1. I2.1: In-Network Storage PI Presentation by Arun Iyengar NS-CTA INARC Meeting 23-24 March 2011 Cambridge, MA

2. Key Aspects of this Work Make better use of space within network storage – Remove redundant content From communications between network nodes From network storage itself From images which may have similar content – Only store most relevant content Use proper cache replacement policies Handle disruptions and failures – Nodes may fail, become unreachable – Packet losses may occur Data Issues – Dealing with data provenance – Data consistency How provenance affects data consistency decisions

3. Key Problem Nodes within a network need adequate storage and memory – Mobile devices may have limited storage/memory – Content may be replicated for high availability, increasing storage/memory requirements – Even if persistent storage is sufficient, maintaining as much content in main memory may be desirable for performance reasons Our work – Redundancy elimination at multiple levels – Making better use of existing memory/storage space

4. Redundancy Elimination at Multiple Levels File Level (Disk) Page Level (main memory) Application Level (e.g. cached items) Communication Protocol Level Semantic Level (e.g. redundancy in visual images) Redundancy elimination at different levels can cause Mutual interference, complicated behavior

5. Redundancy Elimination at Communication Protocol Level In-network caching algorithms to reduce network utilization by removing redundant bytes communicated GW

6. Motivation Several in-network caching algorithms previously suggested (e.g. Spring, Wetherall, SIGCOMM 2000) However, evaluations mainly conducted on packet traces When deploying those algorithms on protocols (TCP/UDP/IP), several issues can arise – E.g., protocol correctness (e.g., termination) In addition, packet-traces studies are restrictive – Can only analyze certain metrics (e.g., bytes savings) – Cannot evaluate other metrics (e.g., TCP delay) and interactions between mechanisms (e.g., TCP exp. Backoff)

7. Goals Analyze Protocol Correctness Conduct a more comprehensive evaluation of performances in real environments – Packet losses, packet re-ordering, etc. – Bytes, Delay Design new algorithms that are more robust Develop analytical models to rigorously analyze and predict performances

8. Redundancy Elimination at Communication Protocol Level Communications between nodes over a network may contain redundant content – Removing those redundancies can reduce network utilization and congestion Fingerprint: number computed from a byte string using a one-way function. – Fingerprint consumes less space than byte string – Low probability of different strings having same fingerprints 1 Rabin fingerprint for string of bytes t 1, t 2, t 3,…, t n : 1 – RF(t 1, t 2, t 3,…, t n ) = (t 1 p n + t 2 p n-1 …+ t n-1 p + t n ) mod M – p and M are constant integers Computationally cheap to compute next fingerprint from previous one: i+1 – RF(t i+1, …, t n+i ) = (RF(t i, …,t n+i-1 ) – t i * p n ) * p + t n+i mod M – For faster execution, all values of t i * p n can be precomputed, stored in a table

9. Redundancy Elimination Algorithm Maintain caches (at both sender and receiver) of recent packets indexed by fingerprints Generate representative fingerprints for packets being sent Look up each fingerprint in cache If match found, compare bytes to make sure that there was no fingerprint collision If bytes match, expand match to largest possible matching string Update sender cache For byte strings with matching content, send tokens indentifying fingerprint instead of actual bytes

10. Our Implementation Original Spring-Weatherall paper did not implement a system with this redundancy scheme Our implementation encountered following issues

11. Illustration of Interactions RE creates dependencies between packets Wireless links are loss-prone Chains of packets may be undecodable TCP performance may be severely affected (e.g., exp. backoff) GW IP 1 IP 2 IP 3 x IP 1 IP 2 IP 3 IP 2 IP 3 IP 2, IP 3 can not be decoded Network in-caching alg. create dependencies between IP packets, which increase correlated losses and trigger TCP algorithms (e.g., exp. Backoff)

12. Illustration of Protocol Correctness Violation GW IP 1 IP 2 x IP 1 IP 2 IP 3 Re-transmit IP 1 IP 2 cannot be decoded IP 3 IP 2 IP 3 cannot be decoded Re-transmit IP 1 IP 4 IP 3 IP 4 IP 4 cannot be decoded A single lost (or re-ordered) packet can stall a TCP connection! IP 1 IP 3 IP 4 Same object

13. New Algorithms Algorithm 1: – Check TCP sequence numbers – Packets only encoded with previous packets – Variant: I-P frames (similar to MPEG) Algorithm 2: – Flush caches upon packet retransmissions

14. Results

15. Results: I/P frames

16. Memory Deduplication Memory system, cache, file system may have multiple entities which are identical – For example, two pages in a memory system may be identical Challenge is to identify duplicate entities, combine them so that only one copy is stored and shared Maintain a hash of stored entities. When two items hash to same value, do a byte-by- byte comparison to verify that the entities are in fact identical – Use of hash function significantly reduces number of comparison operations to check for identical entities

17. Delta Encoding Multiple objects within a cache may be similar but not identical – Deduplication will not work Identify similar cached objects by taking Rabin fingerprints of parts cached objects, looking for objects with similar Rabin fingerprints For objects with similar content, some of the objects o 1, o 2,…, o n can be stored as differences from one or more base objects – No need to store complete data for o 1, o 2,…, o n If overhead of unencoding a differenced object is an issue, delta encoding can be restricted to cached objects which are infrequently requested

18. Compression Cached objects can be compressed to further decrease memory/storage consumption If computational overhead of compression is a problem, compression should only be applied to cached objects which are infrequently accessed.

19. Cache Replacement Determine how to make best use of cache space LRU, Greedy-dual-size have been used in the past Have developed new cache replacement algorithms which are superior for DTNs

20. Caching: Basic Idea Utility-based data placement – A unified probabilistic framework – Ensure that the more popular data is always cached nearer to the brokers Data utility is calculated based on – Its chance to be forwarded to the brokers – Its popularity in the network Each two caching nodes optimize their data placement upon contact

21. Data Utility Data specifications – Data i is destined for brokers B 1, …, B k – Data i has a finite lifetime T The utility u ij of data i at node j evaluates the benefit of caching data i at node j – c ij : The probability that i can be forwarded to the brokers within T – w i : The probability that data i will be retrieved in the future

22. Cached Data Placement Whenever two nodes contact, they exchange their cached data to maximize the utilities of the data they cache – Hard replacement: only for data which is currently requested by the brokers – Soft replacement: for the other unrequested data Hard replacement is prioritized to ensure that the requested data is forwarded to the brokers

23. Unified Knapsack Formulation When nodes A and B contact, put the data cached on both A and B into the same selection pool with size k – – u iA : utility of data i at A – s i : size of data I – S A : buffer size of A Similar for node B

24. Hit Rate

25. Data access delay

26. Overhead

27. Military Relevance Our In-Network storage techniques are important for communications between soldiers in real deployments – Handling disruptions – Dealing with failed/captured nodes and unreliable network links – Handling lost communications, packet losses Collaborations with Robert Cole (CERDEC), John Hancock (ArtisTech/IRC), Matthew Aguirre (ArtisTech/IRC), Alice Leung (BBN/IRC) – They are studying how are techniques can be applied to military scenarios – Studying our DTN caching techniques to run experiments on traces typical of military scenarios Joint work with Guohong Cao’s group, CNARC

28. Impact and Collaborations ICDCS 2011 paper co-authored with Guohong Cao (Penn State) of CNARC Collaborations with Robert Cole (CERDEC), John Hancock (ArtisTech/IRC), Matthew Aguirre (ArtisTech/IRC), Alice Leung (BBN/IRC) – They have used our DTN caching code to run experiments on traces typical of military scenarios

29. Future Work Refine techniques on redundancy elimination in network communications Study redundancy at other levels in the system Study interactions, interferences, and synergies between redundancy elimination at different levels of the system

30. Summary and Conclusion New techniques for redundancy elimination – Reduces space requirements for in-network storage Redundancy elimination being performed at multiple levels of the entire system New methods for making better use of caches in DTNs – These methods are of interest to other collaborators in NS-CTA

31. Arun Iyengar’s NS-CTA Contributions I2.1 - In-Network Storage is only task funding Arun’s research Research Contributions in caching, redundancy elimination summarized in previous slides Publications: – “Supporting Cooperative Caching in Disruption Tolerant Networks”. W. Gao, G. Cao, A. Iyengar, M. Srivatsa. Accepted in ICDCS 2011 – "Social-Aware Data Diffusion in Delay Tolerant MANETs", Yang Zhang, Wei Gao, Guohong Cao, Tom La Porta, Bhaskar Krishnamachari, Arun Iyengar. Book chapter to appear in Handbook of Optimization in Complex Networks: Communication and Social Networks, Springer. – “Provenance driven data dissemination in disruption tolerant networks”. M. Srivatsa, W. Gao and A. Iyengar. Under submission, Fusion 2011 – “Resolving Negative Interferences between In-Network Caching Methods”. F. Le, M. Srivatsa, A. Iyengar and G. Cao. Under preparation Patent Application: – "System and method for caching provenance information", Wei Gao, Arun Iyengar (lead inventor), Mudhakar Srivatsa, IBM patent application. Initiated and established collaboration with Guohong Cao’s group (CNARC). – Mentor for Wei Gao (Guohong Cao’s PhD student) for internship at IBM. Initiated and established collaboration with Robert Cole (CERDEC), John Hancock (ArtisTech/IRC), Matthew Aguirre (ArtisTech/IRC), Alice Leung (BBN/IRC)

32. Data Consistency Problem: How to make sure cached data is current – Resolving inconsistencies between different copies Data consistency in DTNs – Limited connectivity can make it difficult to achieve strong consistency – Expiration times can be used for maintaining cache consistency