Autonomic and Collaborative Protocols in Wireless Delay Tolerant Networks

1. Autonomic and Collaborative Protocols in Wireless Delay Tolerant Networks Stavros Toumpis Department of Informatics Athens University of Economics and Business Athens, Greece CROWN KICKOFF, 11/5/12

2. PART A: Delay Tolerant Networks

3. Definition • Delay in the delivery of packets is very large (specifically, comparable to the time needed for the topology to change substantially) • Two cases • Very large delays are necessarily large (e.g., interplanetary networks [Burleigh et al. 2003]) • Very large delays are a design choice (e.g., Zebranet, Juang et al. 2002). Delay is conscientiously traded off. • In the context of wireless networks, large delays typically translate to communication by physical transportation of data (either partially or exclusively)

4. Applications • Interplanetary networks • Sensor networks (Zebranet) • The Internet • Vehicular Networks, for certain kinds of traffic (GeoDTN+Nav)

5. Recent History • Infostations [Goodman et al. ‘97] • Epidemic Routing [Vahdat/Becker ‘00] • Mobility Increases the Capacity of Wireless networks [Grossglauser/Tse ‘01, Toumpis/Goldsmith ‘04] • Data Mules [Shah et al. ‘03] • Zebranet [Juang et al. 02, Small/Hass ‘03] • Delay Tolerant Architecture [Jain et al. ‘03] • Spray and Wait [Spyropoulos et al. ‘05] • MaxProp [Burgess et al. ‘06]

6. Earlier History • Much work in Operations Research in the context of dynamic flows and networks (which are functions of time) • Ford/Fulkerson constructed maximal flows in ‘54 and maximal dynamic flows in ’58! • Ogier studied minimum delay routing and related problems in the ‘80s. • Ferreira et al. [Ferreira 04,10] and Merugu et al. [Merugu et al. ‘04] applied dynamic flows in the context of (wireless) DTNs

7. Classification of DTN Analysis • Do we know the topology evolution of the network? • If YES, then we can study it using tools from network optimization theory, notably dynamic flows and networks • If NO, then we can use tools from probability and related fields (e.g., stochastic control)

8. Why are DTNs interesting in the context of this project? • If Wireless Networks added a spatial component to the analysis of networks… • … then Delay Tolerant Networks add a time component… • …and room for innovation is still there… • … particularly in the areas covered by this project.

9. Part B: Autonomous and Collaborative Protocols in DTNs

10. Organization of the project WP1: Understanding and influencing uncoordinated interactions of autonomic wireless networks WP2: Optimization through network coordination WP3: Autonomic and collaborative protocols in Wireless DTNs Task 3.1: Autonomic operation of wireless DTNs Task 3.2: Coordinated operation of wireless DTNs Task 3.3 Realistic wireless DTN protocol design

11. Task 3.1: Autonomic operation of wireless DTNs • Main tool: probability theory • DTNs are frequently partitioned, therefore autonomic operation based on local decisions is appealing/necessary • Nodes decide on next hop of packet • Nodes decide on what packet to transmit/delete • etc. • Resources (bandwidth, buffer spaces) are typically constrained, so selfish behavior is expected • Nodes would like to only transmit/store their own packets, etc.

12. Sub-Task 3.1.1: Geographic Routing • In geographic routing, next hop for a packet is decided according to location of destination and topology near the current packet holder. • Problem: find optimal behavior for current holder, based on local knowledge. • An obvious tradeoff exists between transportation cost and packet delivery delay. • Tool for performing the analysis: Stochastic Geometry

13. Subtask 3.1.2: Delay-Throughput tradeoff of DTNs • Fundamental tradeoff: The more copies of a packet are transmitted, the faster it will arrive at its destination, but the smaller the throughput becomes. • Goal: evaluate the performance of network coding particularly in terms of this tradeoff • What is the optimal tradeoff • How well do practical protocols achieve it?

14. Task 3.2: Coordinated operation of wireless DTNs • Main tool: network optimization theory and dynamic flows. • Main goal: study the tradeoff of delay with other metrics in a systematic manner. • Common approach: • First, find optimum tradeoffs • Then, find good heuristics and compare them with optimum tradeoffs

15. Current status of Task 3.2 • Delay versus cost tradeoff • i.e., if we wait more, we will transport data with smaller cost [Tasiopoulos et al. 12] • Delay versus data volume tradeoff • i.e., if we wait more, we will transport more data [Gitzenis et al. ‘12] • Delay versus storage capacity tradeoff • i.e., if we wait more, we need less storage capacity [Iosifidis/Koutsopoulos ‘11]

16. Task 3.3 Realistic wireless DTN protocol design • Main tool: simulations • Aim: develop simulator for simulating the operation of DTNs in the 10,000 node regime • Currently available tools: • Generic, i.e., NS2, OMNET, etc. These are not good fits for DTN research, because large delays mean large buffers. • Specialized (for example, ONE), but slow • Our approach: fast, dedicated simulator written in C.

17. Current status of Task 3.3 • Features of our partially built simulator: • Can handle 10,000+ nodes • Uses realistic channel model and includes a realistic slotted MAC protocol • Can handle a variety of mobility models • We have evaluated a variety of well known DTN protocols such as Spray and Wait, GeoDTN+Nav, Geocross, etc. • We have created our own protocol, DTFR (more later) [Sidera ‘11] • Main aim: use simulator to test ideas and protocols coming from other workpackages and tasks.

18. Part C: Flow Optimization in Delay Tolerant Networks using Dual Decomposition Savvas Gitzenis (Informatics and Telematics Institute, CERTH, Greece) George Konidaris, Stavros Toumpis (Informatics Department, AUEB, Greece) (RAWNET 2012, 18/5/12, Paderborn

19. Our work • Mobile Wireless DTNs • Topology changes due to node mobility • Objective: Single commodity flow optimization • Challenge: Flow Optimization is a hard problem in wireless networks even in non-DTN setting • Contributions: • Fast non-causal centralized algorithm (taking into account the structure of the problem) • Heuristic causal centralized algorithms (Heuristic causal decentralized algorithms are subject of future work)

20. Network Model (1/2) • N nodes 1,2,…,N with set of links A • T time epochs 1,2,…, T • Topology (i.e., link properties) remains fixed during each epoch • (following Ferreira ‘02 and others) • Traffic flow in epoch t is x(t)={xij(t), (i,j) inA}, t=1,2,…, T • NB: x(t) describes volume of data, not data rate. • x(t) must be inside capacity region R(t) • At transition from epoch t-1 to epoch t, node i must have volume of data less than buffer size Bi(t), i=1,2,…, N, t=2,…, T • Internal buffer size vectors B(t)={Bi(t), i=1,2,…N}

21. Network Model (2/2) • Let yi(t) be the data volume at node i at start of epoch t. Let y(t)={yi(t), i=1,2,…, N} • Let zi(t) be the data volume at node i at end of epoch t. Let z(t)={zi(t), i=1,2,…, N} • Let input cost function Ci(yi(1)), i=1,2,…, N • Let utility function Ui(zi(T)), i=1,2,…, N • Let external buffer size vectorsB(1), B(T+1) such that

22. Capacity Region Evolving Graph(CREG) • Replica t ↔epoch t • Vertices it , t=1,…,T correspond to node i for different replicas • Storage vertices stiwill be used in dual decomposition

23. Problem DTN Utility Maximization (DTNUM)

24. A key idea • Complexity of problem is dominated by the capacity regions, which are often very hard to describe accurately (e.g., Johansson&Soldati, ’06) • Even simple flow maximization problems can be shown to be NP-complete (e.g., Ephremides&Truong ’90). • Therefore, lumping multiple capacity regions in same problem is a bad idea. • We will use duality to make sure that we never have to worry about more than one capacity region at a time.

25. Solving DTNUM directly Computation Time, T (sec) Nodes, N Epochs, T

26. Solving DTNUM by Dual Decomposition Computation Time, T Computation Time, T (sec) Nodes, N Epochs, T

27. Causal Algorithms • Finding optimum involves knowing complete network evolution beforehand. • Greedy DTNUM Algorithm: • Do greedy maximization at each epoch • Initial costs are assumed 0, and buffer spaces increase • Geographic DTNUM Algorithm: • Greedy optimization takes into account node locations and direction of their movement • May be thought of as generalization of geographic routing

28. Performance Volume (a): Optimal algorithm, (b) Geographic algorithm (c): Greedy algorithm, (d): Optimal algorithm, with 1/10 the speed of nodes Total Epochs

29. Part D: On the Cost/Delay Tradeoff of Wireless Delay Tolerant Geographic Routing Tasiopoulos*, Ch. Tsiaras$, S. Toumpis* *Informatics Department, Athens University of Economics and Business, Greece $Department of Informatics, Communication Systems Group, University of Zurich, Switzerland (WOWMOM 2012, 25-28/6/12, San Francisco, CA)

30. Basic Idea • In DTNs, there is a tradeoff between packet delay and cost (including transmission and storage cost) • Currently, tradeoff appears implicitly in formulations [Juang et al. ‘02, Jain et al. 04, Laoutaris et al. 09, Small et al. 03, etc.] • We want to capture this tradeoff formally and explicitly • Under optimal operation • Using practical protocols

31. Cost/Delay Evolving Graphs (C/DEGs) • Time is divided in epochs • The C/DEG is comprised of one subgraph for each epoch • Transmission delay is 0.

32. Optimal Cost/Delay Curves (OC/DCs) • Let two nodes i, j in a network • The OC/DC Cij(t) is the minimum cost with which i can send a packet to j with a delay of at most t epochs. • To calculate it, we need to find the minimum cost path between node i0 and the set of nodes i0, j1,…, jt • Simple minimum cost path problem • Special structure permits fast calculation

33. Example OC/DCs

34. Achievable Cost/Delay Curves (AC/DCs) • Let two nodes i, j in a network • The AC/DC Cij(t) is the minimum cost with which i can send a packet to j with a delay of at most t epochs, assuming optimization over the parameters of the protocol • To calculate it, we need simulations

35. Example AC/DCs

36. Delay Tolerant Geographic Routing • When node S has a packet for node D, it sends it to one of its neighbors using only its local topology and the location of D. • Traditionally, there is no delay. • Newer approach: wait for topology to change. • MoVe [Lebrun et al. ‘05] • AeroRP [Peters et al. ‘11] • GeOpps [Leontiadis et al ‘07.] • BRR, CR [Tasiopoulos et al. ‘12]

37. Rules for selecting next hop • MoVe: A selects node that will pass closest to D • AeroRP: A selects node that approaches D fastest • Min-Cost-per-Progress Rule: minimize • Balanced Ratio Rule: minimize • Composite Rule: minimize

39. Part E: Delay Tolerant Firework Routing Sidera$, S. Toumpis* $ Department of Electrical and Computer Engineering, University of Cyprus, Cyprus *Department of Informatics, Athens University of Economics and Business, Greece (Med-Hoc-Net 2011, Sicily, Italy)

40. DTFR Operation • Homing Phase: travel to estimated location of destination using delay tolerant geographic routing • Explosion Phase: create multiple copies • Spread Phase: systematically search for destination • Lock Phase: do routing in usual sense when in same partition with destination

41. Performance

42. Analysis of Delay Tolerant Geographic Forwarding • Assume mobile node, placed according to spatial Poisson process at any given time. • Assume a packet destination at an infinite distance. • There is an obvious tradeoff between • Speed vp with which packet moves towards the destination • Transmission cost per distance, cp • We find cp(vp) curve for specific forwarding protocol, under some approximations.

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