Machine Learning methods in tasks load balancing between IoT devices and the Cloud

Mikhail Tishin, Constandinos X. Mavromoustakis, Jordi Mongay Batalla

Research output: Contribution to journalArticlepeer-review

Abstract

Nowadays, with the ongoing, wide scale digitization and development of AI in pursuit of automation, the IoT industry becomes one of the very important parts in this process. The development of IoT devices computational capabilities, as well as the massive amounts of data which is being generated by them, create a need for methods to load balance workloads efficiently. Since the IoT devices are receiving more processing power, it becomes important to leverage that power for executing curtain tasks inside an IoT ecosystem itself, rather than delegating to the Cloud. One of the main goals of the research is to understand, to what extent an IoT ecosystem can be self reliant in managing various tasks. In particular, what mechanism would allow to load balance tasks among IoT devices and Cloud servers. The paper focuses on exploration of the existing solutions and offers an alternative concept of such mechanism, which includes application of Machine Learning methods to load balance the workloads. It proposes tasks distribution based on runtime complexity estimated by Machine Learning and historical data from the previous tasks.

Original languageEnglish
JournalIEEE Access
DOIs
Publication statusAccepted/In press - 2024
Externally publishedYes

Keywords

  • Cloud computing
  • Internet of Things (IoT)
  • task load balancing

Fingerprint

Dive into the research topics of 'Machine Learning methods in tasks load balancing between IoT devices and the Cloud'. Together they form a unique fingerprint.

Cite this