A Scheduling-based Routing Network Architecture

1. A Scheduling-based Routing Network Architecture Omar Y. Tahboub & Javed I. Khan Multimedia & Communication Networks Research Lab (MediaNet) Kent State University

2. Outline • Introduction • The Scheduling-based Routing Network Architecture • Case Study: An Institutional Remote Data Backup & Recovery Network • Performance Evaluation. • Conclusion and Future Work

3. Outline • Introduction • The Scheduling-based Routing Network Architecture • Case Study: An Institutional Remote Data Backup & Recovery Network • Performance Evaluation. • Conclusion and Future Work

4. Introduction • Bandwidth-intensity will be dominating aspect in future emergent Internet applications. • Will pose network capacity demands beyond imagination reaching Gigabytes and yet Terabytes per day. • Internet2 [1] model will likely be the reference architectural model for the next generation high-performance networks. • The Internet2 Dynamic Circuit Networking (DCN) [2] will also be the key communication paradigm. • Multi Protocol Labeling Switching (MPLS) [3] play a central role massive data flow routing, switching and forwarding

5. Introduction • Finally, on the basis of the case study, we carried out a performance evaluation study: • Demonstrated two simulation experiments. • Compared the performance between the scheduled data backup transfer to the conventional non-scheduled. • We first describe a scheduling-based routing network architecture namely DCN@MPLS [4,5]. • Implements DCN operation at the MPLS level. • Enables time-scheduled route (LSP) information to be disseminated into MPLS domains. • Second, we present a case study focusing on remote backup and recovery networking application. • Utilized the Ohio Super Computing Network OSCnet backbone. • Connects 11 universities in the state of Ohio,

6. Outline • Introduction • The Scheduling-based Routing Network Architecture • Case Study: An Institutional Remote Data Backup & Recovery Network • Performance Evaluation • Conclusion and Future Work

7. The Scheduling-based Routing Network Architecture Figure 1: The DCN@MPLS Network Architecture [4][5]

8. The Network Tier • For each edge eiE, bwi denotes its bandwidth (bps) and li denotes its propagation delay in seconds. • Represented by G = (N, E). • N = {n1, n2, …, nm} be the set of m label switch routers. • For each switch router niinN, • ci : service rate in bits per second (bps) and • bi: the available storage buffer in bits. Figure 2: The Network Tier • E = {e1, e2, …, en} be the set of edges (links), • Each edge ei in E connects a pair of label switch routers (nu, nv) N.

9. The Edge Tier • Clients of this architecture are multi-disciplinary demanding various communication services: • Telemedicine • Content Distribution • Distance Learning • Represents the user-groups requesting on-demand data flow transmissions via the network tier. Figure 3: The Edge Tier

10. The Edge Tier • A FEC is further presented by a task t defined by the tuple (u , v , o, dl, s), where • u: the ingress LER. • v: the egress LER. • o: the task origination time in seconds. • dl: the task completion time deadline in seconds. • s: the task size in bits. Figure 4: The FEC as a Task

11. The Routing Tier • The main task of the route scheduling tier is computing time-scheduled routes in the underlying network domain. • Consists of the route scheduling solver. Figure 5: The Routing Tier

12. The Routing Tier • Let set RT ={ r1, r2, …, ri,…, rn} defines a route schedule as a set of routes, where each task has a route (is committed to a task). • Given a MPLS domain G = (N, E) • Let the route (LSP) ri be a solution to task ti, defined as an ordered set of k node hops (switch routers) • Li = {ei,1, ei,2,.., ei,j, …, ei,k-1}, where ei,j connects ni,(j-1) and ni,j. • Hi = {ui, ni,2,…, ni,j, …, ni,(k-1), vi}, or as k-1 link (edge) hops. • Let T denote the set of n tasks Figure 6: The LSP Specifications

13. The Routing Tier

14. The Scheduling Tier • This tier consists of three entities: • Node Resource Information Base (NRIB) • Link Resource Information Base (LRIB) and • Router server. Figure 7: The Scheduling Tier

15. The Scheduling TierThe Node Resource Information Base (NRIB) • Node resources information includes: • Available service capacity (bps) • Total service capacity (bps) • Total input/output buffers capacity (bytes) and • Available input/output buffer capacities (bytes). Figure 8: The NRIB

16. The Scheduling TierThe Link Resource Information Base (NRIB) • Link resources information includes: • Source LSR • Destination LSR, • Total link capacity (bps), and • Propagation delay (seconds). Figure 9: The NRIB

17. The Scheduling Tier Network Resource Reservation Figure 11: Network Resource Reservation

18. The Scheduling Tier Route Schedule Dissemination Figure 12:Route (LSP) Schedule Dissemination

19. Outline • Introduction • The Scheduling-based Routing Network Architecture • Case Study: An Institutional Remote Data Backup & Recovery Network • Performance Evaluation. • Conclusion and Future Work

20. Case Study:An Institutional Remote Data Backup & Recovery Network • We utilize the Ohio Supercomputing Computing [6] network OSCnet as practical network backbone. • Safeguarding data and information against all types of disasters is an urgency. • Offline remote data backup & Recovery Networks serves an efficient solution. • In organizational Information centers, data & information backup is performed in a daily, weekly and monthly basis.

21. Case Study:The OSCnet Network Backbone Figure 13: The OSCnet Network Backbone

22. Case Study:Backup Mirror Site Assignment Table 1: Backup Mirror Sites Assignment

23. Case Study:Data Backup Transfer Demands Table 2: Projected Daily Transfer Demands

24. Case Study:Critical Performance Challenges Chaotic Will Chock out other bandwidth contending Applications Stable Figure 14: Sample Average Shortest Path Length

25. Case Study:Critical Performance Challenges Figure 15: SampleAggregate Shortest Path Load

26. Case Study:The Network Architecture Figure 16: The Four-Tier OSCnet-based Network Architecture

27. Outline • Introduction • The Scheduling-based Routing Network Architecture • Case Study: An Institutional Remote Data Backup & Recovery Network • Performance Evaluation. • Conclusion and Future Work

28. Performance Evaluation • To demonstrate the performance incentives of scheduled-based data transfer over the classical transfer scheme. • Compares the performance achieved by scheduled backup data transfer to the classical unscheduled scheme. • This study is conducted as a simulation study of the OSCnet network backbone shown by Figure 13.

29. Simulation Experiment-1 Setup • Link capacity allocation: • Unscheduled: Day = 100%, Night = 100% • Scheduled: Day = 10%, Night = 90% • Number of Tasks:156. • Performance Metrics: • Average Shortest Path Length at Link Load 90% • Aggregate Shortest Path Load at Link Load 90%

30. Simulation Experiment-2 Setup • Link capacity allocation:Day = 100%, Night = 100% • Number of Tasks:156. • Performance Metrics: • Overall Task Schedulability Percentage • The ration of number of tasks completed by their deadline to total of all tasks * 100%

31. Simulation Experiment-1 Results Unscheduled Scheduled Figure 16: Average Shortest Path Length

32. Simulation Experiment-1 Results Figure 17: Aggregate Shortest Path Load

33. Simulation Experiment-2 Results Figure 17: Overall Task Schedulability

34. Outline • Introduction • The Scheduling-based Routing Network Architecture • Case Study: An Institutional Remote Data Backup & Recovery Network • Performance Evaluation. • Conclusion and Future Work

35. Conclusion and Future Work • On the basis of the performance evaluation stud, it can be concluded that Scheduling-based routing significantly improves: • The Average Shortest Path Length. • The Aggregate Load of the Shortest Path. • The Overall Task Schedulability. • Presented a four-tier scheduling-based routing architecture namely DCN@MPLS. • Demonstrated a OSCnet-based remote data backup case study.

36. Conclusion and Future Work • The Scheduling-based data backup and recovery • Near-optimal mirror site exploration and Selection Heuristics. • Hierarchical scheduling-based routing network architecture • DCN@MPLS is a centralized architecture. • MPLS & CR-LDP Protocol Extensions • Timed Route Schedule Dissemination in MPLS networks • Pathway Intermittency • Route Scheduling in Physically/Logically Intermittent Networks

37. Thank You !

38. References [1] The Internet2, Wikipedia, url: http://en.wikipedia.org/wiki/Internet2. [2] Internet2 Consortium, “Internet2’s Dynamic Circuit Network”, 2008. [3] E. Rosen, A. Viswanathan, and R. Callon, “Multiprotocol Label Switching Architecture”, RFC 3031, January, 2001. [4] O. Tahboub, “DCN@MPLS: A Network Architectural Model for Dynamic Circuit Networking at Multiple Protocol Label Switching”, TR-2009-02-01, MediaNet Lab, , 2009. [5] Tahboub, O., Khan, J., “DCN@MPLS: A Network Architectural Model for Dynamic Circuit Networking at Multiple Protocol Label Switching”, The First International Workshop on Concurrent Communication ConCom 2009, Seattle, WA, 2009. [6] The Ohio Super Computing Network, url: http://www.osc.edu/oscnet/