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Department of Computer Science
Empirical Evaluation ofLatency-Sensitive Application
Performance in the Cloud
Sean Barker and Prashant ShenoyUniversity of Massachusetts Amherst
University of Massachusetts Amherst - Department of Computer Science
Cloud Computing
! Cloud platforms built with data centers: large-scale, concentrated servers clusters• Machines rented out to
companies or individuals• Hosting for arbitrary applications• May supplement local resources
! Cheap enough to rent machines by the hour
2
Type CPUs Memory Disk Cost/hr
Small 1 1.7 GB 160 GB $0.085
Large 4 7.5 GB 850 GB $0.34
XL 8 15 GB 1690 GB $0.68
Current prices on Amazon Elastic Compute Cloud (EC2)
University of Massachusetts Amherst - Department of Computer Science
Multimedia Cloud Computing Scenarios
! Clouds designed primarily for web & e-commerce apps, but may also be used for multimedia
! Rent game server for an evening• No firewall or bandwidth issues, only a few dollars
! Rent high-CPU machines for HD video transcoding• Home PC may take several hours to transcode one video,
cloud can transcode many in a fraction of this time
! Rent servers for webcast of live event• Large, inexpensive temporary bandwidth allocation
3
! Data center servers are typically well-equipped• Providers share individual
machines machines among multiple users
! Example: one user runs game server, another runs high-performance database on same machine
! Multimedia has unique performance requirements• Low latency games, low jitter & high bandwidth streaming
! Are cloud platforms designed for conventional web applications suitable for multimedia?
University of Massachusetts Amherst - Department of Computer Science
Resource Sharing in the Cloud
4
8 GB RAM
Core 1
Core 2
Core 3
Core 4
1000 GB Disk
1000 GB Disk
4 GB RAM
Core 1
Core 2
Core 3
Core 4
1000 GB Disk
1000 GB Disk
4 GB RAM
University of Massachusetts Amherst - Department of Computer Science
Outline
! Motivation
! Virtualized clouds
! Amazon EC2 study
! Laboratory cloud study
! Real world multimedia case studies
! Related work & conclusions
5
University of Massachusetts Amherst - Department of Computer Science
Virtualized Clouds
! Cloud platforms are virtualized data centers! Virtualization facilitates machine distribution
among multiple users with virtual machines (VMs)
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VM
Hardware
VM VM
Game Server
Web Server
Media Server
Customer A
Users
Customer C
Customer B
! Each VM is assigned slice of physical resources! VM access to hardware managed by hypervisor• Enforces limits and isolates VMs from each other
! Are these resource sharing mechanisms suitable for the timeliness constraints of multimedia?
VM VM VM
AppA
App C
Users
App B
Hardware
Hypervisor
University of Massachusetts Amherst - Department of Computer Science
Virtual Machine Isolation
8
resourcestarvation
Hypervisor
VM VM VM
App A
Users
Hardware
App B App C
University of Massachusetts Amherst - Department of Computer Science
Outline
! Motivation
! Virtualized clouds
! Amazon EC2 study
! Laboratory cloud study
! Real world multimedia case studies
! Related work & conclusions
9
University of Massachusetts Amherst - Department of Computer Science
EC2 Study – Overview
! Amazon Elastic Compute Cloud (EC2)• Popular virtualized cloud platform
! Unknown applications coexisting on machine• No control over VM placement
! Goal: evaluate performance with unknown background server load
! Methodology: measured CPU, disk, and network consistency over period of days
10
University of Massachusetts Amherst - Department of Computer Science
EC2 CPU Performance
0
200
400
600
800
1000
1200
1400CPU time (ms)
Time (5 minute intervals)
EC2Local
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• Volatility on EC2 vs stability on dedicated server
2.5x average outliers:
1.5-2x avg
no competing VMs: no outliers
University of Massachusetts Amherst - Department of Computer Science
EC2 Disk Performance
0
10000
20000
30000
40000
50000
60000
70000
80000
90000Long write time (ms)
Time (5 minute intervals)
EC2Local
12
• Similarly: inconsistent EC2 disk performance
widely fluctuatingdisk performance
University of Massachusetts Amherst - Department of Computer Science
EC2 Network Latency (LAN)
0
50
100
150
200
250First three hops latency (ms)
Time (5 minute intervals)
13
• Latency variations in EC2 LAN
University of Massachusetts Amherst - Department of Computer Science
EC2 Study – Summary
! Performance variations observed on EC2• Not observed on local server running a single VM
! Can only speculate on causes without access to the hypervisor
! Need to experiment on a controlled platform similar to Amazon’s
14
University of Massachusetts Amherst - Department of Computer Science
Laboratory Cloud Study – Overview
! Local cloud running the Xen hypervisor• Same virtualization technology used by EC2• Advantage: local cloud gives us control of interference
! Built-in mechanisms for sharing hardware between VMs• CPU credit scheduler• Round-robin disk servicing• Linux-level tool tc for network sharing
! How well do these tools isolate background work?
! Methodology: evaluated performance impact of competing VM
15
University of Massachusetts Amherst - Department of Computer Science
CPU Performance with Background Load
0
50
100
150
200CPU time (ms)
Time (5 second intervals)
16
• Default 1 to 1 sharing with variable background load
No background work: VM gets 100% CPU
Max background work: VM gets 50% CPU
University of Massachusetts Amherst - Department of Computer Science
Disk Performance with Background Load
0
20
40
60
80
100
1 2 3 4 8
Performance Impact (%)
Disk Thread Pairs on Collocated VM
Fair ShareSmall Read
Small WriteRead Throughput
Write Throughput
17
• Degraded by half over ‘fair’, but stable with increasing load
‘unfair’ impact
University of Massachusetts Amherst - Department of Computer Science
Laboratory Cloud Study – Summary
! Significant interference possible from background VMs
! Xen configuration can guarantee share of CPU• Default settings allow fluctuation in shared CPU
! Disk sharing less fair and harder to control• Consistent with observed EC2 behavior
! Network sharing effects evaluated in case studies on laboratory cloud (next)
18
University of Massachusetts Amherst - Department of Computer Science
Case Study 1 – Doom 3 Game Server
! Multiplayer Doom 3 game server
! Introduced controlled interference as before
! Measured map load times and server latency
! Network sharing configuration via tc:• Idle: No bandwidth usage by resource-hog VM• Off (default): No rate-limiting, network free-for-all• Shared: 50% (min) to 100% (max) of bandwidth per VM• Dedicated: 50% (max) of bandwidth per VM
19
University of Massachusetts Amherst - Department of Computer Science
Game Server Map Load
0
1000
2000
3000
4000
5000
Idle Disk CPU Disk + CPU
Average Server Load Time (ms)
Collocated VM Activity
20
• Interference produces up to 50% degradation
University of Massachusetts Amherst - Department of Computer Science
Game Server Latency
21
! Server crippled without bandwidth controls (tc off)
! Dedicated vs shared bandwidth:• Dedicated: lower latency, higher jitter• Sharing: higher latency, lower jitter
Configuration Avg. Latency (ms)
Std. Deviation (jitter) Timeouts
No interference 8.1 10.2 0%
tc off (free-for-all) N/A N/A 100%
tc, sharing b/w 33.9 16.9 2%
tc, dedicated b/w 23.6 29.6 7%
University of Massachusetts Amherst - Department of Computer Science
Case Study 2 – Darwin Streaming Server
! Streaming video to multiple clients
! Introduced controlled interference as before
! Measured sustained streaming bandwidth and stream jitter (latency variation)
! Varied tc settings and number of clients• Max video stream rate of 1 Mbps per client
22
University of Massachusetts Amherst - Department of Computer Science
Streaming Server Bandwidth
0
200
400
600
800
1000
idle (fair) off shared dedicated
average bitrate per stream (kbps)
tc sharing type
4 streams8 streams
23
• both tc configurations recovered bandwidth
decreased stream quality
University of Massachusetts Amherst - Department of Computer Science
Streaming Server Jitter
0
2
4
6
8
10
12
14
16
idle (fair) off shared dedicated
average stream jitter (ms)
tc sharing type
4 streams8 streams
24
• Jitter improved by shared, but worsened by dedicated
University of Massachusetts Amherst - Department of Computer Science
Real World Case Studies – Summary
! Real applications show substantial impacts from background interference
! Network is particularly vulnerable without administrative controls
! Proper configuration is important• CPU and network isolation tools fairly well-developed• Disk isolation needs better mechanisms
25
University of Massachusetts Amherst - Department of Computer Science
Related Work
! Fair-share schedulers and quality-of-service• Nieh and Lam (SOSP ‘97) for multimedia• Sundaram et al. (ACM MM ‘00) for QoS-aware OS
! Virtualization and hypervisors• Xen, VMware ESX Server
! Improving performance isolation• Gupta et al. (Middleware ‘06) for Xen mechanisms
! We focus on evaluation of existing mechanisms with specific attention to multimedia
26
University of Massachusetts Amherst - Department of Computer Science
Conclusions
! Clouds exhibit performance variations• Applications with timeliness requirements are
particularly sensitive
! Appropriate hypervisor configuration can help• In some cases, prevents resource starvation• Some resource sharing mechanisms need improvement
! Future work: evaluation of non-Xen platforms
! Questions?
27