Division of IT Convergence Engineering Introduction and Motivation  Design of a QoE model based on the DEN-ng model  Define appropriate QoE metrics and.

Slides:



Advertisements
Similar presentations
OGSA Use Case Description Online Media & Entertainment v 0.1 mini 06-Feb-2002 Tan LuIBM Corporation Boas BetzlerIBM Corporation
Advertisements

General A smart global supply chain solution of forward and reverse logistics. Business Process Outsourcing (BPO). Web Based System. Inventory planning.
KAIS T The Vision of Autonomic Computing Jeffrey O. Kephart, David M Chess IBM Watson research Center IEEE Computer, Jan 발표자 : 이승학.
May 12, 2015IEEE Network Management Symposium Page-1 Requirements for Configuration Management of IP-based Networks Luis A. Sanchez Chief Technology Officer,
Towards a Framework for QoE Sergio Beker and Frédéric Guyard Orange Labs, Sophia-Antipolis, France.
SLA Basics Describes a set of non functional requirements of the service. Example : RTO time – Return to Operation Time if case of failure SLO – Service.
How Can You Have QoS When… Jennifer Rexford AT&T Labs--Research.
Version : 11 December 2008 Workshop on “Monitoring Quality of Service and Quality of Experience of Multimedia Services in Broadband/Internet Networks”
1 SAFIRE Project DHS Update – July 15, 2009 Introductions  Update since last teleconference Demo Video - Fire Incident Command Board (FICB) SAFIRE Streams.
Domain-Specific Software Engineering (DSSE). Software Engineering Concerns  There are many of them  “Classical” software architecture research has focused.
Introduction and Overview “the grid” – a proposed distributed computing infrastructure for advanced science and engineering. Purpose: grid concept is motivated.
1 Presentation_ID © 1999, Cisco Systems, Inc. Programmable Networks OPENSIG-99 Industry Panel John Hopprich.
Study Period Report: Metamodel for On Demand Model Selection (ODMS) Wang Jian, He Keqing, He Yangfan, Wang Chong State Key Lab of Software Engineering,
Lock Inference for Systems Software John Regehr Alastair Reid University of Utah March 17, 2003.
Policy Framework Status aaaarch mtg, irtf, Aug. 2, 2000 Ed Ellesson co-chairs of policy framework wg: Ed Ellesson: John Strassner:
1 FM Overview of Adaptation. 2 FM RAPIDware: Component-Based Design of Adaptive and Dependable Middleware Project Investigators: Philip McKinley, Kurt.
By- Jaideep Moses, Ravi Iyer , Ramesh Illikkal and
Community Manager A Dynamic Collaboration Solution on Heterogeneous Environment Hyeonsook Kim  2006 CUS. All rights reserved.
Frequently asked questions about software engineering
Abstract Cloud data center management is a key problem due to the numerous and heterogeneous strategies that can be applied, ranging from the VM placement.
Chapter 1- Introduction
A Research Agenda for Accelerating Adoption of Emerging Technologies in Complex Edge-to-Enterprise Systems Jay Ramanathan Rajiv Ramnath Co-Directors,
© Drexel University Software Engineering Research Group (SERG) 1 Based on the paper by Philippe Kruchten from Rational Software.
Introduction Due to the recent advances in smart grid as well as the increasing dissemination of smart meters, the electricity usage of every moment in.
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
DPNM, POSTECH 1/23 NOMS 2010 Jae Yoon Chung 1, Byungchul Park 1, Young J. Won 1 John Strassner 2, and James W. Hong 1, 2 {dejavu94, fates, yjwon, johns,
Day 2 – Topic 1 (and 4) Dagstuhl Seminar April 2015.
An approach to Intelligent Information Fusion in Sensor Saturated Urban Environments Charalampos Doulaverakis Centre for Research and Technology Hellas.
Social Computing Networks: A New Paradigm for Engineering Pervasive Software Systems Naeem Esfahani Sam Malek 32th International Conference on Software.
Copyright © 2013 Curt Hill The Zachman Framework What is it all about?
Division of IT Convergence Engineering Optimal Demand-Side Energy Management Under Real-time Demand-Response Pricing Jin Xiao 1, Jae Yoon Chung 2, Jian.
©Ian Sommerville 2004Software Engineering, 7th edition. Chapter 1 Slide 1 Software Engineering The first lecture.
Division of IT Convergence Engineering Towards Unified Management A Common Approach for Telecommunication and Enterprise Usage Sung-Su Kim, Jae Yoon Chung,
TELEFÓNICA I+D © 2008 Telefónica Investigación y Desarrollo, S.A. Unipersonal NETQOS Policy Management for flexible QoS Provisioning in Future Internet.
IPPM metrics registry extension draft-stephan-ippm-registry-ext-00.txt 79th IETF Meeting – November 2010 IPPM Working Group Emile Stephan.
Management for IP-based Applications Mike Fisher BTexaCT Research
Dimitrios Skoutas Alkis Simitsis
Nikos Kefalakis, John Soldatos, Efstathios Mertikas, Neeli R. Prasad Generating Business Events in an RFID Network.
Division of IT Convergence Engineering Related Work Knee Rehabilitation Using Range of Motion Exercise Feedback Yeongrak Choi 1, Sangwook Bak 1, Sungbae.
Efficient Provisioning of Service Level Agreements for Service Oriented Applications Valeria Cardellini, Emiliano Casalicchio, Vincenzo Grassi, Francesco.
Illustrations and Answers for TDT4252 exam, June
Division of IT Convergence Engineering Towards a Context-Aware Information Model to Support Virtualization Yeongrak Choi 1, Jian Li 2, Yoonseon Han 1,
Integrated Systems Division Service-Oriented Programming Guy Bieber, Lead Architect Motorola ISD C4I 2000 OOPSLA Jini Pattern Language Workshop Guy Bieber,
The world of autonomous reconfigurable systems Intelligent Interactive Distributed Systems Group Vrije Universiteit Amsterdam /
Wireless communications and mobile computing conference, p.p , July 2011.
KUKDM’2011, Zakopane Semantic Based Storage QoS Management Methodology Renata Słota, Darin Nikolow, Jacek Kitowski Institute of Computer Science AGH-UST,
Infrastructure & Methodology (The cool group). Problem space What is “self-management” anyway? –Defined broadly - anything that does not require (or reduces)
QoE Definition WG1 Subgroup “Web and Cloud Applications” Tobias Hoßfeld, Raimund Schatz, Martin Varela, Christian Timmerer WG1 Applications.
Toward a cooperative programming framework for context-aware applications B. Guo, D. Zhang Telecom. Network and Service Dept. Institut TELECOM SudParis.
© Drexel University Software Engineering Research Group (SERG) 1 The OASIS SOA Reference Model Brian Mitchell.
03/03/051 Performance Engineering of Software and Distributed Systems Research Activities at IIT Bombay Varsha Apte March 3 rd, 2005.
Introduction to virtual engineering László Horváth Budapest Tech John von Neumann Faculty of Informatics Institute of Intelligent Engineering.
Policy Based Management for Internet Communities Kevin Feeney, Dave Lewis, Vinny Wade, Knowledge and Data Engineering Group Trinity College Dublin Policy.
Policy Modeling in a PBM Architecture 6WIND / Euronetlab
Hierarchical Management Architecture for Multi-Access Networks Dzmitry Kliazovich, Tiia Sutinen, Heli Kokkoniemi- Tarkkanen, Jukka Mäkelä & Seppo Horsmanheimo.
Implementation of Ontology Based Context-awareness Framework Ki-Chul Lee, Jung-Hoon Kim International Conference on Multimedia and Ubiquitous Engineering.
Sharing personal knowledge over the Semantic Web ● We call personal knowledge the knowledge that is developed and shared by the users while they solve.
1 Ji Wang and Dongsheng Li National Lab for Parallel and Distributed Processing Introduction of iVCE ( Internet-based V irtual C omputing E nvironment.
Division of IT Convergence Engineering POSTECH’s U-Health Monitoring and Support Smart Home for the Elderly  Improve quality of life & reduce healthcare.
Www3.informatik.uni-wuerzburg.de Institute of Computer Science Chair of Communication Networks Prof. Dr.-Ing. P. Tran-Gia Simulation Framework for Live.
Design for a generic knowledge base for autonomic QoE optimization in multimedia access networks September 9, 2008 Bong-Kyun Lee Dept. of Information and.
Scott C. Johnson Lecturer Rochester Institute of Technology Spring 2016.
RESERVOIR Service Manager NickTsouroulas Head of Open-Source Reference Implementations Unit Juan Cáceres
EOSC MODEL Pasquale Pagano CNR - ISTI
Frequently asked questions about software engineering
Intelligent Agents Chapter 2.
Online Compliance Monitoring of Service Landscapes
Frequently asked questions about software engineering
Towards Unified Management
A framework for ontology Learning FROM Big Data
Presentation transcript:

Division of IT Convergence Engineering Introduction and Motivation  Design of a QoE model based on the DEN-ng model  Define appropriate QoE metrics and their relationship with performance indicators and consumers  Present a control loop to optimize the quality of services based on the measured QoE among networks, end-users and service providers QoE Metrics Model  Define a new Customer Role Quality Indicator (CRQI)  Roles abstract people and organizations  ConsumerRole abstracts different responsibilities and functionality that a customer has  CRQI is a measurement of a specific aspect of the quality of a product, service, or resource as perceived by a particular customer  CRQI captures the semantics between a given ConsumerRole and a particular context as represented by QoE data Future Research  Create an Autonomic SLA management system  Define semantics to create a knowledge continuum to relate diverse business, management, and operational data to each other  Validate using a simulator and/or a network testbed  Evolution of the Network Based Services  Advanced multimedia services (e.g., IPTV, VoD, VoIP)  Different services have conflicting resource demands on a shared infrastructure  Quality of Experience (QoE)  A subjective measure of a customer’s experiences for services  Relate objective network data to customer experience to improve QoE  Use a control loop to manage services based on the measured QoE Research Goal QoE Control Loop Model Performance Indicators DEN-ng Model  QoE Control Loop  Many types of changes to the service can be managed by a single control loop  Subjective, objective, and contractual changes are captured as QoE data  Our model manages QoE data changes using QoS mechanisms  Our control loop self-regulates  QoEServiceFeedback  Triggers the change of network configuration according to management policy when any SLA data changes  Key Performance Indicator (KPI):  Quantifiable measurements that reflect the critical successful or unsuccessful factors of a particular resource or service  Key Quality Indicator (KQI):  An indicator for a specific performance aspect of the product or product components  Customer Quality Indicator (CQI):  Quality indicators that are experienced and perceived by customers Network Service for QoE Ontology and SWRL rules  We designed an ontology for detecting SLA changes by relating MIB data to SLA data  Service Level Agreement (SLA): high-level business descriptions  Management Information Base (MIB): low-level network data  Semantic Web Rule Language (SWRL) for  Computing SLA changes from lower level performance indicators (Network Performance, KPI, and KQI)  Mapping between different performance indicators  Calculating performance indicators  Mapping between network data, KPIs, KQIs, and SLAs  SLAViolationCheckEqTrue  SLA(?sla) ∧ hasPerformanceInfo(?sla, ?indicator) ∧ hasThreshold(?sla, ?threshold) ∧ hasOperator(?sla, ?operator) ∧ hasValue(?indicator, ?value) ∧ swrlb:equal(?operator, "eq") ∧ swrlb:notEqual(?value, ?threshold) → isViolated(?sla, "true") PerformanceInfo KQIKPINP ProductKQIServiceKQI ResourceKQIProductKPIServiceKPI ResourceKPISTBNP Infrastructure NP Aggregation SwitchNP influence SLA PerformanceInfo Threshold Operator True or False hasPerformanceInfo hasOperatorisViolated hasThreshold Service Provider Calculat ed QoE SLA QoE Control Consume r Role QoS Mechanis m Networ k A Quality of Experience Model and an Ontology for High Quality Multimedia Services Arum Kwon 1, Joon-Myung Kang 1, Sin-seok Seo 1, Sung-Su Kim 1, Jae Yoon Chung 1, John Strassner 2, and James Won-Ki Hong 2 1 Dept. of Computer Science and Engineering, 2 Division of IT Convergence Engineering, Pohang University of Science and Technology (POSTECH), Korea