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by: Alayna Veum
Alayna Veum

GPA 3.81

Michael Best

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Michael Best
Class Notes
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This 0 page Class Notes was uploaded by Alayna Veum on Monday November 2, 2015. The Class Notes belongs to CS 4803 at Georgia Institute of Technology - Main Campus taught by Michael Best in Fall. Since its upload, it has received 9 views. For similar materials see /class/234092/cs-4803-georgia-institute-of-technology-main-campus in ComputerScienence at Georgia Institute of Technology - Main Campus.

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Date Created: 11/02/15
Lecture 9 Introduction to Enterprise Computing CS 48038803ENC Application Servers Ling Liu Associate Professor Georgia Tech Lecture 9 Lecture Outline 0 Application Servers oConoept 9 Design Principles 9 Key Technology ITP Monitors IWorkflow Systems oMarketplaoe 0 Information Aggregation Services 0 MediatorWrapper Systems I Online shopping SupplierChain Management Metacrawier 1 ng Liu 2 Application Server Definition osoftware that runs on a middle tier between W b browserbased thin clients and back end databases and business applications a Basic functionalit o Provide Connectivity to legacy systems 9 Support for business and application logic Su on server clustering load balancing and fallover features Multi tier Architecture Applicatinn Server Tier pate piesznla m mien in Application Server Tier B ackend Data Server Architectural Evolution of Web Applications Databases o Twotier architecture Sam o Threetier or Multitier architecture Application Server Client Data Server Databases Lemme 9 Example Application Services o Webbased Supply Chain Management o Comparative Shopping Robots 0 Meta Search Engines o Continuous Internet Services olnformation tracking and notification oData streaming Lemme 9 Comparative Shopping 0 Application Logic K J Comparative Shopping Junglee and Jango initial success in industry Lemme 9 Why Application Servers o portability of application software 0 interoperability of applications 0 availability of application services 0 performance and scalability of application service Lemme 9 Design Principles m39 y 17321 o Multitier Architecture 0 Load Balancing 0 Availability 0 Message Queuing Asynchronous messaging Store amp FonNard o Replication o Failover Clustering o Protecting Business Uptime Scalability and Availability ng Llu Message Queuing 0 Message Queuing and Scalability 0 Asynchron ous Messa ing 0 Allow to write applications that do not require immediate responses 0 Provide an ability to handle delays between a request and a response Message Queuing and Availability 0 isolating the imp one component on the rest 0 th 0 allowing continuation ofwork act of failure or unavailability of e system A Scalable Work ow Architecture Prenmamp mam mum om mmmmn mmmmn Dewy mam pmzssng Shuan Dimma Failover Clustering Provides a server with a backup partner in case of mummy nadela E0 i saw paw commkmbu mumu ry aut mm m m y shared disk array cmnec vity in a unmade dusts Business Application Integration 0 Application Servers ans Mom M n lse an undez minimum SImd bley Protecting Business Uptime 0 Business uptime othe ability to conduct business in the event of partial system failure or unexpected demand Maintaining Uptime Mechanisms Best Practice Providing Redundancia o Prioritizing oThe rst step in the ltering process involves prioritizing all the business services 0 Modularizing othe process of dividing the essential and non essential services into modules on the basis ofthe similarity ofthe function 0 Segregating o a process ofimplementing the prioritized list of business functions so that nonessential functions can be turned offwhen needed am angm Lecture 9 Supporting Application Logic 0 Today s application servers 9 support simple application logic integration and classification of user s info 9 provide access and information integration to heterogeneous collection of Webbased data servers 0 Key technology for tomorrow s aggregators 9 Workflow Management Software Lecture 9 Information Aggregation Service o A special type of service supported by application server technology 0 Also called aggregator 0 Features 9 it has complex application logic 9 it requires connectivity and uniform access to multiple and heterogeneous backend legacy data servers web based Example Aggregation Service Veyueamne anu nyDay cum Gvegg Fveisntauuunu AttamabasedVemca OnE 5251 DUB Ennis uwn easn Me When m ventuve eapnai Them m une mace ne can see batancestuv W5 9 Ban n checkm o munEv39mavkel accounts 9 Am n nExm say 9 Deuammnesneuuent 9 Man men Pumt Internet Aggregation Services Verticalonecom 9 focuses aggreganon on nnanoat emaH and bonus rnne accounts 9 grab data from 450 Web Sites 9 brought by 511999for166rnnnon32 ownership of Vooiee yodleecom Anil Arora over180m hons n aggregated assests ogatners oersonai nnanoai accounts emaH oonus mite e commerce HeWS etc ab data from 1450 Web StteS Maunched Sept 99 teamed thh Chase Manhattan MemH Lynch Amercia Onhne Quicken com Lemme 9 Today s Aggregators o gather up all your financial information in one place accessible by a single password 0 offer only basic information 9 account balances and recent transactions or an email subject heading 0 If you want more you need merely click through to the original site 0N0 need to enter your usual password Lemme 9 Marketplace Outlook 0 User Space USA Today in 2000 o gt400000 people now use aggregation services ototal will double by year s end 0 8 30 million Americans in 45 years 0 Profits 0 Bank or portal pays the aggregator in order to keep customers at its site and hawk new offerings I 100000 500000 upfront a monthly persubscriber fee or a cut of advertisement revenue Lemme 9 Challenges for Aggregation Services o Privacy and Security Concerns oone loginpassword for all I convenient but how about security 9 several layers of encryption I but no proofthat none can break into 0 Legal and competition issues 9 The famous screen scrapers problem I no need for permission from banks to cull info 9 a threat to banks and online brokerages I lose the touch point with the customers Lemme 9 Supporting Application Logic 0 Today s application servers 9 support simple application logic integration and classification of user s info 9 provide access and information integration to heterogeneous collection of Webbased data servers 0 Key technology for tomorrow s aggregators 39gt 9 Workflow Management Software 9 TP monitors 4 ng Liu 24 Lecture 9 Requirements for Enterprise Networking effectively share distributed information and computing resources interoperability between 7 onsumefs loosely coupled legacy systems and new heterogeneous and distributed HAD systems support USECA properties gniform access calability Product Evolution Qomposability Autonomy Network Cap abilities Service ensure correctness and reliability of various distributed processing Technology Push a lications pp To create an enterprise wide information unity ng Ltu Lecture 9 Work ow Terminology 0 Workflow 0A workflow is a sequence of actions or steps used in business process 0 Workflow management ois a software tool lfor structuring and optimizing business processing and lfor supporting the practical implementation of business process reengineering ng L 26 Lemme 9 Business ReEngineering o Enterprisewide reengineering oMotivated by global competition oTotal quality and greater efficiency 0 Modeling the business process oTransform Legacy into Next Generation oEmail and collaborative tools eg Notes oBusiness activities and messaging oFleXible glues that capture the process Lemme 9 Work ow Terminology 0 Workflow Management System WfMS orefers to a set of software and tools for automating and improving business processes 0 Workflow Technology oTechnology to streamline coordinate and monitor a process or an activity I involving human and automated tasks that spread across multiple enterprises with heterogeneous existing and new computing environments Lecture 9 Why Work ow technology 0 Organize schedule control and monitortasks 0 Support onIine data entry where data originates support data exchange and transactions across independent enterprises EDI reduce paperwork 0 Use standard interface for database access and updates 0 help understandimprove process reengineering 0 Can be seen as programmingintheIarge Lecture 9 WfMS Conceptual Architecture Workflow WMS runtime Development SyStem and BPM Toolkit Toolkit tools I O scheduler 0 process View graphical o task manager 0 0r9 ViEW deSign too39 interfaces 0 data view lt gt O developer s E i h processing 0 reengineerinJ workbench entities analyzer teSting too39 0 monitoring tocl O SI39mU39at39on o tracking tool 0 o reporting tool ng Llu CS 4803 Computer and Network Security Alexandra Sasha Boldyreva Block ciphers Pseudorandom functions Block ciphers Building blocks for symmetric cryptography o A block cipher E is a collection of functions from n bits to n bits Each function is fully specified by a kbit key Examples DES 3DES AES 0 Notation for every Ke01k Me01n EKM is nbit output 0 For every Ke01k EK is a permutation onetoone and onto function For every Ce01n there is a single Me01n M 0 Thus each block cipher has an inverse for every key EK1 st EKEK391CC EK391EKMM for all MCe01n For every Ke01k EK EK391 01n 3901n DES Key length k56 input and output length n64 1973 NBS National Bureau of Standards announced a search for a data protection algorithm to be standardized 1974 IBM submits a design based on Lucifer algorithm 1975 The proposed DES is published 1976 DES approved as a federal standard DES is highly efficient z2S107 DES computations per second Security of block ciphers 0 Any block cipher E is subject to exhaustive keysearch given M1C1EKM1MqCqEKMq an adversary can recover K or another key consistent with the given pairs as follows EKSEM1C1MqCq For i12k o if ETiM1C1 then Ti is ith kbit string if ETiMjCj for all ZSqu then return Ti EndIf EndIf EndFor Security of block ciphers Exhaustive key search takes 2k block cipher computations in the worst case On the average 2k 1 DES has a property that DESKI DESFG this speeds up exhaustive search by a factor of 2 For DES k56 exhaustive search takes 255225107 that is about 23 years Security of DES 0 There are more sophisticated attacks known differential cryptoanalysis finds the key given about 247 chosen plaintexts and the corresponding ciphertexts linear cryptoanalysis finds the key given about 242 known plaintext and ciphertext pairs These attacks require too many data hence exhaustive key search is the best known attack And it can be mounted in parallel o A machine for DES exhaustive key search was built for 250000 It finds the key in about 56 hours on average 0 A new block cipher was needed TripleDES 3DESK1 K2MDESK2 DES 1Kl DESK2M 3DES s keys are 112bit long Good but needs 3 DES computations Advanced Encryption Standard AES 1998 NIST announced a search for a new block cipher 15 algorithms from different countries were submitted 2001 NIST announces the winner an algorithm Rijndael designed by Joan Daemen and Vincent Rijmen from Belgium AES block length n128 key length k is variable 128 192 or 256 bits Exhaustive key search is believed infeasible Limitations of keyrecovery based security 0 A classical approach to block cipher security key recovery should be infeasible 0 Le given M1EKM1MqEKMq where K is chosen at random an M1Mq are chosen at random or by an adversary the adversary cannot compute K in time t with probability a 0 Necessary but is it sufficient 0 Consider E39KM1 M2EKM1M2 for some good E Key recovery is har for E39 as well but it does not look secure 0 Q What property ofa block cipher as a building block would ensure various security properties of different constructions Intuition 0 We want that informally 0 key search is hard 0 a block cipher output does not leak the input a block cipher output does not leak bits of the input 0 a block cipher output does not leak any function of the input 0 there is a master property of a block cipher as a building block that enables security analysis of protocols based on block ciphers 0 It is good if the block cipher outputs look random o Pseudorandom functions PRFs is a very important notion in cryptography o A good block cipher should be a pseudorandom function ie informally its instances behave like a random function and thus no information can be learned from its behavior 0 What is a random function our ideal object o It is a function chosen at random from the set of ALL possible functions from n bits to n bits 0 We are interested in the inputoutput behavior of a random function Let s imagine that we have access to a subroutine that implements such a function gXe01 global arra If TX is not defined then TX 3 01n EndIf pick a random nbit string Return TX Black box access Imagine a computer has an executable program for a random function and you can use it via an inputoutput interface 90 gt global array T If TX is not defined then Y Tx o1L EnIf lt Return YT Black box access Imagine the computer also has an executable program for a block cipher E specified by a random key K and you can use the program via an inputoutput interface X EK


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