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  1. Proceedings of the Workshop on Mobile Edge Communications (MECOMM '17)
  2. RAERA: A Robust Auctioning Approach for Edge Resource Allocation
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Virtualization and Programmability in Mobile Wireless Networks: Architecture and Resource Management
Mobility-based Proactive Multicast for Seamless Mobility Support in Cellular Network Environments
Geo-partitioning of MEC Resources
Information-Centric Mobile Edge Computing for Connected Vehicle Environments: Challenges and Research Directions
Optimization of Webpage Downloading Performance with Content-aware Mobile Edge Computing
RAERA: A Robust Auctioning Approach for Edge Resource Allocation
Wi-Stitch: Content Delivery in Converged Edge Networks
Realization of CDMA-based IoT Services with Shared Band Operation of LTE in 5G
SDLB: A Scalable and Dynamic Software Load Balancer for Fog and Mobile Edge Computing
Managing Data in Computational Edge Clouds

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RAERA: A Robust Auctioning Approach for Edge Resource Allocation

Content Provider ACM Digital Library
Author Koll, David Fu, Xiaoming Prasad, Abhinandan S. Arumaithurai, Mayutan
Abstract In edge computing, content and service providers aim at enhancing user experience by providing services closer to the user. At the same time, infrastructure providers such as access ISPs aim at utilizing their infrastructure by selling edge resources to these content and service providers. In this context, auctions are widely used to set a price that reflects supply and demand in a fair way. In this work, we propose RAERA, the first robust auction scheme for edge resource allocation that is suitable to work with the market uncertainty typical for edge resources---here, customers typically have different valuation distribution for a wide range of heterogeneous resources. Additionally, RAERA encourages truthful bids and allows the infrastructure provider to maximize its break-even profit. Our preliminary evaluations highlight that REARA offers a time dependent fair price. Sellers can achieve higher revenue in the range of 5%-15% irrespective of varying demands and the buyers pay up to 20% lower than their top bid amount.
Starting Page 49
Ending Page 54
Page Count 6
File Format PDF
ISBN 9781450350525
DOI 10.1145/3098208.3098217
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-08-09
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Robust optimization Mobile edge computing Pricing Auctions
Content Type Text
Resource Type Article
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