关键词： framework   mobile cloud computing   mobile edge computing   offloading  

摘要： With mobile devices' limited resources, computing the increasing amount of data locally puts strain on their batteries, processors, storage and bandwidth making it necessary to offload computeheavy tasks to a close proximity edge cloud server. A research field known as Mobile Cloud Computing (MCC) has emerged to address the shortcomings of mobile devices by means of offloading. However, cost of operation and maintenance of cloud servers alongside the mobile nature of devices that could potentially cause connectivity issues are some of the most prominent challenges that face an efficient offloading process.

关键词： Mobile edge computing   Computation offloading   CMOP   Fine-grained task offloading scheduling   NSGA-II   Parallel computing  

摘要： In mobile edge computing (MEC), task offloading can solve the problem of resource constraints on mobile devices effectively, but it is not always optimal to offload all the computation subtasks of an application. In order to solve the energy and delay of fine-grained offloading in MEC, this paper proposes a novel offloading scheduling based on multi-user fine-grained for IoT. In order to optimize the execution location and scheduling order of subtasks, we regard the computation task as a directed acyclic graph (DAG). Considering the energy consump -tion and delay, the computation offloading is considered as a constrained multi-objective optimization problem (CMOP), and then an improved NSGA-II algorithm is proposed to solve the CMOP. The proposed algorithm can realize local and edge parallel processing, which can effectively reduce the delay and energy consumption. Finally, extensive experiments show that the proposed algorithm is significantly better than the related algorithms in terms of energy and delay. Compared with no-segmentation and related segmentation algorithms, the proposed algorith -m can achieve up 10-50% reduction about energy saving. And the proposed algorithm can make the optimal decision in practical applications.

关键词： Task analysis   Processor scheduling   Scheduling   Servers   Cloud computing   Optimization   Energy consumption   Cuckoo search   mobile edge computing   multiobjective optimization   task scheduling  

摘要： 6G wireless networks have raised increasing attention with computation-sensitive services such as AI Internet of things (AIoT) and mobile augmented reality/virtual reality (AR/VR) applications. Mobile edge computing (MEC) provides rich computation resources for user equipments (UE) at the edge of networks. Aided by MEC servers, computation-intensive applications that are commonly modeled as Directed Acyclic Graphs (DAG) can be performed locally and offloaded to MEC servers to enhance execution efficiency. However, it is a key issue to efficiently provide low latency with limited energy. In this paper, we investigate a multiobjective task scheduling problem in MEC-aided 6G network. Then, an improved multiobjective cuckoo search (IMOCS) algorithm is proposed to deal with a DAG-based task scheduling problem, which aims to reduce the execution latency and energy consumption of UE. Particularly, the proposed IMOCS algorithm is based on the single-objective cuckoo search algorithm and Pareto dominance. An external archive is used to record nondominated solutions, whose update strategy improves the quality of solutions by the aid of fast nondominated sorting and crowding distance sorting. Simulation results demonstrate that IMOCS algorithm outperforms other four benchmark algorithms, which can provide optimal task scheduling policy for MEC severs in 6G networks.

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： 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： 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.