Priority queues are used in many applications including real-time systems, operating systems, and simulations. Their implementation may have a profound effect on the performance of such applications. In this article, we study the performance of well-known sequential priority queue implementations and the recently proposed parallel access priority queues. To accurately assess the performance of a priority queue, the performance measurement methodology must be appropriate. We use the Classic Hold, the Markov Model, and an Up/Down access pattern to measure performance and look at both the average access time and the worst-case time that are of vital interest to real-time applications. Our results suggest that the best choice for priority queue algorithms depends heavily on the application. For queue sizes smaller than 1,000 elements, the Splay Tree, the Skew Heap, and Henriksen's algorithm show good average access times. For large queue sizes of 5,000 elements or more, the Calendar Queue and the Lazy Queue offer good average access times but have very long worst-case access times. The Skew Heap and the Splay Tree exhibit the best worst-case access times. Among the parallel access priority queues tested, the Parallel Access Skew Heap provides the best performance on small shared memory multiprocessors.

In Time Warp optimistic discrete event simulation, there exists a need to occasionally save the states of the logical processes. The state saving often constitutes a substantial overhead. However it is not necessary to save each state of a logical process since states can be restored from earlier states by re-executing intermediate events. In this paper, we analyse the effects of doing the state saving less frequently and present a method that allows each logical process to adapt its state saving interval to its rollback behaviour. Experimental results indicate that the proposed method improves performance of the Time Warp system.

No abstract

In Time Warp optimistic discrete evenr simulation, there

Pervasive environments are characterized by a large number of embedded devices offering their services to the user. Which of the available services are of most interest to the user considerably depends on the user's current context. User context is often rich and very dynamic; making an explicit, user-driven discovery of services impractical. Users in such environments would instead like to be continuously informed about services relevant to them. Implicit discovery requests triggered by changes in the context are therefore prevalent. This paper proposes a proactive service discovery approach for pervasive environments addressing these implicit requests. Services and user preferences are described by a formal context model called Hyperspace Analogue to Context, which effectively captures the dynamics of 296 World Wide Web (2011) 14:295-319 context and the relationship between services and context. Based on the model, we propose a set of algorithms that can continuously present the most relevant services to the user in response to changes of context, services or user preferences. Numeric coding methods are applied to improve the algorithms' performance. The algorithms are grounded in a context-driven service discovery system that automatically reacts to changes in the environment. New context sources and services can be dynamically integrated into the system. A client for smart phones continuously informs users about the discovery results. Experiments show, that the system can efficiently provide the user with continuous, up-to-date information about the most useful services in real time.