Replicated services that allow to scale dynamically can adapt to requests load. Choosing the right number of replicas is fundamental to avoid performance worsening when input spikes occur and to save resources when the load is low. Current mechanisms for automatic scaling are mostly based on fixed thresholds on CPU and memory usage, which are not sufficiently accurate and often entail late countermeasures. We propose Make Your Service Elastic (MYSE), an architecture for automatic scaling of generic replicated services based on queuing models for accurate response time estimation. Requests and service times patterns are analyzed to learn and predict over time their distribution so as to allow for early scaling. A novel heuristic is proposed to avoid the flipping phenomenon. We carried out simulations that show promising results for what concerns the effectiveness of our approach. © 2014 Springer International Publishing.

An architecture for automatic scaling of replicated services / Aniello, Leonardo; Bonomi, Silvia; Lombardi, Federico; Alessandro, Zelli; Baldoni, Roberto. - 8593 LNCS(2014), pp. 122-137. ((Intervento presentato al convegno 2nd International Conference on Networked Systems, NETYS 2014 tenutosi a Marrakech nel 15 May 2014 through 17 May 2014 [10.1007/978-3-319-09581-3_9].

An architecture for automatic scaling of replicated services

ANIELLO, LEONARDO;BONOMI, Silvia;LOMBARDI, FEDERICO;BALDONI, Roberto
2014

Abstract

Replicated services that allow to scale dynamically can adapt to requests load. Choosing the right number of replicas is fundamental to avoid performance worsening when input spikes occur and to save resources when the load is low. Current mechanisms for automatic scaling are mostly based on fixed thresholds on CPU and memory usage, which are not sufficiently accurate and often entail late countermeasures. We propose Make Your Service Elastic (MYSE), an architecture for automatic scaling of generic replicated services based on queuing models for accurate response time estimation. Requests and service times patterns are analyzed to learn and predict over time their distribution so as to allow for early scaling. A novel heuristic is proposed to avoid the flipping phenomenon. We carried out simulations that show promising results for what concerns the effectiveness of our approach. © 2014 Springer International Publishing.
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