关键词： Information-centric networking   mobile edge computing   video multicast   wireless network virtualization  

摘要： To support the massive content delivery, a novel virtualized heterogeneous network framework is proposed to enable content transcoding, caching, and multicast. In addition, a virtual multi-resources allocation problem is formulated to jointly optimize the utilities of computing, caching, and communication for the proposed framework. Simulations are conducted to show the effectiveness of the proposed scheme.

关键词： Mobile edge computing  

摘要： In the evolution toward fifth-generation networks, Mobile Edge Computing (MEC) is an emerging paradigm, conceived to meet the ever-increasing computational demands of mobile applications. Within the access range of mobile devices, the MEC technique promises the enablement of efficient Mobile Cloud Computing services. In an MEC system, virtual machine (VM) migration is a key issue; VM migration is the process of moving a VM from an edge node to another edge node. To improve service quality and system performance, the VM migration method has a dual focus on the MEC system's computation and communication resources. In this study, we formulate the VM migration problem as a one-on-one contract game model and develop a learning-based price control mechanism to effectively handle the MEC's resource. By using the game methodology and learning process, our approach is able to capture the dynamics of MEC systems, and it interacts continually with an unknown system environment. Finally, extensive simulation results are provided to demonstrate the capability of the proposed approach in achieving, with respect to existing MEC schemes, both higher resource utilization and system throughput, as well as reduced service drop ratio and reduced service delay.

关键词： Mobile edge computing   resource allocation   fairness   system throughput maximization   minimum rate requirement   POWER ALLOCATION   MULTIUSER OFDM   NETWORKS   RADIO   SUBCARRIER   CHANNEL  

摘要： Communication resource allocation is important for improving the performance of users in mobile edge computing (MEC) scenarios. In existing studies, the users in the MEC system typically suffer from unfair resource allocation, which results in the inefficient resource utilization and degraded user performance. To address this challenge, in this paper we propose a fair resource allocation approach to maximize the overall network throughput, under the constraint of each mobile user's minimum transmission rate. We formulate the problem as a fair Nash bargaining resource allocation game, and the existence and uniqueness of the solution to this game model are analyzed. By adopting the time-sharing variable, we obtain the near optimal bargaining resource allocation strategy for the mixed integer nonlinear programming optimization. The user's priority is further considered in the iterative implementation of the proposed algorithm by considering the time delay constraint of users. Simulation results show that the proposed scheme outperforms existing methods in terms of resource allocation fairness and overall system throughput.

关键词： Cloud Computing   Internet Of Things   Mobile Computing   Network Servers   Wireless LAN   Wireless Sensor Networks   Central Data Server   User Location   Data Collection   End User Devices   Fog Computing   Mobile Edge Computing   Io T Perspective   Processing Appetites   Personal Computing   Cloud Servers   Cloud Computing   Io T Idea   Sensors   Ubiquitous Computing   Pervasive Computing   High Speed Networks   Interconnections   Everything As A Service   Cloudlets   Cloud Computing   Sensors   Servers   Edge Computing   Wireless Fidelity   Wireless Sensor Networks   Internet   Mobile Cloud Computing   Cloudlet   Edge Computing   Computation Offload   Internet  

摘要： When the requirements and processing appetites grew sufficiently in the era of personal computing, users began to think about cloud servers. This initiated the growth of cloud computing and set up the era of Everything-as-a-Service. When the IoT idea spread, lots of sensors and smaller devices were introduced for ubiquitous and pervasive computing. The simplest way to collect and process all this data was to connect the devices to the cloud. This required high-speed networks and interconnections. However, no matter how fast they could go, engineers understood that there's no need to transfer all the data through links to the central data server, and that it would be much more efficient to distribute smaller servers in front of the central data server and closer to the location of the user. This began the evolution of pushing data collection and processing back to end-user devices, as a new computing approach called edge computing, which appears in different forms such as fog computing, cloudlets, and mobile edge computing.

关键词： Caching   computation offloading   mobile edge computing   energy efficient   ARCHITECTURE   NETWORKS  

摘要： While augment reality applications are becoming popular, more and more data-hungry and computation-intensive tasks are delay-sensitive. Mobile edge computing is expected to an effective solution to meet the low latency demand. In contrast to previous work on mobile edge computing, which mainly focus on computation offloading, this paper introduces a new concept of task caching. Task caching refers to the caching of completed task application and their related data in edge cloud. Then, we investigate the problem of joint optimization of task caching and offloading on edge cloud with the computing and storage resource constraint. We formulate this problem as mixed integer programming which is hard to solve. To solve the problem, we propose efficient algorithm, called task caching and offloading (TCO), based on alternating iterative algorithm. Finally, the simulation experimental results show that our proposed TCO algorithm outperforms others in terms of less energy cost.

关键词： Mobile crowdsensing   mobile edge computing   MCS functional architecture   MEC reference architecture   INTERNET   IOT  

摘要： Mobile crowdsensing (MCS) is a human-driven Internet of Things service empowering citizens to observe the phenomena of individual, community, or even societal value by sharing sensor data about their environment while on the move. Typical MCS service implementations utilize cloud-based centralized architectures, which consume a lot of computational resources and generate significant network traffic, both in mobile networks and toward cloud-based MCS services. Mobile edge computing (MEC) is a natural choice to distribute MCS solutions by moving computation to network edge, since an MEC-based architecture enables significant performance improvements due to the partitioning of problem space based on location, where real-time data processing and aggregation is performed close to data sources. This in turn reduces the associated traffic in mobile core and will facilitate MCS deployments of massive scale. This paper proposes an edge computing architecture adequate for massive scale MCS services by placing key MCS features within the reference MEC architecture. In addition to improved performance, the proposed architecture decreases privacy threats and permits citizens to control the flow of contributed sensor data. It is adequate for both data analytics and real-time MCS scenarios, in line with the 5G vision to integrate a huge number of devices and enable innovative applications requiring low network latency. Our analysis of service overhead introduced by distributed architecture and service reconfiguration at network edge performed on real user traces shows that this overhead is controllable and small compared with the aforementioned benefits. When enhanced by interoperability concepts, the proposed architecture creates an environment for the establishment of an MCS marketplace for bartering and trading of both raw sensor data and aggregated/processed information.

关键词： Heterogeneous networks   matching theory   mobile edge computing   resource allocation   WIRELESS CELLULAR NETWORKS   OPTIMIZATION   RADIO  

摘要： In this paper, we propose an optimization framework of computation offloading and resource allocation for mobile-edge computing with multiple servers. Concretely, we aim to minimize the system wide computation overhead by jointly optimizing the individual computation decisions, transmit power of the users, and computation resource at the servers. The crux of the problem lies in the combinatorial nature of multi-user offloading decisions, the complexity of the optimization objective, and the existence of inter cell interference. To overcome these difficulties, we adopt a suboptimal approach by splitting the original problem into two parts: 1) computation offloading decision and 2) joint resource allocation. To enable distributed computation offloading, two matching algorithms are investigated. Moreover, the transmit power of offloading users is found using a bisection method with approximate inter-cell interference, and the computation resources allocated to offloading users is achieved via the duality approach. Simulation results validate that the proposed framework can significantly improve the percentage of offloading users and reduce the system overhead with respect to the existing schemes. Our results also show that the proposed framework performs close to the centralized heuristic algorithm with a small optimality gap.

关键词： Internet of Thing   low power wide area network   mobile edge computing   remote software update   trusted connection   DATA-TRANSMISSION   INTERNET   ARCHITECTURE   THINGS  

摘要： The Internet of Things (IoT) leads to intelligent services by collecting information from tiny sensor devices. In recent years, storage-less sensing devices have been used to implement IoT services. They depend on delivered software from a network server to operate service functions, and IoT services are based on collected user information. Therefore, it is important to maintain trusted connections during software delivery or data transmission. If a network connection is untrustworthy, stable data transmission cannot be achieved. Untrustworthy data connections cause many problems in IoT services. Therefore, this paper proposes a software update method in trusted connection of IoT networking. The proposed method employs a low-power wide area network (LPWAN) as a long-range IoT networking technology and uses a mobile edge cloud to improve computing efficiency in an access network that consists of IoT devices with insufficient resources. In the proposed method, the mobile edge cloud is integrated into a gateway and processes sensing data and remote software updates of LPWAN. IoT devices can receive software functions from the mobile edge cloud. The proposed method analyzes statistical information about connections in an access network and determines the LPWAN trusted connections. Then, software updates can be performed over the trusted connection. Using trusted connections leads to an increased packet delivery rate and reduced transmission energy consumption. The proposed method is compared with currently available systems through computer simulation and the proposed method's efficiency is validated.

关键词： Cloudlets   deep learning   Internet of Things (IoT)   mobile edge computing   mobile healthcare   preventive healthcare   traffic classification   traffic prediction   survey   fog computing   cloud computing   multimedia applications   smart cities   BIG DATA   WIRELESS SENSOR   CHRONIC DISEASES   SIGNAL STRENGTH   MOBILE-HEALTH   AREA NETWORKS   INTERNET   TECHNOLOGIES   CHALLENGES   THINGS  

摘要： Smart city advancements are driving massive transformations of healthcare, the largest global industry. The drivers include increasing demands for ubiquitous, preventive, and personalized healthcare, to be provided to the public at reduced risks and costs. Mobile cloud computing could potentially meet the future healthcare demands by enabling anytime, anywhere capture and analyses of patients' data. However, network latency, bandwidth, and reliability are among the many challenges hindering the realization of next-generation healthcare. This paper proposes a ubiquitous healthcare framework, UbeHealth, that leverages edge computing, deep learning, big data, high-performance computing (HPC), and the Internet of Things (IoT) to address the aforementioned challenges. The framework enables an enhanced network quality of service using its three main components and four layers. Deep learning, big data, and HPC are used to predict network traffic, which in turn are used by the Cloudlet and network layers to optimize data rates, data caching, and routing decisions. Application protocols of the traffic flows are classified, enabling the network layer to meet applications' communication requirements better and to detect malicious traffic and anomalous data. Clustering is used to identify the different kinds of data originating from the same application protocols. A proof of concept UbeHealth system has been developed based on the framework. A detailed literature review is used to capture the design requirements for the proposed system. The system is described in detail including the algorithmic implementation of the three components and four layers. Three widely used data sets are used to evaluate the UbeHealth system.

关键词： Mobile edge computing   Internet of Thing   service provisioning   service composition   RADIO  

摘要： The increasing number of Internet of Thing (IoT) devices and services makes it convenient for people to sense the real world and makes optimal decisions or complete complex tasks with them. However, the latency brought by unstable wireless networks and computation failures caused by constrained resources limit the development of IoT. A popular approach to solve this problem is to establish an IoT service provision system based on a mobile edge computing (MEC) model. In the MEC model, plenty of edge servers are placed with access points via wireless networks. With the help of cached services on edge servers, the latency can be reduced, and the computation can be offloaded. The cache services must be carefully selected so that many requests can by satisfied without overloading resources in edge servers. This paper proposes an optimized service cache policy by taking advantage of the composability of services to improve the performance of service provision systems. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of these IoT services.