NDLI: Enriching software architecture models with statistical models for performance prediction in modern storage environments

Please wait, while we are loading the content...

Principles and methods for elastic computing

Modular coordination of multiple autonomic managers

Injecting quality attributes into software architectures with the common variability language

Using a model to generate reconfiguration plans at runtime

A new approach to detect potential race conditions in component-based systems

Design-by-contract for reusable components and realizable architectures

Quantifying the parallelism in BPMN processes using model checking

Software component models: past, present and future

A component-based meta-level architecture and prototypical implementation of a reflective component-based programming and modeling language

Enriching software architecture models with statistical models for performance prediction in modern storage environments

AO-OpenCom: an AO-middleware architecture supporting flexible dynamic reconfiguration

Memory monitoring in a multi-tenant OSGi execution environment

A specification schema for software connectors

Architecture internalisation in BIP

Rigorous component-based design in BIP: half-day tutorial

Towards a meta-model for dynamic applications

Enabling collaborative testing across shared software components

Coqcots & pycots: non-stopping components for safe dynamic reconfiguration

A memory isolation method for OSGi-based home gateways

Ensuring application integrity in shared sensing environments

Robust reconfiguration of cloud applications

Development of smart cyber-physical systems

Runtime modularity in complex structures: a component model for fine grained runtime adaptation

Strengthening architectures of smart CPS by modeling them as runtime product-lines

Execution cost estimation for software deployment in component-based embedded systems

Enriching software architecture models with statistical models for performance prediction in modern storage environments

Content Provider	ACM Digital Library
Author	Rentschler, Andreas Kounev, Samuel Reeb, Roland Reussner, Ralf Noorshams, Qais
Abstract	Model-based performance prediction approaches on the software architecture-level provide a powerful tool for capacity planning due to their high abstraction level. To process the increasing amount of data produced by today's applications, modern storage systems are becoming increasingly complex having multiple tiers and intricate optimization strategies. Current software architecture-level modeling approaches, however, struggle to account for this development and are not well-suited in complex storage environments due to overly simplistic storage assumptions, which consequently leads to inaccurate performance predictions. To address this problem, in this paper we present a novel approach to combine software architecture-level performance models with statistical models that capture the complex behavior of modern storage systems. More specifically, we first propose a general methodology for enriching software architecture modeling approaches with statistical I/O performance models. Then, we present how we realize the modeling concepts as well as model solving to obtain performance results. Finally, we evaluate our approach extensively in the context of three case studies with two state-of-the-art environments based on Sun Fire and IBM System z server hardware. Using our approach, we are able to successfully predict the application performance within 20 % prediction error in almost all cases.
Starting Page	45
Ending Page	54
Page Count	10
File Format	PDF
ISBN	9781450325776
DOI	10.1145/2602458.2602475
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-06-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Software architecture Storage Statistical model Prediction I/o Performance
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A generic approach for architecture-level performance modeling and prediction of virtualized storage systems

Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments

Experimental evaluation of a flexible I/O architecture for accelerating workflow engines in cloud environments

Predictive performance modeling of virtualized storage systems using optimized statistical regression techniques

I/O Performance Modeling of Virtualized Storage Systems

Collective I/O Tuning Using Analytical and Machine Learning Models

Automated Modeling of I/O Performance and Interference Effects in Virtualized Storage Systems

Deriving Queuing Network Model for UML for Software Performance Prediction

Markov model prediction of I/O requests for scientific applications

Enriching software architecture models with statistical models for performance prediction in modern storage environments

Similar Documents

A generic approach for architecture-level performance modeling and prediction of virtualized storage systems

Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments

Experimental evaluation of a flexible I/O architecture for accelerating workflow engines in cloud environments

Predictive performance modeling of virtualized storage systems using optimized statistical regression techniques

I/O Performance Modeling of Virtualized Storage Systems

Collective I/O Tuning Using Analytical and Machine Learning Models

Automated Modeling of I/O Performance and Interference Effects in Virtualized Storage Systems

Deriving Queuing Network Model for UML for Software Performance Prediction

Markov model prediction of I/O requests for scientific applications

Enriching software architecture models with statistical models for performance prediction in modern storage environments