1. Scalable Resource Information Service for Computational Grids Nian-Feng Tzeng Center for Advanced Computer Studies University of Louisiana at Lafayette December 7, 2007

2. Computational Grids Comp. Grid

3. Grid Resource Information Service Computational Grid

4. Core GIIS Core Grid Grid Resource Information Service

5. Under GRAM reporter of GT: – at least, one system-wide GIIS – each resource provider (e.g., cluster header) runs GRIS to provide queuing information and others Grid Resource Information Service

6. Typical MDS-4 deployment

8. What is P2P? A distributed system architecture: No centralized control Nodes are symmetric in function Typically many nodes, but unreliable and heterogeneous Client Internet

9. Example P2P problem: lookup Internet N1N1 N2N2 N3N3 N6N6 N5N5 N4N4 Publisher Key=“title” Value=file data… Client Lookup(“title”) ? publish/lookup at the heart of all P2P systems

10. Another approach: distributed hash tables (DHTs) Nodes are the hash buckets Key identifies data uniquely DHT balances keys and data across nodes DHT replicates, caches, routes lookups, etc. Distributed hash tables Distributed applications Lookup (key) data node …. Insert(key, data)

11. Chord lookups Map keys to nodes in a load- balanced way Hash a node IP addr. into a long string of digit (node ID) Hash a key to the same string length (key ID) Assign hashed key to “closest” node (i.e., its successor) Refer hashed node ID & key ID, as ID & key, respectively K20 K5 K80 Circular ID space N32 N90 N105 N60 Forward key lookup to a closer node Insert: lookup + store Join: insert node in ring

12. Chord’s routing table: fingers N80 ½ ¼ 1/8 1/16 1/32 1/64 1/128

13. Lookups take O( log(N) ) hops N32 N10 N5 N20 N110 N99 N80 N60 Lookup(K19) K19 Lookup: route to closest predecessor

14. Figure. Finger tables at existing nodes 0, 1, 3, to specify subsequent intervals and their corresponding successors. Steps for Node 3 to find successor of key = 1: a. key = 1 belongs to 3.finger[3].interval b. Node 3 checks its 3 rd finger entry, succ. = 0 c. Node 0 checks its 1 st finger entry, gets its succ. = 1 (key is within the smallest possible interval, 1 st entry  answer = succ.) 3 keys existing, 1, 2, 6, held in different nodes held

15. Prefix Hash Trees (PHTs) Easy deployment using OpenDHT 3 APIs – put, delete, get Application – Place Lab Range queries Multiple attributes – combined using linearization Hash on prefixes Beacon ID hashed to SHA-1

17. PHT search takes O(log(log(N))) hops

18. Fast and Scalable Resource Discovery Our Work by Denvil Smith well ID name bore pressure temp user attribute/query translation DHT (Chord) user notification Well data query well ID = 2701 get hash search PHT until finding leaf node check node capacity create leaf nodes update PHT update DHT get hash search PHT over DHT validate selection internal nodes find successors until getting leaf node Well ID = 2701 Name = geiser Bore = 89.99 Pressure = 28.8 Temp = 512 query lookups resource publishing

19. Our Work by Denvil Smith

21. Questions? Please Ask!