Abstract-SPEC CPU2006 benchmark suite has been extensively studied, with efforts focusing on the requirement understanding of memory workloads from the SPEC CPU2006 suite. However, characterizing SPEC CPU2006 workloads from a time dependence perspective has attracted little attention. This paper studies the auto-correlation functions of the arrival intervals of memory accesses in all SPEC CPU2006 traces, and concludes that correlations in memory inter-access times are inconsistent, either with evident correlations or with little and no correlation. Different with the studies focused on the prior suites, we present that self-similarity exists only in a small number of SPEC2006 workloads. In addition, we implement a memory access series generator in which the inputs are the measured properties of the available trace data. Experimental results show that this model can more accurately emulate the complex access arrival behaviors of real memory systems than the conventional self-similar and independent identically distributed methods, particularly the heavy-tail characteristics under both Gaussian and non-Gaussian workloads.

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Abstract-One of the challenging issues in performance evaluation of parallel storage systems through synthetic-trace-driven simulation is to accurately characterize the I/O demands of data-intensive scientific applications. This paper analyzes several I/O traces collected from different distributed systems and concludes that correlations in parallel I/O inter-arrival times are inconsistent, either with little correlation or with evident and abundant correlations. Thus conventional Poisson or Markov arrival processes are inappropriate to model I/O arrivals in some applications. Instead, a new and generic model based on the α-stable process is proposed and validated in this paper to accurately model parallel I/O burstiness in both workloads with little and strong correlations. This model can be used to generate reliable synthetic I/O sequences in simulation studies. Experimental results presented in this paper show that this model can capture the complex I/O behaviors of real storage systems more accurately and faithfully than conventional models, particularly for the burstiness characteristics in the parallel I/O workloads.

The paper is concerned with the controllability of nonlinear neutral stochastic differential inclusions with infinite delay in a Hilbert space. Sufficient conditions for the controllability are obtained by using a fixed-point theorem for condensing maps due to O'Regan.

With the development of network storage technology, object-based storage (OBS), as the next wave of storage technology and devices, provides the advantages of both file storage and block storage. However, the performance of such network storage hinges on knowing the I/O traffic patterns and optimizing the network for such patterns.Recently the notion of self-similarity has been applied to I/O workload, just like to wide-area and local-area network traffic. In this paper, basing on self-similar stochastic processes, we study the long-range dependencies in data traffic for object-based storage system (OBSS) and discuss the relation between I/O workload and network traffic about self-similarity.