An Efficient Topology-Adaptive Membership Protocol for Large-Scale
                 Cluster-Based Services


           Jingyu Zhou, Lingkun Chu, and Tao Yang


A highly available large-scale service cluster often requires the system
to discover new nodes and identify failed nodes quickly in order to 
handle a high volume of traffic. Determining node membership promptly 
in such an environment is critical to location-transparent service 
invocation, load balancing, and failure shielding.  In this paper, we
present a topology-adaptive hierarchical membership service which
dynamically divides the entire cluster into membership groups based on
the network topology among nodes so that the liveness of a node within
each group is published to others in a highly efficient manner.  The
proposed approach has been compared with two alternatives: an all-to-all
multicast approach and a gossip based approach.  The results show that
the proposed approach is scalable and effective in terms of high
membership accuracy, short view convergence time, and low communication
cost.