This document discusses recovery management and concurrency controls in databases. It defines recovery management as the process of restoring a database to its most recent consistent state after a failure. There are three states in recovery: pre-condition (consistent), condition (failure occurs), and post-condition (restore to pre-failure state). The types of failures are transaction, system, and media failures. Concurrency control manages simultaneous transactions to maintain consistency and prevent issues like lost updates, temporary updates, and incorrect summaries that can occur from concurrent execution.
A presentation on different CPU scheduling algorithms such as SJF, RR and FIFO detailed explanation with advantages and disadvantages of each algorithm. This ppt also contains brief information about the multiprocessor scheduling and the performance evaluation of Scheduling algorithms.
Operating Systems Process Scheduling Algorithmssathish sak
CPU scheduling big area of research in early ‘70s
Many implicit assumptions for CPU scheduling:
One program per user
One thread per program
Programs are independent
These are unrealistic but simplify the problem
Does “fair” mean fairness among users or programs?
If I run one compilation job and you run five, do you get five times as much CPU?
Often times, yes!
Goal: dole out CPU time to optimize some desired parameters of the system.
A presentation on different CPU scheduling algorithms such as SJF, RR and FIFO detailed explanation with advantages and disadvantages of each algorithm. This ppt also contains brief information about the multiprocessor scheduling and the performance evaluation of Scheduling algorithms.
Operating Systems Process Scheduling Algorithmssathish sak
CPU scheduling big area of research in early ‘70s
Many implicit assumptions for CPU scheduling:
One program per user
One thread per program
Programs are independent
These are unrealistic but simplify the problem
Does “fair” mean fairness among users or programs?
If I run one compilation job and you run five, do you get five times as much CPU?
Often times, yes!
Goal: dole out CPU time to optimize some desired parameters of the system.
This presentation describes about the various memory allocation methods like first fit, best fit and worst fit in memory management and also about fragmentation problem and solution for the problem.
The Least Cost Method is another method used to obtain the initial feasible solution for the transportation problem. Here, the allocation begins with the cell which has the minimum cost. The lower cost cells are chosen over the higher-cost cell with the objective to have the least cost of transportation.
Lecture notes of production & operation managementComplaint2015
Lectures notes
On
Production and Operation Management
Prepared by
Dr. Sarojrani Pattnaik
Dr. Swagatika Mishra
Assistant Professor
Department of Mechanical Engineering
VSSUT Burla
.
This presentation explains about the Operations Management concept Reorder point, different cases with examples, fixed order interval model, single period model etc.
Queuing is the common activity of customers or people to avail the desired service, which could be processed or distributed one at a time. Bank ATMs would avoid losing their customers due to a long wait on the line. The bank initially provides one ATM in every branch. But, one ATM would not serve a purpose when customers withdraw to use ATM and try to use other bank ATM. Thus the service time needs to be improved to maintain the customers. This paper shows that the queuing theory used to solve this problem. We obtain the data from a bank ATM in a city. We then derive the arrival rate, service rate, utilization rate, waiting time in the queue and the average number of customers in the queue based on the data using Little’s theorem and M/M/I queuing model. The arrival rate at a bank ATM on Sunday during banking time is 1 customer per minute (cpm) while the service rate is 1.50 cpm. The average number of customer in the ATM is 2 and the utilization period is 0.70. We conclude the paper by discussing the benefits of performing queuing analysis to a busy ATM.
This presentation describes about the various memory allocation methods like first fit, best fit and worst fit in memory management and also about fragmentation problem and solution for the problem.
The Least Cost Method is another method used to obtain the initial feasible solution for the transportation problem. Here, the allocation begins with the cell which has the minimum cost. The lower cost cells are chosen over the higher-cost cell with the objective to have the least cost of transportation.
Lecture notes of production & operation managementComplaint2015
Lectures notes
On
Production and Operation Management
Prepared by
Dr. Sarojrani Pattnaik
Dr. Swagatika Mishra
Assistant Professor
Department of Mechanical Engineering
VSSUT Burla
.
This presentation explains about the Operations Management concept Reorder point, different cases with examples, fixed order interval model, single period model etc.
Queuing is the common activity of customers or people to avail the desired service, which could be processed or distributed one at a time. Bank ATMs would avoid losing their customers due to a long wait on the line. The bank initially provides one ATM in every branch. But, one ATM would not serve a purpose when customers withdraw to use ATM and try to use other bank ATM. Thus the service time needs to be improved to maintain the customers. This paper shows that the queuing theory used to solve this problem. We obtain the data from a bank ATM in a city. We then derive the arrival rate, service rate, utilization rate, waiting time in the queue and the average number of customers in the queue based on the data using Little’s theorem and M/M/I queuing model. The arrival rate at a bank ATM on Sunday during banking time is 1 customer per minute (cpm) while the service rate is 1.50 cpm. The average number of customer in the ATM is 2 and the utilization period is 0.70. We conclude the paper by discussing the benefits of performing queuing analysis to a busy ATM.
Process management in Operating System_Unit-2mohanaps
In this PPT Of operating system it covers:
Process Concept; Process Control Block; Process Scheduling; CPU Scheduling - Basic Concepts; Scheduling Algorithms – FIFO; RR; SJF; Multi- level; Multi-level feedback. Process Synchronization and deadlocks: The Critical Section Problem; Synchronization hardware; Semaphores; Classical problems; Deadlock: System model; Characterization; Deadlock prevention; Avoidance and Detection.
The Power of Determinism in Database SystemsDaniel Abadi
Slides for Daniel Abadi talk at UC Berkeley on 10/22/2014. Discusses the problems with traditional database systems, especially around modularity and horizontal scalability, and shows how deterministic database systems can help.
Distributed Deadlock & Recovery Deadlock concept, Deadlock in Centralized systems, Deadlock in Distributed Systems – Detection, Prevention, Avoidance, Wait-Die Algorithm, Wound-Wait algorithm Recovery in DBMS - Types of Failure, Methods to control failure, Different techniques of recoverability, Write- Ahead logging Protocol, Advanced recovery techniques- Shadow Paging, Fuzzy checkpoint, ARIES, RAID levels, Two Phase and Three Phase commit protocols
What is Database Backup? The 3 Important Recovery Techniques from transaction...Raj vardhan
What is Database Backup?
What is Database recovery techniques
Why recovery is needed? (What causes a Transaction to fail?)
The 3 Important Recovery Techniques from transaction failures:
The figure below illustrates the use of Shadow paging techniques:
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
1. Recovery management with
concurrent controls
D. Shanmugapriya
II-Msc (IT)
Department of CS & IT
Nadar saraswathi college of arts and science
Theni.
2. Recovery Management
• Recovery manager is a utility that manages the
process of creating backups of all database files.
• Is the process of restoring the database to the most
recent consistent state that existed just failure the
system.
• Is the process of planning,testing and
implementing the recovery procedures and
standards required to restore service in the event
of a component failure.
3. 3 states of recovery
• Pre-condition:In a consistant state.
• Condition:Occurs some kind of system failure.
• Post condition:Restore the data base to the
consistent state of the existed before the failure.
4. Types
• Transaction failure :Occurs when it fails to execute or when
it reaches a point from where it can’t go any further. If a
few transaction is hurt this is called as transaction
failure.(incorrect IP, deadlock)
• System failure :May result from a hard drive with bad
sectors, causing the operating system to not able to read
data from the hard drive. (is failure, ram failure).
• Media failure :A condition of not being able to read from
or write to a storage device such as a disk or tape due to a
defect in the recording surface. (power problem).
5. Database basic update strategies
• Deferred update:Transaction operating do not immediately
update the physical database.
• Database is physically updated only after the transaction
reaches it’s commit point.
• Immediate update:database is immediately updated by
the transaction operation during the execution of
transactions even before it reaches commit point.
• Example :word-to save the file and immediately updated.
6. Concurrent control
• The process of managing simultaneous operation on the
database without having them interface with each other.
• The problem of synchronizing concurrent transaction
such that the consistency of the database of the
maintained same time.
• Concurrent controls invlove identifying and preventing
problems in a organizing as they occur this means that as
they occur.
7. Why we need concurrency control
• Simultaneous execution of transaction over a shared
database can create several data integrity and consistency
problem.
• Three types
• 1)lost update
• 2)temporary uodate
• 3)incorrect summary
8. Lost update
• This problem occurs when two transactions that access
the same database items.
• Have their operation interlaced in a way that makes the
values of same database item incorrect.
• Successfully completed update is overridden by another
user.
• Example:T1 withdraws $10 from an account with
balance initially $ 100.
• T2 deposits $100 into same accoint
• Serially final balance would be $190.
9. Temporary update
• This problem occurs when one transaction updates a
database item and then the transaction fails for some
reasons.
• The updated item is accessed by another transactions
before it is changed back to its original value.
• Example :T4 updates balance to 200 but it aborts so
balance should be back at original value 100
• T3 has read new value of balance 200 and users value as
basis of 10 reduction giving a new balance of 190 instead
of 90.
10. Advantage
• Waiting time decrease
• Response time decrease.
• Resourse utilization increase.
• Efficiency utilization increase.