Stream processing applications became a representative workload in current computing systems. A significant part of these applications demands parallelism to increase performance. However, programmers are often facing a trade-off between coding productivity and performance when introducing parallelism. SPar was created for balancing this trade-off to the application programmers by using the C++11 attributes’ annotation mechanism. In SPar and other programming frameworks for stream processing applications, the manual definition of the number of replicas to be used for the stream operators is a challenge. In addition to that, low latency is required by several stream processing applications. We noted that explicit latency requirements are poorly considered on the state-of-the-art parallel programming frameworks. Since there is a direct relationship between the number of replicas and the latency of the application, in this work we propose an autonomic and adaptive strategy to choose the proper number of replicas in SPar to address latency constraints. We experimentally evaluated our implemented strategy and demonstrated its effectiveness on a real-world application, demonstrating that our adaptive strategy can provide higher abstraction levels while automatically managing the latency.

Autonomic and latency-aware degree of parallelism management in SPar / Vogel, A; Griebler, D; De Sensi, D; Danelutto, M; Fernandes, L. G.. - 11339:(2019), pp. 28-39. (Intervento presentato al convegno 24th International Conference on Parallel and Distributed Computing, Euro-Par 2018 tenutosi a ita) [10.1007/978-3-030-10549-5_3].

Autonomic and latency-aware degree of parallelism management in SPar

De Sensi D;
2019

Abstract

Stream processing applications became a representative workload in current computing systems. A significant part of these applications demands parallelism to increase performance. However, programmers are often facing a trade-off between coding productivity and performance when introducing parallelism. SPar was created for balancing this trade-off to the application programmers by using the C++11 attributes’ annotation mechanism. In SPar and other programming frameworks for stream processing applications, the manual definition of the number of replicas to be used for the stream operators is a challenge. In addition to that, low latency is required by several stream processing applications. We noted that explicit latency requirements are poorly considered on the state-of-the-art parallel programming frameworks. Since there is a direct relationship between the number of replicas and the latency of the application, in this work we propose an autonomic and adaptive strategy to choose the proper number of replicas in SPar to address latency constraints. We experimentally evaluated our implemented strategy and demonstrated its effectiveness on a real-world application, demonstrating that our adaptive strategy can provide higher abstraction levels while automatically managing the latency.
2019
24th International Conference on Parallel and Distributed Computing, Euro-Par 2018
Adaptive degree of parallelism; Autonomic computing; Parallel programming; Stream processing
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Autonomic and latency-aware degree of parallelism management in SPar / Vogel, A; Griebler, D; De Sensi, D; Danelutto, M; Fernandes, L. G.. - 11339:(2019), pp. 28-39. (Intervento presentato al convegno 24th International Conference on Parallel and Distributed Computing, Euro-Par 2018 tenutosi a ita) [10.1007/978-3-030-10549-5_3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1656234
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