Modelling and Validation of Response Times in Zoned RAID

We present and validate an enhanced analytical queueing network model of zoned RAID. The model focuses on RAID levels 01 and 5, and yields the distribution of I/O request response time. Whereas our previous work could only support arrival streams of I/O requests of the same type, the model presented here supports heterogeneous streams with a mixture of read and write requests. This improved realism is made possible through multiclass extensions to our existing model. When combined with priority queueing, this development also enables more accurate modelling of the way subtasks of RAID 5 write requests are scheduled. In all cases we derive analytical results for calculating not only the mean but also higher moments and the full distribution of I/O request response time. We validate our mode

SIMULATION AND MODELLING OF RAID 0 SYSTEM PERFORMANCE

RAID systems are fundamental components of modern storage infrastructures. It is therefore important to model their performance effectively. This paper describes a simulation model which predicts the cumulative distribution function of I/O request response time in a RAID 0 system consisting of homogeneous zoned disk drives. The model is constructed in a bottom-up manner, starting by abstracting a single disk drive as an M/G/1 queue. This is then extended to model a RAID 0 system using a split-merge queueing network. Simulation results of I/O request response time for RAID 0 systems with various numbers of disks are computed and compared against device measurements

Impact of Rituximab on Immunoglobulin Concentrations and B Cell Numbers after Cyclophosphamide Treatment in Patients with ANCA-Associated Vasculitides

OBJECTIVE: To assess the impact of immunosuppressive therapy with cyclophosphamide (CYC) and rituximab (RTX) on serum immunoglobulin (Ig) concentrations and B lymphocyte counts in patients with ANCA-associated vasculitides (AAVs). METHODS: Retrospective analysis of Ig concentrations and peripheral B cell counts in 55 AAV patients. RESULTS: CYC treatment resulted in a decrease in Ig levels (median; interquartile range IQR) from IgG 12.8 g/L (8.15-15.45) to 9.17 g/L (8.04-9.90) (p = 0.002), IgM 1.05 g/L (0.70-1.41) to 0.83 g/L (0.60-1.17) (p = 0.046) and IgA 2.58 g/L (1.71-3.48) to 1.58 g/L (1-31-2.39) (p = 0.056) at a median follow-up time of 4 months. IgG remained significantly below the initial value at 14.5 months and 30 months analyses. Subsequent RTX treatment in patients that had previously received CYC resulted in a further decline in Ig levels from pre RTX IgG 9.84 g/L (8.71-11.60) to 7.11 g/L (5.75-8.77; p = 0.007), from pre RTX IgM 0.84 g/L (0.63-1.18) to 0.35 g/L (0.23-0.48; p<0.001) and from pre RTX IgA 2.03 g/L (1.37-2.50) to IgA 1.62 g/L (IQR 0.84-2.43; p = 0.365) 14 months after RTX. Treatment with RTX induced a complete depletion of B cells in all patients. After a median observation time of 20 months median B lymphocyte counts remained severely suppressed (4 B-cells/µl, 1.25-9.5, p<0.001). Seven patients (21%) that had been treated with CYC followed by RTX were started on Ig replacement because of severe bronchopulmonary infections and serum IgG concentrations below 5 g/L. CONCLUSIONS: In patients with AAVs, treatment with CYC leads to a decline in immunoglobulin concentrations. A subsequent RTX therapy aggravates the decline in serum immunoglobulin concentrations and results in a profoundly delayed B cell repopulation. Surveying patients with AAVs post CYC and RTX treatment for serum immunoglobulin concentrations and persisting hypogammaglobulinemia is warranted