Topic
Edge computing
About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.
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TL;DR: A dynamic network virtualization technique to integrate the network resources, and furtherly design a cooperative computation offloading (CCO) model to achieve parallel computation in STNs is proposed.
Abstract: The high-speed satellite-terrestrial network (STN) is an indispensable alternative in future mobile communication systems. In this article, we first introduce the architecture and application scenarios of STNs, and then investigate possible ways to implement mobile edge computing (MEC) technique for QoS improvement in STNs. We propose satellite MEC (SMEC), in which a user equipment without a proximal MEC server can also enjoy MEC services via satellite links. We propose a dynamic network virtualization technique to integrate the network resources, and furtherly design a cooperative computation offloading (CCO) model to achieve parallel computation in STNs. Task scheduling models in SMEC are discussed in detail, and an elemental simulation is conducted to evaluate the performance of the proposed CCO model in SMEC.
223 citations
TL;DR: This article presents a mobility-aware hierarchical MEC framework for green and low-latency IoT, and deploys a game theoretic approach for computation offloading in order to optimize the utility of the service providers while also reducing the energy cost and the task execution time of the smart devices.
Abstract: IoT, a heterogeneous interconnection of smart devices, is a great platform to develop novel mobile applications. Resource constrained smart devices, however, often become the bottlenecks to fully realize such developments, especially when it comes to intensive-computation-oriented and low-latency-demanding applications. MEC is a promising approach to address such challenges. In this article, we focus on MEC applications for IoT, and address energy efficiency as well as offloading performance of such applications in terms of end-user experience. In this regard, we present a mobility-aware hierarchical MEC framework for green and low-latency IoT. We deploy a game theoretic approach for computation offloading in order to optimize the utility of the service providers while also reducing the energy cost and the task execution time of the smart devices. Numerical results indicate that the proposed scheme does brings significant enhancement in both energy efficiency and latency performance of MEC applications for IoT.
222 citations
TL;DR: The experiments show that the ECC-based healthcare system provides a better user experience and optimizes the computing resources reasonably, as well as significantly improving in the survival rates of patients in a sudden emergency.
221 citations
TL;DR: Fogs can largely improve the performance of smart city analytics services than cloud only model in terms of job blocking probability and service utility.
Abstract: Analysis of Internet of Things (IoT) sensor data is a key for achieving city smartness. In this paper a multitier fog computing model with large-scale data analytics service is proposed for smart cities applications. The multitier fog is consisted of ad-hoc fogs and dedicated fogs with opportunistic and dedicated computing resources, respectively. The proposed new fog computing model with clear functional modules is able to mitigate the potential problems of dedicated computing infrastructure and slow response in cloud computing. We run analytics benchmark experiments over fogs formed by Rapsberry Pi computers with a distributed computing engine to measure computing performance of various analytics tasks, and create easy-to-use workload models. Quality of services (QoS) aware admission control, offloading, and resource allocation schemes are designed to support data analytics services, and maximize analytics service utilities. Availability and cost models of networking and computing resources are taken into account in QoS scheme design. A scalable system level simulator is developed to evaluate the fog-based analytics service and the QoS management schemes. Experiment results demonstrate the efficiency of analytics services over multitier fogs and the effectiveness of the proposed QoS schemes. Fogs can largely improve the performance of smart city analytics services than cloud only model in terms of job blocking probability and service utility.
219 citations
TL;DR: This paper introduces a new concept of task caching, and proposes efficient algorithm, called task caching and offloading (TCO), based on alternating iterative algorithm, which outperforms others in terms of less energy cost.
Abstract: 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.
219 citations