Analysis of self-similarity in I/O workload using structural modeling

Gómez, María E.; Santonja, V.

doi:10.1109/mascot.1999.805060

Cited by 27 publications

(24 citation statements)

References 8 publications

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“…As shown in the figure, the on periods appear to be heavy-tailed but not the off periods. This is consistent with results reported in [13] where the lack of heavy-tailed behavior for the off periods is attributed to periodic activity such as the sync daemon traffic. Having heavy-tailed on periods is sufficient, however, to result in self-similar aggregate traffic.…”

Section: Underpinnings Of Self-similar I/o Trafficsupporting

confidence: 92%

“…Recently, file system activities [15] and I/O traffic [13] have been found to exhibit scale-invariant burstiness. Local and wide-area network traffic may also be more accurately modeled using statistically self-similar processes than the Poisson model(e.g., [27]).…”

Section: I/o Traffic Be Characterized By a Three-tuple Consisting Of mentioning

confidence: 99%

“…As in [13], we define an off period for a process as any interval longer than 0.2s during which the process does not generate any I/O. All other intervals are considered on periods for the process.…”

Section: Underpinnings Of Self-similar I/o Trafficmentioning

confidence: 99%

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Characteristics of I/O traffic in personal computer and server workloads

Hsu

Smith

2003

IBM Syst. J.

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Understanding the characteristics of physical I/O traffic is increasingly important as the performance gap between processor and disk-based storage continues to widen. Moreover, recent advances in technology, coupled with market demands, have led to several new and exciting developments in storage, including network storage, storage utilities, and intelligent self-optimizing storage. In this paper, we empirically examine the I/O traffic of a wide range of real PC and server workloads with the intent of understanding how well they will respond to these new storage developments. As part of our analysis, we compare our results with historical data and reexamine rules of thumb that have been widely used for designing computer systems. Our results show that there is a strong need to focus on improving I/O performance. We find that the I/O traffic is bursty and appears to exhibit self-similar characteristics. In addition, our analysis indicates that there is little cross-correlation in traffic volume among the server workloads, which suggests that aggregating these workloads will likely help to smooth out the traffic and enable more efficient utilization of resources. We also discover that there is a lot of potential for harnessing

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Section: Underpinnings Of Self-similar I/o Trafficsupporting

confidence: 92%

Section: I/o Traffic Be Characterized By a Three-tuple Consisting Of mentioning

confidence: 99%

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Characteristics of I/O traffic in personal computer and server workloads

Hsu

Smith

2003

IBM Syst. J.

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“…• Self-similarity in access patterns [303,292,293,396]. A possible model for file access patterns is based on a hidden Markov model (HMM) [556].…”

Section: File System Access Patternsmentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

191

131

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Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.

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“…However, over the last decade, there have been many studies of storage system I/O traces (e.g. [16][17][18][19][20][21]) which consistently show that real-life arrivals to storage systems exhibit burstiness and a variety of request size distributions. Consequently, we have extended the simulator to support bulk arrivals of I/O requests at the RAID controller, making use of JINQS 's in-built support for arrivals that consist of a number of requests defined by a chosen probability distribution.…”

Section: Bulk Arrivalsmentioning

confidence: 99%

Modelling Zoned RAID Systems Using Fork-Join Queueing Simulation

Lebrecht

Dingle

Knottenbelt

2009

Computer Performance Engineering

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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.

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Analysis of self-similarity in I/O workload using structural modeling

Cited by 27 publications

References 8 publications

Characteristics of I/O traffic in personal computer and server workloads

Characteristics of I/O traffic in personal computer and server workloads

Workload Modeling for Computer Systems Performance Evaluation

Modelling Zoned RAID Systems Using Fork-Join Queueing Simulation

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