关键词： Software-defined networks   Fault tolerance   Distributed control plane   Stochastic network calculus   Mobile edge computing  

摘要： Mobile edge computing (MEC) is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the users. With the advent of the next-generation mobile networks, researchers have turned their attention to various aspects of edge computing in an effort to leverage the new capabilities offered by 5G. So, the integration of software defined networking (SDN) and MEC techniques was seriously considered to facilitate the orchestration and management of Mobile Edge Hosts (MEH). Edge clouds can be installed as an interface between the local servers and the core to provide the required services based on the known concept of the SDN networks. Nonetheless, the problem of reliability and fault tolerance will be of great importance in such networks. The paper introduced a dynamic architecture that focuses on the end-to-end mobility support required to maintain service continuity and quality of service. This paper also presents an SDN control plane with stochastic network calculus (SNC) framework to control MEC data flows. In accordance with the entrance processes of different QoS-class data flows, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Compared to other solutions investigated in the literature, the proposed approach exhibits a significant increase in the throughput distributed over the active links of mobile edge hosts. It also proved that the outage index and the system's aggregate data rate can be effectively improved by up to 32%. (C) 2022 Published by Elsevier B.V.

关键词： Mobile edge computing   computation offloading   network uncertainties   energy efficiency  

摘要： Computation offloading is indispensable for mobile edge computing (MEC). It uses edge resources to enable intensive computations and save energy for resource-constrained devices. Existing works generally impose strong assumptions on radio channels and network queue sizes. However, practical MEC systems are subject to various uncertainties rendering these assumptions impractical. In this paper, we investigate the energy-efficient computation offloading problem by relaxing those common assumptions and considering intrinsic uncertainties in the network. Specifically, we minimize the worst-case expected energy consumption of a local device when executing a time-critical application modeled as a directed acyclic graph. We employ the extreme value theory to bound the occurrence probability of uncertain events. To solve the formulated problem, we develop an c-bounded approximation algorithm based on column generation. The proposed algorithm can efficiently identify a feasible solution that is less than (1 + epsilon) of the optimal one. We implement the proposed scheme on an Android smartphone and conduct extensive experiments using a real-world application. Experiment results corroborate that it will lead to lower energy consumption for the client device by considering the intrinsic uncertainties during computation offloading. The proposed computation offloading scheme also significantly outperforms other schemes in terms of energy saving.

关键词： Cost optimization   Facility location   Mobile edge computing   Player assignment   Server provisioning   Social interactivity  

摘要： Distributed interactive applications (DIAs), like immersive virtual reality and multiplayer cloud games, where players team-up and form social communities to participate in collaborative virtual events, dominate the modern cloud multimedia industry. Still, due to the massive workload, intensive graphics processing and delay intolerance, conventional cloud-only models are increasingly becoming ineffective. Thankfully, with the shift towards Mobile Edge Computing, game providers can leverage existing cellular infrastructure to deploy edge servers that supply gaming services in a highly accessible fashion. One fundamental challenge, that naturally arises, is the discovery of the most suitable player-to-edge server assignment strategy, that achieves a balanced trade-off between conflicting objectives that refer to high social interactivity on the one hand and low-cost server provisioning on the other. In this article, the core properties of social interactivity for gaming DIAs are investigated through formal formulation under diverse social aspects and strict assignment rules, respecting, in the meantime, the provisioning cost and constraints of the edge servers. We call this the "Social Interactivity oriented Edge Allocation"(SIEA) problem and prove its NP-hardness. Based on our theoretical analysis, we present SIEA-H, a two-phase heuristic algorithm to efficiently assign players to servers and then iteratively refine the edge allocation strategy. SIEA-H is evaluated using comprehensive trace-driven simulations. The results demonstrate how it surpasses the baseline and state-of-the-art assignment alternatives in reducing the total cost, especially as the number of servers and their capacity grows, or the coverage area expands, while for players it exhibits the highest admission rate and the best overall resource management.

关键词： Mobile edge computing  

摘要： Recently, mobile edge computing (MEC) has been widely applied into Internet of Things (IoT) networks, which has attracted a lot of attention from researchers. A critical challenge in the MEC-aided IoT networks is that the performance analysis is often complicated, where it is quite difficult for us to obtain some analytical or closed-form solution to the performance analysis, such as outage probability and bit error rate. This has been the bottleneck of the development of MEC-aided IoT networks. To address this challenge, we deeply investigate the Chebyshev-Gauss approximation method and derive the analytical solution to implement this powerful and useful approximation. We then give several examples to show the effectiveness of the Chebyshev-Gauss approximation in the performance analysis for the MEC-aided IoT systems. The results in this work can serve as an important reference and reveal some important inherent mechanisms for the MEC-aided IoT networks.

关键词： Mobile edge computing   Service function chain   Instance reuse  

摘要： The combination of mobile edge computing and network function virtualization has led to the emergence of Virtualized Network Function (VNF) in a broader range of application scenarios. These latency-sensitive and highly dynamic services can be provided by combining multiple VNFs into Service Function Chains (SFCs). However, existing work has conspicuously neglected that online placing SFC with instance reuse can significantly improve resource utilization and save initialization time, which requires considering both the dynamic distribution of required VNFs over time and resource constraints on the edge network. In this paper, we initiate the study of Online SFC placement combined with Instance Reuse. An OSIR algorithm is proposed to gain a tradeoff between service costs and users' quality of experience. The OSIR is designed based on deep reinforcement learning, which improves the system performance by maximizing the long-term cumulative reward. In OSIR, an SFC queue network is designed to extract the dynamic distribution of required VNFs over time, composed of memory space and the long short-term memory learning approach. The experimental results with real-world data traces show that OSIR can efficiently and effectively improve system performance and outperform the best result of all existing algorithms ranging from 17% to 26%.

关键词： Mobile edge computing   non-clairvoyant task offloading   randomised online task offloading  

摘要： In this paper, we consider non-clairvoyant task offloading for random tasks in mobile edge computing within the framework of combinatorial optimisation. For offline non-clairvoyant task offloading, we propose a non-clairvoyant task offloading algorithm, which is able to determine a task offloading strategy without knowing the amount of computation and communication of any task. For online non-clairvoyant task offloading, we propose a randomised online task offloading algorithm, which is able to make an offloading decision for an arrival task without knowing anything about future tasks and other tasks. For both algorithms, we analyse the probability of certain performance guarantee. We also demonstrate numerical data. To the best of the author's knowledge, this is the first paper which considers both offline and online non-clairvoyant task offloading in mobile edge computing, together with analytical results on performance guarantee with high probability.

关键词： distributed unsupervised learning   energy efficiency   mobile edge computing   task offloading  

摘要： Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud *** conserve energy as well as maintain quality of service,low time complexity algorithm is proposed to complete task offloading and server *** this paper,a multi-user with multiple tasks and single server scenario is considered for small network,taking full account of factors including data size,bandwidth,channel state ***,we consider a multi-server scenario for bigger network,where the influence of task priority is taken into *** jointly minimize delay and energy cost,we propose a distributed unsupervised learning-based offloading framework for task offloading and server *** exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization *** further reduce time cost and achieve near-optimal performance,we use convolutional neural networks to process mass data based on fully connected *** results show that the proposed algorithm performs better than other offloading schemes,which can generate near-optimal offloading decision timely.

关键词： Task analysis   Processor scheduling   Robustness   Upper bound   Servers   Edge computing   Linear programming   Mobile edge computing   robust offloading scheduling   submodular optimization  

摘要： In this paper, we study the problem of Robust offloading schEduling for mobIle edge computiNg (REIN), i.e., in the presence of uncertain offloading failures, how to determine an offloading schedule to minimize the overall latency of all computation-intensive tasks. We mathematically formulate the problem in the form of min-max robust optimization, based on the twice equivalent transformations of the scheduling problem that originally does not consider robustness. REIN is challenging to solve because the min-max robust objective is computationally intractable with existing approaches, and the monotonicity of the objective function is uncertain, even if we transform the objective into the popular max-min form by introducing an appropriate constant upper bound. To solve the above challenges, we first construct a constant upper bound and a monotone modular function to approximate the transformed max-min objective function, and then propose a computationally feasible solution with provable performance bound. Moreover, given the fact of the weak computation ability of users in practical, we construct a tighter constant upper bound and a monotone submodular approximation function, and propose a feasible solution with possibly improved performance bound. Extensive results show that, given a maximum number of offloading failures, our proposed algorithms outperform three benchmark algorithms, and approach the optimum at small time costs.

关键词： Internet of Things   Task analysis   Bandwidth   Reliability   Virtual machining   Cloud computing   Collaboration   Bandwidth consumption   Internet of Things (IoT) application   mobile edge computing   reliability level   task offloading  

摘要： Internet of Things (IoT) devices have become an integral part of our lives and are increasingly used in almost every field. Subsequently, there are a large number of latency-sensitive IoT applications (e.g., face recognition and autonomous driving) targeted for mobile edge computing environments. These IoT applications are often split into multiple collaborative tasks and offloaded onto containers or virtual machines (VMs) with certain failure rates and recovery rates. If these containers or VMs are not deployed in the same edge servers, the bandwidth resources of edge clouds must be consumed to transfer data. These factors increase the completion time of IoT applications to different degrees, and then affect their reliability level. Therefore, there exists equilibrium between the reliability level and bandwidth consumption. In this article, we investigate the equilibrium of minimizing the bandwidth consumption of IoT applications while maximizing the reliability level of these IoT applications during task offloading. We propose a multiobjective optimization problem, and transform it to a single-objective optimization problem. Furthermore, we introduce two efficient approaches to acquire two near-optimal solutions. The results of simulation experiments demonstrate that our proposed approaches can observably enhance the reliability level and reduce the bandwidth consumption of IoT applications compared with other related approaches. Meanwhile, we also make a comparative analysis of our proposed approaches.

关键词： Edge computing   Green cloudlet   Task offloading   Mobile edge computing  

摘要： Edge computing places cloudlets with high computational capabilities near mobile devices to reduce the latency and network congestion encountered in cloud server-based task offloading. However, many cloudlets are required in such an edge computing network, leading to a tremendous increase in carbon emissions of computing networks globally. This increase in carbon emission envisages the need to employ green energy resources to power these cloudlets. This need has led to the concept of Green Cloudlet Networks (GCNs). But GCNs must deal with the problem of the unpredictability of green energy available to them while optimizing the performance (in terms of latency) delivered to the mobile user. This paper proposes a novel task-assignment called Green Energy and Latency Aware Task Assignment (Ge-LATA) for GCNs to address this issue. The primary aim of Ge-LATA is to optimize the latency and the green energy consumed in processing the offloaded tasks from the mobile devices. In this GCN, the cloudlets are connected in a network to process the incoming tasks cooperatively to ensure load-balancing at the cloudlets. Ge-LATA considers various factors like the current load, available green energy, service rate offered by cloudlets, and the distance from the mobile user, leading to optimal decisions in terms of latency and green energy consumed. Simulations are performed using the actual solar insolation data taken from the NREL database. Ge-LATA is tested with other offloading schemes for latency in processing the offloaded tasks and green energy consumed under different solar insolation scenarios in these simulations. Simulation results show that Ge-LATA achieves up to 31.87% of reduction in the latency while ensuring up to 50.15% of reduction in the energy consumption than other comparable task-assignment ***, Ge-LATA suggests that it leads to an optimal task assignment by considering the various factors mentioned above during the task assignment process. Thus, Ge-LA