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Algorithms 2018, 11(9), 134; https://doi.org/10.3390/a11090134

Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach

Department of Civil Engineering and Computer Science Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
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Received: 5 July 2018 / Revised: 31 August 2018 / Accepted: 5 September 2018 / Published: 7 September 2018
(This article belongs to the Special Issue Algorithms for the Resource Management of Large Scale Infrastructures)
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Abstract

The capability of efficiently processing the data streams emitted by nowadays ubiquitous sensing devices enables the development of new intelligent services. Data Stream Processing (DSP) applications allow for processing huge volumes of data in near real-time. To keep up with the high volume and velocity of data, these applications can elastically scale their execution on multiple computing resources to process the incoming data flow in parallel. Being that data sources and consumers are usually located at the network edges, nowadays the presence of geo-distributed computing resources represents an attractive environment for DSP. However, controlling the applications and the processing infrastructure in such wide-area environments represents a significant challenge. In this paper, we present a hierarchical solution for the autonomous control of elastic DSP applications and infrastructures. It consists of a two-layered hierarchical solution, where centralized components coordinate subordinated distributed managers, which, in turn, locally control the elastic adaptation of the application components and deployment regions. Exploiting this framework, we design several self-adaptation policies, including reinforcement learning based solutions. We show the benefits of the presented self-adaptation policies with respect to static provisioning solutions, and discuss the strengths of reinforcement learning based approaches, which learn from experience how to optimize the application performance and resource allocation. View Full-Text
Keywords: Data Stream Processing; self-adaptive; hierarchical control; MAPE; reinforcement learning Data Stream Processing; self-adaptive; hierarchical control; MAPE; reinforcement learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Russo Russo, G.; Nardelli, M.; Cardellini, V.; Lo Presti, F. Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach. Algorithms 2018, 11, 134.

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