The authors describe a novel approach to scalable group re-keying for secure multicast. Our approach, which we call Kronos, is based upon the idea of periodic group re-keying. We first motivate our approach by showing that if a group is re-keyed on each membership change, as the size of the group increases and/or the rate at which members leave and join the group increases, the frequency of rekeying becomes the primary bottle neck for scalable group re-keying. In contrast, Kronos can scale to handle large and dynamic groups because the frequency of re-keying is independent of the size and membership dynamics of the group. Next, we describe how Kronos can be used in conjunction with distributed key management frameworks such as IGKMP (T. Hardjono et al., 1998) that use a single group-wide session key for encrypting communications between members of the group. Using a detailed simulation, we compare the performance tradeoffs between Kronos and other key management protocols.

In this paper, we present the design and implementation of Dodo, an efficient user-level system for harvesting idle memory in off-the-shelf clusters of workstations. Dodo enables data-intensive applications to use remote memory in a cluster as an intermediate cache between local memory and disk. It requires no modifications to the operating system and/or processor firmware and is hence portable to multiple platforms. Further, the memory recruitment policy used by Dodo is designed to minimize any delays experienced by the owner of desktop machines whose memory is harvested by Dodo. Our implementation of Dodo is operational and currently runs on Linux 2.0.35. For communication, Dodo can use either UDP/IP or U-Net, the low-latency user-level network architecture developed by von Eicken et al. (1995). We evaluated the performance improvements that can be achieved by using Dodo for two real applications and three synthetic benchmarks. Our results show that speedups obtained for an application are highly dependent on its I/O access pattern and data set sizes. Significant speedups (between 2 and 3) were obtained for applications whose working sets are larger than the local memory on a workstation but smaller than aggregate memory available on the cluster and for applications that can benefit from the zero-seek nature of remote memory.