Presentation on miscreant jobs in HTCondor presented at HTCondor week 2013. Showing how to reduce the number of bad jobs run and increase the chances of good jobs running quickly.
This document discusses self-learning cloud controllers that can dynamically scale cloud resources. It notes that current auto-scaling approaches require deep application knowledge and expertise to determine scaling parameters and policies. The paper proposes a type-2 fuzzy logic approach called RobusT2Scale that uses fuzzy rules and monitoring data to determine scaling actions. It aims to handle uncertainty in elastic systems and accommodate different user preferences through fuzzy reasoning over workload and response time data. The approach pre-computes scaling decisions to enable efficient runtime elasticity control. It is evaluated based on its ability to meet an SLA target response time compared to over- and under-provisioning approaches.
This document describes HFSP, a fair scheduling protocol for Hadoop that aims to improve performance for interactive jobs. It does so by estimating job sizes and simulating a processor-sharing model to determine job completion order. Key aspects include initial job size estimation that is refined over time, treating map and reduce phases separately, and using OS signals to suspend and resume reduce tasks for preemption instead of waiting or killing tasks. Experiments on Facebook workload traces showed HFSP significantly reduced average job completion times compared to Hadoop's default scheduler, especially for smaller clusters.
Performance Analysis and Optimizations of CAE Applications (Case Study: STAR_...Fisnik Kraja
This document summarizes the results of performance analysis and optimizations done on the STAR-CCM+ application run on different Intel CPU configurations. The analysis showed that the application's performance was highly dependent on CPU frequency (85-88%) and benefited from optimizations like CPU binding, huge pages, and scatter task placement. Comparing CPU types showed the 12-core CPU was 8-9% faster. Hyperthreading had a minimal impact on performance. Turbo Boost was effective but its benefits reduced as fewer cores were utilized.
Performance Optimization of HPC Applications: From Hardware to Source CodeFisnik Kraja
The document summarizes optimization techniques for HPC applications from hardware selection to application code tuning. It describes analyzing application performance, choosing an appropriate system, efficiently using resources, tuning system parameters, and optimizing code. Examples are provided for AVL Fire and OpenFOAM simulations, analyzing scalability, hardware dependencies, and reducing runtime through MPI and system tuning.
Autonomic Resource Provisioning for Cloud-Based SoftwarePooyan Jamshidi
This document proposes using fuzzy logic and type-2 fuzzy sets to develop an autonomous resource provisioning system for cloud-based software. Current auto-scaling solutions have limitations including requiring deep application knowledge and performance modeling expertise from users. The proposed system would use fuzzy inference to map monitored performance data to scaling actions, eliminating the need for users to specify scaling parameters or policies. It would incorporate uncertainty into the modeling and use expert knowledge from multiple users to develop robust and adaptive provisioning behavior.
The document describes research on developing learning strategies for network fault detection and remediation. It presents a Cost-Sensitive Fault Remediation (CSFR) model for planning repair actions while minimizing costs. The objective is to extend this model to optimize a minimax criterion for safer policies when prior probabilities are uncertain. A MiniMax CSFR planning algorithm is developed using a minimax cost function. Action ordering heuristics are also implemented to reduce repair costs. A simulated network environment and CSFR planning and learning agents are developed and evaluated.
Detailed design for a robust counter as well as design for a completely on-line multi-armed bandit implementation that uses the new Bayesian Bandit algorithm.
The document discusses several myths about system performance and CPU utilization. It debunks the myths that CPU requirements are proportional to the number of users, and that the CPU must be upgraded when utilization is above 60%. It explains that response time is determined by I/O service time, I/O queueing delay, CPU service time, and CPU queueing delay. A CPU upgrade can sometimes hurt response times by increasing I/O contention for I/O-intensive users. The system does not truly "lock up" when CPU utilization is 100%; a high priority runaway process can cause the perception of a lock up by starving other processes.
This document discusses self-learning cloud controllers that can dynamically scale cloud resources. It notes that current auto-scaling approaches require deep application knowledge and expertise to determine scaling parameters and policies. The paper proposes a type-2 fuzzy logic approach called RobusT2Scale that uses fuzzy rules and monitoring data to determine scaling actions. It aims to handle uncertainty in elastic systems and accommodate different user preferences through fuzzy reasoning over workload and response time data. The approach pre-computes scaling decisions to enable efficient runtime elasticity control. It is evaluated based on its ability to meet an SLA target response time compared to over- and under-provisioning approaches.
This document describes HFSP, a fair scheduling protocol for Hadoop that aims to improve performance for interactive jobs. It does so by estimating job sizes and simulating a processor-sharing model to determine job completion order. Key aspects include initial job size estimation that is refined over time, treating map and reduce phases separately, and using OS signals to suspend and resume reduce tasks for preemption instead of waiting or killing tasks. Experiments on Facebook workload traces showed HFSP significantly reduced average job completion times compared to Hadoop's default scheduler, especially for smaller clusters.
Performance Analysis and Optimizations of CAE Applications (Case Study: STAR_...Fisnik Kraja
This document summarizes the results of performance analysis and optimizations done on the STAR-CCM+ application run on different Intel CPU configurations. The analysis showed that the application's performance was highly dependent on CPU frequency (85-88%) and benefited from optimizations like CPU binding, huge pages, and scatter task placement. Comparing CPU types showed the 12-core CPU was 8-9% faster. Hyperthreading had a minimal impact on performance. Turbo Boost was effective but its benefits reduced as fewer cores were utilized.
Performance Optimization of HPC Applications: From Hardware to Source CodeFisnik Kraja
The document summarizes optimization techniques for HPC applications from hardware selection to application code tuning. It describes analyzing application performance, choosing an appropriate system, efficiently using resources, tuning system parameters, and optimizing code. Examples are provided for AVL Fire and OpenFOAM simulations, analyzing scalability, hardware dependencies, and reducing runtime through MPI and system tuning.
Autonomic Resource Provisioning for Cloud-Based SoftwarePooyan Jamshidi
This document proposes using fuzzy logic and type-2 fuzzy sets to develop an autonomous resource provisioning system for cloud-based software. Current auto-scaling solutions have limitations including requiring deep application knowledge and performance modeling expertise from users. The proposed system would use fuzzy inference to map monitored performance data to scaling actions, eliminating the need for users to specify scaling parameters or policies. It would incorporate uncertainty into the modeling and use expert knowledge from multiple users to develop robust and adaptive provisioning behavior.
The document describes research on developing learning strategies for network fault detection and remediation. It presents a Cost-Sensitive Fault Remediation (CSFR) model for planning repair actions while minimizing costs. The objective is to extend this model to optimize a minimax criterion for safer policies when prior probabilities are uncertain. A MiniMax CSFR planning algorithm is developed using a minimax cost function. Action ordering heuristics are also implemented to reduce repair costs. A simulated network environment and CSFR planning and learning agents are developed and evaluated.
Detailed design for a robust counter as well as design for a completely on-line multi-armed bandit implementation that uses the new Bayesian Bandit algorithm.
The document discusses several myths about system performance and CPU utilization. It debunks the myths that CPU requirements are proportional to the number of users, and that the CPU must be upgraded when utilization is above 60%. It explains that response time is determined by I/O service time, I/O queueing delay, CPU service time, and CPU queueing delay. A CPU upgrade can sometimes hurt response times by increasing I/O contention for I/O-intensive users. The system does not truly "lock up" when CPU utilization is 100%; a high priority runaway process can cause the perception of a lock up by starving other processes.
Este documento proporciona instrucciones para crear y programar un nuevo proyecto en Microsoft Project 2010, incluyendo cómo definir las propiedades del archivo, abrir un proyecto existente, guardar el proyecto en formato PDF o XPS, y guardar el proyecto con Project Standard 2010. También explica cómo agregar tareas, hitos, duraciones de tareas, dependencias, esquemas de proyecto, calendarios y publicar proyectos.
Google is transitioning Google Shopping from free listings to a paid model called Product Listing Ads. To succeed under the new model, advertisers must set up Product Listing Ad campaigns in Google AdWords. This involves adding fields to product feed data, linking AdWords and merchant accounts, creating campaigns with auto-targeting and bidding. Tracking performance requires modifying the redirect URL field to include analytics parameters to differentiate traffic sources in Google Analytics reports.
1) The document discusses friction and provides examples of static and kinetic friction. It defines the coefficients of static (μs) and kinetic (μk) friction.
2) An example of a block on an inclined plane is used to illustrate static friction. The angle at which the block will start to slide down the plane depends on the coefficient of static friction.
3) Another example examines the range of applied force where a block placed on a horizontal plane will remain in equilibrium. The range depends on the coefficient of static friction and angles of the forces.
Organizations can make better decisions by leveraging business analytics and new decision tools. While organizations have access to more data and information than ever before, few have systematically worked to improve decision making across various business functions. Business analytics allows organizations to incorporate more data and viewpoints into decisions to anticipate outcomes and maximize opportunities. However, most companies have not fully examined current decision processes or investigated new analytics options to enhance decision making. Prioritizing decision improvement and applying analytics can help organizations make more informed choices that positively impact performance and strategy.
1) There is currently weak coordination of open government data (OGD) among different levels of government and across topics in Switzerland.
2) Standardization can help encourage reuse of public data by providing better coordination, clarity on what data should be published, and tools for implementation.
3) The eCH-Group focuses on standardization to improve coordination of OGD, promote reuse of data, and build trust through addressing coordination challenges and promoting integrated data publishing and use systems.
Learning Management System og jobbytelse: En kvalitativ tilnærmingTom Erik Holteng
En hovedfagsavhandling om hvordan forelesere beskriver sine forventninger til jobbrelatert ytelse når de bruker et
Learning Management System (LMS) i forbindelse med undervisning?
The document provides an overview of how social sciences and humanities (SSH) are integrated into Horizon 2020, the EU framework programme for research and innovation from 2014-2020. SSH is supported through various parts of Horizon 2020 including the European Research Council and Marie Skłodowska-Curie actions. SSH is also integrated into the societal challenges and industrial leadership priorities. For the societal challenges, SSH can be embedded in all topics or have dedicated SSH components. Around 26% of topics across the societal challenges are considered SSH relevant. The Societal Challenge 6 on inclusive, innovative and reflective societies has a dedicated budget for SSH-related research.
Este documento presenta la distribución de programas para la Escuela del Aire en el año 2009. Los programas se transmitirán los sábados de febrero a agosto y cubrirán temas relacionados al desarrollo infantil, la educación y el apoyo a padres. Cada programa consta de dos segmentos presentados por diferentes profesores.
Research On Hong Kong Tourism Websites Tiffanie_Chan
This document summarizes research Tiffanie Chan conducted on other Hong Kong tourism websites to improve her own website. She analyzed four websites: Discover Hong Kong, Travel Hong Kong, Trip Advisor Hong Kong, and Hong Kong Tourism Asia. For each site, she identified pros and cons and aspects she could apply to her website, such as using colorful banners and sidebars, providing detailed mall information, and including photos and visitor reviews. The research will help Tiffanie create a better website for her clients.
To hit Ruby3x3, we must first figure out **what** we're going to measure, **how** we're going to measure it, in order to get what we actually want. I'll cover some standard definitions of benchmarking in dynamic languages, as well as the tradeoffs that must be made when benchmarking. I'll look at some of the possible benchmarks that could be considered for Ruby 3x3, and evaluate them for what they're good for measuring, and what they're less good for measuring, in order to help the Ruby community decide what the 3x goal is going to be measured against.
Deadlock happens when two threads are waiting for a mutex owned by the other (circular deadlock between multiple threads is also possible). Therefore, we need to check for deadlock only when a thread fails to lock a mutex. At that point, the Thread Manager needs to suspend all threads and take over to perform a cycle check on mutex dependency. Finding such a cycle is easily done by performing a tree traversal of the dependencies, and marking threads and mutexes along the way. Using this method, we can detect deadlock and identify all threads and mutexes involved in the deadlock.
The document discusses energy-saving policies for grid-computing and smart environments. It analyzes seven energy policies for managing resource states in the Grid'5000 infrastructure to reduce energy consumption. The policies are tested through simulation and evaluated using data envelopment analysis. The best policy was found to save up to 162,000 euros, 318 tons of CO2, and 1,163,286 kWh per year for Grid'5000. Locations and policies are also compared to identify efficiency improvements needed based on the results.
Have you ever wondered how to speed up your code in Python? This presentation will show you how to start. I will begin with a guide how to locate performance bottlenecks and then give you some tips how to speed up your code. Also I would like to discuss how to avoid premature optimization as it may be ‘the root of all evil’ (at least according to D. Knuth).
On the way to low latency (2nd edition)Artem Orobets
This is the second edition of the story about how we struggled to implement strict latency requirements in a service implemented with Java and how we managed to do that.
The most common latency contributors are an in-process locking, thread scheduling, I/O, algorithmic inefficiencies and, of course, garbage collector.
I will share our experience of dealing with the causes. And tell what you can do to prevent them from affecting the production.
Team Backrow proposes using idle computer time at institutions for backend processes like cryptocurrency mining or machine learning tasks. Their system uses a distributed hypervisor architecture with a central supervisor to switch machines between user and research VMs. Simulation results found that a shortest remaining time first plus longest remaining time first scheduling policy completed the most jobs while maintaining equitable distribution of work. The team's system was able to effectively utilize idle computer resources but faced limitations due to the pandemic preventing on-campus testing.
Identifying Optimal Trade-Offs between CPU Time Usage and Temporal ConstraintsLionel Briand
This document describes a method for identifying optimal trade-offs between CPU time usage and temporal constraints in software integration using multi-objective search. The method models the problem as a constrained optimization problem to minimize CPU time usage and number of time slots while satisfying timing constraints. A multi-objective genetic algorithm searches over task offset vectors to find Pareto optimal solutions representing different trade-offs. The approach is evaluated on a large automotive case study with 430 tasks, finding solutions that reduce CPU usage by 60-70% compared to a naive approach.
This document discusses CPU scheduling in operating systems. It covers basic scheduling concepts like multiprogramming and preemptive scheduling. It then describes the role of the scheduler and dispatcher in selecting which process runs on the CPU. Several common scheduling algorithms are explained like first-come first-served, shortest job first, priority scheduling, and round robin. Factors for evaluating scheduling performance and examples of scheduling in Linux and real-time systems are also summarized.
Real-time systems are systems whose specifications include both logical and temporal correctness requirements. They must produce outputs at the right time in addition to producing logically correct outputs. Many embedded systems are real-time systems. Real-time systems have characteristics like being event-driven, having high failure costs, requiring concurrency and reliability. They can be hard real-time systems where all deadlines must be met or soft real-time systems where some missed deadlines are allowed. Real-time operating systems help manage resources and priorities to meet the timing constraints of real-time applications.
dataprocess using different technology.pptssuserf6eb9b
The document discusses various CPU scheduling algorithms used in operating systems. It begins by describing assumptions made in early CPU scheduling research, such as one program per user and independent programs. Common scheduling algorithms are then examined, including first-come, first-served (FCFS), round robin (RR), shortest job first (SJF), and shortest remaining time first (SRTF). The key factors of response time, throughput, and fairness are evaluated for each algorithm. SRTF is shown to provide optimal average response time but is difficult to implement due to inability to accurately predict job lengths. Later sections discuss using historical data to estimate future CPU burst lengths.
Optimizing for performance and reducing latency is a hard problem. Examples could be: choosing a different algorithm and data structures, improving SQL queries, adding a cache, serving requests asynchronously, or some low-level optimization that requires a deep understanding of the OS, kernel, compiler, or the network stack. The engineering effort is usually nontrivial, and only if you're lucky, you'll see some tangible results.
That being said, there are some performance optimization techniques, with a few lines of code — even exist in the built-in library — it can lead to noticeable surprising results. One of these techniques is to "fail fast, retry soon". These techniques are often neglected or taken for granted.
In distributed systems, a service or a database consists of a fleet of nodes that functions as one unit. It is not uncommon for some nodes to go down, usually, for a short time. When this occurs, failures can happen on the client-side and can lead to an outage. To build resilient systems, and reduce the probability of failure, we're going to explore these topics: timeouts, backoff, and jitter. We'll talk about timeouts, what timeout to set, pitfalls of retries, how backoff improves resource utilization, and jitters reduce congestion. Furthermore, we're going to see an adaptive mechanism to dynamically adjust these configurations.
This is inspired by a real-production use case where DynamoDB latency p99 & max went down from > 10s to ~500ms after employing these three techniques: timeouts, backoff, and jitter.
This is inspired by a real-production use case where DynamoDB latency p99 & max went down from > 10s to ~500ms. AWS articles, specifically M. Brooker’s writings, and SDKs code have been great resources to dive into these techniques:
- Timeouts, retries and backoff with jitter in the AWS Builder's Library, 2019 (https://aws.amazon.com/builders-library/timeouts-retries-and-backoff-with-jitter/)
- Exponential Backoff and Jitter on the AWS Architecture Blog, 2016 (https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/)
- Fixing retries with token buckets and circuit breakers, Marc's Blog, 2022 (https://brooker.co.za/blog/2022/02/28/retries.html)
This document provides information about line balancing processes in a textile factory. It begins with an introduction to line balancing and its importance for improving production throughput and reducing costs. It then discusses various line balancing methods like time study analysis, bottleneck identification, and work allotment. Specific steps for balancing a production line are outlined, including determining operator requirements, work-in-process inventory levels, and establishing rules to ensure maximum operator capacity. Formulas for calculating standard minute values and organization efficiency are also presented.
CPU Scheduling is a process of determining which process will own CPU for execution while another process is on hold. The main task of CPU scheduling is to make sure that whenever the CPU remains idle, the OS at least select one of the processes available in the ready queue for execution.
Este documento proporciona instrucciones para crear y programar un nuevo proyecto en Microsoft Project 2010, incluyendo cómo definir las propiedades del archivo, abrir un proyecto existente, guardar el proyecto en formato PDF o XPS, y guardar el proyecto con Project Standard 2010. También explica cómo agregar tareas, hitos, duraciones de tareas, dependencias, esquemas de proyecto, calendarios y publicar proyectos.
Google is transitioning Google Shopping from free listings to a paid model called Product Listing Ads. To succeed under the new model, advertisers must set up Product Listing Ad campaigns in Google AdWords. This involves adding fields to product feed data, linking AdWords and merchant accounts, creating campaigns with auto-targeting and bidding. Tracking performance requires modifying the redirect URL field to include analytics parameters to differentiate traffic sources in Google Analytics reports.
1) The document discusses friction and provides examples of static and kinetic friction. It defines the coefficients of static (μs) and kinetic (μk) friction.
2) An example of a block on an inclined plane is used to illustrate static friction. The angle at which the block will start to slide down the plane depends on the coefficient of static friction.
3) Another example examines the range of applied force where a block placed on a horizontal plane will remain in equilibrium. The range depends on the coefficient of static friction and angles of the forces.
Organizations can make better decisions by leveraging business analytics and new decision tools. While organizations have access to more data and information than ever before, few have systematically worked to improve decision making across various business functions. Business analytics allows organizations to incorporate more data and viewpoints into decisions to anticipate outcomes and maximize opportunities. However, most companies have not fully examined current decision processes or investigated new analytics options to enhance decision making. Prioritizing decision improvement and applying analytics can help organizations make more informed choices that positively impact performance and strategy.
1) There is currently weak coordination of open government data (OGD) among different levels of government and across topics in Switzerland.
2) Standardization can help encourage reuse of public data by providing better coordination, clarity on what data should be published, and tools for implementation.
3) The eCH-Group focuses on standardization to improve coordination of OGD, promote reuse of data, and build trust through addressing coordination challenges and promoting integrated data publishing and use systems.
Learning Management System og jobbytelse: En kvalitativ tilnærmingTom Erik Holteng
En hovedfagsavhandling om hvordan forelesere beskriver sine forventninger til jobbrelatert ytelse når de bruker et
Learning Management System (LMS) i forbindelse med undervisning?
The document provides an overview of how social sciences and humanities (SSH) are integrated into Horizon 2020, the EU framework programme for research and innovation from 2014-2020. SSH is supported through various parts of Horizon 2020 including the European Research Council and Marie Skłodowska-Curie actions. SSH is also integrated into the societal challenges and industrial leadership priorities. For the societal challenges, SSH can be embedded in all topics or have dedicated SSH components. Around 26% of topics across the societal challenges are considered SSH relevant. The Societal Challenge 6 on inclusive, innovative and reflective societies has a dedicated budget for SSH-related research.
Este documento presenta la distribución de programas para la Escuela del Aire en el año 2009. Los programas se transmitirán los sábados de febrero a agosto y cubrirán temas relacionados al desarrollo infantil, la educación y el apoyo a padres. Cada programa consta de dos segmentos presentados por diferentes profesores.
Research On Hong Kong Tourism Websites Tiffanie_Chan
This document summarizes research Tiffanie Chan conducted on other Hong Kong tourism websites to improve her own website. She analyzed four websites: Discover Hong Kong, Travel Hong Kong, Trip Advisor Hong Kong, and Hong Kong Tourism Asia. For each site, she identified pros and cons and aspects she could apply to her website, such as using colorful banners and sidebars, providing detailed mall information, and including photos and visitor reviews. The research will help Tiffanie create a better website for her clients.
To hit Ruby3x3, we must first figure out **what** we're going to measure, **how** we're going to measure it, in order to get what we actually want. I'll cover some standard definitions of benchmarking in dynamic languages, as well as the tradeoffs that must be made when benchmarking. I'll look at some of the possible benchmarks that could be considered for Ruby 3x3, and evaluate them for what they're good for measuring, and what they're less good for measuring, in order to help the Ruby community decide what the 3x goal is going to be measured against.
Deadlock happens when two threads are waiting for a mutex owned by the other (circular deadlock between multiple threads is also possible). Therefore, we need to check for deadlock only when a thread fails to lock a mutex. At that point, the Thread Manager needs to suspend all threads and take over to perform a cycle check on mutex dependency. Finding such a cycle is easily done by performing a tree traversal of the dependencies, and marking threads and mutexes along the way. Using this method, we can detect deadlock and identify all threads and mutexes involved in the deadlock.
The document discusses energy-saving policies for grid-computing and smart environments. It analyzes seven energy policies for managing resource states in the Grid'5000 infrastructure to reduce energy consumption. The policies are tested through simulation and evaluated using data envelopment analysis. The best policy was found to save up to 162,000 euros, 318 tons of CO2, and 1,163,286 kWh per year for Grid'5000. Locations and policies are also compared to identify efficiency improvements needed based on the results.
Have you ever wondered how to speed up your code in Python? This presentation will show you how to start. I will begin with a guide how to locate performance bottlenecks and then give you some tips how to speed up your code. Also I would like to discuss how to avoid premature optimization as it may be ‘the root of all evil’ (at least according to D. Knuth).
On the way to low latency (2nd edition)Artem Orobets
This is the second edition of the story about how we struggled to implement strict latency requirements in a service implemented with Java and how we managed to do that.
The most common latency contributors are an in-process locking, thread scheduling, I/O, algorithmic inefficiencies and, of course, garbage collector.
I will share our experience of dealing with the causes. And tell what you can do to prevent them from affecting the production.
Team Backrow proposes using idle computer time at institutions for backend processes like cryptocurrency mining or machine learning tasks. Their system uses a distributed hypervisor architecture with a central supervisor to switch machines between user and research VMs. Simulation results found that a shortest remaining time first plus longest remaining time first scheduling policy completed the most jobs while maintaining equitable distribution of work. The team's system was able to effectively utilize idle computer resources but faced limitations due to the pandemic preventing on-campus testing.
Identifying Optimal Trade-Offs between CPU Time Usage and Temporal ConstraintsLionel Briand
This document describes a method for identifying optimal trade-offs between CPU time usage and temporal constraints in software integration using multi-objective search. The method models the problem as a constrained optimization problem to minimize CPU time usage and number of time slots while satisfying timing constraints. A multi-objective genetic algorithm searches over task offset vectors to find Pareto optimal solutions representing different trade-offs. The approach is evaluated on a large automotive case study with 430 tasks, finding solutions that reduce CPU usage by 60-70% compared to a naive approach.
This document discusses CPU scheduling in operating systems. It covers basic scheduling concepts like multiprogramming and preemptive scheduling. It then describes the role of the scheduler and dispatcher in selecting which process runs on the CPU. Several common scheduling algorithms are explained like first-come first-served, shortest job first, priority scheduling, and round robin. Factors for evaluating scheduling performance and examples of scheduling in Linux and real-time systems are also summarized.
Real-time systems are systems whose specifications include both logical and temporal correctness requirements. They must produce outputs at the right time in addition to producing logically correct outputs. Many embedded systems are real-time systems. Real-time systems have characteristics like being event-driven, having high failure costs, requiring concurrency and reliability. They can be hard real-time systems where all deadlines must be met or soft real-time systems where some missed deadlines are allowed. Real-time operating systems help manage resources and priorities to meet the timing constraints of real-time applications.
dataprocess using different technology.pptssuserf6eb9b
The document discusses various CPU scheduling algorithms used in operating systems. It begins by describing assumptions made in early CPU scheduling research, such as one program per user and independent programs. Common scheduling algorithms are then examined, including first-come, first-served (FCFS), round robin (RR), shortest job first (SJF), and shortest remaining time first (SRTF). The key factors of response time, throughput, and fairness are evaluated for each algorithm. SRTF is shown to provide optimal average response time but is difficult to implement due to inability to accurately predict job lengths. Later sections discuss using historical data to estimate future CPU burst lengths.
Optimizing for performance and reducing latency is a hard problem. Examples could be: choosing a different algorithm and data structures, improving SQL queries, adding a cache, serving requests asynchronously, or some low-level optimization that requires a deep understanding of the OS, kernel, compiler, or the network stack. The engineering effort is usually nontrivial, and only if you're lucky, you'll see some tangible results.
That being said, there are some performance optimization techniques, with a few lines of code — even exist in the built-in library — it can lead to noticeable surprising results. One of these techniques is to "fail fast, retry soon". These techniques are often neglected or taken for granted.
In distributed systems, a service or a database consists of a fleet of nodes that functions as one unit. It is not uncommon for some nodes to go down, usually, for a short time. When this occurs, failures can happen on the client-side and can lead to an outage. To build resilient systems, and reduce the probability of failure, we're going to explore these topics: timeouts, backoff, and jitter. We'll talk about timeouts, what timeout to set, pitfalls of retries, how backoff improves resource utilization, and jitters reduce congestion. Furthermore, we're going to see an adaptive mechanism to dynamically adjust these configurations.
This is inspired by a real-production use case where DynamoDB latency p99 & max went down from > 10s to ~500ms after employing these three techniques: timeouts, backoff, and jitter.
This is inspired by a real-production use case where DynamoDB latency p99 & max went down from > 10s to ~500ms. AWS articles, specifically M. Brooker’s writings, and SDKs code have been great resources to dive into these techniques:
- Timeouts, retries and backoff with jitter in the AWS Builder's Library, 2019 (https://aws.amazon.com/builders-library/timeouts-retries-and-backoff-with-jitter/)
- Exponential Backoff and Jitter on the AWS Architecture Blog, 2016 (https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/)
- Fixing retries with token buckets and circuit breakers, Marc's Blog, 2022 (https://brooker.co.za/blog/2022/02/28/retries.html)
This document provides information about line balancing processes in a textile factory. It begins with an introduction to line balancing and its importance for improving production throughput and reducing costs. It then discusses various line balancing methods like time study analysis, bottleneck identification, and work allotment. Specific steps for balancing a production line are outlined, including determining operator requirements, work-in-process inventory levels, and establishing rules to ensure maximum operator capacity. Formulas for calculating standard minute values and organization efficiency are also presented.
CPU Scheduling is a process of determining which process will own CPU for execution while another process is on hold. The main task of CPU scheduling is to make sure that whenever the CPU remains idle, the OS at least select one of the processes available in the ready queue for execution.
This document provides an overview of CPU scheduling concepts and algorithms. It discusses key scheduling concepts like multiprogramming and processes. It then covers various scheduling algorithms like first-come first-served, shortest job first, priority-based, and round robin. It also discusses scheduling criteria, multilevel queues, multiple processor scheduling, real-time scheduling, and how scheduling algorithms are evaluated. The goal of scheduling is to optimize criteria like wait time, response time, and throughput.
For more course tutorials visit
Uophelp is now newtonhelp.com
www.newtonhelp.com
1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
The centroid method for plant location uses which of the following dataramuaa128
For more course tutorials visit
Uophelp is now newtonhelp.com
www.newtonhelp.com
1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
Which of the following is an input to the master production schedule (mps)ramuaa130
For more course tutorials visit
Uophelp is now newtonhelp.com
www.newtonhelp.com
1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
In hau lee's uncertainty framework to classify supply chainsramuaa127
This document provides a guide to the OPS 571 Final Exam, including 29 multiple choice practice questions covering topics like operations and supply chain management, production processes, inventory management, project management, forecasting, and more. The questions assess understanding of key concepts and tools used in operations, supply chain, and project management.
This document discusses different CPU scheduling algorithms used in operating systems. It begins by explaining the assumptions made in early CPU scheduling research and goals of scheduling algorithms. It then covers First Come First Served (FCFS) scheduling and provides an example. Next it introduces Round Robin (RR) scheduling and compares it to FCFS. Shortest Job First (SJF) and Shortest Remaining Time First (SRTF) algorithms are presented as optimal approaches but difficult to implement due to lack of knowledge about future job lengths. The document concludes by discussing predicting future job behavior to improve scheduling decisions.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
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Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
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4. Deployment Using ArgoCD for Edge Devices
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5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
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9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
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Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
1. Making
HTCondor
Energy
Efficient
by
iden5fying
miscreant
jobs
Stephen
McGough,
Ma@hew
Forshaw
&
Clive
Gerrard
Newcastle
University
Stuart
Wheater
Arjuna
Technologies
Limited
2. Mo5va5on
Policy
and
Simula5on
Conclusion
Task
lifecycle
5me
HTC
user
submits
task
resource
selec5on
Interac5ve
user
logs
in
Task
evic5on
resource
selec5on
Computer
reboot
resource
selec5on
Interac5ve
user
logs
in
…
resource
selec5on
Task
evic5on
Task
evic5on
Task
comple5on
5me
…
Good
Bad
‘Miscreant’
–
has
been
evicted
but
don’t
know
if
it’s
good
or
bad
Mo5va5on
3. Mo5va5on
Policy
and
Simula5on
Conclusion
Mo5va5on
• We
have
run
a
high-‐throughput
cluster
for
~6
years
– Allowing
many
researchers
to
perform
more
work
quicker
• University
has
strong
desire
to
reduce
energy
consump5on
and
reduce
CO2
produc5on
– Currently
powering
down
computer
&
buying
low
power
PCs
– “If
a
computer
is
not
‘working’
it
should
be
powered
down”
• Can
we
go
further
to
reduce
wasted
energy?
– Reduce
5me
computers
spend
running
work
which
does
not
complete
– Prevent
re-‐submission
of
‘bad’
jobs
– Reduce
the
number
of
resubmissions
for
‘good’
jobs
• Aims
– Inves5gate
policy
for
reducing
energy
consump5on
– Determine
the
impact
on
high-‐throughput
users
Mo5va5on
4. Mo5va5on
Policy
and
Simula5on
Conclusion
Can
we
fix
the
number
of
retries?
0 200 400 600 800 1000 1200 1400 1600 1800 200
0
100
200
300
400
500
600
700
800
900
Number of evictions
Cumulativewastedseconds(millions)
Good Jobs
Bad Jobs
• ~57
years
of
compu5ng
5me
during
2010
• ~39
years
of
wasted
5me
– ~27
years
for
‘bad’
tasks
:
average
45
retries
:
max
1946
retries
– ~12
years
for
‘good’
tasks
:
average
1.38
retries
:
max
360
retries
• 100%
‘good’
task
comple5on
-‐>
360
retries
• S5ll
wastes
~13
years
on
‘bad’
tasks
– 95%
‘good’
task
comple5on
-‐>
3
retries
:
9,808
good
tasks
killed
(3.32%)
– 99%
‘good’
task
comple5on
-‐>
6
retries
:
2,022
good
tasks
killed
(0.68%)
Mo5va5on
5. Mo5va5on
Policy
and
Simula5on
Conclusion
0 200 400 600 800 1000 1200 1400
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
Idle time (minutes)
Cumulativefreeseconds
Can
we
make
tasks
short
enough?
• Make
tasks
short
enough
to
reduce
miscreants
• Average
idle
interval
–
371
minutes
• But
to
ensure
availability
of
intervals
– 95%
:
need
to
reduce
5me
limit
to
2
minutes
– 99%
:
need
to
reduce
5me
limit
to
1
minute
• Imprac5cal
to
make
tasks
this
short
Mo5va5on
6. Mo5va5on
Policy
and
Simula5on
Conclusion
Cluster
Simula5on
• High
Level
Simula5on
of
Condor
– Trace
logs
from
a
twelve
month
period
are
used
as
input
• User
Logins
/
Logouts
(computer
used)
• Condor
Job
Submission
5mes
(‘good’/’bad’
and
dura5on)
end applications are unlikely to migrate to virtual desktops
or user owned devices due to hardware requirements and
icensing conditions, so we expect to need to maintain a
pool of hardware that will be useful for Condor for some
ime.
PUE values have been assigned at the cluster level with
values in the range of 0.9 to 1.4. These values have not been
empirically evaluated but used here to steer jobs. In most
cases the cluster rooms have a low enough computer density
not to require cooling giving these clusters a PUE value of
1.0. However, two clusters are located in rooms that require
air conditioning, giving these a PUE of 1.4. Likewise, four
clusters are based in a basement room, which is cold all
year round; hence computer heat is used to offset heating
requirements for the room, giving a PUE value of 0.9.
By default computers within the cluster will enter the
sleep state after a given interval of inactivity. This time will
depend on whether the cluster is open or not. During open
hours computers will remain in the idle state for one hour
before entering the sleep state whilst outside of these hours
he idle interval before sleep is reduced to 15 minutes. This
policy (P2) was originally trialled under Windows XP where
he time for computers to resume from the shutdown state
was considerable (sleep was an unreliable option for our
environment). Likewise the time interval before a Condor
ob could start using a computer (M1) was set to be 15
minutes during cluster opening hours and 0 minutes outside
Table I: Computer Types
Type Cores Speed Power Consumption
Active Idle Sleep
Normal 2 ⇠3Ghz 57W 40W 2W
High End 4 ⇠3Ghz 114W 67W 3W
Legacy 2 ⇠2Ghz 100-180W 50-80W 4W
Figure 3 illustrates the interactive logins for this period
showing the high degree of seasonality within the data. It
is easy to distinguish between week and weekends as well
as where the three terms lie along with the vacations. This
represents 1,229,820 interactive uses of the computers.
Figure 4 depicts the profile for the 532,467 job submis-
sions made to Condor during this period. As can be seen
the job submissions follow no clearly definable pattern. Note
that out of these submissions 131,909 were later killed by the
original Condor user. In order to simulate these killed jobs
the simulation assumes that these will be non-terminating
jobs and will keep on submitting them to resources until the
time at which the high-throughput user terminates them. The
graph is clipped on Thursday 03/06/2010 as this date had
93,000 job submissions.
For the simulations we will report on the total power
consumed (in MWh) for the period. In order to determine
the effect on high-throughput users of a policy we will also
report the average overhead observed by jobs submitted to
Condor (in seconds). Where overhead is defined to be the
amount of time in excess of the execution duration of the job.
Other statistics will be reported as appropriate for particular
!"!#$
!"#$
#$
#!$
#!!$
!"#$%&'()'*"$#+,,+(-,'."-/&%/,'
012%'
Figure 4: Condor job submission profile
Ac5ve
User
/
Condor
Idle
Sleep
WOL
Z
Z
Z
Cycle Stealing Users
Interactive Users
High-Throughput
Management
Z
Z
Z
Management policy
Policy
and
Simula5on
7. Mo5va5on
Policy
and
Simula5on
Conclusion
0 5 10 15 20 25 30
10
1
10
2
10
3
10
4
10
5
Number of retries (n)
Numberofgoodtaskskilled
N1 C1
N1 C2
N1 C3
N2 C1
N2 C2
N2 C3
N3 C1
N3 C2
N3 C3
n
realloca5on
policies
• N1(n):
Abandon
task
if
deallocated
n
5mes.
• N2(n):
Abandon
task
if
deallocated
n
5mes
ignoring
interac5ve
users.
• N3(n):
Abandon
task
if
deallocated
n
5mes
ignoring
planned
machine
reboots.
• C1:
Tasks
allocated
to
resources
at
random,
favouring
awake
resources
• C2:
Target
less
used
computers
(longer
idle
5mes)
• C3:
Tasks
are
allocated
to
computers
in
clusters
with
least
amount
of
5me
used
by
interac5ve
users
0 5 10 15 20 25 30
3
3.5
4
4.5
5
5.5
6
x 10
6
Number of retries (n)
Energyconsumption(MWh)
N1 C1
N1 C2
N1 C3
N2 C1
N2 C2
N2 C3
N3 C1
N3 C2
N3 C3
0 5 10 15 20 25 30
0
2
4
6
8
10
12
x 10
5
Number of retries (n)
Overheadsonallgoodtasks
N1 C1
N1 C2
N1 C3
N2 C1
N2 C2
N2 C3
N3 C1
N3 C2
N3 C3
Policy
and
Simula5on
9. Mo5va5on
Policy
and
Simula5on
Conclusion
Individual
Time
Policy
• Impose
a
limit
on
individual
execu5on
5me
for
a
task.
– Nightly
reboots
bound
this
to
24
hours.
– What
is
the
impact
of
lowering
this?
• I1(t):
Abandon
if
individual
5me
>
t.
0 5 10 15 20 25 30
3.5
4
4.5
5
5.5
6
x 10
6
Individual time (hours)
Energyconsumption(MWh)
I1 C1
I1 C2
I1 C3
0 5 10 15 20 25
10
0
10
1
10
2
10
3
10
4
10
5
Individual time (hours)
Numberofgoodtaskskilled
I1 C1
I1 C2
I1 C3
0 5 10 15 20 25 30
3
4
5
6
7
8
9
10
11
12
x 10
5
Individual time (hours)
Overheadsonallgoodtasks
I1 C1
I1 C2
I1 C3
Policy
and
Simula5on
10. Mo5va5on
Policy
and
Simula5on
Conclusion
20 40 60 80 100 120 140
10
6
10
7
10
8
10
9
10
10
10
11
10
12
Maximum execution duration (hours)
Energyconsumption(MWh)
m=10, n=10
m=10, n=20
m=10, n=30
m=20, n=10
m=20, n=20
m=20, n=30
m=30, n=10
m=30, n=20
m=30, n=30
m=40, n=10
m=40, n=20
m=40, n=30
Dedicated
Resources
D1(m,d):
Miscreant
tasks
are
permi@ed
to
con5nue
execu5ng
on
a
dedicated
set
of
m
resources
(without
interac5ve
users
or
reboots),
with
a
maximum
dura5on
d.
20 40 60 80 100 120 140
0
2
4
6
8
10
12
14
16
Maximum execution duration (hours)
Numberofgoodtaskskilled
m=10, n=10
m=10, n=20
m=10, n=30
m=20, n=10
m=20, n=20
m=20, n=30
m=30, n=10
m=30, n=20
m=30, n=30
m=40, n=10
m=40, n=20
m=40, n=30
20 40 60 80 100 120 140
1.12
1.14
1.16
1.18
1.2
1.22
1.24
1.26
1.28
1.3
x 10
6
Maximum execution duration (hours)
Overheadsonallgoodtasks
m=10, n=10
m=10, n=20
m=10, n=30
m=20, n=10
m=20, n=20
m=20, n=30
m=30, n=10
m=30, n=20
m=30, n=30
m=40, n=10
m=40, n=20
m=40, n=30
Policy
and
Simula5on
11. Mo5va5on
Policy
and
Simula5on
Conclusion
Conclusion
• Simple
policies
can
be
used
to
reduce
the
effect
of
miscreant
tasks
in
a
mul5-‐use
cycle
stealing
cluster.
– N2
(total
evic5ons
ignoring
users)
• Order
of
magnitude
reduc5on
in
energy
consump5on
– Reduce
amount
of
effort
wasted
on
tasks
that
will
never
complete
• Policies
may
be
combined
to
achieve
further
improvements.
– Adding
in
dedicated
computers
Conclusion
12. Ques5ons?
stephen.mcgough@ncl.ac.uk
m.j.forshaw@ncl.ac.uk
More
info:
McGough,
A
Stephen;
Forshaw,
Ma@hew;
Gerrard,
Clive;
Wheater,
Stuart;
Reducing
the
Number
of
Miscreant
Tasks
ExecuBons
in
a
MulB-‐use
Cluster,
Cloud
and
Green
Compu5ng
(CGC),
2012