Evaluate The Performance Of Storage System By Mathematical Modeling Methods

As an important resource, the storage system is of great significance. With the explosivegrowth of digital information, users and various applications, the demands of high-capacitystorage systems is increasing very fast. The development of the storage system is thepreferred and prevalent way for the good use of many resources.In today’s storage system, the performance of I/O system is an essential part of theperformance of storage system. Therefore, how to construct an I/O system with highperformance, low power consumption and suitable for different applications becomes animportant task. The performance of storage system is vital to the storage system itself and theapplication of storage systems. I/O performance is one of the most important performanceindicators for evaluating the storage system, and it has great theoretical and practical value forthe study of I/O performance of storage system. At present, because I/O performance ofcomputer has already become the bottleneck of the whole system for a long time, I/Operformance analysis and optimization method looks especially important and valuable.In recent years, as the main methods of storage system performance, the queuing theorymethod has been analyzed and discussed. Therefore, in the thesis, the queuing theory is takenas the main tool to analyzes and study the performance of I/O aspects of storage system.Because of the growth of information and the existence of many storage devices in thelarge-scale storage nowadays, the storage device failures can occur almost every day, thus, therepairability of storage devices must to be a considerable factor. There are too many factorsinfluencing storage system performance. The paper focuses on the front-end queue of thewhole storage system.This thesis mainly analyzes some local characteristics, and aiming atthe I/O problem of storage system, the service equipment, structural arrangement, andoperation model of the I/O and so on are analyzed by establishing corresponding model. Inthe thesis, I/O performance model is analyzed by the use of the method of the combination ofquasi birth and death process and queuing model, and it is studied mainly passing theindicator distribution of queuing theory and the Q matrix pattern of quasi birth and deathprocess, such as service efficiency submit to megative exponential distribution and so on. Theanalytical method of matrix pattern is presented, and the I/O operational specific performanceindicator expression is summed up. Then after many simulations, the regular pattern isobtained. The data are use for the I/O concrete operation process, finally, the I/O operation rule under the different conditions and all the universal law under the operation mode aresummarized. The results may be used to evaluate storage performance, and are the basis fordeciding I/O scheduling strategy.Cache is one of the important factors affect the storage system performance. This paperproposes a visit sequence generation process comply with the principle of locality. Theprocess has locality, continuity and mutability properties and so on, and can control thestrength of the above properties by a few parameters set. The digital flow is generated in linewith the various access patterns, and compared with various cache replacement algorithm. Onthe basis of this method, a two-level buffer cache structure that is constituted by the buffercaches of application servers and storage servers is studied. The existing ULC (UnifiedLevel-Aware Caching) protocol can effectively solve the problems that redundantly cachedblocks in multilevel hierarchy and weakly localized at storage server cache. However, whenthere are multiple application servers sharing one storage server, the ULC adopts LRUstrategy to allocate cache capacity of storage server to each application server, and thismethod can not gain the maximal marginal profits of the storage server cache. A second-levelbuffer cache dynamic allocation strategy called MG-ULC (Marginal Gain-based ULC) isproposed, and it is designed for storage servers in which multiple applications share the samecache resources. Based on the ULC protocol, the MG-ULC dynamically allocates cachecapacity in accordance with the second-level buffer cache marginal gain of each application.The results shows that, as each application’s access pattern changes, the MG-ULC canallocate second-level buffer cache more rationally than the ULC, thereby realizing a highercache utilization.I/O load is an important factor to affect the performance of storage system, and theresponse time is an important indicator to reflect the performance of storage system. In orderto improve the performance of storage system, the characteristic parameters of the I/O loadand response time are analyzed in this paper. In order to achieve the purpose of forecastingresponse time by some characteristic parameter of I/O load, the model is built by combiningwith the Grey System Theory and BP Artificial Neural Network method, and the nonlinearrelations of characteristic parameter and response time is determined. Make use of the disksim,the trace data is obtained, then the trace data is used in simulating. The predicted result iscompared with the results of using grey and neural network prediction, and the advantages arehighlighted. Due to the considered factors are different, a new prediction model is proposedby combining with the BP neural network and Markov Chain, named as BP neural network-Markov chain(BP-MC) model and applied to I/O load prediction. Through emulatingthe training sample, the rolling prediction of load time series is achieved by BP neuralnetwork, and the relative error of measured and predicted values is acquired. Applying theMarkov Chain to correct the relative error and the accruacy of predicting results is improvedeffectively. The prediction model was used to predict the I/O load of Storage system， theresult shows that the model is high-precision， so it provides a new approach for Storagesystem prediction.The evaluation of I/O performance is very important in the design and use of storagesystem. How to evaluate the I/O performance quickly and efficiently has become an importantprocedure for optimizing I/O performance. In this thesis, both qualitative and quantitativemethod is proposed, which is the combination of analytical hierarchy process level analysismethod and artificial neural network method. I/O performance is analyzed and studied by thismethod. Similarly, the trace data used for simulation is obtained by making use of the disksim.The advantage is found from the simulate result. The evaluation methodology can effectivelyevaluate the I/O performance of storage system.