Analysis of Performance and Energy Consumption in the Cloud

Kandi, Mohamed Mehdi; Aït-Salaht, Farah; Castel-Taleb, Hind; Hyon, Emmanuel

doi:10.1007/978-3-319-66583-2_13

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…Among the most significant works, we can mention the work of Ibe and Keilson [22] refined in Lui and Golubchik [7] which solves the model by partitioning the state space in disjoint sets in order to aggregate the Markov chain. Exhaustive comparisons of the resolution methods are made in [23]. Closed-form solution of the stationary distribution, partition of the state space and matrix geometric methods applied on QBD (Quasi birth and death) processes are studied.…”

Section: Threshold Calculation Methodsmentioning

confidence: 99%

“…The solving of Markov chains (micro and macro) is done with a power method implemented in the marmoteCore framework [28] using a precision of 10 −8 . Indeed, the use of closed formulas suffers from numerical instability [23].…”

Section: Decomposition and Aggregation Methods For Calculation Of The...mentioning

confidence: 99%

“…The MDP algorithm provides the following solution: C * = 25, 239; and [2,4,5,7,8,10,11,13,15,16,18,19,21,23,24]; , 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15].…”

Section: A Numerical Experiments Examplementioning

confidence: 99%

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Efficient Computation of Optimal Thresholds in Cloud Auto-scaling Systems

Tournaire

Castel-Taleb

Hyon

2023

ACM Trans. Model. Perform. Eval. Comput. Syst.

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We consider a horizontal and dynamic auto-scaling technique in a cloud system where virtual machines hosted on a physical node are turned on and off in order to minimise energy consumption while meeting performance requirements. Finding cloud management policies that adapt the system to the load is not straightforward and we consider here that virtual machines are turned on and off depending on queue load thresholds. We want to compute the optimal threshold values that minimize consumption costs and penalty costs (when performance requirements are not met). To solve this problem, we propose several optimisation methods, based on two different mathematical approaches. The first one is based on queueing theory and uses local search heuristics coupled with the stationary distributions of Markov Chains. The second approach tackles the problem using Markov Decision Process (MDP) in which we assume that the policy is of a special multi-threshold type called hysteresis. We improve the heuristics of the former approach with the aggregation of Markov Chains and queues approximation techniques. We assess the benefit of threshold-aware algorithms for solving MDPs. Then, we carry out theoretical analyzes of the two approaches. We also compare them numerically and we show that all of the presented MDP algorithms strongly outperform the local search heuristics. Finally, we propose a cost model for a real scenario of a cloud system to apply our optimisation algorithms and to show their practical relevance. The major scientific contribution of the paper is a set of fast (almost in real time) load-based threshold computation methods that can be used by a cloud provider to optimize its financial costs.

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Section: Threshold Calculation Methodsmentioning

confidence: 99%

Section: Decomposition and Aggregation Methods For Calculation Of The...mentioning

confidence: 99%

See 1 more Smart Citation

Efficient Computation of Optimal Thresholds in Cloud Auto-scaling Systems

Tournaire

Castel-Taleb

Hyon

2023

ACM Trans. Model. Perform. Eval. Comput. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Decomposition and Aggregation Methods For Calculation Of The...mentioning

confidence: 99%

“…The MDP algorithm provides the following solution: C * = 25, 239; and [2,4,5,7,8,10,11,13,15,16,18,19,21,23,24]; , 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15].…”

Section: A Numerical Experiments Examplementioning

confidence: 99%

Optimal control policies for resource allocation in the Cloud: comparison between Markov decision process and heuristic approaches

Tournaire,

Castel-Taleb,

Hyon

2021

Preprint

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We consider an auto-scaling technique in a cloud system where virtual machines hosted on a physical node are turned on and off depending on the queue's occupation (or thresholds), in order to minimise a global cost integrating both energy consumption and performance. We propose several efficient optimisation methods to find threshold values minimising this global cost: local search heuristics coupled with aggregation of Markov chain and with queues approximation techniques to reduce the execution time and improve the accuracy. The second approach tackles the problem with a Markov Decision Process (MDP) for which we proceed to a theoretical study and provide theoretical comparison with the first approach. We also develop structured MDP algorithms integrating hysteresis properties. We show that MDP algorithms (value iteration, policy iteration) and especially structured MDP algorithms outperform the devised heuristics, in terms of time execution and accuracy. Finally, we propose a cost model for a real scenario of a cloud system to apply our optimisation algorithms and show their relevance.

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