Initial Results on Provisioning Variation in Cloud Services

Transcription

1 Initial Results on Provisioning ariation in Cloud Services. Suhail Rehman Research Analyst Cloud Computing Lab Carnegie ellon University in Qatar Collaborators: Prof. ajd F. Sakr, Jim Gargani Carnegie ellon University Supported By: 1

2 Cloud Computing / IaaS What about other Application Domains? Scientific Applications High Performance Computing 2

3 Cloud Computing / IaaS What about other Application Domains? Scientific Applications High Performance Computing Application Performance on the Cloud 3

4 What could affect performance? irtualization irtualized and ultiplexed Hardware ultitenancy Abstraction Simplified, Abstracted Hardware Identical Requests are not guaranteed to give you Identical Hardware 4

5 5 Related Work any Studies on irtualization and Application Performance Application Performance on EC2 Detailed studies on performance variance: Service Oriented Applications : upto 4x [Dejun 2009] ~ 10-25% ariation observed for benchmarks on EC2 [Schad 2010]

6 6 A closer look L3 RA Disk L3 RA Disk L3 RA Disk

7 A closer look L3 RA Disk L3 RA Disk L3 RA Disk 7

8 A closer look L3 RA Disk L3 In Cloud Computing Disk These Details are Abstracted from the User RA L3 RA Disk 8

9 3 Potential Reasons for Performance Issues on the Cloud 1 Loads from other s on the same machine 9

10 3 Potential Reasons for Performance Issues on the Cloud 1 Loads from other s on the same machine 2 ariation in the physical resources being assigned to identical instances 10

11 3 Potential Reasons for Performance Issues on the Cloud 1 Loads from other s on the same machine 2 ariation in the physical resources being assigned to identical instances 3 Configuration of the layout (where the s are placed during provisioning) 11

12 Why does layout matter? I want 4 s each with 1 vcpu, 1 GB RA and 80 GB Disk Client Resource Request Hardware irtual achine Cloud Provider 12

13 Provisioning ariation variation due to ambiguity in the mapping of virtual resources to physical resources in a cloud computing environment Application s from the Cloud Application Performance ariation 13

14 Experimental ethodology Controlled Experimentation on a private cloud Create Identical cluster instances in different physical layouts manually Evaluate the effect on performance for various applications. Client request for 4 s Provisioned on a private cloud Layout 1: 4 s across 4 blades Layout 2: 4 s across 2 blades Layout 3: 4 s across 1 blade 14

15 Testbed Configuration IB Bladecenter H with14 Blades Hadoop RHEL 5.2 Xen RHEL 5.1 Blade CPU: 2 x Quad Xeon E GHz w/ 12B Cache L3 RA Disk RA: 8 GB ECC Disk: 2 x 300 GB SAS Front-Side Bus: 21.6 GB/sec Disk Bandwidth: 600 B/sec Network Interface 2x Gigabit Interfaces to other blades 15

16 Benchmark Tests and Applications Systems Benchmarks CPU: SysTester emory: STREA Disk: Bonnie++ Network: Netperf Hadoop Workloads Executed on Synthetically Configured Infrastructure 4 s across 4 blades 4 s across 2 blades 4 s across 1 blade Hadoop Sort Hadoop Wordcount Hadoop TestDFSIO 16

17 Results of Systems Benchmarks No ariation CPU RA Disk 25% drop in bandwidth for Layouts 2 and % drop in bandwidth for Layouts 2 and 3 Layout 2 Layout 1 Layout 3 Network ~ 4x speedup for Layout 3 17

18 Hadoop Sort Time in Seconds (Log Scale) Layout Layout Layout Layout Layout Layout Size (B) Layout 2 Layout 1 5x performance variation Layout 3 18

19 DFSIO Benchmark Throughput (mb/sec) Read Write Layout Layout 1 Layout 2 Layout 3 1x4 2x2 4x1 Layout (xs) Layout 2 Layout 3 ~ 5x performance variation 19

20 Analysis Correlation between Sort and DFSIO Benchmark Upto 5x performance drop in both Disk contention is the reason Hadoop designed to leverage parallel I/O When all s are on one blade, they compete for disk I/O bandwidth 20

21 Hadoop Wordcount Runtime (Seconds) Layout 4x1B Layout 4x2B Layout 4x4B Input Size (GB) Layout 2 ~20% Layout 1 Layout 3 21

22 Conclusions Tradeoffs placement on same resource Higher bandwidth for inter- communication Constraints emory and Disk Provisioning ariation It s impact on performance varies across application domains Up to 5x performance variation for I/O-bound 22

23 Future Studies and Directions We have only scratched the surface! ore Studies on other Applications Different classes of scientific applications (CPU, emory, I/O Bound) Application Profiling on the Cloud To inform provisioning to meet QoS Resource-aware Applications Dynamic application adaptation to variations in cloud resources 23

24 Join Us! Postdoctoral Positions Carnegie ellon Qatar Contact Prof. ajd Sakr or e: 24