Efficient Event-based Resource Discovery Wei Yan*, Songlin Hu*, Vinod Muthusamy +, Hans-Arno Jacobsen +, Li Zha* * Chinese Academy of Sciences, Beijing + University of Toronto July 9, rd Int’l Conference on Distributed Event- Based Systems (DEBS 2009) MIDDLEWARE SYSTEMS RESEARCH GROUP

MIDDLEWARE SYSTEMS RESEARCH GROUP Composite applications Mashups Service-oriented architectures Cloud computing A fundamental need is to discover resources and services. Many resources Distributed Dynamic attributes Real-time discovery

MIDDLEWARE SYSTEMS RESEARCH GROUP Related work DEBS ’09 Event-based Resource Discovery 3 SchemeComments Centralized index (Condor ’ s Matchmaker) Suffers from large scale and high dynamism. Hierarchical index (Globus ’ s MDS) Root node easily becomes a bottleneck. Federated UDDIExpensive to replicate frequently updated information among repositories. Discovery flooding (Gnutella) Creates a large volume of traffic. DHT (CAN, Chord, Pastry, etc.) Optimized for key-based discoveries. Range queries over DHTs (P-Ring, Mercury, etc.) Multi-attribute lookups can be expensive. Many approaches have limited expressiveness, or support for dynamic attributes or real-time discovery.

MIDDLEWARE SYSTEMS RESEARCH GROUP DEBS ’09 Event-based Resource Discovery 4 Contributions Event-based resource discovery  Distributed architecture  Leverage publish/subscribe system  Support dynamic resource updates  Allow for continuous discovery and real-time results Discovery similarity optimization  Share results among discoveries Evaluations  Tradeoffs of decentralized architecture  Benefits of sharing discovery results

MIDDLEWARE SYSTEMS RESEARCH GROUP DEBS ’09 Event-based Resource Discovery 5 Event-based resource discovery framework

MIDDLEWARE SYSTEMS RESEARCH GROUP DEBS ’09 Event-based Resource Discovery 6 Supported models One-time discovery Continuous discovery Static resource Dynamic resource Static (e.g., find weather service) Dynamic (e.g., find micro- generation power) Static continuous (e.g., monitor real estate) Dynamic continuous (e.g., monitor grid resources) Resources Discoveries Event-based

MIDDLEWARE SYSTEMS RESEARCH GROUP Architecture DEBS ’09 Event-based Resource Discovery 7 Resource providers act as publishers Discovery clients act as subscribers Advertise all attributes: system = linux memory <= 2000 disk <= 320 Publish updates of dynamic attributes: memory = 1500 disk = 80 Subscribe for resources: system = linux disk >= 200 B1 B4 B5 B2 B3 Distributed Content-Based Publish/Subscribe

MIDDLEWARE SYSTEMS RESEARCH GROUP Static model DEBS ’09 Event-based Resource Discovery 8 Discovery is performed locally by any single broker. Advertisement: system = linux, memory = 2, disk = 320 Subscription: memory > 1Publication B1 B4 B5 B2 B3

MIDDLEWARE SYSTEMS RESEARCH GROUP Dynamic model DEBS ’09 Event-based Resource Discovery 9 Resource update publication cached at the resource’s host broker. Discovery subscription routed to potentially matching resource host brokers. Advertisement: system= linux, memory <= 2, disk < 320 Subscription: memory > 1Publication: memory = 1, disk = 200 B1 B3 B4 B5 B2

MIDDLEWARE SYSTEMS RESEARCH GROUP Static continuous model DEBS ’09 Event-based Resource Discovery 10 Discovery is performed locally by any single broker (like static model). Discovery subscription stored at discovery host broker. Advertisement: system = linux, memory = 2, disk = 320 Subscription: memory > 1Publication B1 B4 B5 B2 B3

MIDDLEWARE SYSTEMS RESEARCH GROUP Dynamic continuous model DEBS ’09 Event-based Resource Discovery 11 Traditional pub/sub routing of messages. Discovery subscription is routed to and stored at matching resource host brokers. B5 B4 B3 B2 B1 Advertisement: system= linux, memory <= 2, disk < 320 Subscription: memory > 1Publication: memory = 1, disk = 200

MIDDLEWARE SYSTEMS RESEARCH GROUP Summary of models One-time discovery Continuous discovery Static resource Dynamic resource Static Dynamic Static continuous Dynamic continuous Discovery handled locally at discovery host broker Updates delivered only to interested clients No persistent subscription state Subscription state used to route back updates

MIDDLEWARE SYSTEMS RESEARCH GROUP DEBS ’09 Event-based Resource Discovery 13 Discovery similarity

MIDDLEWARE SYSTEMS RESEARCH GROUP Reuse results of similar discoveries DEBS ’09 Event-based Resource Discovery 14 Find machines with at least 1GB memory S1 Subscription: memory >= 1000 R1 R2 R3 More general Find machines with at least 2GB memory S1 Subscription: memory >= 2000 R1 R2 CoversSuperset

MIDDLEWARE SYSTEMS RESEARCH GROUP Similarity forwarding DEBS ’09 Event-based Resource Discovery 15 To retrieve old results: Send covered sub to the covering sub’s discovery host broker. To intercept new results: Store covered sub at the first broker with a covering sub. Adv: system= linux, memory <= 2, disk < 320 Sub2: memory > 2Pub: memory = 1, disk = 200 Sub1: memory > 1 B1 B5 B2 B3 B4

MIDDLEWARE SYSTEMS RESEARCH GROUP DEBS ’09 Event-based Resource Discovery 16 Evaluations

MIDDLEWARE SYSTEMS RESEARCH GROUP Experimental setup Algorithms implemented in Java  Based on PADRES content-based pub/sub system Run on a cluster of nodes with 1.86 GHz CPU and 4 GB memory Default workload  Topology Decentralized: 24 brokers Centralized: 1 broker  1000 resources Balanced and unbalanced spatial distributions  1000 discoveries Balanced and unbalanced spatial distributions Various degrees of similarity Metrics  Discovery time  Message overhead DEBS ’09 Event-based Resource Discovery 17

MIDDLEWARE SYSTEMS RESEARCH GROUP Discovery time Similarity forwarding optimization is faster Increased discovery similarity  Normal algorithm suffers More matching resources are found  Optimized algorithm benefits Reuse results Spatial clustering of resources  Normal algorithm benefits Smaller subscription propagation tree (more “multicast”)  Optimized algorithm benefits slightly Results are often retrieved from discovery host broker Spatial clustering of discoveries  Normal algorithm suffers Congestion of messages near discovery host brokers  Optimized algorithm suffers slightly Matching of cached results is relatively cheap DEBS ’09 Clustered spatial distribution of discoveries Balanced spatial distribution of discoveries Discovery similarity (%) Avg discovery time (s) Normal(B) Similarity(B) Normal(U) Similarity(U) Discovery similarity (%) Avg discovery time (s) Normal(B) Similarity(B) Normal(U) Similarity(U)

MIDDLEWARE SYSTEMS RESEARCH GROUP Similarity forwarding optimization propagates fewer subscriptions Increased discovery similarity  Normal algorithm suffers slightly More matching resources are found  Optimized algorithm benefits Covered subs only propagate to a single discovery host broker Spatial clustering of resources  Normal algorithm benefits Smaller subscription propagation tree (more “multicast”)  Optimized algorithm benefits (but less than normal algorithm) Covered subs are not affected Spatial clustering of discoveries  Normal algorithm has little effect Subscriptions still propagate to resource host brokers  Optimized algorithm has little effect Cost is dominated by the covering subs, which still need to propagate to resource host brokers DEBS ’09 Clustered spatial distribution of discoveries Balanced spatial distribution of discoveries Subscription messages

MIDDLEWARE SYSTEMS RESEARCH GROUP Decentralized architecture (one-time requests) DEBS ’09 Event-based Resource Discovery 20 Successive discovery groups match increasing number of resources Measure time to find (updated) resources Decentralized architecture distributes the load  Discovery handled locally by discovery host broker  Updates are propagated only to interested discovery host brokers

MIDDLEWARE SYSTEMS RESEARCH GROUP Decentralized architecture (continuous requests) DEBS ’09 Event-based Resource Discovery 21 Decentralized architecture better distributes the load  Results from similar discoveries are reused  Updates are propagated only to interested brokers

MIDDLEWARE SYSTEMS RESEARCH GROUP Conclusions Discovering resources and services is increasingly important in composite distributed applications A distributed event-based resource discovery framework was designed  Parallel discovery of static resources  Efficient dissemination of dynamic resource attributes  Real-time discovery of new resources Optimizations to exploit similarity among discoveries were developed  Find similar discoveries  Reuse results  Exploit publish/subscribe covering techniques Evaluations show that the distributed architecture achieves faster discovery at the expense of increased network traffic The similarity optimization benefits from more skewed spatial and interest distributions DEBS ’09 Event-based Resource Discovery 22

MIDDLEWARE SYSTEMS RESEARCH GROUP DEBS ’09 Event-based Resource Discovery 23 Efficient Event-based Resource Discovery padres.msrg.toronto.edu Open source soon! Q&A