Exploiting hardware heterogeneity in public clouds

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1 Exploiting hardware heterogeneity in public clouds Zhonghong Ou Dept. of Computer Science and Engineering, Aalto University Finland Aalto University 12/11/2013

2 Exploiting hardware heterogeneity in public clouds 1. Z. Ou, H. Zhuang, J.K. Nurminen, A. Ylä-Jääski and P. Hui. Exploiting Hardware Heterogeneity within the Same Instance Type of Amazon EC2. The 4th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud '12) (covered by BBC News, The Register, ACM TechNews etc). 2. Z. Ou, H. Zhuang, A. Lukyanenko, J.K. Nurminen, P. Hui, V. Mazalov, A. Ylä- Jääski. Is the Same Instance Type Created Equal? Exploiting Heterogeneity of Public Clouds. IEEE Transactions on Cloud Computing (accepted). 3. H. Zhuang, X. Liu, Z. Ou, K. Aberer. Impact of Instance Seeking Strategies on Resource Allocation in Cloud Data Centers. IEEE 6th International Conference on Cloud Computing (IEEE Cloud 2013). 2

3 Experiment configurations Detecting hardware info CPUID: non-trapping instruction Confirmed with: cat /proc/cpuinfo Testing period: Amazon EC2 Apr. Jul Jan. Mar Rackspace Cloud May September 2012 Micro-benchmark CPU performance: UnixBench VM scheduling: CPUBench (home made) Memory performance: RAMSpeed Network throughput & latency (home made) Disk performance: Bonnie++ Application benchmark Httperf 3

4 CPUBench Call function gettimeofday () 1 million times If VM is scheduled on, the time acquired is continuous without large gap; If VM is scheduled off, the time acquired will have large gaps in between. 4

5 Local setup Hardware HP ProLiant BL280c G6 Server Blade with one Quad-Core Intel Xeon E MHz processor, 8 GB DDR MHz memory, one HPNC362i dual port Gigabit server adapter Hypervisor Xen (based on simple earliest deadline first, i.e. SEDF scheduler) Xen (Credit scheduler) Dom0 uses CentOS 5.6 Varying number of instances are running on the physical machine 5

6 Hardware information (EC2) Released: E5507: Q1'10 E5430: Q4'07 E5645: Q1' HE:Q3 06 Newer processor models replace older ones progressively Hardware info varies significantly among different availability zones 6

7 Hardware information (Rackspace) 7

8 Outline Motivation Experimental configuration Experimental results Micro-benchmark CPU performance: UnixBench CPU scheduling: CPUBench (home made) Network throughput & latency: TCPBench, UDPBench (home made) Memory performance: RAMSpeed Disk performance: Bonnie++ Application benchmark Httperf Cost analysis Conclusion & future work 8

9 CPU performance: UnixBench 9

10 CPU Utilization and Scheduling 10

11 CPU Utilization and Scheduling (Cont.d) 11

12 Local emulation Different scheduling mechanisms are used in EC2, which exacerbate performance variations in the cloud. EC2 tries to improve the overall performance level of its instances by using newer hardware (e.g. A1-E5645) and allocating more CPU processing time (e.g. A1 can acquire close to 100% CPU time) for the new instances, but within certain limit, to avoid perceivable performance gap for cloud users. 12

13 Network Performance 13

14 Memory performance: RAMspeed 14

15 Disk Performance: Bonnie++ 15

16 Application performance: Httperf 16

17 Short summary of performance 17

18 Cost analysis Saving money by seeking for better performing instances, simply using trial-and-failure method Applying for instances randomly; Checking if performing well; If not, drop and apply for new ones. 18

19 Cost analysis (cont.d) 100 Cost saving(%) p=0.1 p=0.5 p=0.9 P: probability of the betterperforming instance in the overall instances Performance variation (times) 19

20 Conclusion Amazon EC2 uses diversified hardware to host the same type of instances. Hardware diversity is the primary culprit for performance variation in the cloud. Different VM scheduling mechanisms are used in EC2, which exacerbates performance variations, especially for networking related operations. In general, the variation between the fast instances and slow instances can reach 40%. In some applications, the variation can even approach up to 60%. By selecting fast instances within the same instance type, Amazon EC2 users can acquire up to 30% of cost saving, if the fast instances have a relatively low probability. 20

AS an industry-driven initiative, cloud computing gains a

AS an industry-driven initiative, cloud computing gains a IEEE TRANSACTIONS ON CLOUD COMPUTING, VOL. 1, NO. 2, JULY-DECEMBER 2013 201 Is the Same Instance Type Created Equal? Exploiting Heterogeneity of Public Clouds Zhonghong Ou, Member, IEEE, Hao Zhuang, Member,