One of the main characteristics that today makes cloud services so popular is their ability to be elastic, i.e., they can adapt their provisioning to variable workloads, thus increasing resource utilization and reducing operating costs. At the core of any elastic service lies an automatic scaling mechanism that drives provisioning on the basis of a given strategy. In this paper we propose PASCAL , an architecture for Proactive Auto-SCALing of generic distributed services. PASCAL combines a proactive approach, to forecast incoming workloads, with a profiling system, to estimate required provision. Scale-in/out operations are decided according to an application-specific strategy, which aims at provisioning the minimum number of resources needed to sustain the foreseen workload. The main novelties introduced with PASCAL architecture are: (i) a strategy to proactively auto-scale a distributed stream processing system (namely, Apache Storm) with the aim of load balancing operators through an accurate system performance estimation model, and (ii) a strategy to proactively auto-scale a distributed datastore (namely, Apache Cassandra), focused on how to choose when executing scaling actions on the basis of the time needed for the activation/deactivation of storage nodes so as to have the configuration ready when needed. We provide a prototype implementation of PASCAL for both use cases and, through an experimental evaluation conducted on a private cloud, we validate our approach and demonstrate the effectiveness of the proposed strategies in terms of saved resources and response time.
2019, FUTURE GENERATION COMPUTER SYSTEMS, Pages 342-361 (volume: 98)
PASCAL: An architecture for proactive auto-scaling of distributed services (01a Articolo in rivista)
Lombardi F., Muti A., Aniello L., Baldoni R., Bonomi S., Querzoni L.