Decentralized Resource Auctioning for Latency-Sensitive Edge Computing

Abstract

The prevalence of Internet of Things (IoT) in contemporary settings has induced systems composed of heterogeneous devices, computing infrastructures, and cloud services. New paradigms have emerged where computational resources are managed closer to IoT end-devices, within a general theme of decoupling from the cloud. This is because meeting application demands must occur at runtime, in the face of uncertainty and in a decentralized manner. Taking advantage of available resources closer to devices calls for novel resource allocation techniques that comply with latency, privacy and decentralization demands of IoT applications. To this end, we propose a novel decentralized resource management technique and accompanying technical framework for the deployment of latency-sensitive IoT applications on edge devices. Our technique is inspired from the functionality of an auction house and has two objectives; (i) find a deployment mapping for an arbitrary application, compliant with its individual resource requirements and latency constraints, (ii) facilitate privacy, as each device participates at their own will, based on its own availability and privacy preferences. Our approach ensures seamless deployment at runtime, assuming no designtime knowledge of device resources or network topology.

Survey on Placement Methods in the Edge and Beyond

“…1) Offloading in MEC: In terms of Platform components, the papers that tackle offloading decisions always involve terminals, e.g., IoT devices, mobile user equipment. However, we can make a distinction among the collected papers, whether they focus only on edge servers in MEC, e.g., [81], [85], [94], [112], [113], [115], [117], [154], [157]- [160], [173], [183], or they also consider the possibility to offload computational tasks to a central cloud, e.g., [101]- [104], [148], [151], [152]. Furthermore, one can find papers in the related literature that apply moving devices, e.g., UAVs [84], or robots [173], as edge nodes in order to position them to optimal locations anytime the terminals change their location.…”

“…One can find papers in which one of these two goals are set out, e.g., energy in [84], [85], [102], processing time in [94], [104], [115], [148], [152], [157], [173], and there are also related works in which the goals are targeted jointly, e.g., in [81], [103], [112], [113], [117], [183]. While the former goal aims at preserving the limited battery capacity of terminals, e.g., IoT sensors, mobile phones, [57], [60], [63], [69], [74], [78], [79], [81], [82], [84]- [87], [90], [93], [94], [98], [101]- [104], [112], [113], [115], [117], [130], [143], [144], [148], [151], [152], [154], [157]- [160], [1...…”

Computational Resource Allocation Strategy in a Public Blockchain Supported by Edge Computing

“…Meanwhile, the experience-weighted attraction (EWA) algorithm is introduced to reach Nash equilibrium. The authors in [13] proposed a novel edge computing offloading framework, which applies decentralized resource management strategy based on auction to perform resource allocation to ECPs. In order to intelligently manage decentralized applications in a blockchain network, Edgence (EDGe + intelligENCE) is proposed by [14] to realize self-governing of the edge devices.…”

“…In the process of CDA in this paper, we set φ = 0 and used equation (13) to determine the trade price, with δ = 0:5.…”

Artificial Intelligence for Securing IoT Services in Edge Computing: A Survey

“…Today's IoT services are mostly cloud-based and centralized so that all data processing and analyses have to be completed in cloud [41]. With the prosper of IoT, more IoT devices demanding low latency and high response spring up [42].…”