Ziv Dayan Tom Afek Kafka Instructor Ittay Eyal

 What is a failure detector?  Our failure detector  Software Implementation  Gossip style  Independent local unit

 Communication – by messages  Each message contains a list of heartbeats  Each heartbeat contains  IP of creator  Time since creation  Each node contains its own Local Node: Local Node Net Members Node Neighbors Versions Neighbor Version

Repeat periodically:  Choose the node whose threshold is closest to expiration  Wait until the threshold has expired  Check the local time of creation of the last heartbeat received by the suspected node:  If changed – the node is OK  Else – the suspected node had crashed

Computer Listener Main Message Handler Message Sender Sender Detector

 A new abstract class is added – NetMessage  Method 1: Handle() – decodes the received message using the proper version and returns Message  Method 2: toString() – used for serialization NetMessage SHA1MessageNormalMessage Message

 H = f(P, n, threshold)  Assumptions required  Simplicity Vs Efficiency  Full topology  Spread time << threshold

 Assumption – Local Information  Strong Assumption  Reliability  x – number of messages -  Probability for false detection  We want  Result :

 Linear Performance  The bigger is P the bigger is the slope

 Assumptions  Synchrony  Consistency  Calculation for average case

 High Performance

 Comparison Categories  Efficiency  Scalability  Dynamism  Reliability