Sep 15-19, 2008EDOC 2008 Scheduling-capable Autonomic Manager for Policy-based IT Change Management System H. AbdelSalamK. Maly R. MukkamalaM. Zubair Department.

Slides:



Advertisements
Similar presentations
2 Introduction A central issue in supporting interoperability is achieving type compatibility. Type compatibility allows (a) entities developed by various.
Advertisements

Lecture 19: Parallel Algorithms
Query Optimization of Frequent Itemset Mining on Multiple Databases Mining on Multiple Databases David Fuhry Department of Computer Science Kent State.
Towards Self-Testing in Autonomic Computing Systems Tariq M. King, Djuradj Babich, Jonatan Alava, and Peter J. Clarke Software Testing Research Group Florida.
Matrices: Inverse Matrix
1 Parallel Algorithms II Topics: matrix and graph algorithms.
8.2 Discretionary Access Control Models Weiling Li.
Maximal Lifetime Scheduling in Sensor Surveillance Networks Hai Liu 1, Pengjun Wan 2, Chih-Wei Yi 2, Siaohua Jia 1, Sam Makki 3 and Niki Pissionou 4 Dept.
Part 3 Chapter 9 Gauss Elimination
1 2 Extreme Pathway Lengths and Reaction Participation in Genome Scale Metabolic Networks Jason A. Papin, Nathan D. Price and Bernhard Ø. Palsson.
A Grid Parallel Application Framework Jeremy Villalobos PhD student Department of Computer Science University of North Carolina Charlotte.
1 Lecture 25: Parallel Algorithms II Topics: matrix, graph, and sort algorithms Tuesday presentations:  Each group: 10 minutes  Describe the problem,
Chapter 12 Chi-Square Tests and Nonparametric Tests
Guaranteed Smooth Scheduling in Packet Switches Isaac Keslassy (Stanford University), Murali Kodialam, T.V. Lakshman, Dimitri Stiliadis (Bell-Labs)
IT Planning.
1 Introduction to Load Balancing: l Definition of Distributed systems. Collection of independent loosely coupled computing resources. l Load Balancing.
UNC Chapel Hill Lin/Manocha/Foskey Optimization Problems In which a set of choices must be made in order to arrive at an optimal (min/max) solution, subject.
Methods of Image Compression by PHL Transform Dziech, Andrzej Slusarczyk, Przemyslaw Tibken, Bernd Journal of Intelligent and Robotic Systems Volume: 39,
1 Optimizing Utility in Cloud Computing through Autonomic Workload Execution Reporter : Lin Kelly Date : 2010/11/24.
Arrays Data Structures - structured data are data organized to show the relationship among the individual elements. It usually requires a collecting mechanism.
October 14, 2010Neural Networks Lecture 12: Backpropagation Examples 1 Example I: Predicting the Weather We decide (or experimentally determine) to use.
Introduction to Simulated Annealing 22c:145 Simulated Annealing  Motivated by the physical annealing process  Material is heated and slowly cooled.
Association between Variables Measured at the Nominal Level.
Exponential Moving Average Q- Learning Algorithm By Mostafa D. Awheda Howard M. Schwartz Presented at the 2013 IEEE Symposium Series on Computational Intelligence.
Box Method for Factoring Factoring expressions in the form of.
November , 2009SERVICE COMPUTATION 2009 Analysis of Energy Efficiency in Clouds H. AbdelSalamK. Maly R. MukkamalaM. Zubair Department.
Computer Science Department Data Structure & Algorithms Problem Solving with Stack.
Problem Solving Techniques. Compiler n Is a computer program whose purpose is to take a description of a desired program coded in a programming language.
Patricia Méndez Lorenzo & Roberto Alvarez Alonso (IT-DI/SM) 03/10/2013 Calendars Facility in ServiceNow2.
Introduction to Database Systems1. 2 Basic Definitions Mini-world Some part of the real world about which data is stored in a database. Data Known facts.
State Data Center Daylight Saving Time 2007 Overview February 15, 2007.
Selection Relational Expressions A condition or logical expression is an expression that can only take the values true or false. A.
A Comparative Study of Specification Models for Autonomic Access Control of Digital Rights K. Bhoopalam,K. Maly, R. MukkamalaM. Zubair Old Dominion University.
Copyright © 2002 OSI Software, Inc. All rights reserved. PI Application Framework Richard Beeson March 2002.
 The Multi-Tier Mission Architecture and a Different Approach to Entry, Descent and Landing Jeremy Straub Department of Computer Science University of.
Ms. Hammerle Nottingham High School
June 30 - July 2, 2009AIMS 2009 Towards Energy Efficient Change Management in A Cloud Computing Environment: A Pro-Active Approach H. AbdelSalamK. Maly.
June 13-15, 2007Policy 2007 Infrastructure-aware Autonomic Manager for Change Management H. Abdel SalamK. Maly R. MukkamalaM. Zubair Department of Computer.
Optimization Problems In which a set of choices must be made in order to arrive at an optimal (min/max) solution, subject to some constraints. (There may.
What is Science? SECTION 1.1. What Is Science and Is Not  Scientific ideas are open to testing, discussion, and revision  Science is an organize way.
Arrays.
Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs.
A Protocol for Tracking Mobile Targets using Sensor Networks H. Yang and B. Sikdar Department of Electrical, Computer and Systems Engineering Rensselaer.
SWBAT… understand the mistakes they made on the quiz and correct those mistakes. Agenda 1. WU (10 min) 2. Quiz corrections (20 min) 3. Ithaca Car Sharing.
SUBMITTED By: Tasneem Sutarwala (55) Submitted to:- MRS.RUTVI UMRIGAR.
When you first enter the Automation Rules process, there may be no rules listed. In that case the view may appear as shown below and you will have three.
CSC 413/513: Intro to Algorithms Hash Tables. ● Hash table: ■ Given a table T and a record x, with key (= symbol) and satellite data, we need to support:
ELEC692 VLSI Signal Processing Architecture Lecture 12 Numerical Strength Reduction.
May 7-8, 2007ICVCI 2007 RTP Autonomic Approach to IT Infrastructure Management in a Virtual Computing Lab Environment H. Abdel SalamK. Maly R. MukkamalaM.
Hawk Hour Lesson Plans Sept Day 1 – Sept.15, Mon. Essential Question: “How do my daily choices reflect who I am?” Discuss: What do we already have.
Buffering Techniques Greg Stitt ECE Department University of Florida.
Part 3 Chapter 9 Gauss Elimination
Collective Intelligence Week 11: k-Nearest Neighbors
Building Information Systems
Introduction to Load Balancing:
Extreme Learning Machine
Box Method for Factoring
Box Method for Factoring
L9Matrix and linear equation
DISK SCHEDULING FCFS SSTF SCAN/ELEVATOR C-SCAN C-LOOK.
Virtual Memory Networks and Communication Department.
Rate 7/8 (1344,1176) LDPC code Date: Authors:
Lecture 22: Parallel Algorithms
Week #5 – 23/25/27 September 2002 Prof. Marie desJardins
Logo Calendar – January 2012 TO DO LIST 01/04/2012 Example TO DO LIST
CSCI N207 Data Analysis Using Spreadsheet
Managing Economies of Scale in a Supply Chain Cycle Inventory
Arrays Week 2.
Introduction to Graphs
Presentation transcript:

Sep 15-19, 2008EDOC 2008 Scheduling-capable Autonomic Manager for Policy-based IT Change Management System H. AbdelSalamK. Maly R. MukkamalaM. Zubair Department of Computer Science, Old Dominion University D. Kaminsky IBM, Raleigh, North Carolina

Sep 15-19, 2008EDOC 2008 IT Change Management Managing large IT environments is expensive and labor intensive Using ad-hoc and human-based methodologies to manage Change Management in large size IT organizations can be costly, error prone and crisis oriented rather than targeted and predictable. One problem faced by IT administrators, in automating change management, is to arrive at a schedule for applying one or more change requests to the IT infrastructure In this paper, We provide two approaches to schedule change requests in the presence of organizational policies governing the use, access and availability of the IT infrastructure.

Sep 15-19, 2008EDOC 2008 Scheduling Approaches 1.Calendar Based Approach 2.Time – Independent Policies

Sep 15-19, 2008EDOC 2008 Calendar Based Approach Divide the week into time slots of equal size (h). At the beginning of each week as well as whenever the deployed policies or the infrastructure change, create a resource-slot matrix to store the relation between policies, resources, and time slots. For each resource (row) and in each time slot (column), the matrix entry specifies whether taking the specified resource down individually at the specified time slot would violate (value =1) any of the deployed polices or not (value=0).

Sep 15-19, 2008EDOC 2008 To schedule a change request. There are two cases. Case (i): If the request for change involves only one resource Scheduling information is already available in the matrix (i.e., time slots with a zero for the given resource row). Case (ii): To schedule changes that involve more than one resource Evaluate the logical OR between the rows of the resources in the request. Due to redundancy, we need to reevaluate the policies at all the time slots with a zero for the given resource row. (See Next Figure) Return only non violating time slots. Calendar Based Approach (Cont.)

Sep 15-19, 2008EDOC 2008 An example of resources dependency Router R2 Alone doesn’t violates Policy 1. Taking Router R3 alone doesn’t violates Policy 1. Taking R2 & R3 Down violates Policy 1.

Sep 15-19, 2008EDOC 2008 Calendar Based Approach (Cont.) Resource Sunday 12:00 AM- 1:00 AM Monday 8:00 AM- 9:00 AM … Router R10…1… Router R20…0… Router R30…0… DB10…0… DB20…0… Application X0…1… Example 1: Entries in a Time Slot-Resource Matrix

Sep 15-19, 2008EDOC 2008 The idea behind is to represent each policy as a set of time-independent conditions and a set of time ranges when the policy is active. Example: “Application X must be available to students on weekdays from 7:00AM until 6:00PM”. This policy can be expressed as <“Application X must be available to students”, {Mon 7:00AM to 6:00PM, Tue on 7:00AM to 6:00PM … Fri 7:00AM to 6:00PM} >. Time-Independent Approach

Sep 15-19, 2008EDOC 2008 Basic Algorithm ScheduleAChange (change request Cnew) Begin T = Φ For i = 1 to n IF (Pi.violates. Cnew) Then T = T U Ti For i = 1 to m T = T U Qi Return T’ (time range for the next week - T) End

Sep 15-19, 2008EDOC 2008 Improved Algorithm ScheduleAChangeImproved (change request Cnew) Begin T = Φ For i = 1 to n IF (Pi.violates. Cnew) Then T = T U Ti For j = 1 to m { C = Cnew U Ci For i = 1 to n IF (Pi.violates. C) Then T = T U Qi } Return T’ (time range for the next week - T) End

Sep 15-19, 2008EDOC 2008 Implementation

Sep 15-19, 2008EDOC 2008 Conclusion We proposed a simple calendar-based scheduler as well as one where policies are expressed as a pair of pair. This method of representing policies is novel and is shown to improve the efficiency of the scheduler. For small IT environments or for small schedule periods with a relatively large granularity, the calendar approach is found to be adequate. However, for larger environments, longer schedule periods, and a large number of policies, the time-independent approach is found to be essential due to its scalability