In this dissertation we address shortcomings of two important group communication layers, IP Multicast and gossip based message dissemination, both of which have scalability issues when the number of groups grows. We propose a transparent and backward-compatible layer called Dr. Multicast to allow data center administrators to enable IPMC for large numbers of groups without causing stability issues. Dr. Multicast optimizes IPMC resources by grouping together similar groups in terms of membership to minimize redundant transmissions as well as cost of filtering unwanted messages. We then argue that when nodes belong to multiple groups, gossip based communication loses its appealing property of using fixed amount of bandwidth. We propose a platform called GO (for Gossip Objects) that bounds the node’s bandwidth use to a customizable limit, prohibiting applications from joining groups that would cause the limit to be exceeded. Both systems incorporate optimizations that are based on group similarity or affinity. We explore group affinity in real data-sets from social networks and a trace from an industrial setting. We present new models to characterize overlaps between groups, and discuss our results in the context of Dr. Multicast and GO. The chapters on Dr. Multicast and GO are self-contained, extended versions of papers that appeared respectively in the ACM Hot Topics in Networks (HotNets) Workshop 2008 [85] and the International Peer-to-Peer (P2P) Conference 2009 [87].