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SaMW: a probabilistic meta-heuristic algorithm for job scheduling in heterogeneous distributed systems powered by microservices

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Abstract

Applications are evolving in ways that demand geographically distributed resources to co-operate in order to give users better Quality of Service (QoS). There is a plethora of ways to implement such systems, but a Heterogeneous Distributed System such as Jungle Computing System is the one that fits the above scenario the most. Additionally, utilizing the Microservices paradigm results in a dynamic system that can overcome today’s needs for reduced costs, heterogeneity, scalability and fault tolerance. Moreover, Load Balancing is a NP-Hard problem and finding the optimal solution becomes harder when the scale is getting larger. The development and simulation of a Heterogeneous System utilizing Microservices is presented in this article. This work aims at studying how existing load-balancing algorithms can reduce operational costs of such a system and introduces a new algorithm (SaMW) that results in decreasing expenses while keeping relatively low the Mean Response Time.

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Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Dimitrios Tychalas & Helen Karatza

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  1. Dimitrios Tychalas
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  2. Helen Karatza
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Correspondence to Dimitrios Tychalas.

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Tychalas, D., Karatza, H. SaMW: a probabilistic meta-heuristic algorithm for job scheduling in heterogeneous distributed systems powered by microservices. Cluster Comput 24, 1735–1759 (2021). https://doi.org/10.1007/s10586-020-03217-9

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  • DOI: https://doi.org/10.1007/s10586-020-03217-9

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