Whenever data is moved across nodes in the parallel database system, the indexes need to be modified too. Index modification overhead can be quite severe because there can be a large number of indexes on a relation. In this paper, we study two alternatives to index modification, namely OAT (One-At-a-Time page movement) and BULK (bulk page movement). OAT and BULK are two extremes on the spectrum of the granularity of data movement. OAT and BULK differ in two respects: first, OAT uses very little additional disk space (at most one extra page), whereas BULK uses a large amount of disk space. Second, BULK uses sequential prefetch I/O to optimize on the number of I/Os during index modification, while OAT does not. Using an experimental testbed, we show that BULK is an order of magnitude faster than OAT. In terms of the impact on transaction performance during reorganization, BULK and OAT perform differently: when the number of indexes to be modified is either one or two, OAT has a lesser impact on the transaction performance degradation. However, when the number of indexes is greater than two, both techniques have the same impact on transaction performance.

In communication and computer systems, autonomous sources may assign priorities to their messages or jobs locally and independently. When a remote service (e.g., message transmission or RPC) is requested at a shared server, the server cannot use priority scheduling schemes effectively unless it can make a comparison between priorities defined by individual sources. The authors investigate the strategies under which the service received by requests of one source is not affected by the priority assignments at other sources. The first approach is a combination of processor-sharing and priority queue strategies. The second approach is to map locally defined priorities onto a global priority system. The performance of these approaches is examined in terms of the average response time of all requests, the average response time of the highest priority requests and a fairness measure.>