A stable property continues to hold in an execution once it becomes true. Detecting arbitrary stable properties efficiently in distributed executions is still an open problem. The known algorithms for detecting arbitrary stable properties and snapshot algorithms used to detect such stable properties suffer from drawbacks such as the following: They incur the overhead of a large number of messages per global snapshot, or alter application message headers, or use inhibition, or use the execution history, or assume a strong property such as causal delivery of messages in the system. We solve the problem of detecting an arbitrary stable property efficiently under the following assumptions: P1) The application messages should not be modified, not even by timestamps or message coloring. P2) No inhibition is allowed. P3) The algorithm should not use the message history. P4) Any process can initiate the algorithm. This paper proposes a family of nonintrusive algorithms requiring 6(n-1) control messages, where n is the number of processes. A three-phase strategy of uncoordinated observation of local states is used to give a consistent snapshot from which any stable property can be detected. A key feature of our algorithms is that they do not rely on the processes continually and pessimistically reporting their activity. Only the relevant activity that occurs in the thin slice during the algorithm execution needs to be examined.

Flooding and random walk are two basic mechanisms for blind search in unstructured peer-to-peer overlays. Although these mechanisms have been widely studied experimentally and via simulations, they have not been analytically modeled. Time overhead, message overhead, and success rate are often used as metrics for search schemes. This paper shows that node coverage is an important metric to estimate performance metrics such as the message efficiency, success rate, and object recall of a blind search. The paper then presents two simple models to analyze node coverage in random graph overlays. These models are useful to set query parameters, evaluate search efficiency, and to estimate object replication on a statistical basis

Web prefetching is based on Web caching and attempts to reduce user-perceived latency. Unlike on-demand caching, Web prefetching fetches objects and stores them in advance, hoping that the prefetched objects are likely to be accessed in the near future and such accesses would be satisfied from the caches rather than by retrieving the objects from the Web server. This paper reviews the popular prefetching algorithms based on popularity, good fetch, API characteristic, and lifetime, and then makes the following contributions: 1) The paper proposes a family of linear-time prefetching algorithms, objective-greedy prefetching, wherein each algorithm greedily prefetches those Web objects that most significantly improve the performance as per the targeted metric. 2) The hit rate-greedy and bandwidth-greedy algorithms are shown to be optimal for their respective objective metrics. A linear-time optimal prefetching algorithm that maximizes the H/B metric as the performance measure is proposed. 3) The paper shows the results of a performance analysis via simulations, comparing the proposed algorithms with the existing algorithms in terms of the respective objectives - the hit rate, bandwidth, and the H/B metrics. The proposed prefetching algorithms are seen to provide better objective-based performance than any existing algorithms. Further, H/B-greedy performs almost as well as H/B-optimal.

Web prefetching is based on Web caching and attempts to reduce user-perceived latency. Unlike on-demand caching, Web prefetching fetches objects and stores them in advance, hoping that the prefetched objects are likely to be accessed in the near future and such accesses would be satisfied from the cache rather than by retrieving the objects from the Web server. This work reviews the popular prefetching algorithms based on popularity, good fetch, APL characteristic, and lifetime, and then makes the following contributions. (1) The paper proposes a family of prefetching algorithms, objective-greedy prefetching, wherein each algorithm greedily prefetches those Web objects that give the highest performance as per the metric that it aims to improve. (2) The paper shows the results of a performance analysis via simulations, comparing the objective-greedy algorithms with the existing algorithms in terms of the respective objectives - the hit rate, bandwidth, and the H/B metrics. The proposed prefetching algorithms are seen to provide the best objective-based performance. (3) The paper also proves that the algorithms based on good fetch and on the APL characteristic, although using different criteria, are equivalent in terms of their choice of objects selected for prefetching.