Cost-optimized, Policy-based Data Management in Cloud Environments
|
|
- Ashlynn Pitts
- 8 years ago
- Views:
Transcription
1 Cost-optimized, Policy-based Data Management in Cloud Environments Ilir Fetai Filip-Martin Brinkmann Databases and Information Systems Research Group University of Basel
2 Current State in the Cloud: A zoo of different DBMS Current DBMS differ in: APIs Guarantees (Ex: consistency models, replication, etc.) Limitations (Ex: no support for archiving, etc.) Costs 1-2
3 DMS wish list DMS = Data Management System Construct my DBMS via declarative specifications Declarative specifications = Policies Policy management needed A way to express requirements of applications, transactions, end-users, etc., and monitor their fulfilment Examples: Control data consistency Specify possible data replication locations Control data archiving Control costs! Modular DMS architecture needed 1-3
4 Scenario Client: use services have different requirements Service Provider: provides different services uses underlying DMS infrastructure has requirements on data management Cloud DMS Provider: provides the DMS infrastructure 1-4
5 Agenda Policy-based and modular Data Management Systems (POLAR DMS) Cost-effective & Policy-based Data Management Data Consistency Data Replication Data Archiving Architecture of POLAR DMS Conclusion 1-5
6 Agenda Policy-based and modular Data Management Systems (POLAR DMS) Cost-effective & Policy-based Data Management Data Consistency Data Replication Data Archiving Architecture of POLAR DMS Conclusion 1-6
7 POLAR DMS - Requirements Requirements: User-friendly & understandable policy-based and modular DMS Specify policies at different levels: Cloud provider, application/service provider, but even end-customer DMS adjusts at runtime based on policies Minimal core Implement DMS functionality as exchangeable modules 1-7
8 POLAR DMS - Requirements Requirements: User-friendly & understandable policy-based and modular DMS Specify policies at different levels: Cloud provider, application/service provider, but even end-customer DMS adjusts at runtime based on policies Minimal core Implement DMS functionality as exchangeable modules 1-8
9 Policies in POLAR DMS End-user policies May override default transaction policies Ex: paid services Transaction-policies May override default application policies Application-policies Applications may specify default policies valid for entire application System-policies The underlying system may also specify own policies 1-9
10 Conflicting Policies POLAR DMS must also handle policy conflicts! 1-10
11 Modularity in POLAR DMS Build your own DMS by composing it of modules Service Provider: Application - Developers: Specify policies at transaction level. The system may need to adjust at runtime (load modules, etc.) Application - Architects: Choose the modules you need for your architecture or specify application-wide policies Cloud DMS Provider Administrators: Specify system policies and configure the DMS 1-11
12 Cost-effective & Policy-based Data Management POLAR DMS provides Policy Mechanism Modular Architecture In the context of POLAR DMS Analyse a concrete subset of possible data management policies Data management aspects: consistency, replication & archiving 1-12
13 Agenda Policy-based and modular Data Management Systems (POLAR DMS) Cost-effective & Policy-based Data Management Data Consistency Data Replication Data Archiving Architecture of POLAR DMS Conclusion 1-13
14 Agenda Policy-based and modular Data Management Systems (POLAR DMS) Cost-effective & Policy-based Data Management Data Consistency Data Replication Data Archiving Architecture of POLAR DMS Conclusion 1-14
15 Data Consistency Data Consistency Level A contract between DMS provider and customer Customer: From application developer to end-customer A range of consistency levels exists Strong consistency: Low availability, but more user-friendly Weak Consistency: High availability, but less user-friendly EC SI 1SR W e a k c o n s i s t e n c y S t r o n g c o n s i s t e n c y 1-15
16 Data consistency in the Cloud Current Cloud Data Systems provide weak consistency More oriented towards scalability If strong consistency needed, use traditional DBMS Are less scalable due to the consistency overhead Disadvantages of current solutions Different DBMS types for different consistency levels No way to further influence behaviour of data consistency via policies! Kraska et al [3]: Consistency rationing Categorize data based on their consistency requirements: 1SR, EC, Adaptive Our approach: Adaptive consistency based on policies at transaction level! 1-16
17 Idea: Cost-based Consistency Control - C3 Implement a range of consistency levels Provide a policy API at transaction level Adjust consistency at runtime based on the policies - C3 [1] Current Consistency Levels (CL): 1SR, SI and EC Cost models for 1SR, SI and EC defined in [2] 1SR & SI generate no or rarely inconsistencies, but are expensive EC is cheap, but may generate high inconsistency costs Policies: Cost-budget (CB) for a transaction & inconsistency costs (IC) 1-17
18 C3 Architecture Provides an API at transaction level (CB, IC) Different combinations possible Enforce a consistency level by setting IC = Is based on a modular architecture Implement new protocols Define their cost models C3 will choose the cheapest consistency protocol 1-18
19 ECO-1SR C3: Provides a meta-consistency level improve transaction costs & performance by switching between concrete consistency levels Can the concrete levels be improved? Again, based on policies Work-in-Progress: Efficient & cost-optimized 1SR ECO-1SR 1-19
20 ECO-1SR vs. Traditional 1SR Traditional implementation of 1SR ECO-1SR Two-Phase-Locking (2PL) and Two- Phase-Commit (2PC) All replicas synchronized eagerly before transaction returns to the user Optimize transaction execution Optimize transaction commit: combination of eager & lazy replica synchronization 1-20
21 ECO-1SR vs. Traditional 1SR Traditional implementation of 1SR ECO-1SR Two-Phase-Locking (2PL) and Two- Phase-Commit (2PC) All replicas synchronized eagerly before transaction returns to the user Optimize transaction execution Optimize transaction commit: combination of eager & lazy replica synchronization 1-21
22 ECO-1SR Optimizations Transaction execution: which replica is best suited for the transaction execution Choose the replica with highest capacity and freshness, and lowest cost Transaction commit: how many and which replicas to synchronize eagerly How many: Depending on data popularity (i.e. transaction popularity) Which: Choose the one with highest capacity, highest popularity and lowest cost All other replicas updated lazily Active refresh in the case transactions access stale data The 'right' choice reduces the overhead for providing 1SR 1-22
23 Agenda Policy-based and modular Data Management Systems (POLAR DMS) Cost-effective & Policy-based Data Management Data Consistency Data Replication Data Archiving Architecture of POLAR DMS Conclusion 1-23
24 Cost-based Data Replication How to provide efficient and cost-optimized replication? What is the best number of replicas? What is the best replica type? What is the best replica location? Example 1 In the next period many high important transactions expected High importance: high profit or high penalty if constraints violated! Should be more replicas be generated? What replica types are more costeffective? Example 2 Legal issues: Data can be stored/replicated only in specific locations Specify replication policies per data-object or application 1-24
25 Agenda Policy-based and modular Data Management Systems (POLAR DMS) Cost-effective & Policy-based Data Management Data Consistency Data Replication Data Archiving Architecture of POLAR DMS Conclusion 1-25
26 Data Archiving - Motivation Assumption: freshness-aware key-value store timestamped/versioned data items freshness-centric operations Observation: concurrently different versions of the same data item by design different applications/clients: different freshness requirements Key: CharliesAccount Value: Timestamp: 3 Key: CharliesAccount Value: Timestamp: 3 eager replication lazy replication Key: CharliesAccount Value: Timestamp: 1 lazy replication Key: CharliesAccount Value: Timestamp: 2
27 Data Archiving - Motivation Assumption: freshness-aware key-value store timestamped data items freshness-centric operations Observation: concurrently different versions of the same data item by design different applications/clients: different freshness requirements Idea: not deal with, but exploit these properties Goals: archive cloud data in situ allow for policy enforcement more freshness-centric operations enhanced replica placement 1-27
28 K/V Store Infrastructure Example: freshness-aware K/V store for webshop Few writes, many reads few update, many read s -semantics read() latest local data sufficient Update lazy/pull replication Update Update eager replication update(items on stock) webshop read(items on stock) itemsonstock : 100 timestamp: 1
29 K/V Store Infrastructure II Example: freshness-aware K/V store for a webshop Few writes, many reads few update, many read s -semantics read() latest local data sufficient readnotolderthan(time t) when certain freshness level required possibly route / refresh necessary operation is feasible, however potentially expensive Update Update Update refresh() route() itemsonstock : 10 timestamp: 7 itemsonstock : 80 timestamp: 3 itemsonstock : 100 timestamp: 1 readnotolderthan(7) webshop
30 K/V Store Infrastructure III Other read-semantics are necessary as well. However, they are either expensive or impossible. (Or even both!) Examples: readnotyoungerthan(t) How is the stock of a book developing? readbetween(t1, t2) How was it yesterday? readallbetween(t1, t2) How exactly did it fluctuate during our sales week? Data has to be reconstructed via local logs or route/ defresh. Update Update Update log webshop readnotyoungerthan(2) itemsonstock : 80 timestamp: 3 route/defresh() itemsonstock : 100 timestamp: 1
31 Archive Infrastructure An archive helps rolling back time Different possible archive layouts possible: Update Update Archive update Archive update Update Update Update Archive read Archive read Update Archive update Archive read Archive read Dedicated archive Integrated archive
32 Data Archiving with Policies Control over archiving is necessary Which data? How fast? How widely spread? What about successive updates? How long? minimal, maximal TTL Who may access data? How much $ to spend? etc. Policies are needed 1-32
33 Data Archiving with Policies Control over archiving is necessary Which data? How fast? How widely spread? What about successive updates? How long? minimal, maximal TTL Who may access data? How much $ to spend? etc. Data Policy Key: Speed: Spread: Successive updates: TTLmin: TTLmax: Access: Budget: CharliesAccount eager max infinite 10 years 10 years charlie;accountmanager infinite 1-33
34 *-Semantics Rich(er) read*-semantics can be handled efficiently with the archive without archive: read() readnotolderthan(t) with archive: readnotyoungerthan(t) readbetween(t1, t2) readallbetween(t1, t2) Production Update t = 5 t = 3 t = 2 Step 2 Archive Update t = 3..5 t = 1..4 readallbetween(3,4) Step 3 Step 1 readallbetween(2,4) webshop 1-34
35 Putting the Pieces Together High-level, conceptual vision of modular & policy-based DMS Concepts of Data Consistency Data Replication Data Archiving Recall: we want to reach policy-based and modular DMSs In order to put the pieces together: we built a concrete architecture 1-35
36 Agenda Policy-based and modular Database Management Systems (POLAR DMS) Cost-effective & Policy-based Database Management Data Consistency Data Replication Data Archiving Architecture of POLAR DMS Conclusion 1-36
37 Architecture of POLAR DMS 1-37
38 Architecture of POLAR DMS Based on OSGi Module-based framework for Java Facilitates run-time changes of modules Basic architecture Implement core modules: Data Access, Routing, etc. Implement additional functional modules: C3, ECO-1SR, Data Archiving, etc. Policy controlled module selection Finding optimal module selection and configuration POLAR DMS core policy system extensions S3 Access ECO-1SR C3 1-38
39 Agenda Policy-based and modular Data Management Systems (POLAR DMS) Cost-effective & Policy-based Data Management Data Consistency Data Replication Data Archiving Architecture of POLAR DMS Conclusion 1-39
40 Conclusion POLAR DMS consists of a unified model for: categorizing, assessing and creating Data Management Systems (DMS) This is achieved by a policy-driven framework architecture We built concrete functional modules, enabling cost-effective and policy-based Data Consistency Data Replication Data Archiving We provide a concrete architecture of POLAR DMS 1-40
41 Thank You! Questions? 1-41
42 References [1]: Ilir Fetai and Heiko Schuldt, Cost-Based Data Consistency in a Data-as-a-Service Cloud Environment. Proceedings of the 5th International Conference on Cloud Computing (CLOUD 2012), Honolulu, HI, USA, 2012/6. [2]: Ilir Fetai and Heiko Schuldt, Cost-Based Adaptive Concurrency Control in the Cloud. Technical Report, Department of Mathematics and Computer Science, University of Basel, 2012/2. [3]: Kraska et al, Consistency rationing in the cloud: pay only when it matters. Proc. VLDB Endow.,
Cost-Based Adaptive Concurrency Control in the Cloud
Cost-Based Adaptive Concurrency Control in the Cloud Ilir Fetai Heiko Schuldt Technical Report CS-2012-001 University of Basel Email: {ilir.fetai heiko.schuldt}@unibas.ch Abstract The recent advent of
More informationPOLICY BASED MANAGEMENT OF MODULAR CLOUD STORAGE SYSTEMS
POLICY BASED MANAGEMENT OF MODULAR CLOUD STORAGE SYSTEMS MASTER THESIS Natural Science Faculty of the University of Basel Department of Mathematics and Computer Science Databases and Information Systems
More informationData Distribution with SQL Server Replication
Data Distribution with SQL Server Replication Introduction Ensuring that data is in the right place at the right time is increasingly critical as the database has become the linchpin in corporate technology
More informationDistributed Data Management
Introduction Distributed Data Management Involves the distribution of data and work among more than one machine in the network. Distributed computing is more broad than canonical client/server, in that
More informationDATABASE REPLICATION A TALE OF RESEARCH ACROSS COMMUNITIES
DATABASE REPLICATION A TALE OF RESEARCH ACROSS COMMUNITIES Bettina Kemme Dept. of Computer Science McGill University Montreal, Canada Gustavo Alonso Systems Group Dept. of Computer Science ETH Zurich,
More informationIMCM: A Flexible Fine-Grained Adaptive Framework for Parallel Mobile Hybrid Cloud Applications
Open System Laboratory of University of Illinois at Urbana Champaign presents: Outline: IMCM: A Flexible Fine-Grained Adaptive Framework for Parallel Mobile Hybrid Cloud Applications A Fine-Grained Adaptive
More informationData Consistency on Private Cloud Storage System
Volume, Issue, May-June 202 ISS 2278-6856 Data Consistency on Private Cloud Storage System Yin yein Aye University of Computer Studies,Yangon yinnyeinaye.ptn@email.com Abstract: Cloud computing paradigm
More informationA Brief Analysis on Architecture and Reliability of Cloud Based Data Storage
Volume 2, No.4, July August 2013 International Journal of Information Systems and Computer Sciences ISSN 2319 7595 Tejaswini S L Jayanthy et al., Available International Online Journal at http://warse.org/pdfs/ijiscs03242013.pdf
More informationCHAPTER 2 MODELLING FOR DISTRIBUTED NETWORK SYSTEMS: THE CLIENT- SERVER MODEL
CHAPTER 2 MODELLING FOR DISTRIBUTED NETWORK SYSTEMS: THE CLIENT- SERVER MODEL This chapter is to introduce the client-server model and its role in the development of distributed network systems. The chapter
More informationCAP Theorem and Distributed Database Consistency. Syed Akbar Mehdi Lara Schmidt
CAP Theorem and Distributed Database Consistency Syed Akbar Mehdi Lara Schmidt 1 Classical Database Model T2 T3 T1 Database 2 Databases these days 3 Problems due to replicating data Having multiple copies
More informationCloud DBMS: An Overview. Shan-Hung Wu, NetDB CS, NTHU Spring, 2015
Cloud DBMS: An Overview Shan-Hung Wu, NetDB CS, NTHU Spring, 2015 Outline Definition and requirements S through partitioning A through replication Problems of traditional DDBMS Usage analysis: operational
More informationDatabase Replication with Oracle 11g and MS SQL Server 2008
Database Replication with Oracle 11g and MS SQL Server 2008 Flavio Bolfing Software and Systems University of Applied Sciences Chur, Switzerland www.hsr.ch/mse Abstract Database replication is used widely
More informationCHAPTER 7 SUMMARY AND CONCLUSION
179 CHAPTER 7 SUMMARY AND CONCLUSION This chapter summarizes our research achievements and conclude this thesis with discussions and interesting avenues for future exploration. The thesis describes a novel
More informationEventually Consistent
Historical Perspective In an ideal world there would be only one consistency model: when an update is made all observers would see that update. The first time this surfaced as difficult to achieve was
More informationCOMPONENTS in a database environment
COMPONENTS in a database environment DATA data is integrated and shared by many users. a database is a representation of a collection of related data. underlying principles: hierarchical, network, relational
More informationChapter Outline. Chapter 2 Distributed Information Systems Architecture. Middleware for Heterogeneous and Distributed Information Systems
Prof. Dr.-Ing. Stefan Deßloch AG Heterogene Informationssysteme Geb. 36, Raum 329 Tel. 0631/205 3275 dessloch@informatik.uni-kl.de Chapter 2 Architecture Chapter Outline Distributed transactions (quick
More informationADDING A NEW SITE IN AN EXISTING ORACLE MULTIMASTER REPLICATION WITHOUT QUIESCING THE REPLICATION
ADDING A NEW SITE IN AN EXISTING ORACLE MULTIMASTER REPLICATION WITHOUT QUIESCING THE REPLICATION Hakik Paci 1, Elinda Kajo 2, Igli Tafa 3 and Aleksander Xhuvani 4 1 Department of Computer Engineering,
More informationMTCache: Mid-Tier Database Caching for SQL Server
MTCache: Mid-Tier Database Caching for SQL Server Per-Åke Larson Jonathan Goldstein Microsoft {palarson,jongold}@microsoft.com Hongfei Guo University of Wisconsin guo@cs.wisc.edu Jingren Zhou Columbia
More informationClient/Server and Distributed Computing
Adapted from:operating Systems: Internals and Design Principles, 6/E William Stallings CS571 Fall 2010 Client/Server and Distributed Computing Dave Bremer Otago Polytechnic, N.Z. 2008, Prentice Hall Traditional
More informationDatabase Replication Techniques: a Three Parameter Classification
Database Replication Techniques: a Three Parameter Classification Matthias Wiesmann Fernando Pedone André Schiper Bettina Kemme Gustavo Alonso Département de Systèmes de Communication Swiss Federal Institute
More informationImproved Aggressive Update Propagation Technique in Cloud Data Storage
Improved Aggressive Update Propagation Technique in Cloud Data Storage Mohammed Radi Computer science department, Faculty of applied science, Alaqsa University Gaza Abstract: Recently, cloud computing
More informationCloud-dew architecture: realizing the potential of distributed database systems in unreliable networks
Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA'15 85 Cloud-dew architecture: realizing the potential of distributed database systems in unreliable networks Yingwei Wang 1 and Yi Pan 2 1 Department
More informationDeveloping SOA solutions using IBM SOA Foundation
Developing SOA solutions using IBM SOA Foundation Course materials may not be reproduced in whole or in part without the prior written permission of IBM. 4.0.3 4.0.3 Unit objectives After completing this
More informationChapter 3 - Data Replication and Materialized Integration
Prof. Dr.-Ing. Stefan Deßloch AG Heterogene Informationssysteme Geb. 36, Raum 329 Tel. 0631/205 3275 dessloch@informatik.uni-kl.de Chapter 3 - Data Replication and Materialized Integration Motivation Replication:
More informationOutgoing VDI Gateways:
` Outgoing VDI Gateways: Creating a Unified Outgoing Virtual Desktop Infrastructure with Windows Server 2008 R2 and ObserveIT Daniel Petri January 2010 Copyright 2010 ObserveIT Ltd. 2 Table of Contents
More informationDatabase Replication with MySQL and PostgreSQL
Database Replication with MySQL and PostgreSQL Fabian Mauchle Software and Systems University of Applied Sciences Rapperswil, Switzerland www.hsr.ch/mse Abstract Databases are used very often in business
More informationIncreased Security, Greater Agility, Lower Costs for AWS DELPHIX FOR AMAZON WEB SERVICES WHITE PAPER
Increased Security, Greater Agility, Lower Costs for AWS DELPHIX FOR AMAZON WEB SERVICES TABLE OF CONTENTS Introduction... 3 Overview: Delphix Virtual Data Platform... 4 Delphix for AWS... 5 Decrease the
More informationTowards secure and consistency dependable in large cloud systems
Volume :2, Issue :4, 145-150 April 2015 www.allsubjectjournal.com e-issn: 2349-4182 p-issn: 2349-5979 Impact Factor: 3.762 Sahana M S M.Tech scholar, Department of computer science, Alvas institute of
More informationOAK Database optimizations and architectures for complex large data Ioana MANOLESCU-GOUJOT
OAK Database optimizations and architectures for complex large data Ioana MANOLESCU-GOUJOT INRIA Saclay Île-de-France Université Paris Sud LRI UMR CNRS 8623 Plan 1. The team 2. Oak research at a glance
More informationCloud Computing: Meet the Players. Performance Analysis of Cloud Providers
BASEL UNIVERSITY COMPUTER SCIENCE DEPARTMENT Cloud Computing: Meet the Players. Performance Analysis of Cloud Providers Distributed Information Systems (CS341/HS2010) Report based on D.Kassman, T.Kraska,
More informationVirtual Infrastructure Security
Virtual Infrastructure Security 2 The virtual server is a perfect alternative to using multiple physical servers: several virtual servers are hosted on one physical server and each of them functions both
More informationObjectives. Distributed Databases and Client/Server Architecture. Distributed Database. Data Fragmentation
Objectives Distributed Databases and Client/Server Architecture IT354 @ Peter Lo 2005 1 Understand the advantages and disadvantages of distributed databases Know the design issues involved in distributed
More informationEMC DOCUMENTUM MANAGING DISTRIBUTED ACCESS
EMC DOCUMENTUM MANAGING DISTRIBUTED ACCESS This white paper describes the various distributed architectures supported by EMC Documentum and the relative merits and demerits of each model. It can be used
More informationThis paper was presented at the 1996 CAUSE annual conference. It is part of the proceedings of that conference, "Broadening Our Horizons:
This paper was presented at the 1996 CAUSE annual conference. It is part of the proceedings of that conference, "Broadening Our Horizons: Information, Services, Technology -- Proceedings of the 1996 CAUSE
More informationHadoop: A Framework for Data- Intensive Distributed Computing. CS561-Spring 2012 WPI, Mohamed Y. Eltabakh
1 Hadoop: A Framework for Data- Intensive Distributed Computing CS561-Spring 2012 WPI, Mohamed Y. Eltabakh 2 What is Hadoop? Hadoop is a software framework for distributed processing of large datasets
More informationSecure Cloud Transactions by Performance, Accuracy, and Precision
Secure Cloud Transactions by Performance, Accuracy, and Precision Patil Vaibhav Nivrutti M.Tech Student, ABSTRACT: In distributed transactional database systems deployed over cloud servers, entities cooperate
More informationA Tool for Generating Partition Schedules of Multiprocessor Systems
A Tool for Generating Partition Schedules of Multiprocessor Systems Hans-Joachim Goltz and Norbert Pieth Fraunhofer FIRST, Berlin, Germany {hans-joachim.goltz,nobert.pieth}@first.fraunhofer.de Abstract.
More informationSOFT 437. Software Performance Analysis. Ch 5:Web Applications and Other Distributed Systems
SOFT 437 Software Performance Analysis Ch 5:Web Applications and Other Distributed Systems Outline Overview of Web applications, distributed object technologies, and the important considerations for SPE
More informationDesigning a Cloud Storage System
Designing a Cloud Storage System End to End Cloud Storage When designing a cloud storage system, there is value in decoupling the system s archival capacity (its ability to persistently store large volumes
More informationSolr Cloud vs Replication
Solr Cloud vs Replication vs Solr Cloud implementation Single point of failure Single point of failure Data Sources 4 x Solr Servers (Windows) 3 x Zookeeper Servers (Linux) Load Balancer Server (Mule -
More informationComparing Microsoft SQL Server 2005 Replication and DataXtend Remote Edition for Mobile and Distributed Applications
Comparing Microsoft SQL Server 2005 Replication and DataXtend Remote Edition for Mobile and Distributed Applications White Paper Table of Contents Overview...3 Replication Types Supported...3 Set-up &
More informationA framework for web-based product data management using J2EE
Int J Adv Manuf Technol (2004) 24: 847 852 DOI 10.1007/s00170-003-1697-8 ORIGINAL ARTICLE M.Y. Huang Y.J. Lin Hu Xu A framework for web-based product data management using J2EE Received: 8 October 2002
More informationHow To Build Cloud Storage On Google.Com
Building Scalable Cloud Storage Alex Kesselman alx@google.com Agenda Desired System Characteristics Scalability Challenges Google Cloud Storage What does a customer want from a cloud service? Reliability
More informationBest Practices for Programming Eclipse and OSGi
Best Practices for Programming Eclipse and OSGi BJ Hargrave Jeff McAffer IBM Lotus IBM Rational Software 2006 by IBM; made available under the EPL v1.0 March 24, 2006 Introduction During the Eclipse 3.0
More informationiservdb The database closest to you IDEAS Institute
iservdb The database closest to you IDEAS Institute 1 Overview 2 Long-term Anticipation iservdb is a relational database SQL compliance and a general purpose database Data is reliable and consistency iservdb
More informationSoftware Defined Security Mechanisms for Critical Infrastructure Management
Software Defined Security Mechanisms for Critical Infrastructure Management SESSION: CRITICAL INFRASTRUCTURE PROTECTION Dr. Anastasios Zafeiropoulos, Senior R&D Architect, Contact: azafeiropoulos@ubitech.eu
More informationCost-Effective Certification of High- Assurance Cyber Physical Systems. Kurt Rohloff krohloff@bbn.com BBN Technologies
Cost-Effective Certification of High- Assurance Cyber Physical Systems Kurt Rohloff krohloff@bbn.com BBN Technologies Most Important Challenges and Needs Need dynamic behavior in high-confidence systems,
More informationThe Role of the Software Architect
IBM Software Group The Role of the Software Architect Peter Eeles peter.eeles@uk.ibm.com 2004 IBM Corporation Agenda Architecture Architect Architecting Requirements Analysis and design Implementation
More informationchapater 7 : Distributed Database Management Systems
chapater 7 : Distributed Database Management Systems Distributed Database Management System When an organization is geographically dispersed, it may choose to store its databases on a central database
More informationCluster Computing. ! Fault tolerance. ! Stateless. ! Throughput. ! Stateful. ! Response time. Architectures. Stateless vs. Stateful.
Architectures Cluster Computing Job Parallelism Request Parallelism 2 2010 VMware Inc. All rights reserved Replication Stateless vs. Stateful! Fault tolerance High availability despite failures If one
More informationCourse 20465C: Designing a Data Solution with Microsoft SQL Server
Course 20465C: Designing a Data Solution with Microsoft SQL Server Module 1: Introduction to Enterprise Data Architecture As organizations grow to enterprise scale, their IT infrastructure requirements
More informationSharePoint 2010 Interview Questions-Architect
Basic Intro SharePoint Architecture Questions 1) What are Web Applications in SharePoint? An IIS Web site created and used by SharePoint 2010. Saying an IIS virtual server is also an acceptable answer.
More informationWhat is a life cycle model?
What is a life cycle model? Framework under which a software product is going to be developed. Defines the phases that the product under development will go through. Identifies activities involved in each
More informationAutomated file management with IBM Active Cloud Engine
Automated file management with IBM Active Cloud Engine Redefining what it means to deliver the right data to the right place at the right time Highlights Enable ubiquitous access to files from across the
More informationLecture 7: Concurrency control. Rasmus Pagh
Lecture 7: Concurrency control Rasmus Pagh 1 Today s lecture Concurrency control basics Conflicts and serializability Locking Isolation levels in SQL Optimistic concurrency control Transaction tuning Transaction
More informationJBoss Enterprise App. Platforms Roadmap. Rich Sharples Director of Product Management, Red Hat 4th April 2011
JBoss Enterprise App. Platforms Roadmap Rich Sharples Director of Product Management, Red Hat 4th April 2011 Agenda Where we're heading Enterprise Application Platform 6 Enterprise Data Grid 6 Roadmap
More informationWebSphere ESB Best Practices
WebSphere ESB Best Practices WebSphere User Group, Edinburgh 17 th September 2008 Andrew Ferrier, IBM Software Services for WebSphere andrew.ferrier@uk.ibm.com Contributions from: Russell Butek (butek@us.ibm.com)
More informationConverting Java EE Applications into OSGi Applications
Converting Java EE Applications into OSGi Applications Author: Nichole Stewart Date: Jan 27, 2011 2010 IBM Corporation THE INFORMATION CONTAINED IN THIS REPORT IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY.
More informationCloud Computing at Google. Architecture
Cloud Computing at Google Google File System Web Systems and Algorithms Google Chris Brooks Department of Computer Science University of San Francisco Google has developed a layered system to handle webscale
More informationData Modeling Basics
Information Technology Standard Commonwealth of Pennsylvania Governor's Office of Administration/Office for Information Technology STD Number: STD-INF003B STD Title: Data Modeling Basics Issued by: Deputy
More informationSelecting the Right Service Virtualization Tool. www.grid-tools.com E: info@grid-tools.com UK: +44 01865 884 600 US: +1 866 519 3751
Selecting the Right Service Virtualization Tool Selecting Your Service Virtualization Tool In recent years, the adoption of SOA (Service-Oriented Architectures) has become the solution of choice amongst
More informationSuccessfully managing geographically distributed development
IBM Rational SCM solutions for distributed development August 2004 Successfully managing geographically distributed development Karen Wade SCM Product Marketing Manager IBM Software Group Page 2 Contents
More informationA REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM
A REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM Sneha D.Borkar 1, Prof.Chaitali S.Surtakar 2 Student of B.E., Information Technology, J.D.I.E.T, sborkar95@gmail.com Assistant Professor, Information
More informationGlobule: a Platform for Self-Replicating Web Documents
Globule: a Platform for Self-Replicating Web Documents Guillaume Pierre Maarten van Steen Vrije Universiteit, Amsterdam Internal report IR-483 January 2001 Abstract Replicating Web documents at a worldwide
More informationEDOS Distribution System: a P2P architecture for open-source content dissemination
EDOS Distribution System: a P2P architecture for open-source content Serge Abiteboul 1, Itay Dar 2, Radu Pop 3, Gabriel Vasile 1 and Dan Vodislav 4 1. INRIA Futurs, France {firstname.lastname}@inria.fr
More informationStorage and Disaster Recovery
Storage and Disaster Recovery Matt Tavis Principal Solutions Architect The Business Continuity Continuum High Data Backup Disaster Recovery High, Storage Backup and Disaster Recovery form a continuum of
More informationEnterprise Service Bus
We tested: Talend ESB 5.2.1 Enterprise Service Bus Dr. Götz Güttich Talend Enterprise Service Bus 5.2.1 is an open source, modular solution that allows enterprises to integrate existing or new applications
More informationMotivation Definitions EAI Architectures Elements Integration Technologies. Part I. EAI: Foundations, Concepts, and Architectures
Part I EAI: Foundations, Concepts, and Architectures 5 Example: Mail-order Company Mail order Company IS Invoicing Windows, standard software IS Order Processing Linux, C++, Oracle IS Accounts Receivable
More informationUsing OSGi as a Cloud Platform
Jan S. Rellermeyer IBM Austin Research Lab 24 October 2012 Using OSGi as a Cloud Platform The Cloud - Challenges Cloud Computing is the economies of scale E.g., system of engagements Image: Pixomar / FreeDigitalPhotos.net
More informationA new era of PaaS. ericsson White paper Uen 284 23-3263 February 2015
ericsson White paper Uen 284 23-3263 February 2015 A new era of PaaS speed and safety for the hybrid cloud This white paper presents the benefits for operators and large enterprises of adopting a policydriven
More informationRose Business Technologies
Benefits of Software as a Service (SaaS) Software as a Service (SaaS) may be defined simply as software applications deployed over the Internet. With SaaS, a third-party provider licenses an application
More informationDistributed Databases. Concepts. Why distributed databases? Distributed Databases Basic Concepts
Distributed Databases Basic Concepts Distributed Databases Concepts. Advantages and disadvantages of distributed databases. Functions and architecture for a DDBMS. Distributed database design. Levels of
More informationModular Communication Infrastructure Design with Quality of Service
Modular Communication Infrastructure Design with Quality of Service Pawel Wojciechowski and Péter Urbán Distributed Systems Laboratory School of Computer and Communication Sciences Swiss Federal Institute
More informationService Oriented Architecture
Service Oriented Architecture Charlie Abela Department of Artificial Intelligence charlie.abela@um.edu.mt Last Lecture Web Ontology Language Problems? CSA 3210 Service Oriented Architecture 2 Lecture Outline
More information20465: Designing a Data Solution with Microsoft SQL Server
20465: Designing a Data Solution with Microsoft SQL Server Microsoft - Base de Dados Nível: Avançado Duração: 30h Sobre o curso The focus of this five-day instructor-led course is on planning and implementing
More informationThis course is intended for database professionals who need who plan, implement, and manage database solutions. Primary responsibilities include:
Course Page - Page 1 of 5 Designing Solutions for Microsoft SQL Server 2014 M-20465 Length: 3 days Price: $1,795.00 Course Description The focus of this three-day instructor-led course is on planning and
More informationMike Chyi, Micro Focus Solution Consultant May 12, 2010
Mike Chyi, Micro Focus Solution Consultant May 12, 2010 Agenda Load Testing Overview, Best Practice: Performance Testing with Diagnostics Demo (?), Q&A Load Testing Overview What is load testing? Type
More informationMANAGEMENT METHODS IN SLA-AWARE DISTRIBUTED STORAGE SYSTEMS
Computer Science 13 (3) 2012 http://dx.doi.org/10.7494/csci.2012.13.3.35 Darin Nikolow Renata S lota Danilo Lakovic Pawe l Winiarczyk Marek Pogoda Jacek Kitowski MANAGEMENT METHODS IN SLA-AWARE DISTRIBUTED
More informationA Survey Study on Monitoring Service for Grid
A Survey Study on Monitoring Service for Grid Erkang You erkyou@indiana.edu ABSTRACT Grid is a distributed system that integrates heterogeneous systems into a single transparent computer, aiming to provide
More informationDatabase Management. Chapter Objectives
3 Database Management Chapter Objectives When actually using a database, administrative processes maintaining data integrity and security, recovery from failures, etc. are required. A database management
More informationSeamless adaptive multi- cloud management of service- based applications. European Open Cloud Collaboration Workshop, May 15, 2014, Brussels
Seamless adaptive multi- cloud management of service- based applications European Open Cloud Collaboration Workshop, May 15, 2014, Brussels Interoperability and portability are a few of the main challenges
More informationEWeb: Highly Scalable Client Transparent Fault Tolerant System for Cloud based Web Applications
ECE6102 Dependable Distribute Systems, Fall2010 EWeb: Highly Scalable Client Transparent Fault Tolerant System for Cloud based Web Applications Deepal Jayasinghe, Hyojun Kim, Mohammad M. Hossain, Ali Payani
More informationTheodor Borangiu UVHC, ENSIAME 2013
Theodor Borangiu UVHC, ENSIAME 2013 Introduction Manufacturing Systems Performance Monitoring Monitoring Solution for Holonic Manufacturing Systems Conclusions June 19, 2013 2 Three inter-related vectors
More informationEnsuring the Security of Your Company s Data & Identities. a best practices guide
a best practices guide Ensuring the Security of Your Company s Data & Identities Symplified 1600 Pearl Street, Suite 200» Boulder, CO, 80302» www.symplified.com» @Symplified Safe and Secure Identity Management
More informationApache Karaf in real life ApacheCon NA 2014
Apache Karaf in real life ApacheCon NA 2014 Agenda Very short history of Karaf Karaf basis A bit deeper dive into OSGi Modularity vs Extensibility DIY - Karaf based solution What we have learned New and
More informationDistributed Database Management Systems
Distributed Database Management Systems (Distributed, Multi-database, Parallel, Networked and Replicated DBMSs) Terms of reference: Distributed Database: A logically interrelated collection of shared data
More informationMichał Jankowski Maciej Brzeźniak PSNC
National Data Storage - architecture and mechanisms Michał Jankowski Maciej Brzeźniak PSNC Introduction Assumptions Architecture Main components Deployment Use case Agenda Data storage: The problem needs
More informationExtend the value of your service desk and integrate ITIL processes with IBM Tivoli Change and Configuration Management Database.
IBM Service Management solutions and the service desk White paper Extend the value of your service desk and integrate ITIL processes with IBM Tivoli Change and Configuration Management Database. December
More informationSurvey on Comparative Analysis of Database Replication Techniques
72 Survey on Comparative Analysis of Database Replication Techniques Suchit Sapate, Student, Computer Science and Engineering, St. Vincent Pallotti College, Nagpur, India Minakshi Ramteke, Student, Computer
More informationHigh Availability with Postgres Plus Advanced Server. An EnterpriseDB White Paper
High Availability with Postgres Plus Advanced Server An EnterpriseDB White Paper For DBAs, Database Architects & IT Directors December 2013 Table of Contents Introduction 3 Active/Passive Clustering 4
More informationA cloud-based architecture to crowdsource mobile app privacy leaks
Department of Information & Communication Systems Engineering University of the Aegean Karlovasi, Samos, Greece A cloud-based architecture to crowdsource mobile app privacy leaks Dimitrios Papamartzivanos
More informationHow To Build A Cloud Storage System
Reference Architectures for Digital Libraries Keith Rajecki Education Solutions Architect Sun Microsystems, Inc. 1 Agenda Challenges Digital Library Solution Architectures > Open Storage/Open Archive >
More informationInformation Management
Information Management Dr Marilyn Rose McGee-Lennon mcgeemr@dcs.gla.ac.uk What is Information Management about Aim: to understand the ways in which databases contribute to the management of large amounts
More informationWhitepaper. The ERP-Link Software Platform: Counterpart to Duet Enterprise
Whitepaper The ERP-Link Software Platform: Counterpart to Duet Enterprise Table of Contents 1 Introduction... 3 2 ERP-Link Suite for SharePoint and SAP... 3 3 Duet Enterprise... 5 3.1 CAPABILITIES... 5
More informationDesigning a Data Solution with Microsoft SQL Server 2014
Page 1 of 8 Overview The focus of this five-day instructor-led course is on planning and implementing enterprise database infrastructure solutions by using SQL Server 2014 and other Microsoft technologies.
More informationONOS Open Network Operating System
ONOS Open Network Operating System Architecture Overview Thomas Vachuska tom@onlab.us ONOS: SDN OS for Service Provider Networks Scalability, High Availability & Performance Northbound & Southbound Abstractions
More informationRelational Database Basics Review
Relational Database Basics Review IT 4153 Advanced Database J.G. Zheng Spring 2012 Overview Database approach Database system Relational model Database development 2 File Processing Approaches Based on
More informationAnalysis of Cloud Solutions for Asset Management
ICT Innovations 2010 Web Proceedings ISSN 1857-7288 345 Analysis of Cloud Solutions for Asset Management Goran Kolevski, Marjan Gusev Institute of Informatics, Faculty of Natural Sciences and Mathematics,
More informationVDM vs. Programming Language Extensions or their Integration
VDM vs. Programming Language Extensions or their Integration Alexander A. Koptelov and Alexander K. Petrenko Institute for System Programming of Russian Academy of Sciences (ISPRAS), B. Communisticheskaya,
More information