Queueing models of RAID systems with maxima of waiting times

Computer Performance Engineering

2013

Abstract. Fork-join and split-merge queueing systems are well-known abstractions of parallel systems in which each incoming task splits into subtasks that are processed by a set of parallel servers. A task exits the system when all of its subtasks have completed service. Two key metrics of interest in such systems are task response time and subtask dispersion. This paper presents a technique applicable to a class of fork-join systems with heterogeneous exponentially distributed service times that is able to reduce subtask dispersion with only a marginal increase in task response time. Achieving this is challenging since the unsynchronised operation of fork-join systems naturally militates against low subtask dispersion. Our approach builds on our earlier research examining subtask dispersion and response time in split-merge systems, and involves the frequent application and updating of delays to the subtasks at the head of the parallel service queues. Numerical results show the ability to reduce dispersion in fork-join systems to levels comparable with or below that observed in all varieties of split-merge systems while retaining the response time and throughput benefits of a fork-join system.

Section: Numerical Resultsmentioning

confidence: 99%

Reduction of Subtask Dispersion in Fork-Join Systems

Tsimashenka

Computer Performance Engineering

2013

“…In terms of subtask scheduling for RAID 01 read operations, we assume an efficient RAID controller which reads half the data from the primary disks and half the data from the mirror disks [7]. RAID 01 write operations send each subtask to both the primary and mirror disks and create double the number of ForkedCustomer s as for a read request of the same size.…”

Section: Raid 01 Simulationmentioning

confidence: 99%

“…However in RAID 5, particularly with operations involving pre-reads and parity updates, there are many possibilities for scheduling strategies and disk head positioning times within a request. Here we base the design of our RAID 5 simulation upon the operational assumptions of RAID 5 disk behaviour presented in [4,7,11].…”

Section: Raid 5 Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Modelling Zoned RAID Systems Using Fork-Join Queueing Simulation

Lebrecht

Dingle

Computer Performance Engineering

2009

Abstract. RAID systems are ubiquitously deployed in storage environments, both as standalone storage solutions and as fundamental components of virtualised storage platforms. Accurate models of their performance are crucial to delivering storage infrastructures that meet given quality of service requirements. To this end, this paper presents a flexible fork-join queueing simulation model of RAID systems that are comprised of zoned disk drives and which operate under RAID levels 01 or 5. The simulator takes as input I/O workloads that are heterogeneous in terms of request size and that exhibit burstiness, and its primary output metric is I/O request response time distribution. We also study the effects of heavy workload, taking into account the request-reordering optimisations employed by modern disk drives. All simulation results are validated against device measurements.

“…From this, it is straightforward to calculate response time quantiles, as well as the mean, variance and higher moments of I/O request response time. This improves on the state-of-theart in RAID performance models [2][3][4][5][6], all of which yield approximations to the mean response time only.…”

Section: Introductionmentioning

confidence: 88%

A Response Time Distribution Model for Zoned RAID

Lebrecht

Dingle

Analytical and Stochastic Modeling Techniques and Applications

Abstract. RAID systems are widely deployed, both as standalone storage solutions and as the building blocks of modern virtualised storage platforms. An accurate model of RAID system performance is therefore critical to understanding storage system performance. To this end, this paper presents a queueing network-based model of RAID systems comprised of zoned disks and operating at RAID level 0-1 or 5. The contribution over previous work is twofold. Firstly, our analysis approximates full I/O request response time distributions rather than just mean values. This provides the ability to reason about response time quantiles and higher moments of response time -both of which are useful in the context of modern quality of service requirements. Secondly, we validate our model against measurements from a real RAID system rather than a software simulation. The close agreement between predicted and observed response time distributions gives a high level of confidence in the validity of our model.