Electrical and Computer Engineering, University of Thessaly

1. Electrical and Computer Engineering, University of Thessaly “Replication Management and Cache aware Routing in Information-Centric Networking” VasilisSourlas Dissertation Committee: Leandros Tassiulas (UTH,GR), Supervisor SpyrosLalis (UTH, GR) GeorgePavlou (UCL, UK)

2. Outline 1) Introduction 2) Replication Management Framework 3) Storage Planning and Off-line Replica Assignment 4) Distributed Cache Management 5) Opportunistic Caching 6) Cache Aware Routing 7) Conclusions and Future Work

3. Outline 1) Introduction 2) Replication Management Framework 3) Storage Planning and Off-line Replica Assignment 4) Distributed Cache Management 5) Opportunistic Caching 6) Cache Aware Routing 7) Conclusions and Future Work

4. Internet-based Content • The vast majority of interactions relate to content access: • P2P overlays (BitTorrent) • Media aggregators (YouTube) • Content Delivery Networks (Akamai) • Social Networks (Facebook) • Photo sharing sites (Picasa) • New approaches are required to cater for the explosion of video-based content and for creating novel use experiences. • Continue throwing more capacity cannot work anymore!

5. Expected IP Traffic Growth 2012-2017 • According to the Cisco Visual Networking Index: • Global IP traffic will reach 1.3 zettabytes per year. • 3 networked devices per capita in 2016 vs 1 per capita in 2011. • 15 GBytes per capita IP traffic in 2016 vs 4 GBytes in 2011. • Approx. 55% of the overall Internet traffic will be video by 2016, without counting P2P video file sharing (~ 86% including P2P). • It will take over 5 years to watch the amount of video that will cross global IP networks every second in 2015!! • Whatis exchanged is becoming more important than who are exchanging it.

6. Information-Centric Networking • Paradigm shift from the host-to-host Internet to a host-to-content one. • Information-Centric Networking (ICN) targets a general infrastructure that provides in-network caching and multicast communication so that content is distributed in a scalable, cost-efficient & secure manner.

7. ICN Architectural Models • Information-centric (Content-Centric) networks • Content is explicitly named. • Subscriptions/Interests act on the name of each packet. • One-time fetch and ongoing subscribe operation. • DONA, PURSUIT, NDN/CCN, SAIL, ... • Content-Based Publish/Subscribe (CBPS) networks • Overlay event notification services. • Broader request semantics (attribute/value scheme). • One-time fetch only operation. • No content servers assumed. • IBM Gryphon, Siena, REDS, Elvin, …

8. CCN Operation Interest Check Pending Interests Table Data Check Content Store Check Content Store Check Pending Interests Table S1: /spiegel.com/crisisingreece/news.pdf/page34/.... S1: /spiegel.com/crisisingreece/news.pdf/page34/.... Check Forwarding Information Base

9. CBPS Operation Subscribe( ) Subscribe ( ) Publish( ) S1: [type,=,movie/english], [artist,=,Bruce Lee],[year,=,*] S2: [type,=,music/mp3], [artist, =, madonna], [album, =, *], [year, >, 1990] P: [type,=,movie/english], [artist, =, Bruce Lee, Chuck Norris], [title, =, BLvsCN.avi]

10. Research Challenges • Information-Centric Networking Research Group (ICNRG) • Cache management • Traffic engineering • Scalable Routing • QoS approaches • Novel caching strategies • …….

11. Work Synopsis • CDN-like replication management framework. • In-network opportunistic caching framework. • Cache aware routing scheme.

12. Outline 1) Introduction 2) Replication Management Framework 3) Storage Planning and Off-line Replica Assignment 4) Distributed Cache Management 5) Opportunistic Caching 6) Cache Aware Routing 7) Conclusions and Future Work

13. Introduction • CDN-like replication distributes a site’s content across multiple mirror servers. • A request is redirected to the “closest” server. • Replication is used to increase availability and fault tolerance. • Side effects: load balancing and enhanced publisher/subscriber proximity.

14. Contributions • A three phase replication management framework for ICN • Planning phase • Decides the placement of the replication points. • Off-line Assignment phase • Assignment of information items to the replication points based on the observed popularity. • Generalized assignment problem (reduced to NP-complete multiple knapsack problem). • On-line Replacement phase • Replacement of information items in real-time, based on the changing demand pattern.

15. Replication Framework (off-line) Monitoring each node Long-term forecast StoragePlanning NetworkTopology Sub Data SubscriptionForecast Medium-long term forecast Monitor subscriptions ReplicaAssignment Configure (subscribe item t2, publish item t2) Configure (subscribe item t1, publish item t1) Storage device Storage device ForwardingNodes ForwardingNodes … … … Subscribers … Subscribers …

16. Replace item iwith item j? Cache Replacement Substrate Replication Framework (dynamic) client request rates, topology, cache configs coordinate decisions Cache Managers Clients request for items Cache enabled ICN node

17. Outline 1) Introduction 2) Replication Management Framework 3) Storage Planning and Off-line Replica Assignment 4) Distributed Cache Management 5) Opportunistic Caching 6) Cache Aware Routing 7) Conclusions and Future Work

18. Introduction • Replication is thoroughly investigated in the area of CDNs (approximate solutions and optimal algorithms for tree topologies). • 2-aproximation algorithms have been proposed also in the area of distributed replication groups. • In ICN only approaches based on distributed databases. • Less attention has been given to network constraints (limited storage capacity).

19. Contributions • Enhanced the CBPS with an advertisement and a request/response mechanism. • Modified known Greedy algorithm (CDN context). • Used the modified greedy for the proposed placement algorithm. • Proposed a new algorithm for the selection of R storage points among the V network nodes (R < V) based on: a) the locality and the popularity of the interests for each item b) the targeted “replication degree km” of each item m c) the storage capacity “L” of each replication device • Proposed two alternative assignment mechanisms. • Target - Minimize client’s response latency subject to installing the minimum number (or any given number) of replicas in the network.

20. Greedy Algorithm • 1st round:evaluates each of the V nodes to determine its suitability to become a storage. Computes the Gain (traffic served by replica and does not need to access original server) associated with each node and selects the one that maximizes the Gain. • 2nd round: searches for a second storage which, in conjunction with the storage already picked, yields the highest Gain. • Completes: iterates until the requested number of storages have been chosen for the replication of the specific server.

21. Modified Greedy Algorithm • No knowledge of the location of the server, differently there is no server at all. • Repeat Greedy algV times (server j is a different node of the network). • V vectors of possible storages. • Choose as our storages those nodes that appeared more times in the per element summation of the V vectors.

22. Planning and Assignment Planning Steps: • For each item m we execute the modified greedy algorithm and we get M vectors of possible storages. • Each vector is weighted by each item’s weight (significance regarding the traffic of each itemin the network). • Select as storages those M nodes that appeared more times in the per element weighted summation of the M vectors. • For each item m starting from the most significant (based on the weight) assign km storages following the procedure below: • For each entry in the vectorof item m calculated in step 1 assign a storage if that entry also appears in the final storage nodescalculated in step 3 and only if in that storage has been assigned less than L items until we get km storages. • A similar weighted round robin-like mechanism based on the weight of each item has also been proposed. Assignment

23. Evaluation Compare to: • “grd_opt”: each item m is assigned to the km storages produced by the first step of the placement algorithm • “rnd”: no differentiation among items, random assignment after the selection of the storages Metrics: • Mean hop distance between the requesting client and the storage (indicative of the response latency)

24. Predefined Minimum Replication Degree • Off-line Assignment Phase Evaluation

25. Results • The proposed planning and the two off-line placement algorithms perform only 1%-5% worse than greedy, using 50%-80% less storages. • Appropriate solution for real world scenarios where a storage provider has limitations in the number of replicas that can install.

26. Outline 1) Introduction 2) Replication Management Framework 3) Storage Planning and Off-line Replica Assignment 4) Distributed Cache Management 5) Opportunistic Caching 6) Cache Aware Routing 7) Conclusions and Future Work

27. Contributions • Proposed a distributed cache management architecture that dynamically (re-)assigns information items to caches, based on items’ demand patterns in order to minimize the overall network traffic. • Presented four distributed on-line cache management algorithms, categorized them based on the level of cooperation needed between the managers and compared them against their performance, complexity, message overhead and convergence time. • Derived a lower bound of the overall network traffic for regular network topologies.

28. Distributed Cache Management Architecture • Distributed Cache Managers (CM) decide in a coordinated manner whether to cache an item and replace an already cached. • Every CM should have a holistic network-wide view of all the cache configurations and the demand patterns. • Upon a change in a cache configuration the CM should inform (event-based manner) every other CM in the network.

29. Distributed On-Line Cache Manage-ment Algorithms • Known global demand patterns and global replica placement (global cache configu-ration), minimize overall network traffic • Cooperative Cache Management Algorithm • Holistic Cache Management Algorithm • Holistic-all Cache Management Algorithm • Known local demand patterns and global replica placement, minimize local traffic (local clients) 4. Myopic Cache Management Algorithm

30. Cooperative Algorithm • Each CM computes: • For each item min the cache the performance loss lm if item m is removed from the cache. • For each item mnot in the cache of the performance gain gm if item m is cached. • Candidate for insertion the item of maximum performance gain. • Candidate for replacement the items of minimum performance loss. • Maximum local relative gain r= gm- lm and report it to the rest CMs. • CMs calculate the most network-wide beneficial replacement and updated their configuration matrix. • Steps 1-2 are repeated until no further replacements are beneficial for the network. Each replacement decreases the overall network traffic - converges to an equilibrium point (local minimum given the initial cache configuration).

31. Holistic Algorithm Holistic-all Algorithm • Only one CM runs the algorithm at a time e.g. token based decision making. • In holistic only one replacement at each node per iteration. • In holistic-all all possible replacements at each node per iteration.

32. Myopic Algorithm • In highly dynamic environments each CM may don’t have info about the demand pattern in the network. • Decision based on local info only w.r.t to local requests but every CM is aware of the global cache configurations. • Each CM calculates its replacements in order to minimize the traffic cost for the demand it serves. • Same decision making as the holistic.

33. Network Traffic Lower Bound • Assumptions • Uniform request pattern. • Unit size information items. • Regular network topologies (distance regular graphs, n-dim torus). Theorem:

34. Evaluation Metrics: • Overall network traffic, ONT (reqs*hops/sec) at equilibrium. • Total number of replacements per node, RE. • Total number of iterations per node, IT (indicative of the running time). Two sets of experiments • Uniform demand pattern • Synthetic workload & Zoo Topologies

35. Uniform Demand Pattern

36. Synthetic Workload & Zoo Topologies

37. Convergence & Mean Cache Hit Distance

38. Results • The algorithms that use network-wide information are near optimal since the corresponding difference from the lower bound varies between 0,5% and 3,6% regardless of the topology, the size of the network and the storing capacity of each cache and the initial cache assignment. • Network wide knowledge and cooperation give significant performance benefits and reduce the time to convergence at the cost of additional message exchanges and computational effort.

39. Outline 1) Introduction 2) Replication Management Framework 3) Storage Planning and Off-line Replica Assignment 4) Distributed Cache Management 5) Opportunistic Caching 6) Cache Aware Routing 7) Conclusions and Future Work

40. Introduction • In-network opportunistic caching is a salient characteristic of ICNs. • Caching in ICN takes as granted the presence of a hosting server (caches are used to improve delivery of popular items). • In CBPS implementations (or in future P2P ICN implementations) servers do not exist. Caching to preserve information over time instead of making information available in nearer space is missing.

41. Contributions • Enhanced CBPS with a req/resp scheme (subscribers can retrieve already published items). • Decomposed caching mechanism is a set of basic poli-cies/strategies (proposed ICN oriented at each set). • Proposed two duplicate dropping mechanisms (proactive & reactive). • Proposed a stochastic model that captures the dynamics of the new ICN oriented policies. • Described a prototype implementation of the proposed caching mechanisms (Planetlab). • Modified the proposed caching scheme to support mobility of subscribers.

42. Policies • Caching– selects a number of nodes and assigns them as caching points. • Selective caching (SEL) • En-route caching (NRT) • Placement/Replacement- decides a position in the cache where a new message will be cached and which message will be discarded in case of an overflow. • Least Recently Used policy (LRU) • Least Frequently Used policy (LFU) • Priority policy (PRT) • Request– dictates how requests (interests) are propagated in the network. • Subscription-based request policy (SUB) • Flooding request policy (FLD)

43. Handling Multiple Responses • Reactive mechanism, nodes check passing responses whether the item is in its Cache. If true, discards the response packet (Responses follow backwards the same path with Requests). • Proactive mechanism, responded node/cache appends to the Request’s APID (Aggregated Publication Ids) the pub-id of the responded item. Recipients of the request respond with cached items which pub-id are not in the Request.

44. Stochastic Cache Modeling • Use Absorbing Markov Processes to compute the Mean Absorption Time (AT). of an item in the caches of the network. • Present analytical results for a single node network (~ multi-node scenario without item copying). • Reduce the state space with an approxi-mation. • Use the reduced space for the multi-node scenario.

45. Mobility Support • A mechanism that uses a portion of a proxy’s buffer. • Manages subscriptions and publications on behalf of the Mobile Node (MN). • When the MN is disconnected, stores items matching MN’s interests. • During the switch-over phase (reconnection phase) delivers stored items to the MN.

46. Evaluation • Implemented the framework in a Java-based overlay framework/REDS and in a discrete event simulator using MATLAB. • Compared the analytical model with discrete event simulations. • Planetlab and simulation experimentation of various combinations of opportunistic caching schemes. Metrics: • Mean Absorption time, AT – caching capability of the network • Minimum hop distance – delay, perceived QoS • Traffic Overhead –replication and overhead • Satisfaction –perceived QoS

47. Planetlab experimentation

48. Results • The newly proposed ICN oriented policies outperform traditional ones. • The two duplicate dropping mechanisms minimizes the traffic overhead significantly even when used with the flooding request policy. • The Markov model is accurate enough, but looses accuracy when the number of nodes increases. • Prototype implementation results are inline with discrete-event simulator outcome.

49. Outline 1) Introduction 2) Replication Management Framework 3) Storage Planning and Off-line Replica Assignment 4) Distributed Cache Management 5) Opportunistic Caching 6) Cache Aware Routing 7) Conclusions and Future Work

50. Introduction • Performance management and traffic engineering approaches are required in ICN to control routing, configure cache replacement policies, etc. • Routing functionalities is completely missing from the current ICN design. • Only flooding or OSPF-like shortest path mechanisms have been proposed. • Recently hash-routing (similar to datacenters) has been proposed to maximize cache hit within a domain regardless of the traffic.