Magellan Final Report - Office of Science - U.S. Department of Energy

More documents

Recommendations

Info

Magellan Final Report 24-Hour Results The 24-hour cycle tests enable us to study potential changes in performance from typical usage patterns such as peak vs non-peak usage of cloud services. The results in Figure 9.19 shows the timed benchmark results on Amazon and NERSC systems. Figure 9.19a shows I/O performance on the global scratch file system at NERSC. The benchmark was run for a complete 24-hour duration starting at 12 noon PDT. Since the shared file system is being accessed by many users during the daytime, the performance degrades at both ends of the spectrum. After the peak processing hours, there is significant improvement in I/O performance due to limited contention for the shared resources. The I/O performance graphs for the timed benchmark on various virtual environments show more periodic results with occasional spikes. The reason for the occasional spikes is not known at the current time and will be investigated in future work. Figure 9.19g, showing a 24-hour run of the test starting at 12 midnight PDT, suggests a more consistent I/O performance on local disk of a VM instance. The graphs showing the results for EC2 instances clearly signify that the I/O performance doesn’t follow a specific pattern over time, but most of results are either consistent or periodic over time. Figure 9.19c and Figure 9.19e show the I/O performance on local disks for different instances. The occasional drop in performance may be due to the sharing of underlying resources. On the other hand, Figure 9.19d, Figure 9.19f, and Figure 9.19b show the performance graphs on EBS volumes over the small, large, and Cluster Compute instances respectively. Since EBS volumes are attached to every instance separately over the network, there is no sharing of disks or volumes; but interestingly there are certain spikes in the graphs which may be attributed to the network traffic. Large-Scale Tests These tests are run to gauge the variability of performance for multiple instances over a certain time period. These tests are run on 25 Amazon EC2 small instances on local disk in each region (US East and US West) in the same time period but on different days. All the tests were executed on a peak usage period of cloud resources on both the east and the west zones. We selected the time period between 11:00 AM and 2:00 PM PDT or 2:00 PM to 5:00 PM EDT. Timed benchmark was used to capture readings at regular intervals for one hour to understand the variability among instances. The histogram plots and the kernel density plots for the tests are shown in Figure 9.20. The histogram plots show the frequency distribution of throughput performance, and the density plots using the Gaussian kernel show the corresponding probability density function for the throughput variance. These large-scale tests reveal important characteristics of I/O performance on the US East and US West regions. Mean performance in the east zone has a throughput of 30 MB/sec. The area of the graph in Figure 9.20c with the throughput variance between 10 MB/sec–20 MB/sec and 35 MB/sec–45 MB/sec fall under less than one standard deviation from the mean. The peak performance in the east zone is 60 MB/sec. The performance in the west zone, shown in Figure 9.20d, varies significantly, with the peak performance going up to as high as 88 MB/sec. The mean performance in the west zone is between 10 MB/sec–20 MB/sec, which shows a high standard deviation in the west zone. Discussion The performance on virtual hosts tends to show a fair amount of variability due to the contention and sharing of underlying resources. Buffer Caching. Based on our testing, buffer caching does not seem to be enabled on virtualized resources. Scientific applications running in HPC centers are able to use high performance file systems such as GPFS that show significantly higher peak performance than what is seen in today’s virtual environments. This can have a significant impact on overall application performance for I/O intensive applications. Storage Options on VM. A virtual machine has ephemeral local disk and has the option to mount an elastic block storage volume. Typically, the performance of the local disk tends to be slightly higher than 70
Magellan Final Report Histogram Plot− East Zone Histogram Plot− West Zone Frequency 0 100 200 300 400 500 Frequency 0 200 400 600 800 0 10 20 30 40 50 60 Throughput (MB/sec) (a) Large Scale Test on Amazon US-East Region Kernel Density Estimation− East Zone 0 20 40 60 80 100 Throughput (MB/sec) (b) Large Scale Test on Amazon US-West Region Kernel Density Estimation− West Zone Density 0.00 0.01 0.02 0.03 Density 0.00 0.01 0.02 0.03 0.04 0.05 0 10 20 30 40 50 60 Throughput (MB/sec) (c) Large Scale Test on Amazon US-West Region 0 20 40 60 80 Throughput (MB/sec) (d) Large Scale Test on Amazon US-West Region Figure 9.20: Large Scale Test Results Histogram and Kernel Density Plots. 71
Page 1 and 2:
The Magellan Report on Cloud Comput
Page 3 and 4:
Executive Summary The goal of Magel
Page 5 and 6:
Key Findings The goal of the Magell
Page 7 and 8:
Magellan Final Report Finding 8. DO
Page 9 and 10:
Magellan Final Report role in addre
Page 11 and 12:
Contents Executive Summary Key Find
Page 13 and 14:
Magellan Final Report 9.7 Discussio
Page 15 and 16:
Chapter 1 Overview Cloud computing
Page 17 and 18:
Magellan Final Report • The Argon
Page 19 and 20:
Chapter 2 Background The term “cl
Page 21 and 22:
Magellan Final Report 2.1.4 Hardwar
Page 23 and 24:
Magellan Final Report Table 3.1: Ke
Page 25 and 26:
Magellan Final Report Little Magell
Page 27 and 28:
Magellan Final Report 3.2 Advanced
Page 29 and 30:
Chapter 4 Application Characteristi
Page 31 and 32:
Magellan Final Report Table 4.1: Pe
Page 33 and 34: Magellan Final Report Output data
Page 35 and 36: Magellan Final Report of the pipeli
Page 37 and 38: Chapter 5 Magellan Testbed As part
Page 39 and 40: Magellan Final Report Figure 5.1: P
Page 41 and 42: Magellan Final Report Figure 5.2: P
Page 43 and 44: Magellan Final Report NERSC deploye
Page 45 and 46: Magellan Final Report Figure 6.1: A
Page 47 and 48: Magellan Final Report greater than
Page 49 and 50: Magellan Final Report specific QoS
Page 51 and 52: Magellan Final Report configuration
Page 53 and 54: Magellan Final Report 7.4 Summary U
Page 55 and 56: Magellan Final Report Firewalls are
Page 57 and 58: Magellan Final Report Aside from le
Page 59 and 60: Magellan Final Report 9.1 Understan
Page 61 and 62: Magellan Final Report grid) on 256
Page 63 and 64: Magellan Final Report Table 9.1: HP
Page 65 and 66: Magellan Final Report 25  Ping 
Page 67 and 68: Magellan Final Report 100  12 
Page 69 and 70: Magellan Final Report case of GTC,
Page 71 and 72: Magellan Final Report 1.4 IB TCPo
Page 73 and 74: Magellan Final Report only affects
Page 75 and 76: Magellan Final Report Figure 9.11:
Page 77 and 78: Magellan Final Report charted as a
Page 79 and 80: Magellan Final Report Evaluation Cr
Page 81 and 82: Magellan Final Report Write Perform
Page 83: Magellan Final Report 3500 3000 G
Page 87 and 88: Magellan Final Report SATA devices.
Page 89 and 90: Magellan Final Report MB/s Virident
Page 91 and 92: Magellan Final Report and the perfo
Page 93 and 94: Magellan Final Report (a) Hosts (b)
Page 95 and 96: Magellan Final Report Routing IP pa
Page 97 and 98: Chapter 10 MapReduce Programming Mo
Page 99 and 100: Magellan Final Report 10.3 Hadoop E
Page 101 and 102: Magellan Final Report 35000  3500
Page 103 and 104: Magellan Final Report summarize som
Page 105 and 106: Magellan Final Report Processing ti
Page 107 and 108: Magellan Final Report in the networ
Page 109 and 110: Magellan Final Report Workload Patt
Page 111 and 112: Magellan Final Report This benchmar
Page 113 and 114: Magellan Final Report Task Tracker
Page 115 and 116: Magellan Final Report processing ti
Page 117 and 118: Magellan Final Report Using ESnet
Page 119 and 120: Magellan Final Report Figure 11.2:
Page 121 and 122: Magellan Final Report data collecte
Page 123 and 124: Magellan Final Report comparison to
Page 125 and 126: Magellan Final Report 11.2.5 Integr
Page 127 and 128: Magellan Final Report very large (4
Page 129 and 130: Magellan Final Report for optimizat
Page 131 and 132: Magellan Final Report One of the ad
Page 133 and 134: Magellan Final Report commercial cl
Page 135 and 136:
Magellan Final Report Table 12.2: H
Page 137 and 138:
Magellan Final Report Cost per TF t
Page 139 and 140:
Magellan Final Report Productivity.
Page 141 and 142:
Magellan Final Report compute insta
Page 143 and 144:
Chapter 13 Conclusions Cloud comput
Page 145 and 146:
Magellan Final Report Inherently, t
Page 147 and 148:
Bibliography [1] G. Aldering, G. Ad
Page 149 and 150:
Magellan Final Report [30] I. Foste
Page 151 and 152:
Magellan Final Report [67] M. Palan
Page 153 and 154:
Appendix A Publications Selected Pr
Page 155 and 156:
Magellan Final Report Magellan Rese
Page 157 and 158:
Magellan Final Report Selected Mage
Page 159 and 160:
Appendix B Surveys B1
Page 161 and 162:
• Nuclear Physics - Accelarator P
Page 163 and 164:
Allow users to edit responses. What
Page 165 and 166:
Amazon Eucalyptus OpenStack Other:
Page 167 and 168:
Please list any publications/report
Page 169 and 170:
Hadoop Streaming Hadoop Native Prog
show all

Magellan Final Report - Office of Science - U.S. Department of Energy

Create successful ePaper yourself

Delete template?

Save as template?