Performance and Scalability of Networks, Systems, and Services

Transcription

1 Performance and Scalability of Networks, Systems, and Services Niklas Carlsson Linkoping University, Sweden Student Sweden, Oct. 10, 2012 Primary collaborators: Derek Eager (University of Saskatchewan, Canada) Anirban Mahanti (NICTA, Australia) Martin Arlitt (HP Labs, USA) György Dan (KTH, Sweden) Carey Williamson (University of Calgary, Canada) Phillipa Gill (University of Toronto, Canada) Aniket Mahanti (University of Auckland, New Zeeland) 1

2 Background Research overview: Niklas Carlsson Design, modeling, and performance evaluation of distributed systems and networks Methodologies include measurements analytic modeling, optimization, simulations, and implementation Current topics include Scalable content delivery Measurement analysis and modeling Energy-efficient and sustainable ICT Security and emerging services

3 Some recent example studies 3

4 Motivation Video dissemination (e.g., YouTube) can have widespread impacts on opinions, thoughts, and cultures 4

5 Motivation Not all videos will reach the same popularity and have the same impact 5

6 views Motivation Not all videos will reach the same popularity and have the same impact 6

7 Video popularity dynamics Example questions What makes one file more popular than another? How does popularity evolve over time? E.g., KDD 2012, Performance 2011, TWEB

8 Motivation 8 Content provider wanting to minimize its delivery cost Catalogue of many contents Different popularity Average service guarantees

9 Motivation Cost-efficient solution must scale with regards to both: Request rate Number of availible contents

10 Motivation Cost-efficient solution must scale with regards to both: Request rate Number of availible contents

11 Motivation Cost-efficient solution must scale with regards to both: Request rate Number of availible contents Chunk-based typically efficient

12 Motivation Cost-efficient solution must scale with regards to both: Request rate Number of availible contents Not as efficient in offloading server

13 Motivation Cost-efficient solution must scale with regards to both: Request rate Number of availible contents

14 Cost model cloud servers peers Client can download from either Origin servers (all contents) Cloud storage/servers (subset of contents) Other clients (peers)

15 Cost model cloud servers peers Client can download from either Origin servers (all contents) Cloud storage/servers (subset of contents) Other clients (peers)

16 Cost model cloud servers peers Client can download from either Origin servers (all contents) Cloud storage/servers (subset of contents) Other clients (peers)

17 Cost model cloud servers peers Client can download from either Origin servers (all contents) Cloud storage/servers (subset of contents) Other clients (peers)

18 Cost model cloud servers peers Client can download from either Origin servers (all contents) Cloud storage/servers (some contents) Other clients (peers)

19 Cost model cloud servers peers Client can download from either Origin servers (all contents) Cloud storage/servers (subset of contents) Other clients (peers)

20 Cost model cloud servers peers Simple cost model Server b/w Cloud b/w Cloud storage Three (3) basic cost components

21 Cost model cloud servers peers Server b/w Cloud b/w Cloud storage

22 Cost model cloud servers peers Server b/w Cloud b/w Cloud storage i N / M B s i c i M B c i C M

23 Cost model cloud servers peers Server b/w Cloud b/w Cloud storage i N / M B s i c i M B c i C M

24 Cost model cloud servers peers Server b/w Cloud b/w Cloud storage i N / M B s i c i M B c i C M

25 Cost model cloud servers peers Server b/w Cloud b/w Cloud storage i N / M B s i c i M B c i C M

26 Cost model cloud servers peers Server b/w Cloud b/w Cloud storage i N / M B s i c i M B c i C M

27 Remote access extension cloud servers peers i N Extensions Full version (in paper) B s i c i M j P i B c i, j (1 q ij f ij ) C i N P i

28 Hybrid (UT > L) Inflate most popular Depends less on cloud Seeding (only) Bundling (only) Hybrid

29 29

30 Passive measurements E.g., Organizational service usage Internet **Extract packet and protocol heathers, dns information, etc. ** Store securely ** Analyze ACM TWEB 11, ACM TOIT 11 30

31 Passive measurements E.g., Organizational service usage IEEE ICC 12 31

32 Popularity Active + passive measurement E.g., Peer-to-peer Head 10 6 Trunk 10 4 Tail 10 2 Zipf(1e+007,1) MZipf(1e+007,50,1) 10 0 GZipf(2e+005,0.02,1e-005,1) Rank Swarm Torrent IPTPS 09, IPTPS 10, IEEE P2P 11 32

33 Global vs. local access patterns Popularity dynamics Example questions Differences in access and service patterns? E.g., PAM 2012, TOIT 2011

34 Content Popularity Dynamics (and content discovery) How can users find content in this ocean of content? How are people creating and consuming content? What role social relationships play in content consumption? What fundamental laws govern content creation/consumption? A Tweet Movie B Under submission (and ongoing) 34

35 Active measurements E.g., C D B A ACM IMC '09, ICWSM '09 35

36 Mathematical modeling 36

37 Preferential Attachment (PA) Does PA explain the observed data? p i proportional to k α i For source node selection (Out-degree, α = 0.8) A B C For destination node selection (In-degree, α = 0.9) 37

38 Peer-to-peer dynamics Arrivals x(t) Downloaders Conversions Swarm Torrent dx dt dy dt ( x y) UC, ( x y) UC y Seeds y(t) Departures

39 Streaming using BT Downloader Downloader Seed Downloader Seed Arrivals Torrent (x downloaders; y seeds) Departures Much improved performance!! Peers (sorted by age) 39

40 Scalability and the long tail Consider a peer-assisted system Clients contribute their upload bandwidth when downloading a file from the server (files seeded only by server) Solves scalability problem, right? 40

41 Scalability and the long tail Consider a peer-assisted system Clients contribute their upload bandwidth when downloading a file from the server (files seeded only by server) Solves scalability problem, right? Perhaps not 41

42 Optimized Periodic Broadcasts Channel 1 Channel 2 Channel 3 Channel 4 Channel 5 Channel 6 r = segment streaming rate = 1 s = maximum # streams client listens to concurrently = 2 b = client data rate = s x r = 2 k length of first s segments: lk l1 l j r r j 1 length of segment k s: k 1 1 l k l j r j k s

43 Optimized Periodic Broadcasts Channel 1 Channel 2 Channel 3 Channel 4 Channel 5 Channel 6 r = segment streaming rate = 1 s = maximum # streams client listens to concurrently = 2 b = client data rate = s x r = 2 length of first s segments: length of segment k s: 1 lk r k 1 1 l k l j r j k s k 1 1 l1 l j r j 1 Including extension to non-linear media and/or multiple servers

44 Server selection problem A B C D Clients Requests / Demand? Task: Determine which replica should be serving each client at each point in time A B A C A A A A B Client schedule 44

45 Locality vs. aggregation Efficiency? use of a relatively small number of distributed servers, using request aggregation, may be as, or more, energy friendly than peer-assisted designs S But now have server selection problem S S C S S S C S C S C C S C C C network resource usage is minimized by selecting nearest server server resource usage is minimized by directing closely-spaced requests for the same file to the same server ACM TOMCCAP 2010; IEEE/ACM MASCOTS

46 Simple cost model Server i file delivery cost (concave due to aggregation) Fraction of requests served remotely Remote access cost per unit of data C N i 1 N C ( λ r ) cl i i i i 1 Total file delivery cost Server costs Network costs Policy dependent costs Neglected: Policy independent system costs 46

47 Example results N = 16, c = 0.5, equal request rates Candidate policy gives close to optimal performance 47

48 Workload management (and/or traffic/workload mixing) Maximize server resource usage Prioritized delay-sensitive workload(s) Background delay-tolerant workload(s) Workload management Split vs. shared resources 48

49 Demand Management ACM/SPEC ICPE 11 Steady state Diurnal 49

50 Mobile CDN and intelligent proxies making the mobile Web faster... and more efficient. 50

51 Scalability and efficiency matters -- Making the mobile Web cheaper Bandwidth, processing, energy, and other resources costs money Bandwidth, energy, other resources 51

52 Scalability and efficiency matters -- Making the mobile Web cheaper Bandwidth, processing, energy, and other resources costs money Bandwidth, energy, other resources Goal: Improving user experience while also reducing overall delivery cost, resource and energy usage... 52

53 Consider a proxy-based approach Move complexity to proxies Optimize processing, as well as server-proxy and proxy-client communication 53

54 Requirement prediction improving service and system utilization. 54

55 Many other ongoing projects incl. Scalable and Efficient Content Delivery Adaptive streaming How to best allow server selection and quality adaption to be done dynamically?? Hybrid delivery systems: How to best leverage and combine different techniques and solutions?? Emerging services: E.g., how to best enable emerging service; e.g., participatory sensing and smart grid solutions?? Non-linear media: How to best serve non-linear (or customizable) media?? Sustainability: How to best build sustainable ICT systems, and/or how to use ICT to improve sustainability of non-ict systems?? 55

56 Long-term goal (or mission statement ) Contribute towards providing better and more resource effective services and systems Focus on performance and resource usage/cost aspects of protocols, services, and systems Focus efforts towards the problems that we believe have the potential to yield the most significant contributions Try to use the best tools for the problem at hand 56

57 A final note I am looking for ambitious hardworking students (both thesis and PhD positions). If you are interested in one (or more) topic(s) related to computer networks and distributed systems, please do not hesitate to contact me... There are always lots interesting problems!!! You can also find out more about my research here: 57

58 Thank you! Scalable content delivery Traffic measurements, analysis, and modeling Energy-efficient and sustainable ICT Security and emerging services Niklas Carlsson 58