Attribute-Based Prediction of File Properties

We present evidence that attributes that are known to the file system when a file is created, such as its name, permission mode, and owner, are often strongly related to future properties of the file such as its ultimate size, lifespan, and access pattern. More importantly, we show that we can exploit these relationships to automatically generate predictive models for these properties, and that these predictions are sufficiently accurate to enable optimizations.Engineering and Applied Science

A survey and classification of software-defined storage systems

The exponential growth of digital information is imposing increasing scale and efficiency demands on modern storage infrastructures. As infrastructure complexity increases, so does the difficulty in ensuring quality of service, maintainability, and resource fairness, raising unprecedented performance, scalability, and programmability challenges. Software-Defined Storage (SDS) addresses these challenges by cleanly disentangling control and data flows, easing management, and improving control functionality of conventional storage systems. Despite its momentum in the research community, many aspects of the paradigm are still unclear, undefined, and unexplored, leading to misunderstandings that hamper the research and development of novel SDS technologies. In this article, we present an in-depth study of SDS systems, providing a thorough description and categorization of each plane of functionality. Further, we propose a taxonomy and classification of existing SDS solutions according to different criteria. Finally, we provide key insights about the paradigm and discuss potential future research directions for the field.This work was financed by the Portuguese funding agency FCT-Fundacao para a Ciencia e a Tecnologia through national funds, the PhD grant SFRH/BD/146059/2019, the project ThreatAdapt (FCT-FNR/0002/2018), the LASIGE Research Unit (UIDB/00408/2020), and cofunded by the FEDER, where applicable

NEOS AND CONDOR: SOLVING OPTIMIZATION PROBLEMS OVER THE INTERNET

We discuss the use of Condor, a distributed resource management system, as a provider of computational resources for NEOS, an environment for solving optimization problems over the Internet. We also describe how problems are submitted and processed by NEOS, and then schedules and solved by Condor on available (idle) workstations

Modeling the Relative Fitness of Storage Devices

Relative fitness modeling is a new approach for predicting the performance and resource utilization of a workload when running on a particular storage device. In contrast with conventional device models, which expect deviceindependent workload characteristics as input, a relative fitness model makes predictions based on characteristics measured on a specific other device. As such, relative fitness models explicitly account for the workload changes that almost always result from moving a workload across storage devices---for example, higher I/O performance usually leads to faster application execution which results in higher I/O rates. Further, relative fitness models allow service observations (e.g., performance and resource utilizations) from the measured device to be used in making predictions on the modeled device---such observations often provide more predictability than basic workload characteristics. Overall, we find that relative fitness models reduce prediction error by over 60% on average when compared to conventional modeling techniques

New York, NY. May 2004. File classification in self- * storage systems

To tune and manage themselves, file and storage systems must understand key properties (e.g., access pattern, lifetime, size) of their various files. This paper describes how systems can automatically learn to classify the properties of files (e.g., read-only access pattern, short-lived, small in size) and predict the properties of new files, as they are created, by exploiting the strong associations between a file’s properties and the names and attributes assigned to it. These associations exist, strongly but differently, in each of four real NFS environments studied. Decision tree classifiers can automatically identify and model such associations, providing prediction accuracies that often exceed 90%. Such predictions can be used to select storage policies (e.g., disk allocation schemes and replication factors) for individual files. Further, changes in associations can expose information about applications, helping autonomic system components distinguish growth from fundamental change. 1

Modeling the Relative Fitness of Storage Devices (CMU-PDL-05-106)

Relative fitness modeling is a new approach for predicting the performance and resource utilization of a workload when running on a particular storage device. In contrast with conventional device models, which expect device independent workload characteristics as input, a relative fitness model makes predictions based on characteristics measured on a specific other device. As such, relative fitness models explicitly account for the workload changes that almost always result from moving a workload across storage devices—for example, higher I/O performance usually leads to faster application execution which results in higher I/O rates. Further, relative fitness models allow service observations (e.g., performance and resource utilizations) from the measured device to be used in making predictions on the modeled device—such observations often provide more predictability than basic workload characteristics. Overall, we find that relative fitness models reduce prediction error by over 60% on average when compared to conventional modeling techniques