2026-04-17T05:57:05Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3426052026-02-09T10:00:39Zcom_2072_1033col_2072_452950

On the optimality of concurrent container clusters scheduling over heterogeneous smart environments Asensio Garcia, Adrian Masip Bruin, Xavier García Almiñana, Jordi Sánchez López, Sergio Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors Electronic villages (Computer networks)) Cloud computing Artificial intelligence Computer networks Edge computing Edge-cloud systems Optimal scheduling Heterogeneous system Smart environment Ciutats digitals (Xarxes d'ordinadors) Computació en núvol Intel·ligència artificial Ordinadors, Xarxes d' Smart environments rely on the cloud for most computation activities; however, leveraging the availability of resources at the edge could complement the cloud capabilities to provide a resources continuum spectrum that takes advantage of the benefits of both technologies, cloud and edge computing. In this scenario, applications are usually decomposed for execution in sets of tasks, which are in turn encapsulated into virtual components such as containers. Containers are lightweight implementations of virtual machines, improving efficiency and portability in distributed applications. Traditional containers scheduling in cloud is a well-known problem, but when the environment is heterogeneous, as it is in the scope of edge computing and edge-cloud systems, the problem becomes more challenging. In this paper we present the Concurrent Container Clusters Scheduling problem (C3S) aimed at optimizing the problem of placing containers in clusters of heterogeneous nodes satisfying a set of resource requirements, quality of service limitations, and considering additional stringent constraints in terms of applications execution in isolation for security guaranteeing. The C3S problem has been formulated using Integer Linear Programming with the dual objective of minimizing the number of applications rejected while minimizing the number of nodes used for computation. We have evaluated the optimality of this approach, analyzed the performance in terms of solving time and, finally, created a heuristic approach to solve the problem in realistic high demanding scenarios. Peer Reviewed Postprint (author's final draft) 2021-05-01 Article https://www.sciencedirect.com/science/article/abs/pii/S0167739X21000054 http://creativecommons.org/licenses/by-nc-nd/3.0/es/ Open Access Attribution-NonCommercial-NoDerivs 3.0 Spain Elsevier