| Texto completo | |
| Autor(es): |
Rappa, Frederico Meletti
;
Rodrigues-Filho, Roberto
;
Panisson, Alison R.
;
Marcolino, Leandro Soriano
;
Bittencourt, Luiz F.
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PROCEEDINGS OF 2024 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM, NOMS 2024; v. N/A, p. 6-pg., 2024-01-01. |
| Resumo | |
The investigation of stateful service mobility across networking infrastructures is becoming increasingly important as applications require stateful services capable of migrating from centralized cloud data centers to edge computing infrastructures. State-of-the-art approaches propose either machine learning solutions for stateless service placement or stateful service mobility using static and inflexible state management strategies. We believe these approaches fall short of addressing the full length of the stateful service mobility problem. In this paper, we revisit an emerging concept named self-distributing systems, where a local executing application manages to detach some of its constituent (often stateful) components and place them in remote machines as a solution for stateful service mobility. In previous work, a machine learning approach to support self-distributing systems has not been thoroughly investigated. We model the distribution of stateful components across networking infrastructures as a multi-armed bandits problem and use the UCB1 algorithm to solve it as a first attempt at a flexible solution for stateful service mobility. We conclude the paper by discussing the main challenges and opportunities in this area. (AU) | |
| Processo FAPESP: | 21/00199-8 - Redes e serviços inteligentes rumo 2030 (SMARTNESS) |
| Beneficiário: | Christian Rodolfo Esteve Rothenberg |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia |