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In asynchronous real-time systems the time when all events occur can not be predicted beforehand. Systems with sporadic tasks, or that operate a protocol for sharing resources like the priority ceiling protocol, for example, are asynchronous real-time systems. In this paper, we present a sufficient and efficient response time based analysis technique for computing Ri(k), the worst case response time at each invocation k of the periodic tasks of real-time asynchronous systems. In addition, efficient idle time computation for asynchronous systems is presented. This analysis technique can be applied to the analysis of several process models including weakly hard real-time systems, and slack management techniques like aperiodic servers and slack stealing algorithms. It is also shown that the pattern of response times of tasks in a hyperperiod is pseudoperiodic and that the maximum response time instants tend to occur evenly separated within the hyperperiod.

The aperiodic server scheduling mechanism (like the Deferrable Server or Sporadic Server) is an adequate technique to provide service for soft and firm tasks in flexible environments. For handling multiple sources of events either a single server or multiple servers can be used. A single server minimizes the number of capacity exhaustions but provides a poor performance when the sources have different temporal requirements. This problem can be solved using multiple servers at different priorities, however this approach suffers from much more capacity exhaustions as the capacity has to be statically partitioned among them. We overcome these two problems by introducing the capacity sharing protocol in which a server can use the unused capacity of other servers. In this paper, we describe the protocol, evaluate its performance in comparison with single and multiple servers without capacity sharing and we analyze its implementation complexity. The results of the simulation analysis show that the capacity sharing protocol exhibits a better performance than single server and multiple servers without capacity sharing.

Using the constant value for the worst case execution time for all calls to a subprogram is safe but pessimistic. We address the problem of tailoring the WCET of each call to a subprogram by building the WCET as an algebraic expression parameterised with some of the subprogram parameters. The WCET of each call may then be constrained to particular ranges of values that lead to tighter WCET. The technique relies on the power of computational algebra systems to perform sophisticated simplifications and evaluation of the intermediate expressions. The same approach also allows to obtain tighter estimations of nested loop iterations which may also be tailored for different execution calls. This approach requires the description of the non-functional behaviour of the code with annotations included as specially formatted comments.