SWORD: workload-aware data placement and replica selection for cloud data management systems

K. Ashwin Kumar*, Abdul Quamar, Amol Deshpande, Samir Khuller

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

Cloud computing is increasingly being seen as a way to reduce infrastructure costs and add elasticity, and is being used by a wide range of organizations. Cloud data management systems today need to serve a range of different workloads, from analytical read-heavy workloads to transactional (OLTP) workloads. For both the service providers and the users, it is critical to minimize the consumption of resources like CPU, memory, communication bandwidth, and energy, without compromising on service-level agreements if any. In this article, we develop a workload-aware data placement and replication approach, called SWORD, for minimizing resource consumption in such an environment. Specifically, we monitor and model the expected workload as a hypergraph and develop partitioning techniques that minimize the average query span, i.e., the average number of machines involved in the execution of a query or a transaction. We empirically justify the use of query span as the metric to optimize, for both analytical and transactional workloads, and develop a series of replication and data placement algorithms by drawing connections to several well-studied graph theoretic concepts. We introduce a suite of novel techniques to achieve high scalability by reducing the overhead of partitioning and query routing. To deal with workload changes, we propose an incremental repartitioning technique that modifies data placement in small steps without resorting to complete repartitioning. We propose the use of fine-grained quorums defined at the level of groups of data items to control the cost of distributed updates, improve throughput, and adapt to different workloads. We empirically illustrate the benefits of our approach through a comprehensive experimental evaluation for two classes of workloads. For analytical read-only workloads, we show that our techniques result in significant reduction in total resource consumption. For OLTP workloads, we show that our approach improves transaction latencies and overall throughput by minimizing the number of distributed transactions.

Original languageEnglish (US)
Pages (from-to)845-870
Number of pages26
JournalVLDB Journal
Volume23
Issue number6
DOIs
StatePublished - Nov 15 2014

Keywords

  • Cloud data management
  • Data placement
  • Hypergraph partitioning
  • Replication
  • Resource minimization
  • Scalability

ASJC Scopus subject areas

  • Information Systems
  • Hardware and Architecture

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