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

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...

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Published in:2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing pp. 445 - 452
Main Authors: Ardagna, Danilo, Ciavotta, Michele, Lancellotti, Riccardo
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2014
Subjects:
Algorithm design and analysis
Auto-Scaling
Capacity Allocation
Noise
Optimization
Prediction algorithms
QoS
Quality of service
Resource management
Time factors
ISBN:9781479984473, 1479984477
Online Access:Get full text
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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.
AbstractList 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.
Author Lancellotti, Riccardo
Ciavotta, Michele
Ardagna, Danilo
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  surname: Ardagna
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  givenname: Michele
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  givenname: Riccardo
  surname: Lancellotti
  fullname: Lancellotti, Riccardo
  email: riccardo.lancellotti@unimore.it
  organization: Dipt. di Ing. "Enzo Ferrari", Univ. di Modena e Reggio Emilia, Modena, Italy
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Snippet 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...
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StartPage 445
SubjectTerms Algorithm design and analysis
Auto-Scaling
Capacity Allocation
Noise
Optimization
Prediction algorithms
QoS
Quality of service
Resource management
Time factors
Title A Receding Horizon Approach for the Runtime Management of IaaS Cloud Systems
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