Disk-cache and parallelism aware I/O scheduling to improve storage system performance

Ramya Prabhakar, Mahmut Kandemir, Myoungsoo Jung

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations


Modern large computing systems employ sophisticated disk I/O systems that are configured to deliver high throughput, low-latency disk I/O to multiple clients accessing them. However, due to potential interferences among concurrent I/O accesses issued by multiple clients, a disk-cache and disk-level parallelism unaware I/O scheduling algorithm employed by the operating system/storage controller may have a significant impact on both system throughput and I/O latency. In this paper, we propose two fundamentally new disk I/O scheduling techniques. The first technique, called DCAP, performs I/O scheduling in a disk cache aware and parallelism aware manner. The key idea in DCAP is to process simultaneous requests to different disks from the same application/priority class together and reorder them so that they have the highest number of hits in the disk cache. We then propose an enhanced version of DCAP called DCAP-G, that aggregates requests into service groups to alleviate the problem of request starvation that may occur in DCAP in certain cases. We evaluate both DCAP and DCAP-G using a set of I/O workloads from production-based enterprise systems as well as high-performance computing domain. In addition, we also compare the performance of our algorithms to previously proposed I/O scheduling algorithms. Our evaluation shows that, averaged across all our workloads, DCAP improves the average I/O response time, taking maximum advantage of disk access locality and exploiting parallelism among concurrent accesses to multiple disks, by 14.9% over an I/O scheduler that schedules requests on a first-come-first-served (FCFS) basis and also improves by 6.5% over a previously proposed locality-optimal I/O scheduler (SPCTF). In addition to these improvements, DCAP-G improves the average I/O response time by 6.6% over DCAP, leading to an overall 20.7% and 12.0% improvement over FCFS, and SPCTF, respectively.

Original languageEnglish (US)
Number of pages12
StatePublished - 2013
Event27th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2013 - Boston, MA, United States
Duration: May 20 2013May 24 2013


Other27th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2013
Country/TerritoryUnited States
CityBoston, MA

All Science Journal Classification (ASJC) codes

  • Software


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