A dynamic and adaptive load balancing strategy for parallel file system with large-scale I/O servers
Journal of Parallel and Distributed Computing
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Resource performance prediction is known to be useful in resource scheduling in the Grid. The disk I/O workload is another important factor that influences the performance of the CPU and the network which are commonly used in resource scheduling. In the case of disk I/O workload time-series, the adaptation of a prediction algorithm to new time-series should be rapid. Further, the prediction should ensure that the prediction error is minimum in the heterogeneous environment. The storage workload (i.e., the disk I/O load) is a dynamic variable. A prediction parameter based on the characteristics of the current workload must be prepared for prediction purposes. In this paper, we propose and implement the OPHB (On-Line Parameter History Bank). This is a method that stabilizes the incoming disk I/O workload time-series fairly quickly with the help of accurately determined ESM (Exponential Smoothing Method) parameters. The parameters are drawn from a history database. In the case of forecasting with ESM, a smoothing parameter must be specified in advance. If the parameter is statically estimated from observed data found in previous executions, the forecasts would be inaccurate because they do not capture the actual I/O behavior. The smoothing parameter has to be adjusted in accordance with the shape of the new disk I/O workload. The ESM algorithms utilise one of the accumulated parameter histories chronicled by OPHB's Deposit operation. When a new time-series is started, an appropriate parameter value is looked up in the Bank by OPHB's Lookup operation. This is used for the time-series. This process is fully adaptive. We evaluate the proposed method with SES (Single Exponential Smoothing) and ARRSES (Auto-Responsive SES) methods.