Using the distiller to direct the development of self-configuration software

Kurmas, Zachary; Keeton, Kimberly

doi:10.1109/icac.2004.1301361

Cited by 3 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, one must discover the performance-affecting characteristics of a workload. This can be challenging given the heterogeneity of storage devices [15]. For example, a storage array with a large cache may be less sensitive to the spatial access pattern than an array with little cache, so models of the devices would likely focus on different workload characteristics when predicting performance.…”

Section: The Challenges With Absolute Modelsmentioning

confidence: 99%

Modeling the relative fitness of storage

Mesnier

Wachs

Sambasivan

et al. 2007

Proceedings of the 2007 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

View full text Add to dashboard Cite

Relative fitness is a new black-box approach to modeling the performance of storage devices. In contrast with an absolute model that predicts the performance of a workload on a given storage device, a relative fitness model predicts performance differences between a pair of devices. There are two primary advantages to this approach. First, because a relative fitness model is constructed for a device pair, the application-device feedback of a closed workload can be captured (e.g., how the I/O arrival rate changes as the workload moves from device A to device B). Second, a relative fitness model allows performance and resource utilization to be used in place of workload characteristics. This is beneficial when workload characteristics are difficult to obtain or concisely express (e.g., rather than describe the spatio-temporal characteristics of a workload, one could use the observed cache behavior of device A to help predict the performance of B).This paper describes the steps necessary to build a relative fitness model, with an approach that is general enough to be used with any black-box modeling technique. We compare relative fitness models and absolute models across a variety of workloads and storage devices. On average, relative fitness models predict bandwidth and throughput within 10-20% and can reduce prediction error by as much as a factor of two when compared to absolute models.

show abstract

Section: The Challenges With Absolute Modelsmentioning

confidence: 99%

Modeling the relative fitness of storage

Mesnier

Wachs

Sambasivan

et al. 2007

Proceedings of the 2007 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

View full text Add to dashboard Cite

show abstract

“…As a precursor to the cloud based systems of the present day, load balancing was an area of research focus. Work forecasting [28], scheduling algorithms [159], and managing system level properties [287] were all part of the groundwork of this research area, as was the placement of replicants in an edge computing scenario [244].…”

Section: -2005 -Foundational Yearsmentioning

confidence: 99%

Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains

Wong¹,

Wagner²

2021

Preprint

View full text Add to dashboard Cite

Championed by IBM's vision of autonomic computing paper in 2003, the autonomic computing research field has seen increased research activity over the last 20 years. Several conferences (SEAMS, SASO, ICAC) and workshops (SISSY) have been established and have contributed to the autonomic computing knowledge base in search of a new kind of system -a self-adaptive system (SAS). These systems are characterized by being context-aware and can act on that awareness. The actions carried out could be on the system or on the context (or environment). The underlying goal of a SAS is the sustained achievement of its goals despite changes in its environment. Despite a number of literature reviews on specific aspects of SASs ranging from their requirements to quality attributes, we lack a systematic understanding of the current state of the art. This paper contributes a systematic literature review into self-adaptive systems using the dblp computer science bibliography as a database. We filtered the records systematically in successive steps to arrive at 293 relevant papers. Each paper was critically analyzed and categorized into an attribute matrix. This matrix consisted of five categories, with each category having multiple attributes. The attributes of each paper, along with the summary of its contents formed the basis of the literature review that spanned 30 years . We characterize the maturation process of the research area from theoretical papers over practical implementations to more holistic and generic approaches, frameworks, and exemplars, applied to areas such as networking, web services, and robotics, with much of the recent work focusing on IoT and IaaS.

show abstract

“…This can be challenging given the heterogeneity of storage devices. 8 For example, a storage array with a large cache may be less sensitive to the spatial access pattern than an array with little cache, so models of the devices would likely focus on different workload characteristics when predicting performance.…”

Section: The Challenges With Absolute Modelsmentioning

confidence: 99%