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

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      Algorithms

      MDPI AG

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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.

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          Most cited references 22

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          The vision of autonomic computing

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            Edge Analytics in the Internet of Things

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              A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

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                Author and article information

                Journal
                ALGOCH
                Algorithms
                Algorithms
                MDPI AG
                1999-4893
                September 2018
                September 07 2018
                : 11
                : 9
                : 134
                Article
                10.3390/a11090134
                © 2018
                Product
                Self URI (article page): http://www.mdpi.com/1999-4893/11/9/134

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