Edge Computing – EDGE 2019

Third International Conference, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25–30, 2019, Proceedings

herausgegeben von: Tao Zhang, Dr. Jinpeng Wei, Liang-Jie Zhang

Verlag: Springer International Publishing

Buchreihe : Lecture Notes in Computer Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book constitutes the proceedings of the Third International Conference on Edge Computing, EDGE 2019, held in San Diego, CA, USA, in June 2019.
The 5 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 14 submissions. The contributions deal with the latest fundamental advances in the state of the art and practice of edge computing, identifying emerging research topics and defining the future of edge computing.

Inhaltsverzeichnis

Frontmatter

Characterization of IoT Workloads

Abstract

Workload characterization is a fundamental step in carrying out performance and Quality of Service engineering studies. The workload of a system is defined as the set of all inputs received by the system from its environment during one or more time windows. The characterization of the workload entails determining the nature of its basic components as well as a quantitative and probabilistic description of the workload components in terms of both the arrival process, event counts, and service demands. Several workload characterization studies were presented for a variety of domains, except for IoT workloads. This is precisely the main contribution of this paper, which also presents a capacity planning study based on one of the workload characterizations presented here.

Uma Tadakamalla, Daniel A. Menascé

Latency Control for Distributed Machine Vision at the Edge Through Approximate Computing

Abstract

Multicamera based Deep Learning vision applications subscribe to the Edge computing paradigm due to stringent latency requirements. However, guaranteeing latency in the wireless communication links between the cameras nodes and the Edge server is challenging, especially in the cheap and easily available unlicensed bands due to the interference from other camera nodes in the system, and from external sources. In this paper, we show how approximate computation techniques can be used to design a latency controller that uses multiple video frame image quality control knobs to simultaneously satisfy latency and accuracy requirements for machine vision applications involving object detection, and human pose estimation. Our experimental results on an Edge test bed indicate that the controller is able to correct for up to 164% degradation in latency due to interference within a settling time of under 1.15 s.

Anjus George, Arun Ravindran

Energy-Aware Capacity Provisioning and Resource Allocation in Edge Computing Systems

Abstract

Energy consumption plays a key role in determining the cost of services in edge computing systems and has a significant environmental impact. Therefore, minimizing the energy consumption in such systems is of critical importance. In this paper, we address the problem of energy-aware optimization of capacity provisioning and resource allocation in edge computing systems. The main goal is to provision and allocate resources such that the net profit of the service provider is maximized, where the profit is the difference between the aggregated users’ payments and the total operating cost due to energy consumption. We formulate the problem as a mixed integer linear program and prove that the problem is NP-hard. We develop a heuristic algorithm to solve the problem efficiently. We evaluate the performance of the proposed algorithm by conducting an extensive experimental analysis on problem instances of various sizes. The results show that the proposed algorithm has a very low execution time and is scalable with respect to the number of users in the system.

Tayebeh Bahreini, Hossein Badri, Daniel Grosu

Stackelberg Game-Theoretic Spectrum Allocation for QoE-Centric Wireless Multimedia Communications

Abstract

Multimedia Quality of Experience (QoE) is a predominant factor that drives customer satisfaction and user experience in the future wireless networks. This paper proposes a Stackelberg game theoretic spectrum allocation approach for QoE-centric wireless multimedia communication rather than the traditional data traffic. Here, we introduce the cost of utilizing the spectrum as a factor in the utility of the service provider and the client device. Both service provider and client devices are assumed rational and selfishly look to maximize their utility in a non-cooperative manner. Stackelberg game is used to formulate the interaction between the service provider and the client device, and to derive the Nash Equilibrium for the utility maximization problem. The paper proves existence of a Stackelberg game solution such that the utility of both client device and the service provider is maximized. The simulation results demonstrate that QoE and fairness can be achieved by the proposed game-theoretic spectrum allocation scheme.

Krishna Murthy Kattiyan Ramamoorthy, Wei Wang, Kazem Sohraby

Intrusion Detection at the Network Edge: Solutions, Limitations, and Future Directions

Abstract

The low-latency, high bandwidth capabilities promised by 5G, together with the diffusion of applications that require high computing power and, again, low latency (such as videogames), are probably the main reasons—though not the only one—that have led to the introduction of a new network architecture: Fog Computing, that consists in moving the computation services geographically close to where computing is needed. This architectural shift moves security and privacy issues from the Cloud to the different layers of the Fog architecture. In this scenario, IDSs are still necessary, but they need to be contextualized in the new architecture. Indeed, while on the one hand Fog computing provides intrinsic benefits (e.g., low latency), on the other hand, it introduces new design challenges.

In this paper, we provide the following contributions: we analyze the possible IDS solutions that can be adopted within the different Fog computing tiers, together with their related deployment and design challenges; and, we propose some promising future directions, by taking into account the challenges left uncovered by the considered solutions.

Simone Raponi, Maurantonio Caprolu, Roberto Di Pietro

Volunteer Cloud as an Edge Computing Enabler

Abstract

The rapid increase in the number of devices connected to the Internet, due to the Internet of Things, demands new ways of processing data produced by the devices. Edge Computing is one of the solutions that tries to process data close to the origin, which is the edge of networks. Emerging cloud systems, such as volunteer clouds, can also be used towards the processing of data produced by IoT devices. This paper proposes a Volunteer Computing as a Service (VCaaS) based Edge Computing infrastructure. The paper addresses the architectural design of the proposed system together with its research and technical challenges.

Tessema M. Mengistu, Abdullah Albuali, Abdulrahman Alahmadi, Dunren Che

Backmatter

Titel: Edge Computing – EDGE 2019
herausgegeben von: Tao Zhang
Dr. Jinpeng Wei
Liang-Jie Zhang
Verlag: Springer International Publishing
Electronic ISBN: 978-3-030-23374-7
Print ISBN: 978-3-030-23373-0
DOI: https://doi.org/10.1007/978-3-030-23374-7

Springer Professional

Über dieses Buch

Inhaltsverzeichnis

Frontmatter

Characterization of IoT Workloads

Latency Control for Distributed Machine Vision at the Edge Through Approximate Computing

Energy-Aware Capacity Provisioning and Resource Allocation in Edge Computing Systems

Stackelberg Game-Theoretic Spectrum Allocation for QoE-Centric Wireless Multimedia Communications

Intrusion Detection at the Network Edge: Solutions, Limitations, and Future Directions

Volunteer Cloud as an Edge Computing Enabler

Backmatter

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