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Dynamic Control of CPU Cap Allocations in Stream Processing and Data-Flow Platforms
University of Sidney.
Iowa State University.
RMIT University.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-9194-010X
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2019 (English)In: 2019 IEEE 18th International Symposium on Network Computing and Applications, NCA 2019, IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper focuses on Timely dataflow programming model for processing streams of data. We propose a technique to define CPU resource allocation (i.e., CPU capping) with the goal to improve response time latency in such type of applications with different quality of service (QoS) level, as they are concurrently running in a shared multi-core computing system with unknown and volatile demand. The proposed solution predicts the expected performance of the underlying platform using an online approach based on queuing theory and adjusts the corrections required in CPU allocation to achieve the most optimized performance. The experimental results confirms that measured performance of the proposed model is highly accurate while it takes into account the percentiles on the QoS metrics. The theoretical model used for elastic allocation of CPU share in the target platform takes advantage of design principals in model predictive control theory and dynamic programming to solve an optimization problem. While the prediction module in the proposed algorithm tries to predict the temporal changes in the arrival rate of each data flow, the optimization module uses a system model to estimate the interference among collocated applications by continuously monitoring the available CPU utilization in individual nodes along with the number of outstanding messages in every intermediate buffer of all TDF applications. The optimization module eventually performs a cost-benefit analysis to mitigate the total amount of QoS violation incidents by assigning the limited CPU shares among collocated applications. The proposed algorithm is robust (i.e., its worst-case output is guaranteed for arbitrarily volatile incoming demand coming from different data streams), and if the demand volatility is not large, the output is optimal, too. Its implementation is done using the TDF framework in Rust for distributed and shared memory architectures. The experimental results show that the proposed algorithm reduces the average and p99 latency of delay-sensitive applications by 21% and 31.8%, respectively, while can reduce the amount of QoS violation incidents by 98% on average.

Place, publisher, year, edition, pages
IEEE, 2019.
Keywords [en]
Dynamic CPU Resource Allocation, Scalable Data-Stream Processing, Timely Data-Flow Architecture, Computation theory, Control theory, Cost benefit analysis, Data flow analysis, Data handling, Data transfer, Memory architecture, Model predictive control, Network architecture, Quality of service, Queueing theory, Resource allocation, CPU resources, Data stream processing, Data-flow architectures, Delay-sensitive applications, Intermediate buffers, Optimization problems, Optimized performance, Shared memory architecture, Dynamic programming
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-77277DOI: 10.1109/NCA.2019.8935024Scopus ID: 2-s2.0-85077954593ISBN: 9781728125220 (print)OAI: oai:DiVA.org:kau-77277DiVA, id: diva2:1414258
Conference
18th IEEE International Symposium on Network Computing and Applications, NCA 2019, 26 September 2019 through 28 September 2019
Available from: 2020-03-12 Created: 2020-03-12 Last updated: 2020-04-02Bibliographically approved

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Taheri, Javid

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