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Anveshak: Placing Edge Servers In The Wild

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Published in MECOMM workshop colocated with SIGCOMM 2018 held in Budapest, Hungary.

Paper PDF is available at: https://dl.acm.org/citation.cfm?id=3229560

ABSTRACT: In this paper, we present Anveshak, a framework that solves the problem of placing edge servers in a geographical topology and provides the optimal solution for edge
providers. Our proposed solution considers both end-user application requirements as well as deployment and operating costs incurred by edge platform providers. The placement optimization
metric of Anveshak considers the request pattern of users and existing user-established edge servers.

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Anveshak: Placing Edge Servers In The Wild

  1. 1. Anveshak: Placing Edge Servers In The Wild ACM SIGCOMM 2018 Workshop on Mobile Edge Communications (MECOMM’2018), Budapest, Hungary Nitinder Mohan Aleksandr Zavodovski Pengyuan Zhou Jussi Kangasharju University of Helsinki {firstname.lastname@helsinki.fi}
  2. 2. Edge Cloud Overview Network DatacenterEdge Server User 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 1
  3. 3. Edge Cloud Overview Network DatacenterEdge Server User 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 ? 1
  4. 4. Edge Cloud Deployment 1. What are these servers configuration? 2. Who owns and operates them? 3. Where are these servers going to be deployed in the physical world? Network DatacenterEdge Server User 2
  5. 5. Physical Edge Cloud Network 1. Multiple edge cloud deployments can co-exist in same physical space 2. Edge server availability will directly affect end utilization Ø Usage of cellular-based internet in areas where WiFi is available Location ‘L’ 3
  6. 6. 1. Subscribers and requesters of edge resources 2. User density directly impacts local edge server utilization 3. Request distribution is highly dependent on time and behavior ØMore user requests during daytime than in night ØMore user requests in city than suburban areas Users 3
  7. 7. 1. Composed of: self-managing, locally-relevant edge resources Ø e.g. smart speakers, home automation, intelligent WiFi hubs 2. Limited computational-power and network capability 3. High server density Ø Dependent on user population Ø One server caters to small set of users User-Managed Edge Garcia Lopez, Pedro, et al. "Edge-centric computing: Vision and challenges." ACM SIGCOMM Computer Communication Review, 2015 Management/Control → End-users 3
  8. 8. 1. Composed of: high computation and network capable edge servers 2. Set up specifically by cloud provider in partnership with local ISP 3. Co-located/accessible with cellular base stations for ease-of operation and maintenance 4. Low server density Ø One server caters to large set of users Service Provider Managed Edge Management/Control → Cloud provider/ISP Ceselli, Alberto, Marco Premoli, and Stefano Secci. "Mobile edge cloud network design optimization." IEEE/ACM Transactions on Networking (TON) (2017) 3
  9. 9. New Server Deployment* *from Service Provider perspective Which location to deploy new edge server? Over-provisioning! Ø Connectivity can be overlapping Ø Future utilization can be minimal OR Under-provisioning! Ø Maintain a Quality-of-Service Ø Must support peak request traffic 4
  10. 10. Considerations Deploying managed edge servers is expensive! An efficient server deployment algorithm must: 5
  11. 11. Considerations Deploying managed edge servers is expensive! An efficient server deployment algorithm must: 1. Prioritize areas with high user requests 5
  12. 12. Deployment Considerations Deploying managed edge servers is expensive! An efficient server deployment algorithm must: 1. Prioritize areas with high user requests 2. Avoid areas with high user- managed edge resources 5
  13. 13. System Workflow Ø Three-phase waterfall-based workflow Ø Intermediate phase checkpoints for recoverability Ø Swappable phase modules for incorporating improved algorithms and parameters Ø Anveshak is first-of-its-kind framework which considers user- managed edge in location selection Phase I User Mapping Phase II User Edge Incorporation Phase III Edge Location Selection User Requests Edge Installation Locations 6
  14. 14. Phase I: User Mapping AIM: Identify and prioritize areas of high user communication requests Ø High server utilization Ø Low user-server connection latency Phase I Divide location L into NxN grid Map user request pattern to L Cluster user requests to grid Generate request density heatmap Candidate Grid Locations (GL) 7
  15. 15. Phase I: User Mapping Phase I Divide location L into NxN grid Map user request pattern to L Cluster user requests to grid Generate request density heatmap Candidate Grid Locations (GL) Divide map into equi-spaced NxN grid Ø Grid division allows for consistent clustering Ø Grid size directly affects problem search space 1 2 3 n. . . . . . . . 123n........ 7
  16. 16. Phase I: User Mapping Past user communication requests such as CDRs, internet initiation are mapped on location Ø Requests are averaged over time to remove temporal outliers Phase I Divide location L into NxN grid Map user request pattern to L Cluster user requests to grid Generate request density heatmap Candidate Grid Locations (GL) 7
  17. 17. Phase I: User Mapping Cluster user requests based on inter-request distances and densities Ø Choice of clustering algorithms and their parameters can be easily tweaked [DBScan used as example] Phase I Divide location L into NxN grid Map user request pattern to L Cluster user requests to grid Generate request density heatmap Candidate Grid Locations (GL) 7
  18. 18. Phase I: User Mapping Generate heatmap for arbitrary cluster shapes Ø Handles over-lapping shapes, small/dense clusters Ø Handles any inefficiency of clustering algorithm Phase I Divide location L into NxN grid Map user request pattern to L Cluster user requests to grid Generate request density heatmap Candidate Grid Locations (GL) 7
  19. 19. Phase I: User Mapping Phase I Divide location L into NxN grid Map user request pattern to L Cluster user requests to grid Generate request density heatmap Candidate Grid Locations (GL) 1 2 3 n. . . . . . . . GL = {Grid ID, Request Density} 123n........ 7
  20. 20. Phase II: User Edge Incorporation • Anveshak estimates future deployment of user- managed edge resources • Availability of user-edge servers will limit utilization of deployed edge in same location • Such devices are highly dependent of user population and interaction in an area Estimated via current deployment of WiFi access points Phase II Filter APs based on ownership Map filtered APs in GL Adjust GL request density Selected grids !" #$%&'(% INPUT: GL = {Grid ID, Request Density} 8
  21. 21. Phase II: User Edge Incorporation Map currently deployed WiFi access points in the same area Ø Utilize open datasets for WiFi access points such as wigle.net Ø Filter out mobile and temporary access points Phase II Filter APs based on ownership Map filtered APs in GL Adjust GL request density Selected grids !" #$%&'(% 8
  22. 22. Phase II: User Edge Incorporation Map all filtered access points on Phase I heatmap Ø Cluster nearby access points based on densities Phase II Filter APs based on ownership Map filtered APs in GL Adjust GL request density Selected grids !" #$%&'(% 8
  23. 23. Phase II: User Edge Incorporation Reduce grid densities based access point availability density Ø Resulting heatmap denotes grids with overflowing user requests Phase II Filter APs based on ownership Map filtered APs in GL Adjust GL request density Selected grids !" #$%&'(% 8
  24. 24. Phase II: User Edge Incorporation Phase II Filter APs based on ownership Map filtered APs in GL Adjust GL request density Selected grids !" #$%&'(% 1 2 3 n. . . . . . . . 123n........ )* +,-./0- = {g11, d11; g12, d12; g13, d13; . . . gnn, dnn} 8
  25. 25. Phase III: Edge Location Selection 1. Select the best set of deployment locations considering connectivity to end users 2. Base stations are taken as possible deployment locations Phase III Rank GL on decreasing request density Identify base station l ∈ GL Solve Facility Location Problem in GL R(u,S) Location selection best resembles Facility Location Problem (FLP) Please check our paper for more details 9
  26. 26. Evaluation
  27. 27. Evaluation Evaluate Anveshak’s placement of ‘k’ edge servers on ‘n’ possible locations in Milan, Italy 10
  28. 28. Evaluation Evaluate Anveshak’s placement of ‘k’ edge servers on ‘n’ possible locations in Milan, Italy 11
  29. 29. Evaluation 100 100 Evaluate Anveshak’s placement of ‘k’ edge servers on ‘n’ possible locations in Milan, Italy 11
  30. 30. Evaluation 235m 235m Evaluate Anveshak’s placement of ‘k’ edge servers on ‘n’ possible locations in Milan, Italy 11
  31. 31. Evaluation Evaluate Anveshak’s placement of ‘k’ edge servers on ‘n’ possible base-stations in Milan, Italy A. With real datasets 1. User requests: Published by Telecom Italia 2. Service-provider edge locations: Published by OpenCellid 3. User-managed edge locations: Published by WiGLE 12
  32. 32. Evaluation Evaluate Anveshak’s placement of ‘k’ edge servers on ‘n’ possible base-stations B. With two approaches 1. Greedy: Allocates user request densities to grids and selects top-k maximum serving base-stations 2. Random: Randomly chooses k valid base-stations 13
  33. 33. Evaluation Setup 1. User is mapped to nearest base station using coordinate based latency approximation 2. Deployment framework selects 50 edge location out of 850+ 14
  34. 34. User Request Satisfaction Q. How many user requests are handled by Anveshak’s placed edge servers? 1. 67% more requests than Greedy 2. 25% of total requests handled by 8% of selected base stations 01-D ec03-D ec06-D ec09-D ec12-D ec15-D ec18-D ec21-D ec24-D ec27-D ec30-D ec 0 0.1 0.2 0.3 0.4 UserRequest Satisfaction Anveshak Greedy Random Can achieve 90% user satisfaction by placing 124 servers over 218 and 300+ by Greedy and Random 15
  35. 35. Server Utilization Q. How busy are the deployed servers? Assumption: All user requests in a grid are first handled by user- managed edge More user request density areas have more user-managed edge resources leading to less utilization of deployed server AnveshakGreedy Random 0 0.2 0.4 0.6 0.8 1 EdgeServerUtilization 66% 83% 12% 16
  36. 36. Conclusion 1. Anveshak is a deployment framework designed to assist service providers 2. It efficiently identifies optimal locations for edge server placement while considering: Ø Density of user requests Ø Density of future deployment of user-managed edge resources 3. We evaluate Anveshak and other deployment algorithms on real datasets 4. Anveshak achieves 67% increase in user satisfaction with 83% server utilization 17
  37. 37. Thank You! Questions? nitinder.mohan@helsinki.fi CleanSky - EU FP7 Marie Curie Initial Training Network
  38. 38. Backup Slides
  39. 39. How is Anveshak different from CDN server placement? Similarities: 1. Both problems must ensure consistent and least connectivity to end-clients 2. Both problems must optimize for cost of deployment Differences: 1. Unlike content, edge server handles requests which are short-lived and locally-relevant 2. Focuses more on server availability and network latency than network bandwidth
  40. 40. Phase III: Edge Location Selection Anveshak assumes that the deployed edge server will be co-located with a base station Ø From Phase II heatmap, select exact base station location which satisfies maximum user requests Ø Selected base station must be able to service maximum users in 1-2 hops Ø List of exact base station locations can be made available with partnering ISP Phase III Rank GL on decreasing request density Identify base station l ∈ GL Solve Facility Location Problem in GL
  41. 41. Phase III: Edge Location Selection Prioritize the grid locations based on updated user request density Phase III Rank GL on decreasing request density Identify base station l ∈ GL Solve Facility Location Problem in GL GL= {g11, d11; g12, d12; g13, d13; . . . gnn, dnn}
  42. 42. Phase III: Edge Location Selection Phase III Rank GL on decreasing request density Identify base station l ∈ GL Solve Facility Location Problem in GL Prioritize the grid locations based on updated user request density GL= {g11, d11; g12, d12; g13, d13; . . . gnn, dnn}
  43. 43. Phase III: Edge Location Selection Phase III Rank GL on decreasing request density Identify base station l ∈ GL Solve Facility Location Problem in GL where, GL{d1} > GL{d2} > . . . > GL{dlast} GL= {g13, d13; g32, d32; g61, d61; . . . gn7, dn7} Prioritize the grid locations based on updated user request density
  44. 44. Phase III: Edge Location Selection Phase III Rank GL on decreasing request density Identify base station l ∈ GL Solve Facility Location Problem in GL GL= {g13, d13; g32, d32; g61, d61; . . gn7, dn7}
  45. 45. Phase III: Edge Location Selection Phase III Rank GL on decreasing request density Identify base station l ∈ GL Solve Facility Location Problem in GL Select optimal base station (Sl) in GL[i] satisfying maximum user requests [Phase I] Ø We focus on location and not density
  46. 46. Phase III: Edge Location Selection Phase III Rank GL on decreasing request density Identify base station l ∈ GL Solve Facility Location Problem in GL Select optimal base station (Sl) in GL[i] satisfying maximum user requests [Phase I] Ø We focus on location and not density
  47. 47. Phase III: Edge Location Selection Phase III Rank GL on decreasing request density Identify base station l ∈ GL Solve Facility Location Problem in GL Network cost of connectivity (n(u,S)) R(u,S) n(u,S) = " ∗ R(u,S) where, $(&',)') +,- = max[u - Sl] ∀ ul ∈ U & l ∈ L " = maximum server access cost
  48. 48. Phase III: Edge Location Selection Objective: Minimize one-hop latency between users and selected server location Phase III Rank GL on decreasing request density Identify base station l ∈ GL Solve Facility Location Problem in GL "# = min ∑% ∈& "% "% ∈ ", ( #,)* < (,-.} 0% where, 0% ∈ {0,1}
  49. 49. Phase III: Edge Location Selection Ø NP-hard problem Ø Approximation-based solvers available [ODL, Sitation] Anveshak’s Advantage: Ø Grid size as additional constraint, reduce problem size Ø Due to problem’s future outlook, exact optimization is not required! Phase III Rank GL on decreasing request density Identify base station l ∈ GL Solve Facility Location Problem in GL "# = min ∑% ∈& "% "% ∈ ", ( #,)* < (,-.} 0%
  50. 50. Datasets (1/3) User Request Dataset *https://dandelion.eu/datamine/open-big-data/ • Published by Telecom Italia* for Milan, Italy • Anonymized details of Call Detail Records (CDRs), internet connectivity of users in the region • Data of November 1st to December 31st 2013
  51. 51. Datasets (2/3) Service-provider Edge Dataset *https://www.opencellid.org • Published by OpenCellid* • Details of cellular base stations such as connectivity type (3G/4G), area etc. along with their GPS coordinates • Post filteration: 800+ LTE base stations • Each base station is associated to the map grid
  52. 52. Datasets (3/3) User-managed Edge Dataset *https://wigle.net/ • Published by WiGLE* • Details of WIFi access points such as SSID, signal strength, channel number etc. along with GPS coordinates • 800+ base stations Milan, Italy serving LTE connection

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