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Approximate mean value analysis of client-server systems with multi-class requests

Stochastic Rendezvous Networks (SRVNs) are performance models for multitasking parallel software with intertask communication via rendezvous introduced in [1], which are very appropriate to model client-server systems. SRVNs differ from Queueing Networks (QNs) in two ways: nodes act as both clients and servers (allowing for nested service), and servers have two distinct phases of service—the first one “in RV” with the client, and the second “after RV”, executed in parallel with the client. Early work on solving SRVN models has used a kind of approximate Mean Value Analysis based on heuristic ad hoc assumptions to determine the task queue properties at the instant of RV request arrivals. Approximation are necessary since SRVN violates product form. Recently, a more rigorous approach was proposed in [2] for the solution of SRVN models, based on a special aggregation (named “Task-Directed Aggregation” TDA) of the Markov chain model describing the interference of different clients that contend for a single server with FIFO queueing discipline and different service times. The algorithm derived in [2] has the limitation that each client may require only a single class of service. In general, a software server offers a range of services with different workloads and functionalities, and a client may need more than one service. The present paper uses the TDA approach to derive an extended algorithm which allows a client to require any number of services from a server by changing randomly the request class. The new algorithm is incorporated into a decomposition method for models with any number of servers. The SRVN modelling technique is applied to a large case study of a distributed database system, giving insight into the behaviour of the system and helping to identify performance problems such as software bottle-neck.