# An On-Line Algorithm for Checkpoint Placement

Save this PDF as:

Size: px
Start display at page:

## Transcription

4 \$ \$ monitoring these operations, the program knows its current state size. Therefore, it can estimate the current cost of checkpointing. In this section, we show how knowledge about the current cost of checkpointing can be used in placement of checkpoints. The main idea in the algorithm described here, is to look for points in the program in which placing a checkpoint is the most beneficial. The algorithm finds points in the program in which the state size is small, and uses these points for checkpoints. If such points are found, checkpoints are placed at these points with small intervals between the checkpoints so that the re-processing time after a fault is small. If no such point is found after a period of time, a checkpoint is placed at a point with higher cost to avoid long re-processing time. In this case the interval between the checkpoints is longer to reduce the checkpointing overhead. To demonstrate how a current knowledge about the checkpointing cost can improve the performance of checkpointing schemes, we use the following example. The program has two possible state sizes, and, such that. The checkpointing cost when the state size is. The state size of the program changes is according to a two state Markov chain with rate of leaving state equal to. The algorithm works in the following way. We define two points in time, and, such that. The algorithm decides whether to place a checkpoint at, where is the time since the last checkpoint, according to the following rules:. If the state size at is, then a checkpoint is placed at. The cost of the checkpoint is.. If the state size at is, the system waits until the state size changes to and a checkpoint is placed at that time. The cost of the checkpoint is. 3. If the state size at is and the state size does not change until, then a checkpoint is placed at. The cost of the checkpoint in this case is. Note that in order to avoid high checkpointing overhead, a checkpoint is never placed before. Also, to avoid long re-processing time, a checkpoint is never placed after. The values of and affect the performance of algorithm. By analyzing the overhead ratio of the algorithm, we can find the values of and that minimize the overhead ratio. In the following section, we calculate the overhead ratio of the on-line algorithm. 3. Analysis of the On-Line Algorithm As we stated earlier, to focus on the benefits of the proposed algorithm, and simplify the analysis of the proposed algorithm, we assume that faults do not occur during checkpointing, and that the recovery time after a fault is 0. We also assume that the faults are detected immediately. Lemma With the above assumptions, the average overhead ratio when the on-line algorithm for checkpointing placement is used is given by (3) where is the probability that the state size at a checkpoint is. Proof: The proof of the lemma consists of the following propositions that derive the probability of placing a checkpoint at and, the average length of an interval between checkpoints and the average execution time of such interval. Proposition 3 In a steady-state, the probability that the state size at a checkpoint is is (4) Proof: In a steady-state the probability that the state size at a checkpoint is satisfies the following equation (5) Pr state is prev. state was! Pr state is prev. state was! The state size at a checkpoint is if, and only if, a checkpoint is placed at, and a checkpoint is placed at if, and only if, the state size at is and the state size does not change in the interval " #. Therefore, Pr state is prev. state was! %\$ & and Pr state is %\$ prev. state was! & where \$ & and \$ & are the transition probabilities from states and respectively to at time given by & & Assigning these values to Eq. (5) and solving for yields Eq. (4).

5 Note that is less than or equal to the steady-state probability of, and it can be close to 0 for high. It means that the proposed algorithm uses the cheaper checkpoint more often than algorithms that do not consider the current checkpointing cost. Proposition 4 In a steady-state, the probability that a checkpoint is placed at is (6) Proof: A checkpoint is placed at if, and only if, it was not placed at and the state size remained in the interval " #. Therefore, or Corollary 5 The probability density function (pdf) of the interval length is (7) where and are the impulse and step functions at respectively. Proposition 6 The average length of an interval between checkpoints is (8) Proof: Let be the probability density function (pdf) of the interval length, then Proposition 7 The average execution time of an interval between checkpoints is (9) Proof: Let be interval of length with checkpoint of cost at of it. From Proposition we know that and the average execution time of an the end Proposition 8 The average overhead ratio of a program is (0) Proof: To calculate the overhead ratio of a program it is not enough to calculate the average overhead of an interval. We need to consider also the length of the intervals, since longer intervals occupy more of the program, and thus they have bigger influence on the overhead ratio. Therefore, using similar arguments to those used when considering the current life of a random point in time in renewal theory [6], the average overhead ratio of a program is given by Assigning the values of from Eq. (8) and from Eq. (9) into the expression of the overhead ratio of a program given in Eq. (0) yields the expression in Eq. (3) and completes the proof of Lemma. Given,,, and, we can numerically find the values of and that minimize the overhead ratio. More on the selection of and can be found in Section On-line Algorithm with More Than Two State Sizes The on-line algorithm we have described in this section is designed for the case when the program has two possible

8 state size state size s s time s s time on-line algorithm rise detection optimal algorithm Figure 3. Placement of checkpoints by the optimal off-line algorithm and the on-line algorithm with and without rise detection its checkpoints. Figure 3 shows two such instances. In both cases the fault rate is 0 and the checkpointing costs are and In the top plot 0, and in the bottom plot 3. The plots show the state size of the program as a function of the time, and the points where each of the algorithms places the checkpoints. The figure also shows the checkpointing placement of a modified version of the on-line algorithm that is described later in the paper, in Section 5. The top plot shows that both algorithms avoid placing checkpoints when the cost is high, even when there are long intervals of high cost. The difference in the algorithms in this plot is the interval between the checkpoints. The optimal algorithm knows exactly the intervals of low and high cost so it can use them to place the checkpoints with the optimal interval between them. On the other hand, the on-line algorithm does not know when the cost is going to change from low to high, and so it prefers to use intervals which are shorter than the optimal interval when the cost is low, instead of losing the possibility to place a checkpoint with a low cost. The top plot also shows an example where the optimal algorithm places a checkpoint at a point with a high cost, while the on-line algorithm avoids it. In this example the on-line algorithm anticipates a fast change in the state size, and therefore it decides to wait for the small state size and place the checkpoint there. On the other hand, the optimal algorithm knows that the interval is going to be long, and therefore it is better to place a checkpoint in it. The second plot gives an example where the on-line algorithm places a checkpoint with high cost while the optimal algorithm avoids the high cost interval. The optimal algorithm knows the length of the high cost interval, and that it is better not to place a checkpoint in it. On the other hand, the on-line algorithm anticipates that the interval is going to be much longer (because of the value of ), and therefore it concludes that it is better to place a checkpoint in it. 5 Detection of Increase in the State Size So far we have assumed that the program does not have any knowledge about future changes in its state size. While this assumption is generally true, there are some cases when a partial knowledge about the future behavior exists. This partial knowledge can be used to improve the placement strategy. The simplest example about future knowledge is knowledge about changes in the state size just before they occur. When the memory allocation or deallocation functions are called, the program knows that state size is going to change before the change actually occur. Detection of changes in the state size before they occur is important when the state size increases. In this case, it might be beneficial to place a checkpoint with lower cost just before the state size increases. The ability to place a checkpoint just before the state size increases can contribute to the performance of the placement strategy in two ways. When the algorithm can place a checkpoint before the state size increases, it does not have to be over-eager when looking for points with low cost (the drop down in the value of & opt in Figure ). Instead, it can wait until is reached, or the state size is about to change, and place the checkpoint at that time. Also, when a checkpoint is placed before the state size increases, the probability of placing a checkpoint with a large state size gets lower, and thus the checkpointing overhead is smaller. In this section we show how to modify the on-line al-

9 gorithm we presented in Section 3 to include the case of detection of an increase in the state size before they occur. We also compare the modified algorithm performance to the original on-line algorithm and optimal off-line placement on line alg. rise detection optimal alg. 5. The Modified Algorithm overhead ratio In the modified algorithm we add another point in time 0, such that 0. A checkpoint is placed at time, 0, if the state size at is and the state size at is. In other words, if the state size is changing from to during the interval " 0, then a checkpoint is placed just before the change. If a checkpoint is not placed in the interval " 0,then the algorithm continues as the algorithm in Section 3. The analysis of the modified algorithm is essentially the same as the analysis of the original on-line algorithm that was shown in Lemma. Due to space limitation, we omit the analysis of the modified algorithm. The analysis can be found in []. Figure 4 shows the overhead ratio and the optimal 0 and as a function of. The figure shows this values when 0, the possible costs of a checkpoint are and 0 005, and. Figure 4a shows the overhead ratio as a function of for the modified algorithm, the original on-line algorithm and the optimal off-line algorithm. The figure shows that the modified algorithm has a lower overhead ratio then the original on-line algorithm, and its behavior is closer to the optimal algorithm. One of the reasons the modified algorithm performs better than the original on-line algorithm, is that it does not have to be over eager when looking for points with a small state size. The original algorithm does not know when the state size is going to increase, therefore, in order not to lose the small state size, it places checkpoints before the optimal interval for the small state size is reached. On the other hand, the modified algorithm can wait until just before the state size changes or is reached before it places a checkpoint, because it knows about the change in the state size before it occurs. Also, when the algorithm knows that the state size is going to increase, it is sometimes better to place a checkpoint after a short interval, specially when is low. Therefore, the optimal value for 0 for the modified algorithm is lower then the optimal value for in the original algorithm. Figure 4b shows the optimal values of 0 and for the modified algorithm as a function of, and for comparison the optimal value of for the original algorithm. The figure confirms that the optimal value of for the modified algorithm is equal to, and the drop in the value of & opt that occur in the original algorithm to avoid losing points with a small state size is not needed in the modified algorithm. optimal t0, t µ (a) Overhead ratio optimal t with rise detection optimal t without rise detection optimal t0 with rise detection µ (b) Optimal Intervals Figure 4. Overhead ratio and optimal 0 and for the modified algorithm The figure also shows that for low values of, when the average time before the state size changes from to is high, it is beneficial to place checkpoints with a very short interval between them to use the small state size. As the value of gets higher, the value of 0 is also getting higher, until it reaches for very high values of. The placement examples that are shown in Figure 3 also help to illustrate the advantages of the modified algorithm over the original algorithm. The plots in the figure show two instances of changes in the state size, and the points where the on-line algorithm, the modified on-line algorithm and the optimal algorithm placed their checkpoints. The figure shows that during long periods of small state size, the modified algorithm places its checkpoints with the same intervals as the optimal algorithm, while the original algorithm uses smaller intervals. Another advantage that the modified algorithm has on the original algorithm is that it can sometime avoid checkpoints with large state size, as can be seen in the bottom plot of Figure 3. Because the modified algorithm places checkpoints just before the state size increases, the probability that the state size will not change to before is smaller than the same probability in the original algorithm that places the checkpoint some time before the state size increases.

### AN OPTIMAL N-JOB BACKUP POLICY MAXIMIZING AVAILABILITY FOR A HARD COMPUTER DISK

Journal of the Operations Research Society of Japan Vo!. 34, No. 4, December 1991 1991 The Operations Research Society of Japan AN OPTIMAL N-JOB BACKUP POLICY MAXIMIZING AVAILABILITY FOR A HARD COMPUTER

### Cloud Storage and Online Bin Packing

Cloud Storage and Online Bin Packing Doina Bein, Wolfgang Bein, and Swathi Venigella Abstract We study the problem of allocating memory of servers in a data center based on online requests for storage.

### Analysis of a Production/Inventory System with Multiple Retailers

Analysis of a Production/Inventory System with Multiple Retailers Ann M. Noblesse 1, Robert N. Boute 1,2, Marc R. Lambrecht 1, Benny Van Houdt 3 1 Research Center for Operations Management, University

Single-Period Balancing of Pay Per-Click and Pay-Per-View Online Display Advertisements Changhyun Kwon Department of Industrial and Systems Engineering University at Buffalo, the State University of New

### Implementing an Approximate Probabilistic Algorithm for Error Recovery in Concurrent Processing Systems

Implementing an Approximate Probabilistic Algorithm for Error Recovery in Concurrent Processing Systems Dr. Silvia Heubach Dr. Raj S. Pamula Department of Mathematics and Computer Science California State

### APPENDIX 1 USER LEVEL IMPLEMENTATION OF PPATPAN IN LINUX SYSTEM

152 APPENDIX 1 USER LEVEL IMPLEMENTATION OF PPATPAN IN LINUX SYSTEM A1.1 INTRODUCTION PPATPAN is implemented in a test bed with five Linux system arranged in a multihop topology. The system is implemented

### Universal hashing. In other words, the probability of a collision for two different keys x and y given a hash function randomly chosen from H is 1/m.

Universal hashing No matter how we choose our hash function, it is always possible to devise a set of keys that will hash to the same slot, making the hash scheme perform poorly. To circumvent this, we

### Response time behavior of distributed voting algorithms for managing replicated data

Information Processing Letters 75 (2000) 247 253 Response time behavior of distributed voting algorithms for managing replicated data Ing-Ray Chen a,, Ding-Chau Wang b, Chih-Ping Chu b a Department of

### Efficient Fault-Tolerant Infrastructure for Cloud Computing

Efficient Fault-Tolerant Infrastructure for Cloud Computing Xueyuan Su Candidate for Ph.D. in Computer Science, Yale University December 2013 Committee Michael J. Fischer (advisor) Dana Angluin James Aspnes

### Conjugating data mood and tenses: Simple past, infinite present, fast continuous, simpler imperative, conditional future perfect

Matteo Migliavacca (mm53@kent) School of Computing Conjugating data mood and tenses: Simple past, infinite present, fast continuous, simpler imperative, conditional future perfect Simple past - Traditional

### A Petri Net Model for High Availability in Virtualized Local Disaster Recovery

2011 International Conference on Information Communication and Management IPCSIT vol.16 (2011) (2011) IACSIT Press, Singapore A Petri Net Model for High Availability in Virtualized Local Disaster Recovery

### Alok Gupta. Dmitry Zhdanov

RESEARCH ARTICLE GROWTH AND SUSTAINABILITY OF MANAGED SECURITY SERVICES NETWORKS: AN ECONOMIC PERSPECTIVE Alok Gupta Department of Information and Decision Sciences, Carlson School of Management, University

### A Simple Model of Price Dispersion *

Federal Reserve Bank of Dallas Globalization and Monetary Policy Institute Working Paper No. 112 http://www.dallasfed.org/assets/documents/institute/wpapers/2012/0112.pdf A Simple Model of Price Dispersion

### Supplement to Call Centers with Delay Information: Models and Insights

Supplement to Call Centers with Delay Information: Models and Insights Oualid Jouini 1 Zeynep Akşin 2 Yves Dallery 1 1 Laboratoire Genie Industriel, Ecole Centrale Paris, Grande Voie des Vignes, 92290

### Real Time Network Server Monitoring using Smartphone with Dynamic Load Balancing

www.ijcsi.org 227 Real Time Network Server Monitoring using Smartphone with Dynamic Load Balancing Dhuha Basheer Abdullah 1, Zeena Abdulgafar Thanoon 2, 1 Computer Science Department, Mosul University,

### PERFORMANCE STUDY AND SIMULATION OF AN ANYCAST PROTOCOL FOR WIRELESS MOBILE AD HOC NETWORKS

PERFORMANCE STUDY AND SIMULATION OF AN ANYCAST PROTOCOL FOR WIRELESS MOBILE AD HOC NETWORKS Reza Azizi Engineering Department, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran reza.azizi@bojnourdiau.ac.ir

### Lecture 4 Online and streaming algorithms for clustering

CSE 291: Geometric algorithms Spring 2013 Lecture 4 Online and streaming algorithms for clustering 4.1 On-line k-clustering To the extent that clustering takes place in the brain, it happens in an on-line

### The assignment of chunk size according to the target data characteristics in deduplication backup system

The assignment of chunk size according to the target data characteristics in deduplication backup system Mikito Ogata Norihisa Komoda Hitachi Information and Telecommunication Engineering, Ltd. 781 Sakai,

### A Programme Implementation of Several Inventory Control Algorithms

BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume, No Sofia 20 A Programme Implementation of Several Inventory Control Algorithms Vladimir Monov, Tasho Tashev Institute of Information

### 2.3 Scheduling jobs on identical parallel machines

2.3 Scheduling jobs on identical parallel machines There are jobs to be processed, and there are identical machines (running in parallel) to which each job may be assigned Each job = 1,,, must be processed

### The Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy

BMI Paper The Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy Faculty of Sciences VU University Amsterdam De Boelelaan 1081 1081 HV Amsterdam Netherlands Author: R.D.R.

### FAULT TOLERANCE FOR MULTIPROCESSOR SYSTEMS VIA TIME REDUNDANT TASK SCHEDULING

FAULT TOLERANCE FOR MULTIPROCESSOR SYSTEMS VIA TIME REDUNDANT TASK SCHEDULING Hussain Al-Asaad and Alireza Sarvi Department of Electrical & Computer Engineering University of California Davis, CA, U.S.A.

### FPGA area allocation for parallel C applications

1 FPGA area allocation for parallel C applications Vlad-Mihai Sima, Elena Moscu Panainte, Koen Bertels Computer Engineering Faculty of Electrical Engineering, Mathematics and Computer Science Delft University

Adaptive Online Gradient Descent Peter L Bartlett Division of Computer Science Department of Statistics UC Berkeley Berkeley, CA 94709 bartlett@csberkeleyedu Elad Hazan IBM Almaden Research Center 650

### On Admission Control Policy for Multi-tasking Live-chat Service Agents Research-in-progress Paper

On Admission Control Policy for Multi-tasking Live-chat Service Agents Research-in-progress Paper Paulo Goes Dept. of Management Information Systems Eller College of Management, The University of Arizona,

### Effective Page Refresh Policies For Web Crawlers

Effective Page Refresh Policies For Web Crawlers JUNGHOO CHO University of California, Los Angeles and HECTOR GARCIA-MOLINA Stanford University In this paper we study how we can maintain local copies of

### Chapter 14: Recovery System

Chapter 14: Recovery System Chapter 14: Recovery System Failure Classification Storage Structure Recovery and Atomicity Log-Based Recovery Remote Backup Systems Failure Classification Transaction failure

### The CUSUM algorithm a small review. Pierre Granjon

The CUSUM algorithm a small review Pierre Granjon June, 1 Contents 1 The CUSUM algorithm 1.1 Algorithm............................... 1.1.1 The problem......................... 1.1. The different steps......................

### How Useful Is Old Information?

6 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 11, NO. 1, JANUARY 2000 How Useful Is Old Information? Michael Mitzenmacher AbstractÐWe consider the problem of load balancing in dynamic distributed

### OPTIMIZATION OF DATABASE STRUCTURE FOR HYDROMETEOROLOGICAL MONITORING SYSTEM

OPTIMIZATION OF DATABASE STRUCTURE FOR HYDROMETEOROLOGICAL MONITORING SYSTEM Ph.D. Robert SZCZEPANEK Cracow University of Technology Institute of Water Engineering and Water Management ul.warszawska 24,

### Fairness in Routing and Load Balancing

Fairness in Routing and Load Balancing Jon Kleinberg Yuval Rabani Éva Tardos Abstract We consider the issue of network routing subject to explicit fairness conditions. The optimization of fairness criteria

### CHAPTER 8 CONCLUSION AND FUTURE ENHANCEMENTS

137 CHAPTER 8 CONCLUSION AND FUTURE ENHANCEMENTS 8.1 CONCLUSION In this thesis, efficient schemes have been designed and analyzed to control congestion and distribute the load in the routing process of

### Online and Offline Selling in Limit Order Markets

Online and Offline Selling in Limit Order Markets Kevin L. Chang 1 and Aaron Johnson 2 1 Yahoo Inc. klchang@yahoo-inc.com 2 Yale University ajohnson@cs.yale.edu Abstract. Completely automated electronic

### Resilient Cloud Services

Resilient Cloud Services By Hemayamini Kurra, Glynis Dsouza, Youssif Al Nasshif, Salim Hariri University of Arizona First Franco-American Workshop on Cybersecurity 18 th October, 2013 Presentation Outline

### Examining Self-Similarity Network Traffic intervals

Examining Self-Similarity Network Traffic intervals Hengky Susanto Byung-Guk Kim Computer Science Department University of Massachusetts at Lowell {hsusanto, kim}@cs.uml.edu Abstract Many studies have

### LECTURE 15: AMERICAN OPTIONS

LECTURE 15: AMERICAN OPTIONS 1. Introduction All of the options that we have considered thus far have been of the European variety: exercise is permitted only at the termination of the contract. These

### Load Balancing and Switch Scheduling

EE384Y Project Final Report Load Balancing and Switch Scheduling Xiangheng Liu Department of Electrical Engineering Stanford University, Stanford CA 94305 Email: liuxh@systems.stanford.edu Abstract Load

### Ecient approximation algorithm for minimizing makespan. on uniformly related machines. Chandra Chekuri. November 25, 1997.

Ecient approximation algorithm for minimizing makespan on uniformly related machines Chandra Chekuri November 25, 1997 Abstract We obtain a new ecient approximation algorithm for scheduling precedence

### Hydrodynamic Limits of Randomized Load Balancing Networks

Hydrodynamic Limits of Randomized Load Balancing Networks Kavita Ramanan and Mohammadreza Aghajani Brown University Stochastic Networks and Stochastic Geometry a conference in honour of François Baccelli

### Probability distributions

Probability distributions (Notes are heavily adapted from Harnett, Ch. 3; Hayes, sections 2.14-2.19; see also Hayes, Appendix B.) I. Random variables (in general) A. So far we have focused on single events,

### Estimating Frequency of Change

Estimating Frequency of Change Junghoo Cho University of California, LA and Hector Garcia-Molina Stanford University Many online data sources are updated autonomously and independently. In this paper,

### OPTIMAL DESIGN OF A MULTITIER REWARD SCHEME. Amir Gandomi *, Saeed Zolfaghari **

OPTIMAL DESIGN OF A MULTITIER REWARD SCHEME Amir Gandomi *, Saeed Zolfaghari ** Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Ontario * Tel.: + 46 979 5000x7702, Email:

### Load Balancing with Migration Penalties

Load Balancing with Migration Penalties Vivek F Farias, Ciamac C Moallemi, and Balaji Prabhakar Electrical Engineering, Stanford University, Stanford, CA 9435, USA Emails: {vivekf,ciamac,balaji}@stanfordedu

### A Note on Maximum Independent Sets in Rectangle Intersection Graphs

A Note on Maximum Independent Sets in Rectangle Intersection Graphs Timothy M. Chan School of Computer Science University of Waterloo Waterloo, Ontario N2L 3G1, Canada tmchan@uwaterloo.ca September 12,

### Queueing Networks with Blocking - An Introduction -

Queueing Networks with Blocking - An Introduction - Jonatha ANSELMI anselmi@elet.polimi.it 5 maggio 006 Outline Blocking Blocking Mechanisms (BAS, BBS, RS) Approximate Analysis - MSS Basic Notation We

### Optimal Stopping in Software Testing

Optimal Stopping in Software Testing Nilgun Morali, 1 Refik Soyer 2 1 Department of Statistics, Dokuz Eylal Universitesi, Turkey 2 Department of Management Science, The George Washington University, 2115

### SOFTWARE PERFORMANCE EVALUATION ALGORITHM EXPERIMENT FOR IN-HOUSE SOFTWARE USING INTER-FAILURE DATA

I.J.E.M.S., VOL.3(2) 2012: 99-104 ISSN 2229-6425 SOFTWARE PERFORMANCE EVALUATION ALGORITHM EXPERIMENT FOR IN-HOUSE SOFTWARE USING INTER-FAILURE DATA *Jimoh, R. G. & Abikoye, O. C. Computer Science Department,

### Method for detecting software anomalies based on recurrence plot analysis

Journal of Theoretical and Applied Computer Science Vol. 6, No. 1, 2012, pp. 3-12 ISSN 2299-2634 http://www.jtacs.org Method for detecting software anomalies based on recurrence plot analysis Michał Mosdorf

### Competitive Analysis of On line Randomized Call Control in Cellular Networks

Competitive Analysis of On line Randomized Call Control in Cellular Networks Ioannis Caragiannis Christos Kaklamanis Evi Papaioannou Abstract In this paper we address an important communication issue arising

### Real-Time Scheduling 1 / 39

Real-Time Scheduling 1 / 39 Multiple Real-Time Processes A runs every 30 msec; each time it needs 10 msec of CPU time B runs 25 times/sec for 15 msec C runs 20 times/sec for 5 msec For our equation, A

### Dynamic Multi-User Load Balancing in Distributed Systems

Dynamic Multi-User Load Balancing in Distributed Systems Satish Penmatsa and Anthony T. Chronopoulos The University of Texas at San Antonio Dept. of Computer Science One UTSA Circle, San Antonio, Texas

### Analysis of Compression Algorithms for Program Data

Analysis of Compression Algorithms for Program Data Matthew Simpson, Clemson University with Dr. Rajeev Barua and Surupa Biswas, University of Maryland 12 August 3 Abstract Insufficient available memory

### On-line Scheduling of Real-time Services for Cloud Computing

On-line Scheduling of Real-time Services for Cloud Computing Shuo Liu Gang Quan Electrical and Computer Engineering Department Florida International University Miami, FL, 33174 {sliu5, gang.quan}@fiu.edu

### Lecture 6 Online and streaming algorithms for clustering

CSE 291: Unsupervised learning Spring 2008 Lecture 6 Online and streaming algorithms for clustering 6.1 On-line k-clustering To the extent that clustering takes place in the brain, it happens in an on-line

### Chapter 21: The Discounted Utility Model

Chapter 21: The Discounted Utility Model 21.1: Introduction This is an important chapter in that it introduces, and explores the implications of, an empirically relevant utility function representing intertemporal

### Probability Using Dice

Using Dice One Page Overview By Robert B. Brown, The Ohio State University Topics: Levels:, Statistics Grades 5 8 Problem: What are the probabilities of rolling various sums with two dice? How can you

### Optimal proportional reinsurance and dividend pay-out for insurance companies with switching reserves

Optimal proportional reinsurance and dividend pay-out for insurance companies with switching reserves Abstract: This paper presents a model for an insurance company that controls its risk and dividend

### Copyright. Network and Protocol Simulation. What is simulation? What is simulation? What is simulation? What is simulation?

Copyright Network and Protocol Simulation Michela Meo Maurizio M. Munafò Michela.Meo@polito.it Maurizio.Munafo@polito.it Quest opera è protetta dalla licenza Creative Commons NoDerivs-NonCommercial. Per

### Using simulation to calculate the NPV of a project

Using simulation to calculate the NPV of a project Marius Holtan Onward Inc. 5/31/2002 Monte Carlo simulation is fast becoming the technology of choice for evaluating and analyzing assets, be it pure financial

### Optimal Bandwidth Reservation Schedule in Cellular Networks

Optimal Bandwidth Reservation Schedule in Cellular Networks Samrat Ganguly Badri Nath Navin Goyal Department of Computer Science Rutgers University, Piscataway, NJ, 8852 Abstract Efficient bandwidth allocation

### Review: The ACID properties

Recovery Review: The ACID properties A tomicity: All actions in the Xaction happen, or none happen. C onsistency: If each Xaction is consistent, and the DB starts consistent, it ends up consistent. I solation:

### Risk Management for IT Security: When Theory Meets Practice

Risk Management for IT Security: When Theory Meets Practice Anil Kumar Chorppath Technical University of Munich Munich, Germany Email: anil.chorppath@tum.de Tansu Alpcan The University of Melbourne Melbourne,

### Data Storage - II: Efficient Usage & Errors

Data Storage - II: Efficient Usage & Errors Week 10, Spring 2005 Updated by M. Naci Akkøk, 27.02.2004, 03.03.2005 based upon slides by Pål Halvorsen, 12.3.2002. Contains slides from: Hector Garcia-Molina

### Operating Systems, 6 th ed. Test Bank Chapter 7

True / False Questions: Chapter 7 Memory Management 1. T / F In a multiprogramming system, main memory is divided into multiple sections: one for the operating system (resident monitor, kernel) and one

### C. Wohlin, "Managing Software Quality through Incremental Development and Certification", In Building Quality into Software, pp. 187-202, edited by

C. Wohlin, "Managing Software Quality through Incremental Development and Certification", In Building Quality into Software, pp. 187-202, edited by M. Ross, C. A. Brebbia, G. Staples and J. Stapleton,

### Nimble Storage Best Practices for Microsoft SQL Server

BEST PRACTICES GUIDE: Nimble Storage Best Practices for Microsoft SQL Server Summary Microsoft SQL Server databases provide the data storage back end for mission-critical applications. Therefore, it s

### MAXIMUM LIKELIHOOD ESTIMATES FOR THE HYPERGEOMETRIC SOFTWARE RELIABILITY MODEL

International Journal of Reliability, Quality and Safety Engineering c World Scientific Publishing Company MAXIMUM LIKELIHOOD ESTIMATES FOR THE HYPERGEOMETRIC SOFTWARE RELIABILITY MODEL FRANK PADBERG Fakultät

### 6. Storage and File Structures

ECS-165A WQ 11 110 6. Storage and File Structures Goals Understand the basic concepts underlying different storage media, buffer management, files structures, and organization of records in files. Contents

### Offline 1-Minesweeper is NP-complete

Offline 1-Minesweeper is NP-complete James D. Fix Brandon McPhail May 24 Abstract We use Minesweeper to illustrate NP-completeness proofs, arguments that establish the hardness of solving certain problems.

### Cost-Performance of Fault Tolerance in Cloud Computing

Cost-Performance of Fault Tolerance in Cloud Computing Y.M. Teo,2, B.L. Luong, Y. Song 2 and T. Nam 3 Department of Computer Science, National University of Singapore 2 Shanghai Advanced Research Institute,

### MapReduce and Distributed Data Analysis. Sergei Vassilvitskii Google Research

MapReduce and Distributed Data Analysis Google Research 1 Dealing With Massive Data 2 2 Dealing With Massive Data Polynomial Memory Sublinear RAM Sketches External Memory Property Testing 3 3 Dealing With

### High Availability Essentials

High Availability Essentials Introduction Ascent Capture s High Availability Support feature consists of a number of independent components that, when deployed in a highly available computer system, result

### A COOL AND PRACTICAL ALTERNATIVE TO TRADITIONAL HASH TABLES

A COOL AND PRACTICAL ALTERNATIVE TO TRADITIONAL HASH TABLES ULFAR ERLINGSSON, MARK MANASSE, FRANK MCSHERRY MICROSOFT RESEARCH SILICON VALLEY MOUNTAIN VIEW, CALIFORNIA, USA ABSTRACT Recent advances in the

### An Eective Load Balancing Policy for

An Eective Load Balancing Policy for Geometric Decaying Algorithms Joseph Gil y Dept. of Computer Science The Technion, Israel Technion City, Haifa 32000 ISRAEL Yossi Matias z AT&T Bell Laboratories 600

Online Adwords Allocation Shoshana Neuburger May 6, 2009 1 Overview Many search engines auction the advertising space alongside search results. When Google interviewed Amin Saberi in 2004, their advertisement

### Chapter 13 File and Database Systems

Chapter 13 File and Database Systems Outline 13.1 Introduction 13.2 Data Hierarchy 13.3 Files 13.4 File Systems 13.4.1 Directories 13.4. Metadata 13.4. Mounting 13.5 File Organization 13.6 File Allocation

### Chapter 13 File and Database Systems

Chapter 13 File and Database Systems Outline 13.1 Introduction 13.2 Data Hierarchy 13.3 Files 13.4 File Systems 13.4.1 Directories 13.4. Metadata 13.4. Mounting 13.5 File Organization 13.6 File Allocation

### A Near-linear Time Constant Factor Algorithm for Unsplittable Flow Problem on Line with Bag Constraints

A Near-linear Time Constant Factor Algorithm for Unsplittable Flow Problem on Line with Bag Constraints Venkatesan T. Chakaravarthy, Anamitra R. Choudhury, and Yogish Sabharwal IBM Research - India, New

### Completion Time Scheduling and the WSRPT Algorithm

Completion Time Scheduling and the WSRPT Algorithm Bo Xiong, Christine Chung Department of Computer Science, Connecticut College, New London, CT {bxiong,cchung}@conncoll.edu Abstract. We consider the online

### SPARE PARTS INVENTORY SYSTEMS UNDER AN INCREASING FAILURE RATE DEMAND INTERVAL DISTRIBUTION

SPARE PARS INVENORY SYSEMS UNDER AN INCREASING FAILURE RAE DEMAND INERVAL DISRIBUION Safa Saidane 1, M. Zied Babai 2, M. Salah Aguir 3, Ouajdi Korbaa 4 1 National School of Computer Sciences (unisia),

### NOVEL PRIORITISED EGPRS MEDIUM ACCESS REGIME FOR REDUCED FILE TRANSFER DELAY DURING CONGESTED PERIODS

NOVEL PRIORITISED EGPRS MEDIUM ACCESS REGIME FOR REDUCED FILE TRANSFER DELAY DURING CONGESTED PERIODS D. Todinca, P. Perry and J. Murphy Dublin City University, Ireland ABSTRACT The goal of this paper

### Personal Software Process (PSP)

Personal Software Process (PSP) Application of CMM principles to individuals Developed by Watts Humphrey of the Software Engineering Institute (SEI) in the early 1990s Extensive supporting materials: books,

### IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 51, NO. 7, JULY 2005 2475. G. George Yin, Fellow, IEEE, and Vikram Krishnamurthy, Fellow, IEEE

IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 51, NO. 7, JULY 2005 2475 LMS Algorithms for Tracking Slow Markov Chains With Applications to Hidden Markov Estimation and Adaptive Multiuser Detection G.

### A Game Theoretical Framework for Adversarial Learning

A Game Theoretical Framework for Adversarial Learning Murat Kantarcioglu University of Texas at Dallas Richardson, TX 75083, USA muratk@utdallas Chris Clifton Purdue University West Lafayette, IN 47907,

### OPTIMAL SEQUENTIAL BACKUP STRATEGY UNDER CONSTRAINED RESOURCES

Journal of the Operations Research Society of Japan Vol. 42, No. 4, December 1999 1999 The Operations Research Society of Japan OPTIMAL SEQUENTIAL BACKUP STRATEGY UNDER CONSTRAINED RESOURCES Toshio Hamada

### MAS 200. MAS 200 for SQL Server Introduction and Overview

MAS 200 MAS 200 for SQL Server Introduction and Overview March 2005 1 TABLE OF CONTENTS Introduction... 3 Business Applications and Appropriate Technology... 3 Industry Standard...3 Rapid Deployment...4

### A Short Look on Power Saving Mechanisms in the Wireless LAN Standard Draft IEEE 802.11

A Short Look on Power Saving Mechanisms in the Wireless LAN Standard Draft IEEE 802.11 Christian Röhl, Hagen Woesner, Adam Wolisz * Technical University Berlin Telecommunication Networks Group {roehl,

### Operatin g Systems: Internals and Design Principle s. Chapter 10 Multiprocessor and Real-Time Scheduling Seventh Edition By William Stallings

Operatin g Systems: Internals and Design Principle s Chapter 10 Multiprocessor and Real-Time Scheduling Seventh Edition By William Stallings Operating Systems: Internals and Design Principles Bear in mind,

### 2DI36 Statistics. 2DI36 Part II (Chapter 7 of MR)

2DI36 Statistics 2DI36 Part II (Chapter 7 of MR) What Have we Done so Far? Last time we introduced the concept of a dataset and seen how we can represent it in various ways But, how did this dataset came

### Approximating the Partition Function by Deleting and then Correcting for Model Edges

Approximating the Partition Function by Deleting and then Correcting for Model Edges Arthur Choi and Adnan Darwiche Computer Science Department University of California, Los Angeles Los Angeles, CA 995

### Compression algorithm for Bayesian network modeling of binary systems

Compression algorithm for Bayesian network modeling of binary systems I. Tien & A. Der Kiureghian University of California, Berkeley ABSTRACT: A Bayesian network (BN) is a useful tool for analyzing the

### Recovery System C H A P T E R16. Practice Exercises

C H A P T E R16 Recovery System Practice Exercises 16.1 Explain why log records for transactions on the undo-list must be processed in reverse order, whereas redo is performed in a forward direction. Answer:

### A Comparative Performance Analysis of Load Balancing Algorithms in Distributed System using Qualitative Parameters

A Comparative Performance Analysis of Load Balancing Algorithms in Distributed System using Qualitative Parameters Abhijit A. Rajguru, S.S. Apte Abstract - A distributed system can be viewed as a collection

### A Model For Revelation Of Data Leakage In Data Distribution

A Model For Revelation Of Data Leakage In Data Distribution Saranya.R Assistant Professor, Department Of Computer Science and Engineering Lord Jegannath college of Engineering and Technology Nagercoil,

### Performance metrics for parallel systems

Performance metrics for parallel systems S.S. Kadam C-DAC, Pune sskadam@cdac.in C-DAC/SECG/2006 1 Purpose To determine best parallel algorithm Evaluate hardware platforms Examine the benefits from parallelism

### A Determination of g, the Acceleration Due to Gravity, from Newton's Laws of Motion

A Determination of g, the Acceleration Due to Gravity, from Newton's Laws of Motion Objective In the experiment you will determine the cart acceleration, a, and the friction force, f, experimentally for

### 90 Digital igaming Reports: SEO Risk in igaming. Risk of being hit by a Google update has doubled in 17 months. June 2013 - Gillis van den Broeke

90 Digital igaming Reports: SEO Risk in igaming Risk of being hit by a Google update has doubled in 17 months June 2013 - Gillis van den Broeke About the Author Gillis van den Broeke is the Director of

### 2: Computer Performance

2: Computer Performance http://people.sc.fsu.edu/ jburkardt/presentations/ fdi 2008 lecture2.pdf... John Information Technology Department Virginia Tech... FDI Summer Track V: Parallel Programming 10-12