1. A Peer-to-Peer Approach to Resource Discovery in Grid Environments (in HPDC’02, by U of Chicago) Gisik Kwon Nov. 18, 2002

2. Motivation Two general resource discovery systems, Grid & P2P, eventually will have the same goal –Large-scale, decentralized and self-configuring system with complex functionalities So, general guideline is needed for designing the resource discovery system –Proposing 4 axes(components) guiding the design of any resource discovery architecture –Presenting an emulated environment and preliminary performance evaluation with simulations

3. How 4 axes (components) to be considered –Membership protocol How new nodes join and learn the network –Overlay construction function Selecting the set of active collaborators from the local membership list –Preprocessing Off-line preparations for better search performance Caching (X), pre-fetching (O) E.g) Dissemination of resource descriptions –Request processing Local processing –lookup the requested resource in the local info., processing aggregated resources,.. Remote processing –Request propagation rule

4. Evaluation Modeling the Grid environment (4 parameters) –Resource info. distribution and density Some organizations : sharing large number of resources or just a few Some resources : Common or unique –Resource info. dynamism Highly variable(CPU load) or static(type of CPU) –Requests distribution Pattern of Users’ requests E.g) Zipf distribution, uniform,.. –Peer particapation varies over time more significantly in P2P than Grid

5. Evaluation Preliminary Experimental Setup –Optimistic Grid model Static resource attributes, constant peer participation, no failure –Passive membership protocol A new node learns the network through out-of-band When a new node contacts, membership is enriched –Overlay function accepts unlimited number of neighbors –No preprocessing –Request processing Perfect matching 4 request propagation strategies

6. Evaluation Random walk –Choosing randomly learning-based –Answered similar requests previously best-neighbor –Answered the largest number of requests learning-based + best-neighbor –Learning-based first, otherwise best-neighbor

7. Evaluation User requests : Resource distribution

8. Quantitative estimation of resource location costs

10. Grid vs P2P GRIDP2P Scale Thousands of peers, Hundreds of users Hundreds of thousands of peers and users Centralization Some (centralized repositories for shared data) Less than GRID or no central point Functionality Complex (program exe., R/W access to data, resource monitoring,..) Specialized (file sharing, parallel computations) Participation Stable (long, predefined periods of time) Unpredictable User behavior Some homogeneity (incentives, policies) Uneven (free riding in Gnutella) SystemPowerful, good connectionHighly heterogeneous AdministrationYesNo

11. Adaptive Replication in Peer-to-Peer Systems (in ICS’02, by UMD) Gisik Kwon Nov. 18, 2002

12. Motivation Recent P2P systems are good for the uniform query demand But the demand can be heavily skewed So lightweight, adaptive & system-neutral replication mechanism is needed to control the skewed demand –Proposing LAR –Evaluation on Chord and TerraDir systems with simulation

13. How LAR uses two soft states: caches and replicas –Why soft: Created and destroyed by local decision, without any coordination with the item’s home Caches –Consist of –Use LRU replacement strategy Replicas –Contains item data itself and –More states: home address(IP), neighbor of home, known replicas –Should be advertised

14. How Load balancing with creating replicas –Each time a packet is routed, current load(l i ) of server(s i ) is checked –Load is defined in terms of messages sent to a server during a time unit –If l i > l i max (overloaded) s i creates replica at s j (if l i > l j ) –If (l i low <= l i <= l i hi ) (highly loaded) s i creates replica at s j (only if l j <= l j low ) After creating replica, it is disseminated – 2/32 policy : 2 per msg, 32 per server

15. Evaluation Based on Chord simulator –Single network hop = 25ms Query distribution –follows the poisson distribution –Average input rate = 500/sec –def. skewed input : 90-1 90% skewed to one item, other 10% randomly distributed 1k servers, 32k data items l max = 10/sec, l hi = 0.75*l max, l low = 0.3*l max Queue length = 32 Def. Load window size = 2 sec Dissemination policy = 2/32

16. Static vs. adaptive replication

18. Load balancing l hi = 0.75

19. Parameter sensitivity

20. scalability

21. Chord Lookup 33..33N40 34..35N40 36..39N40 40..47N40 48..63N52 64..95N70 96..31N102 N32’s Finger Table Node 32, lookup(82): 32  70  80  85. 71..71N79 72..73N79 74..77N79 78..85N80 86..101N102 102..5N102 6..69N32 N70’s Finger Table (0) N32 N60 N79 N70 N113 N102 N40 N52 N80 N85 81..81N85 82..83N85 84..87N85 88..95N102 96..111N102 112..15N113 16..79N32 N80’s Finger Table

22. TerraDir