关键词： Edge-of-things   Body area networks   Virtual machine   Portfolio optimization   ADMM   Mobile edge computing   Dynamic pricing  

摘要： Edge-of-Things (EoT)-based healthcare services are forthcoming patient-care amenities related to autonomic and persuasive healthcare, where an EoT broker usually works as a middleman between the Healthcare Service Consumers (HSC) and Computing Service Providers (CSP). The computing service providers are the edge computing service providers (ECSP) and cloud computing service provider (CCSP). Sensor observations from a patient's body area networks (BAN) and patients' medical and genetic historical data are very sensitive and have a high degree of interdependency. It follows that EoT based patient monitoring systems or applications are tightly coupled and require obstinate synchronization. Therefore, this paper proposes a portfolio optimization solution for the selection of virtual machines (VMs) of edge and/or cloud computing service providers. The dynamic pricing for an EoT computation service is considered by the EoT broker for optimal VM provisioning in an EoT environment. The proposed portfolio optimization solution is compared with the traditional certainty equivalent approach. As the portfolio optimization is a centralized solution approach, this paper also proposes an alternating direction method of multipliers (ADMM) based distributed provisioning method for the healthcare data in the EoT computing environment. A comparative study shows the cost-effective provisioning for the healthcare data through portfolio optimization and ADMM methods over the traditional certainty equivalent and greedy approach, respectively. (C) 2018 The Author(s). Published by Elsevier Inc.

关键词： 5 G Mobile Communication   Augmented Reality   Internet   Long Term Evolution   Mobile Computing   Mobile Radio   Mobile Users   AR VR Technologies   Research Community   Tactile Internet Applications   Massive Computational Capability   High Communication Bandwidth   Ultra Low Latency   Fifth Generation Networks   Edge Caching   Mobile Edge Computing   AR VR Applications   Mobile AR VR   Augmented Reality   Virtual Reality Applications   Wireless Mobile Networks   Long Term Evolution Networks   5 G Technologies   Tactile Internet   Edge Computing   Videos   Haptic Interfaces   Cloud Computing   5 G Mobile Communication   Servers  

摘要： As a result of increasing popularity of augmented reality and virtual reality (AR/VR) applications, there are significant efforts to bring AR/VR to mobile users. Parallel to the advances in AR/VR technologies, tactile internet is gaining interest from the research community. Both AR/VR and tactile internet applications require massive computational capability, high communication bandwidth, and ultra-low latency that cannot be provided with the current wireless mobile networks. By 2020, long term evolution (LTE) networks will start to be replaced by fifth generation (5G) networks. Edge caching and mobile edge computing are among the potential 5G technologies that bring content and computing resources close to the users, reducing latency and load on the backhaul. The aim of this survey is to present current state-of-the-art research on edge caching and computing with a focus on AR/VR applications and tactile internet and to discuss applications, opportunities and challenges in this emerging field.

关键词： Computation offloading   mobile edge computing   Nash equilibria   decentralized algorithms  

摘要： Offloading computation to a mobile cloud is a promising solution to augment the computation capabilities of mobile devices. In this paper, we consider selfish mobile devices in a dense wireless network, in which individual mobile devices can offload computations through multiple access points or through the base station to a mobile cloud so as to minimize their computation costs. We provide a game theoretical analysis of the problem, prove the existence of pure strategy Nash equilibria, and provide an efficient decentralized algorithm for computing an equilibrium. For the case when the cloud computing resources scale with the number of mobile devices, we show that all improvement paths are finite. Furthermore, we provide an upper bound on the price of anarchy of the game, which serves as an upper bound on the approximation ratio of the proposed decentralized algorithms. We use simulations to evaluate the time complexity of computing Nash equilibria and to provide insights into the price of anarchy of the game under realistic scenarios. Our results show that the equilibrium cost may be close to optimal, and the convergence time is almost linear in the number of mobile devices.

关键词： Mobile edge computing   vehicular networks   computation offloading   resource allocation  

摘要： The emergence of computation-intensive and delay-sensitive vehicular applications poses a great challenge for individual vehicles with limited computation resources. Mobile edge computing (MEC) is a new paradigm shift that can enhance vehicular services through computation offloading. However, the high mobility of vehicles will affect offloading performance. In this paper, we investigate the vehicular user (VU) computation overhead minimization problem in MEC-enabled vehicular networks by jointly optimizing the computation and communication resources' allocation (transmit power and uploading time for communication, and the offloading ratio and local CPU frequency for computation). This optimization problem is nonconvex and difficult to solve directly. To deal with this issue, we first transform the original problem into an equivalent one. Then, we decompose the equivalent problem into a two-level problem. In addition, we develop a low-complexity algorithm to obtain the optimal solution. The numerical results demonstrate that the proposed algorithm can significantly outperform benchmark algorithms in terms of computation overhead.

关键词： computation offloading   software adaptation   mobile edge computing   application refactoring   Android  

摘要： Mobile edge computing(MEC) provides a fresh opportunity to significantly reduce the latency and battery energy consumption of mobile applications. It does so by enabling the offloading of parts of the applications on mobile edges, which are located in close proximity to the mobile devices. Owing to the geographical distribution of mobile edges and the mobility of mobile devices, the runtime environment of MEC is highly complex and dynamic. As a result, it is challenging for application developers to support computation offloading in MEC compared with the traditional approach in mobile cloud computing, where applications use only the cloud for offloading. On the one hand, developers have to make the offloading adaptive to the changing environment, where the offloading should dynamically occur among available computation *** the other hand, developers have to effectively determine the offloading scheme each time the environment changes. To address these challenges, this paper proposes an adaptive framework that supports mobile applications with offloading capabilities in MEC. First, based on our previous study(DPartner), a new design pattern is proposed to enable an application to be dynamically offloaded among mobile devices, mobile edges,and the cloud. Second, an estimation model is designed to automatically determine the offloading *** this model, different parts of the application may be executed on different computation nodes. Finally, an adaptive offloading framework is implemented to support the design pattern and the estimation model. We evaluate our framework on two real-world applications. The results demonstrate that our approach can aid in reducing the response time by 8%–50% and energy consumption by 9%–51% for computation-intensive applications.

关键词： Cloud computing   Edge computing   Internet of Things   Market research   Computer architecture   Licenses   Libraries   Fog computing   cloud computing   edge computing   Internet of Things   mobile cloud computing   mobile edge computing  

摘要： In the last few years, Internet of Things, Cloud computing, Edge computing, and Fog computing have gained a lot of attention in both industry and academia. However, a clear and neat definition of these computing paradigms and their correlation is hard to find in the literature. This makes it difficult for researchers new to this area to get a concrete picture of these paradigms. This work tackles this deficiency, representing a helpful resource for those who will start next. First, we show the evolution of modern computing paradigms and related research interest. Then, we address each paradigm, neatly delineating its key points and its relation with the others. Thereafter, we extensively address Fog computing, remarking its outstanding role as the glue between IoT, Cloud, and Edge computing. In the end, we briefly present open challenges and future research directions for IoT, Cloud, Edge, and Fog computing.

关键词： Mobile edge computing   healthcare applications   5G mobile communications   blockchain   transmission delay   computation delay  

摘要： Mobile edge computing (MEC) is pervasive to support high-speed end-to-end data transmission and computation for 5th generation (5G) mobile communications by offloading computation tasks to the cloud at the edge of a cellular base station. In a cellular network for healthcare applications, we must consider the different priorities of data transmission and computation by referring to the emergency levels of patients. A critical issue of determining the allocation of wireless resources among users is the authenticity of their priorities (i.e., emergency levels). Without authenticating the users' priorities, a user who advocates a higher emergency level can allocate more wireless resources, leading to the insufficient wireless resources to the users who are actually in high priorities. In this paper, we investigate the optimization of end-to-end delay of wireless users in a cellular network with the authenticity of their priorities by blockchain consensus and propose an algorithm of allocating communication and computation resources to minimize the delay of data transmission and computation. The numerical results illustrate that our proposed algorithm is capable of dramatically reducing the end-to-end delay under the various scenarios of healthcare applications.

关键词： Mobile edge computing   Mobile switching center   Reinforcement learning   Resources and computing capabilities  

摘要： Mobile edge computing (MEC) is a recent technology that brings the computing resources too closer to the network edge with reduced response rate in computationally intensive applications. With increasing mobile devices with limited computing and wireless capabilities, the response time of Mobile Switching Center (MSC) deteriorates. Therefore it is necessary to jointly manage the computing and wireless resources in order to avoid deterioration due to limited capabilities. Also, the joint optimisationtechniques limits its capability due to computational complexity and incompatible devices. To avoid all these limitations, in this paper, we propose a joint optimisation model that suitably optimizes the cloud and wireless resources in an optimal way, thereby reducing its response time. An equilibrium model is presented that aims to reduce the trade-offs between the resources and computing capabilities. A state-reward model is developed to maintain the equilibrium between the resources and computational capabilities. To improve the resources allocation capability, we use the reinforcement learning for better allocation of resources in MEC. The performance is compared with other existing methods to test the effectiveness of the model. © 2019, Institute of Advanced Scientific Research, Inc. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) provides cloud-computing services for mobile devices to offload intensive computation tasks to the physically proximal MEC servers. In this paper, we consider a multiserver system where a single mobile device asks for computation offloading to multiple nearby servers. We formulate this offloading problem as the joint optimization of computation task assignment and CPU frequency scaling, in order to minimize a tradeoff between task execution time and mobile energy consumption. The resulting optimization problem is combinatorial in essence, and the optimal solution generally can only be obtained by exhaustive search with extremely high complexity. Leveraging the Markov approximation technique, we propose a light-weight algorithm that can provably converge to a bounded near-optimal solution. The simulation results show that the proposed algorithm is able to generate near-optimal solutions and outperform other benchmark algorithms.

关键词： Computation offloading   Energy optimization   Mobile edge computing   Simulated annealing  

摘要： With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to provide near computing and storage capabilities to smart mobile devices. In addition, mobile devices are most of the time battery dependent and energy constrained while they are characterized by their limited processing and storage capacities. Accordingly, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstances, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, we must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and that retains a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate our solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. Moreover, our solution performs the offloading decisions within an acceptable and feasible timeframes. Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved.