, and traffic efficiency. The results show that our scheme significantly improves the ISP's profit by about 20% while ensuring capacity constraints compared with other baseline schemes.

关键词： task offloading multi-access edge computing data pricing sequential pattern mining

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Joint task offloading and resource allocation in vehicle-assisted multi-access edge computing

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COMPUTER COMMUNICATIONS 2021年 177卷 77-85页

作者： Xue, Jianbin Hu, Qingchun An, Yaning Wang, Lu Lanzhou Univ Technol Sch Comp & Commun Lanzhou Peoples R China

multi-access edge computing (MEC) has significant advantages in improving resource efficiency of Internet of Things (IoT) and 5G networks, however its limited resources cannot meet the demand of data communication and computation capability during off-peak time. Incentivizing intelligent vehicles with idle computation resources as vehicle edge nodes (VENs) to provide computation offloading for nearby user equipments (UEs) is an appealing idea. Thus, we propose a vehicle-assisted MEC (VMEC) paradigm, where tasks can be offloaded to MEC server and VENs. In this paper, we first establish a differentiated pricing model based on different states of resources and a dynamic incentive model according to the demands of UEs. Then, we formulate a Stackelberg game between UEs and MEC service provider (MEC SP) to obtain the optimal offloading strategy and pricing scheme. A gradient-based resource allocation iteration algorithm (GRAIA) is designed for the Nash equilibrium solution. Finally, considering the matching between UEs and vehicles, we present a reverse auction-based task scheduling algorithm (RATSA) to choose VENs. The simulation results demonstrate that the proposed scheme can achieve significant performance improvement and is superior to the existing schemes in improving system utility.

关键词： multi-access edge computing Internet of vehicles Resource allocation Dynamic incentives Stackelberg game

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Throughput Maximization of Offloading Tasks in multi-access edge computing Networks for High-Speed Railways

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第9期70卷 9525-9539页

作者： Xu, Junyi Wei, Zhenchun Lyu, Zengwei Shi, Lei Han, Jianghong Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Anhui Peoples R China Engn Res Ctr Safety Crit Ind Measurement & Contro Minist Educ Hefei 230009 Anhui Peoples R China

With the development of high-speed railways (HSRs), people's lives have become more convenient. At the same time, people are demanding more data services on trains in terms of wireless communications and mobile computation. Recently, multi-access edge computing (MEC), a new computing paradigm, has the great potential to improve the user experience of computing services by placing the cloud infrastructures closer to end-users so that users can offload their computation-intensive and delay-sensitive tasks to the edge servers. In this paper, we concentrate on finding proper data routing paths for each offloaded task and each process result, with the objective of maximizing the network throughput by serving as many tasks as possible while meeting the MEC network resource constraints and the user demand constraints within a finite time horizon of equal time slots. It is challenging because of the unspecific data volume of an offloaded task and handovers of task offloading procedures that may frequently occur. Thus, we first investigate the impact of handovers in uplinks and downlinks. Then we propose a graph-based model to portray the resource availability in the network. Furthermore, a heuristic algorithm is devised, which finds a set of single-source-single-destination delay-constrained shortest paths in a series of auxiliary graphs. Finally, we perform an empirical evaluation of the proposed algorithm by simulations. The simulation results show that the proposed algorithm could achieve a higher network throughput than the other two benchmark heuristics.

关键词： Task analysis Handover Delays Rail transportation Heuristic algorithms edge computing Computational modeling multi-access edge computing High-speed railways Delay-constrained shortest path problem Network resource allocation Admission control

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Service Placement and User Assignment in multi-access edge computing with Base-Station Failure 30

Service Placement and User Assignment in Multi-Access Edge C...

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30th IEEE/ACM International Symposium on Quality of Service (IWQoS)

作者： Taka, Haruto He, Fujun Oki, Eiji Kyoto Univ Kyoto Japan

ISBN: (纸本)9781665468244

multi-access edge computing (MEC) enables users to exploit the resources of cloud computing at a base station (BS) in proximity to the users where an MEC server is hosted. While we have advantage of being able to communicate with low latency and small network load in MEC networks, the resources in BSes are limited. One challenge is where to provide users with services from to make efficient use of resources. Furthermore, to enhance the reliability of MEC system, the case that a BS fails needs to be considered. This paper proposes a service placement and user assignment model with preventive start-time optimization against a single BS failure in MEC networks. The proposed model preventively determines the service placement and user assignment in each BS failure pattern to minimize the worst-case penalty which is the largest penalty among all failure patterns. We formulate the proposed model as an integer linear programming problem. We introduce two algorithms, one is the greedy algorithm with allocation upgrade and the other is with allocation upgrade and preemption, to solve the problem. The results show that the introduced algorithms obtain a solution with the smaller worst-case penalty than the benchmark in a practical time.

关键词： multi-access edge computing service placement user assignment preventive start-time optimization BS failure

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