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MUTE: Multi-Tier Edge networks

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Paper PDF is available: https://dl.acm.org/citation.cfm?id=3195871
Accepted and presented at 5th Workshop on CrossCloud Infrastructures & Platforms, EuroSys Conference, April 2018

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MUTE: Multi-Tier Edge networks

  1. 1. MUTE: MUlti-Tier Edge networks 5th Workshop on CrossCloud Infrastructures & Platforms (CrossCloud’2018) Porto, Portugal Alessio Silvestro△✣ Nitinder Mohan✼✣ Jussi Kangasharju✼ Fabian Schneider△ Xiaoming Fu✝ △NEC Labs Europe ✼University of Helsinki ✝University of Göttingen CleanSky - EU FP7 Marie Curie Initial Training Network✣Joint First Authors
  2. 2. Rise of Edge Clouds Network DatacenterEdge Server User Edge Cloud: Small-scale server(s) deployed at network edge to compute user data Motivation: 2
  3. 3. Rise of Edge Clouds Network DatacenterEdge Server User Edge Cloud: Small-scale server(s) deployed at network edge to compute user data Motivation: üDecreased latency and network traffic 2
  4. 4. Rise of Edge Clouds Network Edge Cloud: Small-scale server(s) deployed at network edge to compute user data Motivation: üDecreased latency and network traffic üComputing data of local relevance 2
  5. 5. Rise of Edge Clouds Network Edge Cloud: Small-scale server(s) deployed at network edge to compute user data Motivation: üDecreased latency and network traffic üComputing data of local relevance üIdeal hosts for on-path vNFs (unlike dedicated middleboxes) 2
  6. 6. Placing SFC on Edge 3
  7. 7. Placing SFC on Edge 3
  8. 8. Placing SFC on Edge ES 1 ES 2 ES 3 ES 1 ES 2 3
  9. 9. Placing SFC on Edge ES 1 ES 2 ES 3 ES 1 ES 2 Requirement: Client to Cloud link must be composed of two service functions Firewall Monitor 3
  10. 10. Placing SFC on Edge ES 1 ES 2 ES 3 ES 1 ES 2 Requirement: Client to Cloud link must be composed of two service functions Objective: Select two Edge Servers with least network and processing cost Firewall Monitor 3
  11. 11. Placing SFC on Edge ES 1 ES 2 ES 3 ES 1 ES 2 SFC 1Objective: Select two Edge Servers with least network and processing cost 3
  12. 12. Placing SFC on Edge ES 1 ES 2 ES 3 ES 1 ES 2 SFC 1 SFC 2 Objective: Select two Edge Servers with least network and processing cost 3
  13. 13. Selecting Optimal Edge Servers ES 1 ES 2 ES 3 ES 1 ES 2 ? ? Q. How do you select which Edge Server will host the service? 3
  14. 14. Selecting Optimal Edge Servers ESA 1 ESA 2 ESA 3 ESB 1 ESB 1 Existing approaches consider all Edge Servers in a single pool 4
  15. 15. Selecting Optimal Edge Servers Existing approaches consider all Edge Servers in a single pool Selection Algorithm (Iterative) ESA 1 ESA 2 ESA 3 ESB 1 ESB 1 selectedServer ← NONE leastproc, leastnetw ← client/service provider requirements for each S in Services do for each ES in EdgePlatform do Calculate ESproc Calculate ESNetw if ESproc < leastproc and ESnetw < leastnetw do selectedServer ← ES 4
  16. 16. Selecting Optimal Edge Servers: Problem! selectedServer ← NONE leastproc, leastnetw ← client/service provider requirements for each S in Services do for each ES in EdgePlatform do Calculate ESproc Calculate ESNetw if ESproc < leastproc and ESnetw < leastnetw do selectedServer ← ES Place " services on # servers → NP-hard 5
  17. 17. Selecting Optimal Edge Servers: Problem! selectedServer ← NONE leastproc, leastnetw ← client/service provider requirements for each S in Services do for each ES in EdgePlatform do Calculate ESproc Calculate ESNetw if ESproc < leastproc and ESnetw < leastnetw do selectedServer ← ES No Scalability! Place " services on # servers → NP-hard 5
  18. 18. MUTE: MUlti-Tier Edge
  19. 19. Edge Platform Perspective Client Server ID: 1 Processing: 3 Bandwidth: 25 Server ID: 2 Processing: 4 Bandwidth: 20 Server ID: 3 Processing: 6 Bandwidth: 30 Edge Platform Cloud 7
  20. 20. Edge Platform Perspective Client Server ID: 1 Processing: 3 Bandwidth: 25 Server ID: 2 Processing: 4 Bandwidth: 20 Server ID: 3 Processing: 6 Bandwidth: 30 Edge Platform Delay= 3 Delay = 2 Delay = 6 Delay = 5 Delay = 6 Delay = 2 Cloud 7
  21. 21. Edge Platform Perspective Client Server ID: 1 Processing: 3 Bandwidth: 25 Server ID: 2 Processing: 4 Bandwidth: 20 Server ID: 3 Processing: 6 Bandwidth: 30 Edge Platform Delay= 3 Delay = 2 Delay = 6 Delay = 5 Delay = 6 Delay = 2 End-to-End delay = 8ms Cloud 7
  22. 22. Edge Platform Perspective Client Tier 1 Tier 2 Server ID: 1 Processing: 3 Bandwidth: 25 Server ID: 2 Processing: 4 Bandwidth: 20 Server ID: 3 Processing: 6 Bandwidth: 30 Edge Platform Delay= 3 Delay = 2 Delay = 6 Delay = 5 Delay = 6 Delay = 2 Cloud Tier ID: 1 Tier ID: 2 Tier ID: 1 7
  23. 23. Architecture ClientClientClient EdgePlatform Cloud Platform CS1 CS2 CS3 8
  24. 24. Architecture ClientClientClient EdgePlatform Cloud Platform CS1 CS2 CS3 Tier1 ES# # ES$ #ES% # Tier2 ES# % ES& % TierN ES# ' ES% ' ES( ' 8
  25. 25. Architecture ClientClientClient EdgePlatform CS 1 CS 2 CS n CS 1 CS 2 CS n Cloud Platform CS1 CS2 CS3 Tier1 ES# # ES$ #ES% # Tier2 ES# % ES& % TierN ES# ' ES% ' ES( ' 8
  26. 26. Architecture ClientClientClient EdgePlatform CS 1 CS 2 CS n CS 1 CS 2 CS n Cloud Platform CS1 CS2 CS3 Tier1 ES# # ES$ #ES% # Tier2 ES# % ES& % TierN ES# ' ES% ' ES( ' 8
  27. 27. Management Plane Architecture ClientClientClient EdgePlatform CS 1 CS 2 CS n CS 1 CS 2 CS n Cloud Platform CS1 CS2 CS3 Tier1 ES# # ES$ #ES% # Tier2 ES# % ES& % TierN ES# ' ES% ' ES( ' 8
  28. 28. Management Plane Architecture ClientClientClient EdgePlatform CS 1 CS 2 CS n CS 1 CS 2 CS n Cloud Platform CS1 CS2 CS3 Tier1 ES# # ES$ #ES% # Tier2 ES# % ES& % TierN ES# ' ES% ' ES( ' Cloud Service Provider 8
  29. 29. Management Plane Architecture ClientClientClient EdgePlatform CS 1 CS 2 CS n CS 1 CS 2 CS n Cloud Platform CS1 CS2 CS3 Tier1 ES# # ES$ #ES% # Tier2 ES# % ES& % TierN ES# ' ES% ' ES( ' Cloud Service Provider Edge Service Provider 8
  30. 30. Edge Tier Grouping Characteristics EdgePlatform Tier1 ES# # ES$ #ES% # Tier2 ES# % ES& % TierN ES# ' ES% ' ES( ' NetworkDelay(Edge) 9
  31. 31. Edge Tier Grouping Characteristics EdgePlatform Tier1 ES# # ES$ #ES% # Tier2 ES# % ES& % TierN ES# ' ES% ' ES( ' NetworkDelay(Edge) ProcessingPower 9
  32. 32. Edge Tier Grouping Characteristics EdgePlatform Tier1 ES# # ES$ #ES% # Tier2 ES# % ES& % TierN ES# ' ES% ' ES( ' NetworkDelay(Edge) UserBandwidth ProcessingPower 9
  33. 33. Edge Tier Grouping Characteristics EdgePlatform Tier1 ES# # ES$ #ES% # Tier2 ES# % ES& % TierN ES# ' ES% ' ES( ' NetworkDelay(Edge) UserBandwidth ProcessingPower ServerDensity 9
  34. 34. Service Placement on Edge 1. Service Placement Terminology 2. Service Operation Cost Optimization 3. Network Delay Optimization 4. Tier-based Optimization 10
  35. 35. Service Placement on Edge 1. Service Placement Terminology 2. Service Operation Cost Optimization 3. Network Delay Optimization 4. Tier-based Optimization For details on optimizations 2 and 3, please refer to the paper 10
  36. 36. Service Placement on Edge Tier-based Optimization Exploits the assumption that lower tiers have smaller delay to clients! selectedServer ← NONE leastnetw ← NONE for each t in tiers do if '()* +, ≥ (+, and '()* ./01 ≥ (./01 do leastnetw ← ∞ for each ES in t do if '(* +, ≥ (+, and '(* ./01 ≥ (./01 do Calculate ESnetw if '(56*, < 89:;<56*, do leastnetw ← ESnetw selectedServer ← ES 11
  37. 37. Service Placement on Edge Tier-based Optimization selectedServer ← NONE leastnetw ← NONE for each t in tiers do if '()* +, ≥ (+, and '()* ./01 ≥ (./01 do leastnetw ← ∞ for each ES in t do if '(* +, ≥ (+, and '(* ./01 ≥ (./01 do Calculate ESnetw if '(56*, < 89:;<56*, do leastnetw ← ESnetw selectedServer ← ES Check if Tier meets service bandwidth and processing requirements 11
  38. 38. Service Placement on Edge Tier-based Optimization selectedServer ← NONE leastnetw ← NONE for each t in tiers do if '()* +, ≥ (+, and '()* ./01 ≥ (./01 do leastnetw ← ∞ for each ES in t do if '(* +, ≥ (+, and '(* ./01 ≥ (./01 do Calculate ESnetw if '(56*, < 89:;<56*, do leastnetw ← ESnetw selectedServer ← ES Filter Edge Servers in the Tier which can support the service requirements 11
  39. 39. Service Placement on Edge Tier-based Optimization selectedServer ← NONE leastnetw ← NONE for each t in tiers do if '()* +, ≥ (+, and '()* ./01 ≥ (./01 do leastnetw ← ∞ for each ES in t do if '(* +, ≥ (+, and '(* ./01 ≥ (./01 do Calculate ESnetw if '(56*, < 89:;<56*, do leastnetw ← ESnetw selectedServer ← ES From filtered list, select Edge Server with least network delay to client 11
  40. 40. Benefits of Tier-Optimization 1. Approximation based algorithm → handles scalability 12
  41. 41. Benefits of Tier-Optimization 1. Approximation based algorithm → handles scalability 2. Optimization problem only considers subset of servers → faster operation 12
  42. 42. Benefits of Tier-Optimization 1. Approximation based algorithm → handles scalability 2. Optimization problem only considers subset of servers → faster operation 3. Demarcation between optimization variables i. Processing Cost and Bandwidth → Best-Fit optimization ii. Networking Cost → Intra-Tier minimization 12
  43. 43. Evaluation
  44. 44. Evaluation Evaluated on Python simulator with RocketFuel topologies Parameters: 1. 61 network graphs of 25-115 nodes 2. Upto 100 edge servers deployed on network topology graphs leading to ≈6000 edge networks 3. Edge servers are clustered in upto 4 tiers 4. Processing and bandwidth capability based on tier membership 14
  45. 45. Evaluation Compared Mute with three other placement algorithms 1. Network Optimizing Server Selection (Netw): iterative search of edge server with least network cost of service deployment 2. Processing Optimizing Server Selection (Proc): iterative search of edge server with least associated processing cost of deploying service 3. Edge-Network Optimizing Server Selection (EdgeNetw): iterative search for edge server with least network cost to client. The algorithm is unaware of multi-tier architecture 15
  46. 46. Evaluation Objective: Find an edge server !" for hosting service " with processing requirement "#$%& and bandwidth requirement "'( 16
  47. 47. Evaluation Objective: Find an edge server !" for hosting service " with processing requirement "#$%& and bandwidth requirement "'( 1. Network cost of selected !" to client 2. Processing cost of selected !" to client 3. Time taken by placement algorithms to find !" 16
  48. 48. Network Cost Comparison 17
  49. 49. Network Cost Comparison 66% reduction in network delay 17
  50. 50. Network Cost Comparison 66% reduction in network delay Optimization of Edge vs. End-to-End network delay 17
  51. 51. Processing Cost Comparison 18
  52. 52. Processing Cost Comparison 20% increase in processing cost (compared to Netw) 18
  53. 53. Processing Cost Comparison 20% increase in processing cost (compared to Netw) Minimization optimization vs. Best-Fit optimization 18
  54. 54. Time Complexity Comparison 19
  55. 55. Time Complexity Comparison Upto 50% reduction in time complexity 19
  56. 56. Time Complexity Comparison Upto 50% reduction in time complexity Always performs better than state-of-the-art 19
  57. 57. Summary 1. MUTE is a multi-tier edge server organization mechanism which enables edge platform providers to manage the servers 2. MUTE categorizes servers based on network delay from client 3. The resulting service deployment optimization efficiently picks edge servers based on requirements 4. Our evaluations via simulations on RocketFuel topologies show i. MUTE optimization results in 66% reduction in network delay while incurring 20% processing cost overhead ii. MUTE optimization reduces computation time by upto 50% 20
  58. 58. Thank You! Questions? nitinder.mohan@helsinki.fi CleanSky - EU FP7 Marie Curie Initial Training Network
  59. 59. Additional Slides 59
  60. 60. Rise of Edge Clouds Network 60 Edge Cloud: Small-scale server(s) deployed at network edge to compute user data Motivation: üDecreased latency and network traffic üComputing data of local relevance üIdeal hosts for on-path vNFs (unlike dedicated middleboxes)
  61. 61. Rise of Edge Clouds Network 61 What? Where? How many? Edge Cloud: Small-scale server(s) deployed at network edge to compute user data Motivation: üDecreased latency and network traffic üComputing data of local relevance üIdeal hosts for on-path vNFs (unlike dedicated middleboxes)
  62. 62. Edge Cloud Models Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., & Koldehofe, B. (2013). Mobile fog. Proceedings of the Second ACM SIGCOMM Workshop on Mobile Cloud Computing - MCC ’13 Cloud Fog What?: Why?: How many?: Network augmented servers Mobile communication Medium 62
  63. 63. Edge Cloud Models Cloud Fog Lopez, P. G., Montresor, A., Epema, D., Iamnitchi, A., Felber, P., & Riviere, E. (2015). Edge-centric Computing : Vision and Challenges. ACM CCR, 45(5), 37–42. What?: Why?: How many?: Network augmented servers Mobile communication Medium What?: Why?: How many?: Compute devices IoT+User tasks Large 63
  64. 64. Edge Cloud Models Cloud Fog Data Store Fog Edge What?: Why?: How many?: Network augmented servers Mobile communication Medium What?: Why?: How many?: Compute devices IoT+User tasks Large What?: Why?: How many?: Compute+Network Hybrid cloud tasks Very large N. Mohan and J. Kangasharju, "Edge-Fog cloud: A distributed cloud for Internet of Things computations," 2016 Cloudification of the Internet of Things (CIoT), pp. 1-6. 64
  65. 65. Service Placement: Terminology 1. Set of all Edge Servers (!"#) !"# = {!"& & , !"( & , !") ( } 2. Edge Server Computational Capability (!"+ ,-./ ) !"/ = {3, 4, 6} 3. Edge Server User Bandwidth (!"+ 34 ) !"34 = {25, 20, 30} 4. Edge Server Cost of Service Deployment (!"+ / ) Client Cloud Tier 1 Tier 2 Device ID: 1 Tier ID: 1 Processing: 3 Bandwidth: 25 Device ID: 2 Tier ID: 1 Processing: 4 Bandwidth: 20 Device ID: 3 Tier ID: 2 Processing: 6 Bandwidth: 30 8/.++ 9, !"& = 3 8/.++ 9, !"( = 2 8 /.++ 9,!") = 6 8/.++ !"&, :; = 5 8/.++ !"(, :; = 6 8/.++ !") ,:; = 2 65
  66. 66. Service Placement: Terminology 1. Set of all Services (!) ! = !#, !% 2. Service Compute Requirements (!& '()* ) !'()* = {2, 1} 3. Service Bandwidth Quota (!& /0 ) !/0 = {20, 15} Client Cloud Tier 1 Tier 2 Device ID: 1 Tier ID: 1 Processing: 3 Bandwidth: 25 Device ID: 2 Tier ID: 1 Processing: 4 Bandwidth: 20 Device ID: 3 Tier ID: 2 Processing: 6 Bandwidth: 30 3*)&& 4, 5!# = 3 3*)&& 4, 5!% = 2 3 *)&& 4,5!7 = 6 3*)&& 5!#, 9: = 5 3*)&& 5!%, 9: = 6 3*)&& 5!7 ,9: = 2 Firewall Monitor Services 66
  67. 67. Service Placement I. Optimizing Operational Cost ! " = $ %&' ( $ )&' * +% ,-./ 0+) ,-./ 0+) / 1%) 1%) = 2 1, 0, if +% deployed on 0+) otherwise 67
  68. 68. Service Placement I. Optimizing Operational Cost ! " = $ %&' ( $ )&' * +% ,-./ 0+) ,-./ 0+) / 1%) Cost for processing the service 1%) = 2 1, 0, if +% deployed on 0+) otherwise 68
  69. 69. Service Placement I. Optimizing Operational Cost ! " = $ %&' ( $ )&' * +% ,-./ 0+) ,-./ 0+) / 1%) Cost for processing the service Cost for deploying the service 1%) = 2 1, 0, if +% deployed on 0+) otherwise 69
  70. 70. Service Placement II. Optimizing Network Delay (End-to-End) ! "# = % &'( ) *+,-- ., 0"& + *+,-- 0"&2(, 0"& + *+,-- 0"&, .345* 6&# *subject to bandwidth constraints 70
  71. 71. Service Placement II. Optimizing Network Delay (End-to-End) ! "# = % &'( ) *+,-- ., 0"& + *+,-- 0"&2(, 0"& + *+,-- 0"&, .345* 6&# Delay from client to Edge Server *subject to bandwidth constraints 71
  72. 72. Service Placement II. Optimizing Network Delay (End-to-End) ! "# = % &'( ) *+,-- ., 0"& + *+,-- 0"&2(, 0"& + *+,-- 0"&, .345* 6&# Delay from client to Edge Server Delay from previous service in SFC *subject to bandwidth constraints 72
  73. 73. Service Placement II. Optimizing Network Delay (End-to-End) ! "# = % &'( ) *+,-- ., 0"& + *+,-- 0"&2(, 0"& + *+,-- 0"&, .345* 6&# Delay from client to Edge Server Delay from previous service in SFC Delay from Edge Server to cloud *subject to bandwidth constraints 73
  74. 74. Service Placement II. Optimizing Network Delay (Edge) ! "# = % &'( ) *+,-- ., 0"& 1&# *subject to bandwidth constraints 74

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