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Optimizing Cost and Performance for Content Multihoming. SIGCOMM’12 -Piggy, 2013.03.18. Outline. What is Content Multihoming Goal Control Framework Global Optimization Local Adaptation Evalution. Content Multihoming. CDN Diversity. CDN DIVERSITY. CDN DIVERSITY. Goal. - PowerPoint PPT Presentation
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OPTIMIZING COST AND PERFORMANCE FOR CONTENT MULTIHOMING
SIGCOMM’12-PIGGY, 2013.03.18
OUTLINE• What is Content Multihoming• Goal• Control Framework• Global Optimization• Local Adaptation• Evalution
CONTENT MULTIHOMING
CDN DIVERSITY
CDN DIVERSITY
CDN DIVERSITY
GOAL• Algorithms and protocols that optimize
• Content publisher cost• Content viewer performance
• A content object can be delivered from multiple CDNs, which CDN(s) should a content viewer use?
NOTATION
CONTROL FRAMEWORK
PASSIVE VS. ACTIVE CLIENT• Passive client
• Use one CDN edge server at a time• Active client
• Adaptation algorithm• Multiple CDN servers for a single content object
PROBLEM STATEMENT (Q)• QoE guarantee
• CDN k is providing the required features to deliver content object i
• exceeds the performance target• Cost optimization
• Balance load to multiple CDNs to minimize total cost
ACTIVE CLIENT• Virtual CDN
• Primary CDN• Backup CDN• k’ = (k, j)
COMPUTING OPTIMIZATION(CMO)• Problem Q has an optimal solution which
assigns a location object into a single CDN
• K|A|N
BASIC IDEA
EXTENSION • CDN subscription levels
• Fix fee to different usage levels• Different levels as an individual CDN
• Per-request cost• Extend vector dimension to R+1
• Multiple streaming rates• Independent content objects
LOCAL ADAPTATION• QoE protection• Prioritized guidance• Low session overhead
LOCAL ADAPTATION• Similar to TCP AIMD• Total workload control• Priority assignment
EVALUATION SETTING
COST SAVING
COST SAVING
ACTIVE CLIENT SETTING• Clients
• 500+ Planetlab nodes with Firefox 8.0 + Adobe Flash 10.1
• Two CDNs• Amazon CloudFront• CDN3
ACTIVE CLIENT TEST CASE
STRESS TESTS (STEP-DOWN)
STRESS TESTS (RAMP-DOWN)
STRESS TESTS (OSCILLATION)
ACTIVE CLIENT QOE GAIN
CONCLUSION• We develop and implement a two-level
approach to optimize cost and performance for content multihoming: • CMO: an efficient algorithm to minimize publisher cost
and satisfy statistical performance constraints• Active client: an online QoE protection algorithm to
follow CMO guidance and locally handle network congestions or server overloading
Q&A