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

SWORD: workload-aware data placement and replica selection for cloud data management systems 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

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

Loading next page...
 
/lp/springer_journal/sword-workload-aware-data-placement-and-replica-selection-for-cloud-83W0fYEsbo
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2014 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-014-0362-1
Publisher site
See Article on Publisher Site

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial