Integrating Concurrency Control in n-Tier Application Scaling Management in the Cloud

Qingyang Wang, Hui Chen, Shungeng Zhang, Liting Hu, Balaji Palanisamy

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Scaling complex distributed systems such as e-commerce is an importance practice to simultaneously achieve high performance and high resource efficiency in the cloud. Most previous research focuses on hardware resource scaling to handle runtime workload variation. Through extensive experiments using a representative n-tier web application benchmark (RUBBoS), we demonstrate that scaling an n-tier system by adding or removing VMs without appropriately re-allocating soft resources (e.g., server threads and connections) may lead to significant performance degradation resulting from implicit change of request processing concurrency in the system, causing either over-or under-utilization of the critical hardware resource in the system. We build a concurrency-aware model that determines a near optimal soft resource allocation of each tier by combining some operational queuing laws and the fine-grained online measurement data of the system. We then develop a dynamic concurrency management (DCM) framework that integrates the concurrency-aware model to intelligently reallocate soft resources in the system during the system scaling process. We compare DCM with Amazon EC2-AutoScale, the state-of-the-art hardware only scaling management solution using six real-world bursty workload traces. The experimental results show that DCM achieves significantly shorter tail latency and higher throughput compared to Amazon EC2-AutoScale under all the workload traces.

Original languageEnglish (US)
Article number8468089
Pages (from-to)855-869
Number of pages15
JournalIEEE Transactions on Parallel and Distributed Systems
Volume30
Issue number4
DOIs
StatePublished - Apr 1 2019
Externally publishedYes

Keywords

  • cloud computing
  • configuration
  • parallel processing
  • Scalability
  • soft resources
  • web application

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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