关键词： mobile edge computing   wireless energy transfer   wireless powered mobile edge computing  

摘要： To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes.

关键词： Mobile Edge Computing   Fog Computing   Classical virtualization   Network function Virtualization (NFV)  

摘要： With the advent of Internet of Things (IoT) connecting billions of mobile and stationary devices to serve real-time applications, cloud computing paradigms face some significant challenges such as high latency and jitter, non-supportive location-awareness and mobility, and non-adaptive communication types. To address these challenges, edge computing paradigms, namely Fog Computing (FC), Mobile Edge Computing (MEC) and Cloudlet, have emerged to shift the digital services from centralized cloud computing to computing at edges. In this article, we analyze cloud and edge computing paradigms from features and pillars perspectives to identify the key motivators of the transitions from one type of virtualized computing paradigm to another one. We then focus on computing and network virtualization techniques as the essence of all these paradigms, and delineate why virtualization features, resource richness and application requirements are the primary factors for the selection of virtualization types in IoT frameworks. Based on these features, we compare the state-of-the-art research studies in the IoT domain. We finally investigate the deployment of virtualized computing and networking resources from performance perspective in an edge-cloud environment, followed by mapping of the existing work to the provided taxonomy for this research domain. The lessons from the reviewed are that the selection of virtualization technique, placement and migration of virtualized resources rely on the requirements of IoT services (i.e., latency, scalability, mobility, multi-tenancy, privacy, and security). As a result, there is a need for prioritizing the requirements, integrating different virtualization techniques, and exploiting a hierarchical edge-cloud architecture. (c) 2020 Elsevier Inc. All rights reserved.

关键词： Internet of things   5G   mobile edge computing   software-defined networking   device-to-device   scalability  

摘要： The Internet of things (IoT) is the third evolution of the traditional Internet that enables interaction and communication among machines. Many IoT platforms and networks have been developed, and recently, market sectors have started to develop specific IoT applications and services. Integrating heterogeneous IoT networks with the existing ones, mainly with the cellular networks, is a great demand. IoT represents one of the main use cases of the fifth-generation (5G) cellular system as announced by the 3rd Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU). Integrating IoT networks with 5G networks face many challenges related to dense deployment and a massive number of expected connected devices. Thus, IoT network availability and scalability are the main requirements that should be achieved. To this end, this work provides a framework for integrating heterogeneous IoT networks with the 5G networks. The proposed system considers dense deployment and system scalability and availability requirements as announced by ITU and 3GPP. Our proposed structure deploys three main communication paradigms;mobile edge computing (MEC), device-to-device communications (D2D), and software-defined networking (SDN). Our proposed system is evaluated over a reliable environment for various deployment scenarios, and the results validate the proposed structure. The proposed IoT/5G reduces the percentage of blocked tasks by an average of 30% than other traditional IoT networks. This increases the overall system availability and scalability since IoT networks can have more devices and tasks than existing IoT networks. Furthermore, our proposed structure reduces the overall consumed energy by an average of 20% than existing IoT networks, which is an effective metric for IoT networks.

关键词： Computation offloading   Task caching   Energy-efficient   Mobile edge computing   Q learning   Deep Q Network  

摘要： Computation offloading at mobile edge computing (MEC) servers can mitigate the resource limitation and reduce the communication latency for mobile devices. Thereby, in this study, we proposed an offloading model for a multi-user MEC system with multi-task. In addition, a new caching concept is introduced for the computation tasks, where the application program and related code for the completed tasks are cached at the edge server. Furthermore, an efficient model of task offloading and caching integration is formulated as a nonlinear problem whose goal is to reduce the total overhead of time and energy. However, solving these types of problems is computationally prohibitive, especially for large-scale of mobile users. Thus, an equivalent form of reinforcement learning is created where the state spaces are defined based on all possible solutions and the actions are defined on the basis of movement between the different states. Afterwards, two effective Q-learning and Deep-Q-Network-based algorithms are proposed to derive the near-optimal solution for this problem. Finally, experimental evaluations verify that our proposed model can substantially minimize the mobile devices' overhead by deploying computation offloading and task caching strategy reasonably.

关键词： Mobile edge computing   Tasks offloading   Data quality   Optimal stopping theory   Sequential decision making  

摘要： An important use case of the Mobile Edge Computing (MEC) paradigm is task and data offloading. Computational offloading is beneficial for a wide variety of mobile applications on different platforms including autonomous vehicles and smartphones. With the envision deployment of MEC servers along the roads and while mobile nodes are moving and having certain tasks (or data) to be offloaded to edge servers, choosing an appropriate time and an ideally suited MEC server to guarantee the Quality of Service (QoS) is challenging. We tackle the data quality-aware offloading sequential decision making problem by adopting the principles of Optimal Stopping Theory (OST) to minimize the expected processing time. A variety of OST stochastic models and their applications to the offloading decision making problem are investigated and assessed. A performance evaluation is provided using simulation approach and real world data sets together with the assessment of baseline deterministic and stochastic offloading models. The results show that the proposed OST models can significantly minimize the expected processing time for analytics task execution and can be implemented in the mobile nodes efficiently. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing  

摘要： With the growing demand for latency-critical and computation-intensive Internet of Things (IoT) services, the IoT-oriented network architecture, mobile edge computing (MEC), has emerged as a promising technique to reinforce the computation capability of the resource-constrained IoT devices. To exploit the cloud-like functions at the network edge, service caching has been implemented to reuse the computation task input/output data, thus effectively reducing the delay incurred by data retransmissions and repeated execution of the same task. In a multiuser cache-assisted MEC system, users’ preferences for different types of services, possibly dependent on their locations, play an important role in joint design of communication, computation and service caching. In this paper, we consider multiple representative locations, where users at the same location share the same preference profile for a given set of services. Specifically, by exploiting the location-aware users’ preference profiles, we propose joint optimization of the binary cache placement, the edge computation resource and the bandwidth allocation to minimize the expected sum-energy consumption, subject to the bandwidth and the computation limitations as well as the service latency constraints. To effectively solve the mixed-integer non-convex problem, we propose a deep learning (DL)-based offline cache placement scheme using a novel stochastic quantization based discrete-action generation method. The proposed hybrid learning framework advocates both benefits from the model-free DL approach and the model-based optimization. The simulations verify that the proposed DL-based scheme saves roughly 33% and 6.69% of energy consumption compared with the greedy caching and the popular caching, respectively, while achieving up to 99.01% of the optimal performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： The body health plays an important metric in people's everyday life, and it directly determines whether people have the ability to preferably contribute to the society. In fact, the physical training is a universal sport to enhance the body health. Therefore, the evaluation and analysis of physical training become particularly significant. With the rapid development and emerging of new techniques and networking paradigms, the traditional offline physical training evaluation and analysis cannot be performed well. Instead, this paper uses Mobile Edge Computing (MEC) and Software-Defined Networking (SDN) to implement the evaluation and analysis of physical training, shortened for MSPT, where MEC is the new computing technique and SDN is the new networking paradigm. The proposed MSPT includes two parts. At first, the physical training data from different mobile devices are migrated into the edge server for computing according to the current condition, in which the game theory is used to complete the task scheduling. Then, SDN is responsible for the global scheduling in the centralized control manner, in which the multigranularity scheduling strategy is used to handle the traffic between the SDN controller and edge computing server. The experiments are driven by OMNet, including three aspects of evaluation, i.e., task offloading of MEC, traffic scheduling of SDN, and performance analysis of physical training, and the results show that the proposed MSPT has better performance than the corresponding baselines.

关键词： Mobile edge computing   Task offloading   Energy and deadline-aware   Deep reinforcement learning (DRL)   Actor-critic framework   K-nearest neighbor (K-NN)  

摘要： With the development of the wireless network, increasing mobile applications are emerging and receiving great popularity. These applications cover a wide area, such as traffic monitoring, smart homes, real-time vision processing, objective tracking, and so on, and typically require computation-intensive resources to achieve a high quality of experience. Although the performance of mobile devices (MDs) has been continuously enhanced, running all the applications on a single MD still causes high energy consumption and latency. Fortunately, mobile edge computing (MEC) allows MDs to offload their computation-intensive tasks to proximal eNodeBs (eNBs) to augment computational capabilities. However, the current task offloading schemes mainly concentrate on average-based performance metrics, failing to meet the deadline constraint of the tasks. Based on the deep reinforcement learning (DRL) approach, this paper proposes an Energy-aware Task Offloading with Deadline constraint (DRL-E2D) algorithm for a multi-eNB MEC environment, which is to maximize the reward under the deadline constraint of the tasks. In terms of the actor-critic framework, we integrate the action representation into DRL-E2D to handle the large discrete action space problem, i.e., using the low-complexity k-nearest neighbor as an approximate approach to extract optimal discrete actions from the continuous action space. The extensive experimental results show that DRL-E2D achieves better performance than the comparison algorithms on all parameter settings, indicating that DRL-E2D is robust to the state changes in the MEC environment.

关键词： Offloading   Mobile edge computing   Machine learning   Neural networks   Hidden markov model   Regression  

摘要： The fast growth of under developing internet-based technologies has been leading to propose promising methods to handle the heterogeneous massive volume of data produced by pervasive smart equipments such as handy mobile devices. Thanks to the mentioned technologies, these mobile devices can run critical business/entertainment applications such as Augmented Reality, Virtual Reality, vehicular networks, and media streaming. However, due to such devices' inherent limitations, some emerging computation environments such as Mobile Edge Computing have been introduced to achieve some essential requirements such as low latency, low energy consumption, and low cost. In the literature, offloading is a technique to transfer the burden of the mobile devices' work incurred by running applications' requests to these computation environments. On the other hand, exploring the computation environment to find the most efficient place to execute such requests is challenging work to achieve. In addition, different researches have been proposed to cope with the management problems of the offloading criterion. In this paper, an autonomous computation offloading framework is proposed to address some challenges related to time-intensive and resource-intensive applications. However, to the best of the authors' knowledge, the proposed autonomous framework has not been explored as a control model for self management in the computation offloading criterion. Besides, to cope with the large dimension of the offloading decision-making problem, different simulations including Deep Neural Networks, multiple linear regression, hybrid model, and Hidden Markov Model as the planning module of the mentioned autonomous methodology have been conducted. Simulation results show that the proposed hybrid model can appropriately fit the problem with near-optimal accuracy regarding the offloading decision-making, the latency, and the energy consumption predictions in the proposed self-management framework.

关键词： Computation offloading   Mobile edge computing   Context‐   aware  

摘要： Along with the rise of mobile devices, the resource demands of respective applications grow. However, mobile devices have limited computation and storage resources. Computation offloading is a prominent solution to overcome the limitations. However, current approaches fail to address the complexity caused by quickly and constantly changing context conditions in mobile edge computing. In this paper, the contexts are gathered and processed using Monitor-Analysis-Plan-Execution (MAPE) loop for making offloading decisions. The results show that the proposed context-aware approach outperforms local computing and offloading without considering context approaches in terms of delay, energy consumption, network usage, and execution cost.