Integrating a performance model in self-managing computer systems under mixed workload conditions

With rapid advances in processing power, network bandwidth, and storage capacity, computer systems are increasingly becoming extremely complex. Consequently, it becomes expensive and difficult for human beings to manually manage complex computer systems. This problem can be effectively tackled by self-managing computer systems, which are intended to meet high performance requirements in a dynamic computing environment. In this paper, we develop a performance model for self-manage computer systems under dynamic workload conditions, where both CPU- and I/O-intensive applications are running in the systems. In particular, we design in this paper a 2-dimensional Markov chain model with two different arrival and service rate of CPU- and I/O-intensive jobs. Importantly, two serving probabilities with respect to CPU- and I/O intensive jobs are derived. To validate the analytical model, we developed an adaptive admission controller in which the model is incorporated. Experimental results demonstratively show that the controller is capable of achieving high performance for computer systems under workloads exhibiting high variabilities.