Publication details

B. Theeten, I. Bedini, P. Cogan, A. Sala, T. Cucinotta, "Towards the Optimization of a Parallel Streaming Engine for Telco Applications," in Bell Labs Technical Journal, Vol. 18, Issue 4, pp. 181-197, March 2014.

Abstract

Parallel and distributed computing is becoming essential to process in real time the increasingly massive volume of data collected by telecommunications companies. Existing computational paradigms such as MapReduce (and its popular open-source implementation Hadoop) provide a scalable, fault-tolerant mechanism for large-scale batch computations. However, many applications in the telco ecosystem require a real time, incremental streaming approach to process data in real time and enable proactive care. Storm is a scalable, fault-tolerant framework for the analysis of real-time streaming data. In this paper we provide a motivation for the use of real time streaming analytics in the telco ecosystem, formalize a performance model to describe the operational functionalities and system costs of Storm and demonstrate, as a motivation for future research, the difficulties inherent to manual parameter tuning of Storm.

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Last updated on
18 March 2024