A probabilistic model for assigning queries at the edge

Kolomvatsos, K. and Anagnostopoulos, C. (2020) A probabilistic model for assigning queries at the edge. Computing, 102, pp. 865-892. (doi: 10.1007/s00607-019-00767-8)

Abstract

Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries/tasks defined by users or applications asking for responses in the form of analytics. Query/task execution can be realized at the edge nodes that can undertake the responsibility of delivering the desired analytics to the interested users or applications. In this paper, we deal with the problem of allocating queries to a number of edge nodes. The aim is to support the goal of eliminating further the latency by allocating queries to nodes that exhibit a low load and high processing speed, thus, they can respond in the minimum time. Before any allocation, we propose a method for estimating the computational burden that a query/task will add to a node and, afterwards, we proceed with the final assignment. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query/task and a probabilistic decision making model. The proposed scheme matches the characteristics of the incoming queries and edge nodes trying to conclude the optimal allocation. We discuss our mechanism and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kolomvatsos, Dr Kostas and Anagnostopoulos, Dr Christos
• Authors: Kolomvatsos, K., and Anagnostopoulos, C.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Computing
• Publisher: Springer
• ISSN: 0010-485X
• ISSN (Online): 1436-5057
• Published Online: 18 November 2019
• Copyright Holders: Copyright © 2019 The Authors
• First Published: First published in Computing 102:865-892
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
301654		Intelligent Applications over Large Scale Data Streams	Christos Anagnostopoulos	European Commission (EC)	745829	Computing Science