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Exploring off path caching with edge caching in information centric networking slides

  1. 1. Exploring Off-Path Caching with Edge Caching in Information Centric Networking* Anshuman Kalla, Sudhir Sharma 1Anshuman Kalla * Proc. IEEE International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), New Delhi, India, March 11, 2016. For reading mode, Adobe Reader – use Ctrl + H and Foxit Reader – use F11
  2. 2. Introduction • Foundation of current (TCP/IP) networking was laid down in early 70s when – Networking resources were scarce – Multiple-accessing of resources was of prime importance – This implies years of experience and mature networking facility 2Anshuman Kalla
  3. 3. Introduction • Foundation of current (TCP/IP) networking was laid down in early 70s when – Networking resources were scarce – Multiple-accessing of resources was of prime importance – This implies years of experience and mature networking facility • Additional support from numerous growth boosters like – emergence of high speed data communication links, – refinement in multi-core processors technology, – exponential and consistent dip in cost of data storage etc. – flooding of economic hand-held networking devices, – simultaneous multiple connectivities 3Anshuman Kalla
  4. 4. Introduction • Foundation of current (TCP/IP) networking was laid down in early 70s when – Networking resources were scarce – Multiple-accessing of resources was of prime importance – This implies years of experience and mature networking facility • Additional support from numerous growth boosters like – emergence of high speed data communication links, – refinement in multi-core processors technology, – exponential and consistent dip in cost of data storage etc. – flooding of economic hand-held networking devices, – simultaneous multiple connectivities, • Thus we expect flawless evolution & facility to be at its best4Anshuman Kalla
  5. 5. Introduction • In spite of years of maturity & technological advancements – Networking facility falls short of users’ expectations – The growth seems to retard 5Anshuman Kalla
  6. 6. Introduction • In spite of years of maturity & technological advancements – Networking facility falls short of users’ expectations – The growth seems to retard • The issues that have in a way plagued the current TCP/IP networking are: – Data Dissemination & Service Accessing (prominent usage) – Named Host (i.e. no contents due to DNS mapping) – Mobility (change in IP leads to ongoing applications restart) – Availability (of content or services preferably close to users) – Security (absence of data level security) – Flash Crowd (leads to congestion, DoS, poor QoS etc.) 6Anshuman Kalla
  7. 7. Introduction • In spite of years of maturity & technological advancements – Networking facility falls short of users’ expectations – The growth seems to retard • The issues that have in a way plagued the current TCP/IP networking are: – Data Dissemination & Service Accessing (prominent usage) – Named Host (i.e. no contents due to DNS mapping) – Mobility (change in IP leads to ongoing applications restart) – Availability (of content or services preferably close to users) – Security (absence of data level security) – Flash Crowd (leads to congestion, DoS, poor QoS etc.) • Trend is to deploy dedicated fix for every issue encountered7Anshuman Kalla
  8. 8. The Facts • First Fact: Increasing add-on patches for various issues – Has transformed TCP/IP into complex and delicate architecture 8Anshuman Kalla
  9. 9. The Facts • First Fact: Increasing add-on patches for various issues – Has transformed TCP/IP into complex and delicate architecture • Second Fact: Today resources are no more limited – In fact more number of networking enabled devices per person 9Anshuman Kalla
  10. 10. The Facts • First Fact: Increasing add-on patches for various issues – Has transformed TCP/IP into complex and delicate architecture • Second Fact: Today resources are no more limited – In fact more number of networking enabled devices per person • Third Fact: Shift in primary usage of networking facility – instead of sharing of network resources the prime usage is content centric 10Anshuman Kalla
  11. 11. The Facts • First Fact: Increasing add-on patches for various issues – Has transformed TCP/IP into complex and delicate architecture • Second Fact: Today resources are no more limited – In fact more number of networking enabled devices per person • Third Fact: Shift in primary usage of networking facility – instead of sharing of network resources the prime usage is content centric 11Anshuman Kalla Thus radical change in its usage is the crux of various issues
  12. 12. Information Centric Networking • Lately researchers have felt the need of clean-slate approach – To reconcile all the issues and shift in usage in a unified manner – This marks the birth of Information Centric Networking (ICN) 12Anshuman Kalla
  13. 13. Information Centric Networking • Lately researchers have felt the need of clean-slate approach – To reconcile all the issues and shift in usage in a unified manner – This marks the birth of Information Centric Networking (ICN) • Various proposals are CCN, PSIRP, DONA, PURSUIT etc. • Albeit design details are different nevertheless all aim – to retire host-centric & bring in place content-centric model 13Anshuman Kalla
  14. 14. Information Centric Networking • Lately researchers have felt the need of clean-slate approach – To reconcile all the issues and shift in usage in a unified manner – This marks the birth of Information Centric Networking (ICN) • Various proposals are CCN, PSIRP, DONA, PURSUIT etc. • Albeit design details are different nevertheless all aim – to retire host-centric & bring in place content-centric model • Content Centric Networking (CCN) has received significant popularity – Thus for present work CCN and its related terminology has been used. 14Anshuman Kalla
  15. 15. Salient Features of ICN • Named content • In-network caching • Named based routing • Data-level security • Multi-path routing • Hop-by-hop flow control • Pull-based communication • Adaptability to Multiple simultaneous connectivities 15Anshuman Kalla
  16. 16. Salient Features of ICN • Named content • In-network caching  secondary point-of-service • Named based routing • Data-level security • Multi-path routing • Hop-by-hop flow control • Pull-based communication • Adaptability to Multiple simultaneous connectivities 16Anshuman Kalla
  17. 17. Types of In-Network Caching 17Anshuman Kalla In-Network Caching in ICN Off-Path Caching Edge CachingOn-Path Caching Hybrid Caching
  18. 18. Types of In-Network Caching 18Anshuman Kalla • On-Path Caching • Off-Path Caching • Edge Caching R1 R2 R3R4 R5R8 R7 R6 Interest Packet
  19. 19. Types of In-Network Caching 19Anshuman Kalla Interest Packet Data Packet R1 R2 R3R4 R5R8 R7 R6 Nodes that could cache data are R1 R2 R3 and R6 • On-Path Caching • Off-Path Caching • Edge Caching
  20. 20. Types of In-Network Caching 20Anshuman Kalla • On-Path Caching • Off-Path Caching • Edge Caching Interest Packet R1 R2 R3R4 R5R8 R7 R6 Node R4 is designated off-path cache
  21. 21. Types of In-Network Caching 21Anshuman Kalla • On-Path Caching • Off-Path Caching • Edge Caching Interest Packet R1 R2 R3R4 R5R8 R7 R6 Data Packet Node R4 is designated off-path cache
  22. 22. Types of In-Network Caching 22Anshuman Kalla • On-Path Caching • Off-Path Caching • Edge Caching Interest Packet R1 R2 R3R4 R5R8 R7 R6
  23. 23. Types of In-Network Caching 23Anshuman Kalla • On-Path Caching • Off-Path Caching • Edge Caching Interest Packet Data Packet R1 R2 R3R4 R5R8 R7 R6 Node R6 is edge cache
  24. 24. Aim - First • To empirically compare the performance of on-path, off-path and edge caching [All Three] – Researchers already compared performance of on-path and edge caching techniques 24Anshuman Kalla
  25. 25. Aim - First • To empirically compare the performance of on-path, off-path and edge caching [All Three] – Researchers already compared performance of on-path and edge caching techniques • If marginal performance gap is affordable then edge caching is better as it involves only edge nodes (Ref this paper for references) 25Anshuman Kalla
  26. 26. Aim - First • To empirically compare the performance of on-path, off-path and edge caching [All Three] – Researchers already compared performance of on-path and edge caching techniques • If marginal performance gap is affordable then edge caching is better as it involves only edge nodes (Ref this paper for references) – However comparison of three would answer the questions • Which one of the three caching technique performs the best? • Is pervasive caching (i.e. caching at all nodes) really beneficial? 26Anshuman Kalla
  27. 27. Performance Metrics Used • Hit Ratio – Indicates availability of contents – Need to be maximized 27Anshuman Kalla
  28. 28. Performance Metrics Used • Hit Ratio – Indicates availability of contents – Need to be maximized • Average Retrieval Delay – Smaller the metric better is QoS perceived by users – Need to be minimized 28Anshuman Kalla
  29. 29. Performance Metrics Used • Hit Ratio – Indicates availability of contents – Need to be maximized • Average Retrieval Delay – Smaller the metric better is QoS perceived by users – Need to be minimized • Unique Contents Cached – Implies cache diversity – Need to be maximized 29Anshuman Kalla
  30. 30. Performance Metrics Used • Hit Ratio – Indicates availability of contents – Need to be maximized • Average Retrieval Delay – Smaller the metric better is QoS perceived by users – Need to be minimized • Unique Contents Cached – Implies cache diversity – Need to be maximized • Percentage of External Traffic – Signifies use of expensive external links and load on server – Need to be minimized 30Anshuman Kalla
  31. 31. Environment Set-up & Parameters Used • Six real network topologies were considered: – Abilene (12 Core nodes), Geant (22), Germany50 (50), India35 (35), Exodus US (79) & Ebone Europe (87) • Number of server – One • Randomly nodes connected to server – 7% to 8% • Randomly nodes connected to clients – 50% to 55% • Size of content population – 1000 * number of core nodes • Cache size per node – 100 • Network cache budget – 10% of content population • Popularity distribution – Zipfian (α = 0.8) • Distance from edge nodes to server – 100 ms • Content Size – homogeneous (unit size) • Network Regime – Congestion free • Replacement policy – LRU • Forwarding over shortest path based on link latency • Total number of requests simulated – 5,00,000 31
  32. 32. Result of Performance Evaluation 32Anshuman Kalla • Six different network topologies and three different caching techniques results in – eighteen different scenarios
  33. 33. Result of Performance Evaluation 33Anshuman Kalla • Six different network topologies and three different caching techniques results in – eighteen different scenarios • Ten simulations per scenario and results depicts mean values with standard deviation
  34. 34. Result of Performance Evaluation 34Anshuman Kalla • Six different network topologies and three different caching techniques results in – eighteen different scenarios • Ten simulations per scenario and results depicts mean values with standard deviation • Overall values of hit ratio or average retrieval delay is computed by considering all the requests concerning all the contents
  35. 35. Result of Performance Evaluation 35Anshuman Kalla • Six different network topologies and three different caching techniques results in – eighteen different scenarios • Ten simulations per scenario and results depicts mean values with standard deviation • Overall values of hit ratio or average retrieval delay is computed by considering all the requests concerning all the contents
  36. 36. Result of Performance Evaluation 36Anshuman Kalla • Six different network topologies and three different caching techniques results in – eighteen different scenarios • Ten simulations per scenario and results depicts mean values with standard deviation • Overall values of hit ratio or average retrieval delay is computed by considering all the requests concerning all the contents
  37. 37. Result of Performance Evaluation 37Anshuman Kalla • Six different network topologies and three different caching techniques results in – eighteen different scenarios • Ten simulations per scenario and results depicts mean values with standard deviation • Overall values of hit ratio or average retrieval delay is computed by considering all the requests concerning all the contents
  38. 38. Result of Performance Evaluation 38Anshuman Kalla • Six different network topologies and three different caching techniques results in – eighteen different scenarios • Ten simulations per scenario and results depicts mean values with standard deviation • Overall values of hit ratio or average retrieval delay is computed by considering all the requests concerning all the contents
  39. 39. Result of Performance Evaluation 39Anshuman Kalla • Six different network topologies and three different caching techniques results in – eighteen different scenarios • Ten simulations per scenario and results depicts mean values with standard deviation • Overall values of hit ratio or average retrieval delay is computed by considering all the requests concerning all the contents Though edge caching performs better than on- path caching, however off-path caching performs the best.
  40. 40. Cumulative External Traffic (Exodus US) 40Anshuman Kalla
  41. 41. Content-Wise Hit Ratio (Exodus US) 41Anshuman Kalla
  42. 42. Content-Wise Average Retrieval Delay 42Anshuman Kalla
  43. 43. Result of Performance Evaluation 43Anshuman Kalla
  44. 44. Off-Path Caching performs the best as compared to On-Path and Edge Caching 44Anshuman Kalla Conclusion & Motivation
  45. 45. Off-Path Caching performs the best as compared to On-Path and Edge Caching However lets review the content-wise average retrieval delay plot 45Anshuman Kalla Conclusion & Motivation
  46. 46. Conclusion & Motivation 46Anshuman Kalla Content-Wise Average Retrieval Delay
  47. 47. Conclusion & Motivation 47Anshuman Kalla Content-Wise Average Retrieval Delay Lets zoom this section
  48. 48. 48Anshuman Kalla Conclusion & Motivation Content-Wise Average Retrieval Delay
  49. 49. 49Anshuman Kalla Conclusion & Motivation Content-Wise Average Retrieval Delay Note the gap in terms of delay for top most popular contents
  50. 50. 50Anshuman Kalla Problem Targeted Content-Wise Average Retrieval Delay Is it possible to devise a caching technique that – could achieve minimum content-wise average retrieval delay for top most popular contents like edge caching while – maintaining overall performance very close to that of off-path caching
  51. 51. Aim - Second • To couple off-path with edge caching  hybrid  That could reduce average retrieval delay for the top most popular contents while 51Anshuman Kalla
  52. 52. Aim - Second • To couple off-path with edge caching  hybrid  That could reduce average retrieval delay for the top most popular contents while  Marginally scarifying other relevant performance metrics 52Anshuman Kalla We propose Hybrid Caching  Coupling Off-Path Caching with Edge Caching
  53. 53. EDOP (EDge Off-Path) Caching • Simple coupling results in two devitalizing issues – Reduction in cache diversity due to content duplication – Blind (edge) caching at boundary nodes  hog the limited space 53Anshuman Kalla
  54. 54. EDOP (EDge Off-Path) Caching • Simple coupling results in two devitalizing issues – Reduction in cache diversity due to content duplication – Blind (edge) caching at boundary nodes  hog the limited space • Flavor of edge caching is being introduced to off-path caching – Caches at the edge nodes are partitioned 54Anshuman Kalla
  55. 55. Anshuman Kalla 55 EDOP (EDge Off-Path) Caching R1 R2 R3R4 R5R8 R7 R6 Partitioned of Content Store at edge nodes
  56. 56. EDOP (EDge Off-Path) Caching • Simple coupling results in two devitalizing issues – Reduction in cache diversity due to content duplication – Blind (edge) caching at boundary nodes  hog the limited space • Flavor of edge caching is being introduced to off-path caching – Caches at the edge nodes are partitioned – Tuning parameter T  percentage of storage for edge caching 56Anshuman Kalla
  57. 57. Anshuman Kalla 57 EDOP (EDge Off-Path) Caching R1 R2 R3R4 R5R8 R7 R6 Partitioned of CS at edge nodes using Tuning Parameter ‘T’
  58. 58. EDOP (EDge Off-Path) Caching • Simple coupling results in two devitalizing issues – Reduction in cache diversity due to content duplication – Blind (edge) caching at boundary nodes  hog the limited space • Flavor of edge caching is being introduced to off-path caching – Caches at the edge nodes are partitioned – Tuning parameter T  percentage of storage for edge caching • Content selection to be made before edge caching – FIFO queue for reference counting i.e. popularity estimation 58Anshuman Kalla
  59. 59. Anshuman Kalla 59 EDOP (EDge Off-Path) Caching R1 R2 R3R4 R5R8 R7 R6 Partitioned of CS at edge nodes
  60. 60. EDOP (EDge Off-Path) Caching • Simple coupling results in two devitalizing issues – Reduction in cache diversity due to content duplication – Blind (edge) caching at boundary nodes  hog the limited space • Flavor of edge caching is being introduced to off-path caching – Caches at the edge nodes are partitioned – Tuning parameter T  percentage of storage for edge caching • Content selection to be made before edge caching – FIFO queue for reference counting i.e. popularity estimation • Pre-fetching of popular contents estimated by FIFO 60Anshuman Kalla
  61. 61. Results and Discussion Caching Hit Ratio Average Retrieval Delay Unique Cached Content On-Path 0.0944 (±0.0003) 100.7412 (±0.0352) 2527 (±13) Edge 0.1027 (±0.0003) 99.6784 (±0.0267) 5810 (±12) Off-Path 0.4637 (±0.0001) 84.4653 (±0.1751) 7900 (±0) EDOP 0.4545 (±0.0003) 84.0432 (±0.1914) 7465 (±2) 61Anshuman Kalla
  62. 62. Results and Discussion Caching Hit Ratio Average Retrieval Delay Unique Cached Content On-Path 0.0944 (±0.0003) 100.7412 (±0.0352) 2527 (±13) Edge 0.1027 (±0.0003) 99.6784 (±0.0267) 5810 (±12) Off-Path 0.4637 (±0.0001) 84.4653 (±0.1751) 7900 (±0) EDOP 0.4545 (±0.0003) 84.0432 (±0.1914) 7465 (±2) 62Anshuman Kalla
  63. 63. Results and Discussion Caching Hit Ratio Average Retrieval Delay Unique Cached Content On-Path 0.0944 (±0.0003) 100.7412 (±0.0352) 2527 (±13) Edge 0.1027 (±0.0003) 99.6784 (±0.0267) 5810 (±12) Off-Path 0.4637 (±0.0001) 84.4653 (±0.1751) 7900 (±0) EDOP 0.4545 (±0.0003) 84.0432 (±0.1914) 7465 (±2) 63Anshuman Kalla <1% <6%
  64. 64. Results and Discussion Caching Hit Ratio Average Retrieval Delay Unique Cached Content On-Path 0.0944 (±0.0003) 100.7412 (±0.0352) 2527 (±13) Edge 0.1027 (±0.0003) 99.6784 (±0.0267) 5810 (±12) Off-Path 0.4637 (±0.0001) 84.4653 (±0.1751) 7900 (±0) EDOP 0.4545 (±0.0003) 84.0432 (±0.1914) 7465 (±2) 64Anshuman Kalla <1% <6% •The gain achieved in content- wise average retrieval delay is between 88% to 3% for the top most popular contents •At the cost of max 6% deterioration in other relevant parameters
  65. 65. Results and Discussion 65Anshuman Kalla
  66. 66. Conclusion and Future Scope • The two fold contribution of the paper is as follow: – Empirically, it has been proven that off-path caching outperforms the on-path and edge caching techniques – Hybrid caching like EDOP caching has potential to improve performance of in-network caching 66Anshuman Kalla
  67. 67. Conclusion and Future Scope • The two fold contribution of the paper is as follow: – Empirically, it has been proven that off-path caching outperforms the on-path and edge caching techniques – Hybrid caching like EDOP caching has potential to improve performance of in-network caching • Issues that will be targeted in future are: – What should be the optimum value of T and how it should be determined? – How to ensure that edge caches retain the most popular contents? 67Anshuman Kalla
  68. 68. Thank You 68Anshuman Kalla

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