Tag Archives: slo_management

Italian for Beginners: The Next Steps for SLO-Based Management

hotstorage11_button.jpgL.N. Bairavasundaram, G. Soundararajan, V. Mathur, K. Voruganti, and S. Kleiman.

This paper investigates the reasons for slow adoption of SLOs and discusses ideas using SLOs that can truly simplify storage management.

Literature is rife with compelling ideas on simplifying storage management using service-level objectives (SLOs). However, very few of these ideas have actually been productized, despite the fact that many of the original ideas came from industry and were developed more than a decade ago. While many good research ideas do not become products, in this case, we believe that there are important reasons why adoption has been slow.

In Proceedings of the USENIX Workshop on Hot Topics in Storage and File Systems  2011 (HotStorage ’11)

Resources

  • A copy of the paper is attached to this posting.
  • A video of the talk from HotStorage ’11 is also available.

hotstorage11-lakshmi.pdf

Harsha Madyastha, University of California, Riverside – July 2011

HarshaMadhyasthaWeb.jpgStorage and Compute Provisioning Informed by Application Characteristics and SLOs

This research addresses the problem of provisioning infrastructure for large-scale distributed applications. The proposed approach, a tool called CProv, takes the characterizations of application demands and hardware capabilities as input to automatically select the most cost-effective storage and compute configurations that meet the SLO requirement of the application. The main objectives of this project are 1) characterizing application behavior, hardware, and performance requirements with sufficient accuracy and manageable complexity 2) solving the constraint optimization problem.

Investigation plan:

  • Leverage expertise at NetApp to inform how the CProv tool should characterize its inputs—cluster building blocks, applications, and SLOs.
  • CProv needs to predict both the architecture and scale of various storage and compute building blocks required to meet input SLOs. They plan to evaluate CProv’s ability to predict the right scale by comparing with other capacity planning tools.
  • With CProv, we will study when inflection points occur in cluster architectures (e.g., scale out architectures aren’t always cost-effective for a given workload) so that administrators who provision clusters can appropriately plan for these inflections.