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.
|Number of pages||12|
|Publication status||Published - 2013|
|Event||27th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2013 - Boston, MA, United States|
Duration: 2013 May 20 → 2013 May 24
|Other||27th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2013|
|Period||13/5/20 → 13/5/24|
All Science Journal Classification (ASJC) codes