Guillermo Navarro

FuSnap: Fuzzy Control of Logical Volume Snapshot Replication for Disk Arrays

This paper presents FuSnap, a fuzzy-logic-based controller that monitors and controls the snapshot process of a logical storage volume in a disk array. As disks do not linearly respond to the arrival rate of user accesses, FuSnap makes use of fuzzy logic as the means to achieve better control of their response time. The goal of the FuSnap controller is to reduce the response time caused by the copy-on-writes (CoWs) that occur during the snapping of a storage logical volume. The FuSnap controller, based on the response time of user accesses, makes the decision on whether to proceed with a CoW or a redirect-on-write when a source logical volume is being copied to a snapshot logical volume. The benefits of FuSnap approach are twofold. First, significant reductions in response time of user requests are obtained with the FuSnap approach over the traditional CoW snap approach. Second, these reductions in response time make the point-in-time copy of data a process less disruptive for database users. FuSnap was verified with two setups using Hewlett-Packard UniX workstations, one setup with eight and the other with 32 disks.

Fuzzy Performability Analysis of Disk Arrays

The performability of disk arrays systems has been studied before. However, in the case of imprecise data, a fuzzy model can be the base for the performability analysis. In this paper a performability analysis of a disk array using a Markov reward model (MRM) is presented. The model considers the repair as the reconstruction (rebuild) of the redundancy, not as a hard drive replacement. With traditional, crisp arithmetic, for each change in a single model parameter the model would need to be run again, resulting in a family of curves difficult to interpret. In the approach presented in this paper, the rewards for each of the states of the MRM, as well as other disk array parameters are expressed through fuzzy numbers. The use of fuzzy arithmetic for the performability estimation of a disk array proved significant advantages. First, the model was able to capture the uncertainty variance of each of the model parameters. Secondly, as opposed to traditional, crisp arithmetic approach, the presented model provides the estimation of the lower and upper boundary of the system performability with a single run of the model

Fuzzy control of sparing in disk arrays

The redundancy regeneration (sparing or rebuild) algorithms in disk arrays face the problem of balancing between the data recovery activity within the array and the user workload acting upon the array at the same time [1]. If the algorithm favors the user workload so the user requests can always preempt the internal data recovery, then the data sparing can stall in the presence of a sustained workload. But on the contrary, if the data recovery is favored over the user requests, the latency of the user requests can be so high to reach unacceptable levels for the data transactions. Using computationally intelligent techniques, like fuzzy logic, better algorithms to balance the level of user requests and the internal data recovery can be achieved. The disk array and data recovery process are modeled using the queue systems with vacations (QSV) [2]. A fuzzy algorithm to control the sparing is presented in this paper. The results indicate that by using fuzzy logic, a better balancing is achieved between the need to have an acceptable response time for the user requests and the data recovered as soon as possible.

Predictive E-Mail Server Performability Analysis Based on Fuzzy Arithmetic

The performability of disk arrays systems has been studied before. However, in the case of imprecise data, a fuzzy model can be the base for the performability analysis. This paper presents a performability analysis of an MSExchange-like e-mail server. The analysis is based on a Markov reward model. The performability analysis is accomplished through the use of fuzzy arithmetic. Unlike traditional Markov chains, fuzzy Markov chains can successfully handle uncertain, imprecise probabilities. In cases where the failure rates, repair rates, or the workload parameters are uncertain, Markov Chains enhanced with fuzzy arithmetic provide means for comprehensive predictive performability analysis of a system. This performability analysis provides a valuable guideline regarding required resources such as the number of mailboxes, and therefore, the number of users the mail server can support with regards to the reliability and performance of the disk array used by the mail server. The fuzzy arithmetic helps in better visualization and estimation of the range of number of users the mail server is capable of servicing over long periods of time.

NFuSA - Neuro-Fuzzy Algorithm for Sparing in RAID Systems

Sparing, the process of rebuilding data in case of disk failure, has been a target of research since early 1990s. The problem that these specific hardware/software control systems typically face in sparing is the tradeoff between serving requests - users versus internal. If the algorithm favors user requests, in the presence of heavy workloads, the internal data recovery gets preempted resulting in risky delay of the data sparing. On the other hand, favoring internal data recovery requests over the user requests can result in high response times per transaction that are unacceptable for the users of the RAID system. Intelligent, neuro-fuzzy controllers (NFCs) offer a way to improve the control process and enhance the ability of a system to achieve faster system response, while serving the internal requests at the same time. This paper presents the neuro-fuzzy enhancement of the traditional data recovery of a RAID system modeled with a queue system with vacations (QSV). Experimental results demonstrated better balancing between an acceptable response time for the user requests and the time for the data to be redundant again, resulting in both higher user satisfaction and better system reliability.