Resource provisioning and task scheduling in Cloud environments are quite challenging because of the fluctuating workload patterns and of the unpredictable behaviors and unstable performance of the infrastructure. It is therefore important to properly master the uncertainties associated with Cloud workloads and infrastructure. In this paper, we propose a probabilistic approach for resource provisioning and task scheduling that allows users to estimate in advance, i.e., offline, the resources to be provisioned, thus reducing the risk and the impact of overprovisioning or underprovisioning. In particular, we formulate an optimization problem whose objective is to identify scheduling plans that minimize the overall monetary cost for leasing Cloud resources subject to some workload constraints. This cost-aware model ensures that the execution time of an application does not exceed with a given probability a specified deadline, even in presence of uncertainties. To evaluate the behavior and sensitivity to uncertainties of the proposed approach, we simulate a simple batch workload consisting of MapReduce jobs. The experimental results show that, despite the provisioning and scheduling approaches that do not take into account the uncertainties in their decision process, our probabilistic approach nicely adapts to workload and Cloud uncertainties.

Della Vedova, M. L., Tessera, D., Calzarossa, M. C., Probabilistic provisioning and scheduling in uncertain Cloud environments, in Proceedings - IEEE Symposium on Computers and Communications, (Messina, 2016-06-27), Institute of Electrical and Electronics Engineers Inc., New York 2016: 797-803. [10.1109/ISCC.2016.7543834] [http://hdl.handle.net/10807/99716]

Probabilistic provisioning and scheduling in uncertain Cloud environments

Della Vedova, Marco Luigi
Primo
;
Tessera, Daniele
Secondo
;
2016

Abstract

Resource provisioning and task scheduling in Cloud environments are quite challenging because of the fluctuating workload patterns and of the unpredictable behaviors and unstable performance of the infrastructure. It is therefore important to properly master the uncertainties associated with Cloud workloads and infrastructure. In this paper, we propose a probabilistic approach for resource provisioning and task scheduling that allows users to estimate in advance, i.e., offline, the resources to be provisioned, thus reducing the risk and the impact of overprovisioning or underprovisioning. In particular, we formulate an optimization problem whose objective is to identify scheduling plans that minimize the overall monetary cost for leasing Cloud resources subject to some workload constraints. This cost-aware model ensures that the execution time of an application does not exceed with a given probability a specified deadline, even in presence of uncertainties. To evaluate the behavior and sensitivity to uncertainties of the proposed approach, we simulate a simple batch workload consisting of MapReduce jobs. The experimental results show that, despite the provisioning and scheduling approaches that do not take into account the uncertainties in their decision process, our probabilistic approach nicely adapts to workload and Cloud uncertainties.
Inglese
Proceedings - IEEE Symposium on Computers and Communications
2016 IEEE Symposium on Computers and Communication, ISCC 2016
Messina
27-giu-2016
30-giu-2017
9781509006793
Institute of Electrical and Electronics Engineers Inc.
Della Vedova, M. L., Tessera, D., Calzarossa, M. C., Probabilistic provisioning and scheduling in uncertain Cloud environments, in Proceedings - IEEE Symposium on Computers and Communications, (Messina, 2016-06-27), Institute of Electrical and Electronics Engineers Inc., New York 2016: 797-803. [10.1109/ISCC.2016.7543834] [http://hdl.handle.net/10807/99716]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10807/99716
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