Conventional gang scheduling has the disadvantage that when processes perform I/O or blocking communication, their processors remain idle because alternative processes cannot be run independently of their own gangs. To alleviate this problem, we suggest a slight relaxation of this rule: match gangs that make heavy use of the CPU with gangs that make light use of the CPU (presumably due to I/O or communication activity), and schedule such pairs together, allowing the local scheduler on each node to select either of the two processes at any instant. As I/O-intensive gangs make light use of the CPU, this only causes a minor degradation in the service to compute-bound jobs. This degradation is more than offset by the overall improvement in system performance due to the better utilization of the resources.

We explore the use of compression methods to improve the middleware-based exchange of information in interactive or collaborative distributed applications. In such applications, good compression factors must be accompanied by compression speeds suitable for the data transfer rates sustainable across network links. Our approach combines methods that continuously monitor current network and processor resources and assess compression effectiveness, with techniques that automatically choose suitable compression techniques. By integrating these techniques into middleware, there is little need for end user involvement, other than expressing the target rates of data transmission. The resulting network-and user-aware compression methods are evaluated experimentally across a range of network links and application data, the former ranging from low end links to homes, to wide-area Internet links, to high end links in intranets, the latter including both scientific (binary molecular dynamics data) and commercial (XML) data sets. Results attained demonstrate substantial improvements of this adaptive technique for data compression over non-adaptive approaches, where better compression methods are used when CPU loads are low and/or network links are slow, and where less effective and typically, faster compression techniques are used in high end network infrastructures.

We explore the possibility of using multiple processors to improve the encoding and decoding times of Lempel-Ziv schemes. A new layout of the processors, based on a full binary tree, is suggested and it is shown how LZSS and LZW can be adapted to take advantage of such parallel architectures. The layout is then generalized to higher order trees. Experimental results show an improvement in compression over the standard method of parallelization and an improvement in time over the sequential method.

We explore the use of compression methods to improve the middleware-based exchange of information in interactive or collaborative distributed applications. In such applications, good compression factors must be accompanied by compression speeds suitable for the data transfer rates sustainable across network links. Our approach combines methods that continuously monitor current network and processor resources and assess compression effectiveness, with techniques that automatically choose suitable compression techniques. By integrating these techniques into middleware, there is little need for end user involvement, other than expressing the target rates of data transmission. The resulting network-and user-aware compression methods are evaluated experimentally across a range of network links and application data, the former ranging from low end links to homes, to wide-area Internet links, to high end links in intranets, the latter including both scientific (binary molecular dynamics data) and commercial (XML) data sets. Results attained demonstrate substantial improvements of this adaptive technique for data compression over non-adaptive approaches, where better compression methods are used when CPU loads are low and/or network links are slow, and where less effective and typically, faster compression techniques are used in high end network infrastructures.