Relevance of Disk Locality in Data Center Computing
Explore the evolution and potential irrelevance of disk locality in data center computing, its impact on performance, and future trends with improving network speeds. Learn about how disk bandwidth surpasses network bandwidth and the shifting dynamics in optimizing data center operations.
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Presentation Transcript
Disk-Locality in Datacenter Computing Considered Irrelevant (and then what?) Ganesh Ananthanarayanan, Ali Ghodsi, Scott Shenker, Ion Stoica University of California Berkeley 1
Data Intensive Computing Driver of modern Internet services Large infrastructure Petabytes of storage Computation Frameworks E.g., MapReduce, Hadoop, Dryad 2
Disk-Locality is the key to improving performance of datacenter jobs Co-locate computation with their input 3
Let there be disk-locality! Programming frameworks supported it MapReduce, Hadoop, DryadLinq Schedulers were modified Delay Scheduling File systems played along Scarlett 4
and more disk-locality! Even fairness was defined using it Quincy, Fair Scheduler Cornerstone of system evaluation Mesos, Dryad 5
Why Disk-Locality? Disk bandwidth >> Network bandwidth 6
So, how effective is it today? Facebook production Hadoop jobs In 85% of jobs, tasks reading from network run just as fast as disk-local tasks Google report says disk-local reads are not faster than rack-local reads Disk-locality not helping much! 7
What does the future hold? Network speeds are improving 1/10 Gbps today, 25 Gbps in couple of years Aggregate link speeds of 100 Gbps Off-rack ~ Rack-local ~ Disk-local Over-subscription is fast reducing Full bisection bandwidth topologies [Fat-tree, VL2, D-Cell, B-Cube] and being adopted in datacenters 8
Disk-Locality will be irrelevant! Disk bandwidth >> Network bandwidth Disk bandwidth >> Network bandwidth Networks are getting faster, disks aren t Disks are the bottleneck 9
Is Locality altogether Irrelevant? No, if data in memory Memory reads are two magnitudes faster Machines have tens of gigabytes of memory Use Memory as Cache But, huge discrepancy between storage and memory capacities Facebook cluster has ~200x more data than memory 10
Unlike traditional caches Working set of datacenter jobs is close to entire input Datacenter Jobs 11
Cache all-or-nothing Job finishes when its last task finishes Even a single task without cached data can significantly slow down job 12
How do we fit data in memory? *Facebook Hadoop Jobs Heavy-tailed fit in the memory cache 96% of jobs can 13
Cache Replacement Traditional cache replacement policies (e.g., LRU, LFU) optimize for hit-ratio Don t perform well for parallel jobs Ignore all-or-nothing caching needs of these jobs We need to look beyond cache hit-ratios 14
