Networked Systems Research Projects @ McGill

1. Networked Systems Research Projects @ McGill Muthucumaru Maheswaran Advanced Networking Research Lab School of Computer Science McGill University Montreal, QC H3A 2A7

2. Outline • Ongoing Projects • Galaxy: A Quality of Service Aware Public Computing Utility • RAN: Resource Addressable Network • Trusted Gossip • GINI: A Toolkit for User-Level Networks • Future Projects: • RASAN: Resource and Service Addressable Network • ALVIN: Application Layer Virtual Internetworking Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

3. Motivation for RASAN RASAN: Resource and Service Addressable Network • New technology trends: • Radio frequency IDs (RFIDs) • Pervasive wireless access • Very low cost/power sensors • Creating new resource and service discovery problems. • Examples of such discovery problems: • Locating the best doctors and nurses who should be brought into a team to respond to particular emergency situations, • Locating and allocating resources and services that are necessary for conducting disaster relief • Logistical scheduling of different types • New “discovery” problems enabled by the evolution of network beyond a system that merely interconnects clients and servers via a packet switched network Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

4. What is RASAN? • RASAN is a real-time large-scale directory service that is targeted to include heterogeneous resources (wired, wireless, sensors, people, etc) • RASAN Goal: • Flexible search (multiple search dimensions) • Minimal overhead • Fast response times • Late binding to determine real-time scenarios Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

5. What is RASAN… • RASAN architecture: • Organized in a P2P manner that self-organizes with resource arrival and departure events • Allows searches along multiple attribute spaces for locating resources and services • Uses space filling curves (SFC) to reduce multi-dimensional search to single dimensional problem (used in RAN with success on the Internet for locations) • Instead of a single SFC, it uses a hierarchy of SFCs • Enables multi-resolution searches to reduce error accumulation Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

6. RASAN Design Requirements • Scalable system: Obvious scalability dimension is the number of devices. Others include number of search attributes and resource classes. • Dynamic system support: Resources and services can attach and detach from the directory services without prior notice. • Heterogeneous and multi-resolution search: RASAN is meant to search along multiple attribute dimensions. One way to make the search efficient is to perform the search in progressively increasing resolutions. Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

7. RASAN Design Req… • Resource efficient implementation: Due to its P2P nature, a RASAN kernel would run on each resource. To include resource challenged sensors into RASAN, the implementation should be able to run with limited memory and processing capacities. Further, resources with restrictive battery capacities should be able to participate in “stub” configurations with minimal transit traffic. • Operation with localized trust: Resource should have some credential to establish it identity. Localized reputation should be used to evaluate “behavior trust” • Shared fate: A resource or service that does not exist need not be indexed by the directory Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

8. RAN discovery substrate Profile-based discovery Location-based discovery Resource Addressable Network • RAN: middle layer between services and resources. • Attribute-based and location-based discovery. ODC Service Naming the resources based on their attributes Profile-based naming Network positioning mechanism, assigning coordinates for each node in the network delay space Landmark-aided positioning Physical Resources Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

9. Node Node (PID) Node (x,y) Node (LID) RAN Overlay Location ID Neighborhood pointers connect the rings Hilbert indexing decides the location LAP decides the ring PBN/Hilbert indexing Type rings Profile ID Resources with the same profile ID form a ring Route pointers in the nodes creates the overlay structure Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

10. Network Positioning • Network positioning: assigning coordinates for the nodes in a virtual Cartesian space, from which real network delay can be predicted. Internet l12 Distance prediction: l12 ≈ √[(x1-x2)2+(y1-y2)2] y (x2, y2) Cartesian space (x1, y1) x Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

11. Landmark Aided Positioning • Landmark aided positioning (LAP): the network positioning scheme for RAN. • Using a set of landmarks. • Normal nodes: • Select a subset of the total landmarks and ping them. • Run optimization algorithm to position themselves to minimize the total error in distance prediction. • Two phases of LAP: • Landmark positioning: positioning the landmarks. • Node positioning: positioning the normal nodes. • Simplex and Spring algorithms. Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

12. Location-based Discovery • Finding a resource at specific coordinate/range: • Multidimensional search. • Chose Hilbert curve as the data structure. • Hilbert curve: • A space filling curve. • Preserving proximity. • Hierarchical Hilbert index  location ID (LID). Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

13. Location-based Discovery (cont…) Routing table at node with LID = 2.3.3.1.0 • Routing table for location-based discovery. • Non-zero error in pings justifies fixed length LIDs. • Ring pointers ensuring connectivity; jump pointers enhancing route complexity. • Average search hop complexity = h (approx. level)  O(1). Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

14. Profile-based Discovery • Discovery systems implements naming schemes: • Label-based naming (LBN): DNS, IP Address. • Scalable, but not flexible. • Description-based naming (DBN): LDAP. • Flexible, but with high overhead due to information maintenance, complex matching algorithms. • Introducing profile based naming (PBN): • Labels popular attribute-value combinations. • Combines the goods of LBN and DBN. • Can not discover all the attribute-value combinations. • Trading off flexibility (performance) for scalability. Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

15. Profile-based Discovery(cont…) Profile-based naming: profiles profile space description 1 2 3 Profile IDs description space Profile 1: {Intel/AMD, ≤ 512MB} : 0.* Profile 2: {Intel with 1GB} : 1.0 Profile 3: {Intel/AMD, > 1GB} : [1.1,1.2] • Profile-based routing table is very similar to location-based routing table. Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

16. The Galaxy Architecture • The following diagram shows a proposal for Galaxy architecture: Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

17. Trust and Incentive Management • Public resources remain under control of local agents whose behavior may change randomly • resource sharing in hostile and friendly environments • Challenges in trust management in a PCU • Internet-scale • manage vast pool of distributed resources • cross boundary; autonomous • span across administrative domains • handle localized policies; varied level of trust requirement • reliable exchange of peer behavior • ensure fair resource exchange; resource participation Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

18. RP1A GRMS Trust Management Model Resource Brokers (RBs) RB1B Resource Peers (RPs) RB1A RB2A RP2B RP1B RP3A RP2A Domain B Domain A Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

19. RP1A Trust Hierarchy • Hierarchy: local, global trust • Helps to reduce overhead needed for computing trust • scalable; flexible; localized policing RB1B local trust RP1B global trust RP1A domains are not connected in hierarchy Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

20. 4. resource negotiation 6. resource rewarding 3. resource reply 1. resource request 2. resource discovery (via RAN) RP1A 5. resource access Provider Requestor PCU Operations for Allocation RB1B RB1A RP1B RP1A Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.

21. 4. resource negotiation RP1A Negotiation: Trust Evaluation RB1A computes RP1B ’s global trust GT_ RP1B = LT_RP1B x REP_DBA RB1B recommends RP1B to RB1A based on RP1B ’s local trust LT_ RP1B Domain C RB1B RB1A REP_DBA = reputation of Domain B as measured by Domain A RP1B RP1A Provider Resource access is authorized if RB1A considers GT_RP1B value as trustworthy Security/fairness mechanisms ensure that RBs and RPs do not collude or lie to each other Domain B Requestor Domain A Advanced Networking Research Laboratory, School of Computer Science, McGill University, Montreal, QC, Canada.