Cross-Site Edge Framework for Location-Awareness Distributed Edge-Computing Applications

Nakata, Yu; Takai, Mayuko; Konoura, Hiroaki; Kinoshita, Masaharu

doi:10.1109/mobilecloud48802.2020.00017

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Chiu et al [11] proposed a server task allocation algorithm to obtain a short response time for clients, where the distributed tasks are allocated to multiple servers according to the communication latency to the servers and the tasks' processing times. In addition, CEF [12] provides an API for IoT application programmers to specify the geographical location of Docker containers running processes in a program. The methods improve the response time while leveraging the task scheduling of an application program.…”

Section: Application-level Proximity Controlmentioning

confidence: 99%

Design and Implementation of an Edge Computing Testbed to Simplify Experimental Environment Setup

Yamanaka

Teranishi

Kawai

et al. 2022

IEICE Trans. Inf. & Syst.

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Running IoT applications on edge computing infrastructures has the benefits of low response times and efficient bandwidth usage. System verification on a testbed is required to deploy IoT applications in production environments. In a testbed, Docker containers are preferable for a smooth transition of tested application programs to production environments. In addition, the round-trip times (RTT) of Docker containers to clients must be ensured, according to the target application's response time requirements. However, in existing testbed systems, the RTTs between Docker containers and clients are not ensured. Thus, we must undergo a large amount of configuration data including RTTs between all pairs of wireless base station nodes and servers to set up a testbed environment. In this paper, we present an edge computing testbed system with simple application programming interfaces (API) for testbed users that ensures RTTs between Docker containers and clients. The proposed system automatically determines which servers to place Docker containers on according to virtual regions and the RTTs specified by the testbed users through APIs. The virtual regions provide reduced size information about the RTTs in a network. In the proposed system, the configuration data size is reduced to one divided by the number of the servers and the command arguments length is reduced to approximately one-third or less, whereas the increased system running time is 4.3 s.

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Section: Application-level Proximity Controlmentioning

confidence: 99%