Adaptive Server Allocation for Peer-assisted VoD
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1 Adaptive Server Allocation for Peer-assisted VoD Konstantin Pussep, Osama Abboud, Florian Gerlach, Ralf Steinmetz, Thorsten Strufe Konstantin Pussep Tel KOM - Multimedia Communications Lab Prof. Dr.-Ing. Ralf Steinmetz (Director) Dept. of Electrical Engineering and Information Technology Dept. of Computer Science (adjunct Professor) TUD Technische Universität Darmstadt Rundeturmstr. 10, D Darmstadt, Germany Tel , Fax KP_HotP2P-final.ppt 2009 author(s) of these slides including research results from the KOM research network and TU Darmstadt. Otherwise it is specified at the respective slide 19. April 2010
2 Motivation By 2013: 91% of global consumer traffic will be Video and P2P Video delivery dominates IP traffic Can server farms deliver all this videos? High costs: E.g. YouTube 1 Mio $/day [Huang2007] Scalability issues Servers must be dimensioned for peak demand KOM Multimedia Communications Lab 2 Data source: Cisco visual networking index, 2009
3 Video-on-Demand over Peer-to-Peer P2P promise Self-scalable, resources grow with demand handle flash crowds Cost-efficient no server costs Availability of local replicas less inter-domain traffic No service guarantees in pure P2P systems Insufficient upload capacity (link asymmetry) Unreliability and dynamics of user behavior Firewalls, NAT boxes etc. Peer-assisted systems Servers as backup (service guarantees) Peers to offload servers Challenge: Provide service guarantees at lowest possible server costs KOM Multimedia Communications Lab 3 Picture source p2p.weblogsinc.com/
4 Overview Motivation and Problem Statement Adaptive Allocation Policies Modeling Global Speed Supporter Evaluation Sensitivity analysis Comparison Summary and Next Steps KOM Multimedia Communications Lab 4
5 Scenario and Problem Statement Commercial video distribution User-generated content (YouTube etc.) Movie trailers (film studios) News Full-length movies etc. Content provider applies peer-assisted streaming to reduce distribution costs Assure high streaming quality Startup delay few seconds Stall time close to zero Streaming quality Server load How much server bandwidth should be allocated per peer and swarm over time? KOM Multimedia Communications Lab 5
6 Bandwidth Demand-Supply-Model Video bitrate Demand: D required = r f P Current downloaders Supply: Bandwidth matching (U D): U s S ' total u s = Peer contribution p P u p max Prefetching factor 1~2 g p P + s S ' ( r f u c Upload utilization <= 1 u f, g, and u p are unknown p g),0 Server contribution KOM Multimedia Communications Lab 6
7 Adaptive Server Allocation Policies Mechanism 1. Peers report their performance to the index server 2. Index server determines required server contribution and 3. allocates or disables servers 4. Servers upload to (some) peers to avoid streaming quality degradation Policy components Monitoring: Data and frequency Decision metric: How much resources are needed? Connection management: How to join the overlay, whom to serve, when to leave? KOM Multimedia Communications Lab 7
8 Global Speed Policy Idea: Peers report their download speed each X seconds Total average speed is calculated over last X seconds Target speed is video bitrate plus prefetching overhead Balance the average download speed at the target level Add or remove server bandwidth d d d average t arg et demand d p P = P = r = d f t arget p Estimated prefetching ' factor f d average KOM Multimedia Communications Lab 8
9 Supporter Policy Idea: Keep peers playout buffer full no stalling, fast startup Avoid unnecessary status reports Report only leaking playout buffers Avoid bad experience for a minority of peers If too many peers cannot fill playout buffers for some time Allocate servers as supporters Supporters Connect only to suffering peers Serve them until they recover KOM Multimedia Communications Lab 9
10 Downloader States Missing blocks in playout buffer DEFAULT WATCHED Buffer filled Supporter disconnected Playout buffer full WATCHED for suffertime Scheduled for support SUPPORTED At least minpeers are starving or supporter has free slots STARVING KOM Multimedia Communications Lab 10
11 Evaluation BitTorrent simulator by Bharambe et al. [Bharambe2006] Highly scalable Fair-share underlay model Additionally implemented: Give-to-Get [Mol2008] as underlying streaming protocol Adaptive policies: Global Speed and Supporter Static policies for comparison Assess the server contribution and user performance Startup and stalling times for fulfilled QoE requirements incl. outliers 50th and 95th percentiles Server load (uploaded data) KOM Multimedia Communications Lab 11
12 Basic Scenario Short videos with variable popularity and session durations Applicable for UG content (like YouTube), trailers, news Video: bitrate = 512 kbps, duration = 5 minutes 10 seconds playout buffers Server dimensioning Up to 10 (virtual) servers 2mbps upload capacity Peer capacities: 200 peers, 3 groups (30, 50, and 20% of peers) 256, 512, 1024 upload 2 mpbs download Peer behavior Exponential arrival rate (6 peers per second) Departure time: ~50% video length on average KOM Multimedia Communications Lab 12
13 Global Speed Policy Performance How to configure the target speed? Too low bad user experience Too high unnecessary server load Operating point: target speed = 1.5*video bitrate KOM Multimedia Communications Lab 13
14 Supporter Policy Performance Sensitivity analysis of relevant parameters minpeers: nr of suffering peers to allocate new servers maxpeers: to take care per supporter suffertime: when a peer really needs help KOM Multimedia Communications Lab 14
15 Policy Comparison (1) Can adaptive policies compete with perfect allocation? Static server allocation Popularity-based, predicted or manual Variable setups Global speed Target speed = 1.5*bitrate Supporter Default configuration Best static allocation Comparable performance For the best static and global speed policies Supporter policy is more efficient (startup) KOM Multimedia Communications Lab 15
16 Policy Comparison (2) Policy Server load Stalling (95%) Startup (95%) Startup (50%) Static (best) 1,75 GB 0s 31.0s 12,2s Global 1,60 GB 0s 29.0s 10,2s Supporter 1,78 GB 0s 15,5s 10,3s Observations Adaptive policies allow to meet streaming quality requirements Median performance similar to best static allocation Focusing on starving peers eliminates most of the outliers ( Supporter policy) KOM Multimedia Communications Lab 16
17 Summary Server allocation policies for peer-assisted VoD Guaranteed user performance Maximized peer contribution minimized server load Proposed policies Global Speed focus on average swarm performance Supporter focus on playout buffers and outliers Parameter study and comparison with static policies Adaptive allocation compete with perfect prediction or complement them Supporter policy is more efficient in outlier elimination KOM Multimedia Communications Lab 17
18 Next Steps Additional evaluations Large videos, more peers, diurnal traffic pattern Overhead measurements Prototype implementation based on the Tribler client: Performance signaling Connection management Upload policy of the server Additional mechanisms Server allocation among swarms Rate allocation for one super - server instead of many smaller (complementary) KOM Multimedia Communications Lab 18
19 Thank You! Questions? KOM Multimedia Communications Lab 19
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