Cloud Computing is emerging as a major trend in ICT industry. As with any new technology, new major challenges lie ahead, one of them concerning the resource provisioning. Modern Cloud applications deal with a dynamic context that requires a continuous adaptation process to meet satisfactory QoS. Unfortunately, current Cloud platforms provide just simple rule-based tools that can be unsuitable in many situations as they do not prevent SLA violations, but only react to them. This situation calls for advanced solutions designed to provide Cloud resources in a predictive and dynamic way. This work presents capacity allocation algorithms whose goal is to minimize the total execution cost while satisfying some constraints on the average response time of Cloud based applications. An extensive evaluation of our solution against an Oracle with perfect knowledge of the future and well-known heuristics presented in the literature is provided. The analysis shows that our solution outperforms the heuristics producing results very close to the optimal ones and reducing the number of QoS violations. Analytical results are validated also through simulation, which analyses the impact of Cloud environment random perturbations. Finally, experiments on a prototype environment demonstrate the effectiveness of our approach under real workloads.
Ardagna, D., Ciavotta, M., Lancellotti, R., & Guerriero, M. (2018). A Hierarchical Receding Horizon Algorithm for QoS-driven control of Multi-IaaS Applications. IEEE TRANSACTIONS ON CLOUD COMPUTING, 1-1.
|Citazione:||Ardagna, D., Ciavotta, M., Lancellotti, R., & Guerriero, M. (2018). A Hierarchical Receding Horizon Algorithm for QoS-driven control of Multi-IaaS Applications. IEEE TRANSACTIONS ON CLOUD COMPUTING, 1-1.|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||No|
|Titolo:||A Hierarchical Receding Horizon Algorithm for QoS-driven control of Multi-IaaS Applications|
|Autori:||Ardagna, D; Ciavotta, M; Lancellotti, R; Guerriero, M|
|Data di pubblicazione:||2018|
|Rivista:||IEEE TRANSACTIONS ON CLOUD COMPUTING|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/TCC.2018.2875443|
|Appare nelle tipologie:||01 - Articolo su rivista|