Transactional Auto Scaler: Elastic Scaling of Replicated In-Memory Transactional Data Grids

Transactional Auto Scaler: Elastic Scaling of Replicated In-Memory Transactional Data Grids 1
Diego Didona, Paolo Romano
INESC-ID / Instituto Superior T ́ecnico, Universidade de Lisboa didona,romanop@gsd.inesc-id.pt
Sebastiano Peluso, Francesco Quaglia Sapienza, Universita` di Roma peluso,quaglia@dis.uniroma1.it
Technical Report RT/12/2014 January 2014
1 This Technical Report supersedes a prelimary version published as INESC-ID Technical Report, 50/2011, December 2011

Abstract
In this paper we introduce TAS (Transactional Auto Scaler), a system for automating the elastic- scaling of replicated in-memory transactional data grids, such as NoSQL data stores or Distributed Transactional Memories. Applications of TAS range from on-line self-optimization of in-production applications, to the automatic generation of QoS/cost driven elastic scaling policies, as well as to support for what-if analysis on the scalability of transactional applications.
In this article we present the key innovation at the core of TAS, namely a novel performance forecasting methodology that relies on the joint usage of analytical modeling and machine-learning. By exploiting these two, classically competing, approaches in a synergic fashion, TAS achieves the best of the two worlds, namely high extrapolation power and good accuracy even when faced with complex workloads deployed over public cloud infrastructures.
We demonstrate the accuracy and feasibility of TAS’ performance forecasting methodology via an extensive experimental study based on a fully fledged prototype implementation integrated with a popular open-source in-memory transactional data grid (Red Hat’s Infinispan) and industry- standard benchmarks generating a breadth of heterogeneous workloads.

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