Network Computing Laboratory Load Balancing and Stability Issues in Algorithms for Service Composition Bhaskaran Raman & Randy H.Katz U.C Berkeley INFOCOM 2003

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 2 Outline One line comment Motivation Assumed environment & Challenges Proposed Mechanisms & Experiments Critique

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 3 One line comment Propose mechanisms to perform scalable and stable service composition in a wide-area network

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 4 Motivation (1/2) Service composition Enables quick & flexible development of new applications Reuse existing services Service Scenario 1 FranceKorea RRS By SKT Translatio n Many Users!

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 5 Service Scenario 2 Scenario 1 Many Users  Issues of scale Scenario 2 Multimedia Session  Availability (Failure detection & Recovery) Motivation (2/2) VoD Server Transcoder 9 시 News Long living multimedia session

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 6 Assumed Environment Services are deployed at service clusters Mechanisms to handle failures and share load are leveraged Service clusters have a cluster manager Perform monitoring & computation required for management Service clusters form an overlay network Stretch across the wide-area Internet Services are deployed by multiple service providers Service clusters may be spread in many different ASes Exit node

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 7 Assumed environment & Challenges System Characteristics Wide area service overlay network Many client sessions sessions last for a long time Requirements Scalability Balance load among replicas Stability Rapid failure detection & recovery

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 8 Proposed Mechanism – Scalability Load balancing Load definition & Load balancing mechanism Metric for load estimation : LIAC (Least Inverse Available Capacity) Side effect of LIAC No cost for intermediary nodes Path length comparison 8000 paths Service 0 Service 1 Exit node cost

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 9 Proposed Mechanism – Scalability Enhanced metric for load estimation Assign a cost to all links Cost: proportional to the AC of the downstream node Effect of the new metric – shorter path length & good load balancing

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 10 Proposed Mechanism – Scalability Load balancing Load information dissemination Propagating load information Simple periodic flooding : incur load oscillation Reduce link-state update period? No! Increase the overhead On-demand link-state update? No! add load during an overloaded period

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 11 Proposed Mechanism – Scalability Piggybacking Feedback load information along the established service path Low control overhead Service 0 Service 1 Exit node

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 12 Proposed Mechanism – Stability Failure detection & Recovery End-to-End recovery Deliver failure notification to an exit node  reconstruct a service path Local Recovery Failure notice  find an alternate path Exit node

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 13 Proposed Mechanism – Stability Failure detection & Recovery Heartbeat mechanism for failure monitoring 300ms period Packet losses are correlated within 1 sec Timeout value Timeout value to distinguish temporary failures and long term failures Trade-off between early detection vs. false detection Empirically found the appropriate value through experiment

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 14 Proposed Mechanism – Stability Failure detection & Recovery Measured the failure gap period of a wide-area Internet path Exchange heartbeat for a week US-Berlin-Austrailia 1.8 sec for the timeout value Early detection & acceptable false detection rate

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 15 Proposed Mechanism – Stability Recovery time End-to-End recovery vs. local recovery Slightly longer recovery time Failure notification is the only additional cost Better path after reconstruction Globally optimized path

Korea Advanced Institute of Science and Technology Network Computing Laboratory | 16 Critique Strong points Simplified the problem well Scalability  load balancing  estimation & propagation of load info Stability  Failure detection & recovery  timeout value selection Emulation strategy Lower cost than real experiment More realistic compared to simulations Weak points Didn’t consider bandwidth in the metric Target applications are bandwidth sensitive Only applicable to service paths There can be requests in the form of graphs Limitation of piggybacking Length of composition is limited to 2 If the length gets longer, path length will be more important α- value selection should be selected carefully