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 prior-dependent graph (PDG) construction method can achieve substantial performance, which can be deployed in edge computing module to provide efficient solutions for massive data management and applications in AIoT.
71 citations
TL;DR: This letter employs an unmanned aerial vehicle (UAV) as the edge computing server to execute offloaded tasks from the ground UEs and jointly optimize user association, UAV trajectory, and uploading power of each UE to maximize sum bits offloaded from all UEs to the UAV, subject to energy constraint.
Abstract: Mobile edge computing (MEC) provides computational services at the edge of networks by offloading tasks from user equipments (UEs). This letter employs an unmanned aerial vehicle (UAV) as the edge computing server to execute offloaded tasks from the ground UEs. We jointly optimize user association, UAV trajectory, and uploading power of each UE to maximize sum bits offloaded from all UEs to the UAV, subject to energy constraint of the UAV and quality of service (QoS) of each UE. To address the non-convex optimization problem, we first decompose it into three subproblems that are solved with integer programming and successive convex optimization methods respectively. Then, we tackle the overall problem by the multi-variable iterative optimization algorithm. Simulations show that the proposed algorithm can achieve a better performance than other baseline schemes.
70 citations
08 May 2017
TL;DR: This paper proposes an architecture for Orchestration for the Fog Computing environment, and developed a prototype to demonstrate some concepts, and discusses in this paper the implementation, and the tools chose, and their roles.
Abstract: The development of Fog Computing technology is crucial to address the challenges to come with the mass adoption of Internet Of Things technology, where the generation of data tends to grow at an unprecedented pace. The technology brings computing power to the surrounds of devices, to offer local processing, filtering, storage and analysis of data and control over actuators. Orchestration is a requirement of Fog Computing technology to deliver services, based on the composition of microservices. It must take into consideration the heterogeneity of the IoT environment and device's capabilities and constraints. This heterogeneity requires a different approach for orchestration, be it regarding infrastructure management, node selection and/or service placement. Orchestrations shall be manually or automatically started through event triggers. Also, the Orchestrator must be flexible enough to work in a centralized or distributed fashion. Orchestration is still a hot topic and can be seen in different areas, especially in the Service Oriented Architectures, hardware virtualization, in the Cloud, and in Network Virtualization Function. However, the architecture of these solutions is not enough to handle Fog Requirements, specially Fog's heterogeneity, and dynamics. In this paper, we propose an architecture for Orchestration for the Fog Computing environment. We developed a prototype to prof some concepts. We discuss in this paper the implementation, and the tools chose, and their roles. We end the paper with a discussion on performance indicators and future direction on the evaluation of non-functional aspects of the Architecture.
70 citations
TL;DR: This work model the problem of joint request offloading and resource scheduling (JRORS) as a mixed-integer nonlinear program to minimize the response delay of requests and proposes a multiple-objective optimization algorithm based on i- NSGA-II, referred to as MO-NSGA.
Abstract: In the era of 5G, with the increasing demands on computation and massive data traffic of the Internet of Things (IoT), mobile edge computing (MEC) and ultradense network (UDN) are considered to be two enabling and promising technologies, which result in the so-called ultradense edge computing (UDEC). Task offloading as an effective solution offers low latency and flexible computation for mobile users in the UDEC network. However, the limited computing resources at the edge clouds and the dynamic demands of mobile users make it challenging to schedule computing requests to appropriate edge clouds. To this end, we first formulate the transmitting power allocation (PA) problem for mobile users to minimize energy consumption. Using the quasiconvex technique, we address the PA problem and present a noncooperative game model based on subgradient (NCGG). Then, we model the problem of joint request offloading and resource scheduling (JRORS) as a mixed-integer nonlinear program to minimize the response delay of requests. The JRORS problem can be divided into two problems, namely, the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a double decision-making problem and propose a multiple-objective optimization algorithm based on i-NSGA-II, referred to as MO-NSGA. The simulation results show that NCGG can save the transmitting energy consumption and has a good convergence property, and MO-NSGA outperforms the existing approaches in terms of response rate and can maintain a good performance in a dynamic UDEC network.
70 citations
TL;DR: In this article, the authors proposed a dynamic network slicing framework for fog computing, in which a regional orchestrator coordinates workload distribution among local fog nodes, providing partitions/slices of energy and computational resources to support a specific type of service with certain quality-of-service guarantees.
Abstract: This paper studies fog computing systems, in which cloud data centers can be supplemented by a large number of fog nodes deployed in a wide geographical area Each node relies on harvested energy from the surrounding environment to provide computational services to local users We propose the concept of dynamic network slicing , in which a regional orchestrator coordinates workload distribution among local fog nodes, providing partitions/slices of energy and computational resources to support a specific type of service with certain quality-of-service guarantees The resources allocated to each slice can be dynamically adjusted according to service demands and energy availability A stochastic overlapping coalition-formation game is developed to investigate the distributed cooperation and joint network slicing between fog nodes under randomly fluctuating energy harvesting and workload arrival processes We observe that the overall processing capacity of the fog computing network can be improved by allowing fog nodes to maintain a belief function about the unknown state and the private information of other nodes An algorithm based on a belief-state partially observable Markov decision process is proposed to achieve the optimal resource slicing structure among all fog nodes We describe how to implement our proposed dynamic network slicing within the 3GPP network sharing architecture and evaluate the performance of our proposed framework using the real base station (BS) location data of a real cellular system with over 200 BSs deployed in the city of Dublin Our numerical results show that our framework can significantly improve the workload processing capability of fog computing networks In particular, even when each fog node can coordinate only with its closest neighbor, the total amount of workload processed by fog nodes can be almost doubled under certain scenarios
70 citations