Cloud Computing is emerging as a major trend in ICT industry. However, as with any new technology it raises new major challenges and one of them concerns the resource provisioning. Indeed, modern Cloud applications deal with a dynamic context and have to constantly adapt themselves in order to meet Quality of Service (QoS) requirements. This situation calls for advanced solutions designed to dynamically provide cloud resource with the aim of guaranteeing the QoS levels. This work presents a capacity allocation algorithm whose goal is to minimize the total execution cost, while satisfying some constraints on the average response time of Cloud based applications. We propose a receding horizon control technique, which can be employed to handle multiple classes of requests. We compare our solution with an oracle with perfect knowledge of the future and with a well-known heuristic described in the literature. The experimental results demonstrate that our solution outperforms the existing heuristic producing results very close to the optimal ones. Furthermore, a sensitivity analysis over two different time scales indicates that finer grained time scales are more appropriate for spiky workloads, whereas smooth traffic conditions are better handled by coarser grained time scales. Our analytical results are also validated through simulation, which shows also the impact on our solution of Cloud environment random perturbations.

A Receding Horizon Approach for the Runtime Management of IaaS Cloud Systems / Ardagna, Danilo; Ciavotta, Michele; Lancellotti, Riccardo. - STAMPA. - (2014), pp. 445-452. (Intervento presentato al convegno 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2014 tenutosi a Timisoara (Romania) nel 22-25 September 2014) [10.1109/SYNASC.2014.66].

A Receding Horizon Approach for the Runtime Management of IaaS Cloud Systems

LANCELLOTTI, Riccardo
2014

Abstract

Cloud Computing is emerging as a major trend in ICT industry. However, as with any new technology it raises new major challenges and one of them concerns the resource provisioning. Indeed, modern Cloud applications deal with a dynamic context and have to constantly adapt themselves in order to meet Quality of Service (QoS) requirements. This situation calls for advanced solutions designed to dynamically provide cloud resource with the aim of guaranteeing the QoS levels. This work presents a capacity allocation algorithm whose goal is to minimize the total execution cost, while satisfying some constraints on the average response time of Cloud based applications. We propose a receding horizon control technique, which can be employed to handle multiple classes of requests. We compare our solution with an oracle with perfect knowledge of the future and with a well-known heuristic described in the literature. The experimental results demonstrate that our solution outperforms the existing heuristic producing results very close to the optimal ones. Furthermore, a sensitivity analysis over two different time scales indicates that finer grained time scales are more appropriate for spiky workloads, whereas smooth traffic conditions are better handled by coarser grained time scales. Our analytical results are also validated through simulation, which shows also the impact on our solution of Cloud environment random perturbations.
2014
16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2014
Timisoara (Romania)
22-25 September 2014
445
452
Ardagna, Danilo; Ciavotta, Michele; Lancellotti, Riccardo
A Receding Horizon Approach for the Runtime Management of IaaS Cloud Systems / Ardagna, Danilo; Ciavotta, Michele; Lancellotti, Riccardo. - STAMPA. - (2014), pp. 445-452. (Intervento presentato al convegno 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2014 tenutosi a Timisoara (Romania) nel 22-25 September 2014) [10.1109/SYNASC.2014.66].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1064083
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