PAC 2019 virtual Hemalatha Murugesan

•

0 likes•103 views

This document discusses using machine learning algorithms for predictive performance modeling of IT systems. It explains that ML can be used to predict quality of service metrics like response time and server utilization for different conditions, such as increased user load or hardware configurations, based on past production and test data. This helps with data-driven decision making for hardware procurement and effective utilization, and can reduce the cost and time of performance testing by complementing benchmarking and application tuning. The key is having sufficient historical data to train accurate models, with various techniques available along a cost-accuracy spectrum, from linear projections to simulation to machine learning.

Predictive Performance Modeling
Using Machine Learning (ML) Algorithms
By Hemalatha Murugesan & Madhu Tanikella

Predictive Modelling Need
• Insights into IT systems behavior (non-functional) ahead
• Better predictability of
➢ System responsiveness
➢ Infrastructure utilizations
• Ability to plan for hardware in advance for Mergers &
Acquisitions / Country Rollouts / New Features etc.
• Minimize surprises w.r.t. performance & scalability

Predictive Performance Modeling
4
What? Predict Quality of Service (QoS) of IT systems (response time, throughput,
server utilization) for varying conditions such as increase in # of users /
transaction load / hardware configurations - by making use of available data
points from Production / QA and forecast
How it helps?
Data-driven decision making for additional hardware procurement for future workloads
Effective hardware utilization
Provides leads for quicker tuning / optimization of specific layers of Application
Landscape
Reduces cost & time to carry out multiple trial-error performance runs
KEY CONSIDERATIONS
 Not a replacement for performance benchmarking /
Application tuning – complements them
 More the data - more accurate the predictions
 Analytical models are cost effective – can be
implemented in multiple ways
Thumb
Rules
Linear
Projections
Analytical
Models
Simulation
Techniques
Real
Environment
Accuracy
Cost
Performance
Prediction
Techniques
QNM
ML
Empirical
Formula

Key Components ofMachine Learning for Performance Modeling
5
Trained Data Set Test Data Set
ML-based Performance
models
Prediction results

Similar to PAC 2019 virtual Hemalatha Murugesan

White paper on testing in cloud

imkulu

AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...

Value Amplify Consulting

This document discusses building a predictive system using machine learning. It describes predicting income using census data with four machine learning algorithms: Two-Class Decision Jungle, Two-Class Averaged Perceptron, Two-Class Bayes Point Machine, and Two-Class Locally-Deep Support Vector Machine. It also discusses tuning hyperparameters, combining results, and benchmarking performance. Additional sections cover predictive analytics processes, digital transformation, and predictive maintenance maturity models.

Effective Software Effort Estimation Leveraging Machine Learning for Digital ...

Shakas Technologies

Effective Software Effort Estimation Leveraging Machine Learning for Digital Transformation. Shakas Technologies ( Galaxy of Knowledge) #11/A 2nd East Main Road, Gandhi Nagar, Vellore - 632006. Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723 Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication | Email : info@shakastech.com | shakastech@gmail.com | website: www.shakastech.com Facebook: https://www.facebook.com/pages/Shakas-Technologies

Primer on application_performance_modelling_v0.1

Trevor Warren

PayPal Decision Management Architecture

Pradeep Ballal

This document discusses decision management and operational decision making. It describes treating decisions as reusable assets and leveraging technology like predictive analytics, business intelligence, and artificial intelligence at key decision points to automate processes. It outlines considerations for developing a decision engine in the cloud, including making components available on a self-service basis and enabling multi-tenancy so each team can be isolated within the shared infrastructure. Finally, it proposes a development workflow for operationalizing strategies, models and rules quickly and at scale to meet business needs.

Cloud impact on IT

Divya Sharma

Cloud computing will impact IT departments, application development, testers, and developers. For IT departments, cloud computing provides hassle-free resources and quicker decommissioning of unused resources, leading to better infrastructure utilization and less management overhead. Application development must consider network bottlenecks, multi-tenancy, and scalability. Test environments are easier to set up but more aspects need testing, like security and compliance. Developers will need to learn new architectural patterns and dynamically scalable application development while some responsibilities like application management will change.

Leveraging Your CMMS - From Selection to Daily Use

johnnyg14

This document discusses leveraging a computerized maintenance management system (CMMS) from selection through daily use. It begins by outlining the business benefits of a 1% improvement in key maintenance metrics. Selection criteria for a CMMS include defining requirements, benchmarking current performance, and selecting a system that integrates with other applications. Proper implementation requires training, documentation, and continuous improvement. Leading practices involve mobile and analytical capabilities to turn maintenance data into actionable insights. Key performance indicators allow measuring progress toward maintenance goals.

Stochastic Scheduling Algorithm for Distributed Cloud Networks using Heuristi...

Eswar Publications

Rule based heuristic scheduling algorithms in real time and cloud computing Systems employ for resource or task scheduling since they are suitable to implement for NP-complete problems. However, they are simple but there is much room to improve these algorithms. This study presents a heuristic scheduling algorithm, called High performance hyper-heuristic scheduling algorithm (HHSA) using detection operator, to find better scheduling solutions for real and cloud computing systems. The two operators - diversity detection and improvement detection operators - are employed in this algorithm to determine the timing to determine the heuristic algorithm.. These two are employed to dynamically determine a low level heuristic that can be used to find better solution. To evaluate the performance of this method, authors examined the above method with several scheduling algorithms and results prove that Hyper Heuristic Scheduling Algorithm can significantly decrease the makespan of task scheduling when compared with all other scheduling algorithms. A novel high-performance hyper-heuristic algorithm is proposed for scheduling on cloud computing systems to reduce the makespan. This algorithm can be applied to both sequence dependent and sequence independent scheduling problems.

Data as the New Oil: Producing Value in the Oil and Gas Industry

VMware Tanzu

Oil and gas exploration and production activities generate large amounts of data from sensors, logistics, business operations and more. Given the data volume, variety and velocity, gaining actionable and relevant insights from the data is challenging. Learn about these challenges and how to address them by leveraging big data technologies in this webinar. During the webinar we will dive deep into approaches for predicting drilling equipment function and failure, a key step towards zero unplanned downtime. In the process of drilling wells, non-productive time due to drilling equipment failure can be expensive. We will highlight how the Pivotal Data Labs team uses big data technologies to build models for predicting drilling equipment function and failure. Models such as these can be used to build essential early warning systems to reduce costs and minimize unplanned downtime. Panelist: Rashmi Raghu, Senior Data Scientist, Pivotal Hosted by: Tim Matteson, Co-Founder -- Data Science Central Video replay is available to watch here: http://youtu.be/dhT-tjHCr9E

Cloud Strategy

Richard Harvey

Azure Analysis Services (Azure Bootcamp 2018)

Turner Kunkel

Smart Manufacturing Suite for Power Industry

A.T.E. Private Limited

The document discusses an IoT-based smart manufacturing solution called EcoAxis for power plants. The solution continuously monitors critical plant parameters to help operators make informed decisions that improve plant performance, availability, and reliability while reducing operating and maintenance costs. It achieves this through remote access to real-time data, predictive maintenance using condition monitoring, and analytics to optimize plant efficiency and operations.

cloud services and providers

Kalai Selvi

Unit 3 covers cloud services and providers, including compute, database, storage, networking, security, identity, and compliance services. It also compares major cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure. Choosing a cloud provider involves considering cost, capabilities, trust, security, and open ecosystem factors. Compute resources in the cloud include CPU, memory, storage, and networking that can be dynamically provisioned on demand.

Ibm test data_management_v0.4

Rosario Cunha

This document discusses test data management strategies and IBM's approach. It begins by explaining how test data management has become essential for software development. A key challenge is ensuring high quality test data. The document then outlines goals for a test data management strategy, such as producing reusable, consumable, and scalable results. It proposes analyzing needs, crafting data models, and establishing governance. IBM's approach involves engaging consultants, conducting a proof of concept, piloting the strategy, and full implementation using test data management tools. The overall goal is to improve testing efficiency and effectiveness.

A Novel Dynamic Priority Based Job Scheduling Approach for Cloud Environment

IRJET Journal

The document proposes a new dynamic priority-based job scheduling algorithm for cloud environments to optimize the problem of starvation. It assigns priority to jobs based on criteria like CPU requirements, I/O requirements, and job criticality. The algorithm aims to reduce wait time, turnaround time, and increase throughput and CPU utilization. It was tested against the Shortest Job First algorithm in CloudSim simulation software. The results showed improvements in wait time, turnaround time, and total finish time compared to the SJF algorithm.

IMPACT OF RESOURCE MANAGEMENT AND SCALABILITY ON PERFORMANCE OF CLOUD APPLICA...

IJCSEA Journal

Cloud computing facilitates service providers to rent their computing capabilities for deploying applications depending on user requirements. Applications of cloud have diverse composition, configuration and deployment requirements. Quantifying the performance of applications in Cloud computing environments is a challenging task. In this paper, we try to identify various parameters associated with performance of cloud applications and analyse the impact of resource management and scalability among them.

IMPACT OF RESOURCE MANAGEMENT AND SCALABILITY ON PERFORMANCE OF CLOUD APPLICA...

IJCSEA Journal

Resource management and scalability significantly impact the performance of cloud applications. Effective resource management is needed to optimize performance, functionality, and costs. Scalability is the ability to scale applications up or down based on demand. The literature identifies key factors like network resources, over-provisioning, under-provisioning, and information proxies that influence performance through their effects on resource management and scalability. Performance analysis requires understanding various attributes and is best done using model-based approaches.

IMPACT OF RESOURCE MANAGEMENT AND SCALABILITY ON PERFORMANCE OF CLOUD APPLICA...

IJCSEA Journal

A Survey on Heuristic Based Techniques in Cloud Computing

IRJET Journal

This document discusses heuristic techniques for scheduling tasks in cloud computing. It begins with an introduction to cloud computing and cloud scheduling. Two common heuristic scheduling techniques are then described: Min-Min scheduling and Max-Min scheduling. Min-Min scheduling assigns the task with the minimum completion time first. Max-Min scheduling assigns the task with the maximum completion time first. The document then reviews several related studies on task scheduling techniques in cloud computing, including studies comparing Min-Min and Max-Min.

SCQAA-SF Meeting on May 21 2014

Sujit Ghosh

This document provides information about the SCQAA-SF organization and an upcoming event. SCQAA-SF is a chapter that sponsors sharing of information to promote quality practices in IT through networking, training and professional development. They meet every two months in San Fernando Valley. The upcoming event will feature presentations on technology advancements and methodology, networking opportunities, and opportunities to earn PDU and other credits. Recently, the organization revised their membership dues policy to better accommodate members' needs.

Similar to PAC 2019 virtual Hemalatha Murugesan (20)

White paper on testing in cloud

AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...

Effective Software Effort Estimation Leveraging Machine Learning for Digital ...

Primer on application_performance_modelling_v0.1

PayPal Decision Management Architecture

Cloud impact on IT

Leveraging Your CMMS - From Selection to Daily Use

Stochastic Scheduling Algorithm for Distributed Cloud Networks using Heuristi...

Data as the New Oil: Producing Value in the Oil and Gas Industry

Cloud Strategy

Azure Analysis Services (Azure Bootcamp 2018)

Smart Manufacturing Suite for Power Industry

cloud services and providers

Ibm test data_management_v0.4

A Novel Dynamic Priority Based Job Scheduling Approach for Cloud Environment

IMPACT OF RESOURCE MANAGEMENT AND SCALABILITY ON PERFORMANCE OF CLOUD APPLICA...

A Survey on Heuristic Based Techniques in Cloud Computing

SCQAA-SF Meeting on May 21 2014

More from Neotys

PAC 2020 Santorin - Giovanni Paolo Gibilisco

Neotys

The document discusses continuous performance optimization of Java applications. It proposes adding an optimization step to the continuous integration pipeline to evolve performance testing beyond just finding regressions. This would allow configurations to be adapted to new application features and releases to find performance improvements. The approach is demonstrated on a flight search microservice, where different garbage collection algorithms and configuration parameters are evaluated to optimize throughput, response times, memory usage and stability under increasing load.

PAC 2020 Santorin - Stijn Schepers

Neotys

The document discusses the concept of observability in performance engineering and its importance for understanding application performance. It defines observability as watching application behavior using response metrics and resource utilization metrics to understand the digital user experience. The document provides examples of integrating load testing tools with application performance monitoring tools to actively monitor applications in production and observe performance across releases. It emphasizes the need to analyze raw metrics from multiple perspectives to gain useful insights.

PAC 2020 Santorin - Edoardo Varani

Neotys

This document discusses measuring and addressing CPU throttling in containerized environments. It describes how CPU limits work at the kernel level and how to measure throttling using cgroup metrics. The author proposes adding 1.3 times the maximum throttled CPUs to the container's CPU limit to eliminate throttling. A case study shows this approach reduced response times and garbage collection pauses. The document also discusses how the JVM can increase demand as CPU limits increase and the importance of tailoring limits to workloads.

PAC 2020 Santorin - Andreas Grabner

Neotys

This document discusses using Keptn to automate service level indicator (SLI) evaluation and performance validation with service level objectives (SLOs). It describes two use cases: 1) automating SLI evaluation over a timeframe, and 2) integrating performance validation as a self-service capability. The document outlines how Keptn works underneath, including defining SLIs and SLOs in YAML and scoring SLIs against SLO criteria. It demonstrates integrating Keptn with existing pipelines and monitoring tools. Finally, it discusses options for installing only the Keptn quality gate functionality or the full Keptn platform.

PAC 2020 Santorin - Vasilis Chatzinasios

Neotys

The document discusses how to automate performance testing in DevOps. It outlines an automated analysis workflow involving defining metrics, comparing metrics to thresholds and baselines, pattern analysis, and test results. It also discusses script automation, reducing false positives, and integrating different types of performance tests like load, stress, and spike tests. The goal is to automate performance testing to support the rapid delivery cycles of DevOps.

PAC 2020 Santorin - Gopalkrishnan Yadav

Neotys

This document discusses monitoring the performance of Azure data pipelines. It recommends using Azure Log Analytics to monitor pipelines near real-time, as traditional testing tools don't support log analytics. It provides steps to configure a Log Analytics workspace and enable diagnostics logging. Sample queries are shown to monitor metrics like pipeline durations, activity times and throughput. Monitoring pipelines helps assess performance, stability and confidence in data processing.

PAC 2020 Santorin - Hari Krishnan Ramachandran

Neotys

This document discusses common performance myths and challenges related to cloud migrations, SaaS deployments, and cloud native applications. It provides approaches to assess performance for each scenario. Some key points covered include: - Common myths like unlimited scaling without effort, cost savings from cloud alone, and SaaS vendors handling all performance. - Challenges for migrations include capacity planning, auto-scaling issues, and latency. For SaaS, risks include customization impacts and integration issues. Cloud native challenges are distributed architectures and configuration problems. - The document recommends approaches like baseline benchmarking, resilience testing, and 360-degree validation across user, cloud, data center and external layers to overcome myths and address challenges.

PAC 2020 Santorin - Joerek Van Gaalen

Neotys

Joerek van Gaalen discussed his experience conducting large-scale performance tests, including a test with 2 million virtual users. He explained how to simulate that level of load using 800 load generators across different cloud providers. He emphasized optimizing tests by tuning scripts, controllers, and agents to minimize resources. Van Gaalen also stressed the importance of making tests realistic by mimicking production usage patterns and balancing load. Specific issues like CDN performance limitations and uneven load distribution were also addressed.

PAC 2020 Santorin - Ankur Jain

Neotys

Ankur Jain presented on using the User Timing API to measure user perceived performance of single-page applications without an APM tool. The User Timing API allows developers to mark milestones and measure the time between them to understand performance. While it requires code changes, it provides accurate, real-user monitoring of applications across browsers. Some limitations are that it requires knowledge of the application's user flow and code access to implement the markers.

PAC 2020 Santorin - Stephen Townshend

Neotys

The document summarizes a presentation given at the Performance Advisory Council in Santorini, Greece in February 2020. The presentation advocated for using cloud-based performance engineering tools for their ease of use, ability to automatically correlate data, and to scale testing on demand. It cautioned that adopting new tools requires maintaining the same performance testing culture to avoid generating misleading or inaccurate results.

PAC 2020 Santorin - Leandro Melendez

Neotys

The document discusses the flawed approach of "racing performance" without proper preparation. It argues that jumping straight into performance testing without checking components like tires, fuel levels, and ensuring the system is functioning properly leads to "fatal consequences" like crashes. Some reasons this approach still occurs include a lack of performance culture, confusion between performance and load testing, and viewing all problems as requiring load automation. The document promotes better approaches like verifying components, defining standards, stress testing parts individually and together before integration, using meters to monitor system functions, conducting lab tests, and test driving the system before attempting high-stakes races.

PAC 2019 virtual Stephen Townshend

Neotys

This document discusses automating performance testing pipelines. It covers value stream mapping to identify tasks that can be removed, simplified or automated. Automating testing provides benefits like reduced time, allowing specialists to focus on higher-value work, and empowering others to run tests. The document demonstrates automating a JMeter load test, providing tips like using JMeter projects and scripts. It notes that significant time savings are possible from automating not just test execution but test development as well through techniques like UI automation.

PAC 2019 virtual Federico Toledo

Neotys

The document discusses adding performance verifications to continuous delivery pipelines. It notes that the typical approach to performance testing does not work well for continuous delivery, as test scripts are fragile and it is difficult to identify the cause of issues. The document recommends taking a different approach with the goal of detecting any degradations as early as possible. It suggests implementing unit performance tests to test endpoints in isolation and detect degradations immediately after they are introduced. While unit tests are important, integration and load tests are still needed periodically to test how components interact as a whole. Client-side performance also needs to be considered.

PAC 2019 virtual Leandro Melendez

Neotys

This document provides an overview of a presentation on the economic impacts of performance testing practices. It discusses two examples: 1) Ford's Pinto car that had a design flaw causing fuel tanks to explode in rear-end collisions. Analyzing the options of fixing the flaw vs paying fines, fixing it would have cost $137 million but saved much higher future costs. 2) An example project prioritizing what processes to automate for performance testing. Following the Pareto principle, automating the top 20% of processes that generate 80% of the load yields far better results than automating all processes, saving significant time and resources. The presentation emphasizes how taking a long-term view of costs and following

PAC 2019 virtual Mark Tomlinson

Neotys

This document discusses web performance optimization techniques. It is a summary of rules for web performance by Mark Tomlinson, who has 27 years of experience in performance. Some of the key techniques discussed include reducing HTTP requests, optimizing file compression, minimizing code, improving web font and image performance, prefetching resources, avoiding unnecessary redirects, and optimizing infrastructure and databases. The document emphasizes measuring performance through load testing and monitoring to identify bottlenecks.

PAC 2019 virtual Srivalli Aparna

Neotys

This document discusses using R for exploratory result analysis of load testing data from JMeter. It provides an introduction to R and highlights benefits like it being programming based, developed for data analysis, supporting exploratory analysis, and including visualization libraries. It also gives examples of base R functions for data manipulation and visualization including aggregate, subset, ifelse, scatter plots, and using color. Finally, it discusses using R to create interactive dashboards for reporting load testing results.

PAC 2019 virtual Reuben Rajan George

Neotys

PAC 2019 virtual Joerek Van Gaalen

Neotys

- Automating performance tests through continuous integration can provide direct feedback on performance changes after code releases and infrastructure changes. It allows performance issues to be detected and addressed earlier. - Key best practices include starting with a single important test scenario, focusing on robustness over realism, visualizing trend data over time, and analyzing results to update thresholds and catch regressions. - The goal is to continuously monitor performance through the pipeline and in production to better understand impacts of changes and flag any performance issues for further investigation. Automated tests complement but do not replace thorough acceptance testing.

PAC 2019 virtual Bruno Audoux

Neotys

This document describes a performance automation solution using load testing scripts to continuously monitor application performance. The solution uses scripts to test functionality, availability, response times, and end-to-end workflows. Load testing engines run the scripts on a periodic schedule and store results. An alerting system analyzes results and sends alerts if response times exceed thresholds or tests fail to run. The system is containerized using Docker for scalability. Potential customers include project managers who need regression testing, monitoring of production applications, and emergency alerts about degradations or failures.

PAC 2019 virtual Arjan Van Den Berg

Neotys

The document provides guidance on developing reliable load test scripts and scenarios. It discusses test data requirements, parameterizing dynamic values, proper use of HTTP protocol versus GUI-level scripting, handling asynchronous requests, implementing think time and pacing, validating scripts, and best practices for load scenario configuration. The goal is to explore main points around scripting best practices, validating load scripts thoroughly, and configuration best practices to build effective performance tests.

More from Neotys (20)

PAC 2020 Santorin - Giovanni Paolo Gibilisco

PAC 2020 Santorin - Stijn Schepers

PAC 2020 Santorin - Edoardo Varani

PAC 2020 Santorin - Andreas Grabner

PAC 2020 Santorin - Vasilis Chatzinasios

PAC 2020 Santorin - Gopalkrishnan Yadav

PAC 2020 Santorin - Hari Krishnan Ramachandran

PAC 2020 Santorin - Joerek Van Gaalen

PAC 2020 Santorin - Ankur Jain

PAC 2020 Santorin - Stephen Townshend

PAC 2020 Santorin - Leandro Melendez

PAC 2019 virtual Stephen Townshend

PAC 2019 virtual Federico Toledo

PAC 2019 virtual Leandro Melendez

PAC 2019 virtual Mark Tomlinson

PAC 2019 virtual Srivalli Aparna

PAC 2019 virtual Reuben Rajan George

PAC 2019 virtual Joerek Van Gaalen

PAC 2019 virtual Bruno Audoux

PAC 2019 virtual Arjan Van Den Berg

Recently uploaded

Evento anual Splunk .conf24 Highlights recap

Rafael Santos

Encontro anual da comunidade Splunk, onde discutimos todas as novidades apresentadas na conferência anual da Spunk, a .conf24 realizada em junho deste ano em Las Vegas. Neste vídeo, trago os pontos chave do encontro, como: - AI Assistant para uso junto com a SPL - SPL2 para uso em Data Pipelines - Ingest Processor - Enterprise Security 8.0 (Maior atualização deste seu release) - Federated Analytics - Integração com Cisco XDR e Cisto Talos - E muito mais. Deixo ainda, alguns links com relatórios e conteúdo interessantes que podem ajudar no esclarecimento dos produtos e funções. https://www.splunk.com/en_us/campaigns/the-hidden-costs-of-downtime.html https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-leading-observability-practice.pdf https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-modern-security-program.pdf Nosso grupo oficial da Splunk: https://usergroups.splunk.com/sao-paulo-splunk-user-group/

How to Manage Internal Notes in Odoo 17 POS

Celine George

Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large...

YanKing2

Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are often heavy in computational complexity, and quadratically with the length of the input code sequence. Toward simplifying the input program of an LLM, the state-of-the-art approach has the strategies to filter the input code tokens based on the attention scores given by the LLM. The decision to simplify the input program should not rely on the attention patterns of an LLM, as these patterns are influenced by both the model architecture and the pre-training dataset. Since the model and dataset are part of the solution domain, not the problem domain where the input program belongs, the outcome may differ when the model is trained on a different dataset. We propose SlimCode, a model-agnostic code simplification solution for LLMs that depends on the nature of input code tokens. As an empirical study on the LLMs including CodeBERT, CodeT5, and GPT-4 for two main tasks: code search and summarization. We reported that 1) the reduction ratio of code has a linear-like relation with the saving ratio on training time, 2) the impact of categorized tokens on code simplification can vary significantly, 3) the impact of categorized tokens on code simplification is task-specific but model-agnostic, and 4) the above findings hold for the paradigm–prompt engineering and interactive in-context learning and this study can save reduce the cost of invoking GPT-4 by 24%per API query. Importantly, SlimCode simplifies the input code with its greedy strategy and can obtain at most 133 times faster than the state-of-the-art technique with a significant improvement. This paper calls for a new direction on code-based, model-agnostic code simplification solutions to further empower LLMs.

SCADAmetrics Instrumentation for Sensus Water Meters - Core and Main Training...

Jim Mimlitz, P.E.

Online music portal management system project report.pdf

Kamal Acharya

The iMMS is a unique application that is synchronizing both user experience and copyrights while providing services like online music management, legal downloads, artists’ management. There are several other applications available in the market that either provides some specific services or large scale integrated solutions. Our product differs from the rest in a way that we give more power to the users remaining within the copyrights circle.

Rohini @ℂall @Girls ꧁❤ 9873777170 ❤꧂VIP Yogita Mehra Top Model Safe

binna singh$A17

Rotary Intersection in traffic engineering.pptx

surekha1287

Best Practices of Clothing Businesses in Talavera, Nueva Ecija, A Foundation ...

IJAEMSJORNAL

This study primarily aimed to determine the best practices of clothing businesses to use it as a foundation of strategic business advancements. Moreover, the frequency with which the business's best practices are tracked, which best practices are the most targeted of the apparel firms to be retained, and how does best practices can be used as strategic business advancement. The respondents of the study is the owners of clothing businesses in Talavera, Nueva Ecija. Data were collected and analyzed using a quantitative approach and utilizing a descriptive research design. Unveiling best practices of clothing businesses as a foundation for strategic business advancement through statistical analysis: frequency and percentage, and weighted means analyzing the data in terms of identifying the most to the least important performance indicators of the businesses among all of the variables. Based on the survey conducted on clothing businesses in Talavera, Nueva Ecija, several best practices emerge across different areas of business operations. These practices are categorized into three main sections, section one being the Business Profile and Legal Requirements, followed by the tracking of indicators in terms of Product, Place, Promotion, and Price, and Key Performance Indicators (KPIs) covering finance, marketing, production, technical, and distribution aspects. The research study delved into identifying the core best practices of clothing businesses, serving as a strategic guide for their advancement. Through meticulous analysis, several key findings emerged. Firstly, prioritizing product factors, such as maintaining optimal stock levels and maximizing customer satisfaction, was deemed essential for driving sales and fostering loyalty. Additionally, selecting the right store location was crucial for visibility and accessibility, directly impacting footfall and sales. Vigilance towards competitors and demographic shifts was highlighted as essential for maintaining relevance. Understanding the relationship between marketing spend and customer acquisition proved pivotal for optimizing budgets and achieving a higher ROI. Strategic analysis of profit margins across clothing items emerged as crucial for maximizing profitability and revenue. Creating a positive customer experience, investing in employee training, and implementing effective inventory management practices were also identified as critical success factors. In essence, these findings underscored the holistic approach needed for sustainable growth in the clothing business, emphasizing the importance of product management, marketing strategies, customer experience, and operational efficiency.

Exploring Deep Learning Models for Image Recognition: A Comparative Review

sipij

Image recognition, which comes under Artificial Intelligence (AI) is a critical aspect of computer vision, enabling computers or other computing devices to identify and categorize objects within images. Among numerous fields of life, food processing is an important area, in which image processing plays a vital role, both for producers and consumers. This study focuses on the binary classification of strawberries, where images are sorted into one of two categories. We Utilized a dataset of strawberry images for this study; we aim to determine the effectiveness of different models in identifying whether an image contains strawberries. This research has practical applications in fields such as agriculture and quality control. We compared various popular deep learning models, including MobileNetV2, Convolutional Neural Networks (CNN), and DenseNet121, for binary classification of strawberry images. The accuracy achieved by MobileNetV2 is 96.7%, CNN is 99.8%, and DenseNet121 is 93.6%. Through rigorous testing and analysis, our results demonstrate that CNN outperforms the other models in this task. In the future, the deep learning models can be evaluated on a richer and larger number of images (datasets) for better/improved results.

Development of Chatbot Using AI/ML Technologies

maisnampibarel

The rapid advancements in artificial intelligence and natural language processing have significantly transformed human-computer interactions. This thesis presents the design, development, and evaluation of an intelligent chatbot capable of engaging in natural and meaningful conversations with users. The chatbot leverages state-of-the-art deep learning techniques, including transformer-based architectures, to understand and generate human-like responses. Key contributions of this research include the implementation of a context- aware conversational model that can maintain coherent dialogue over extended interactions. The chatbot's performance is evaluated through both automated metrics and user studies, demonstrating its effectiveness in various applications such as customer service, mental health support, and educational assistance. Additionally, ethical considerations and potential biases in chatbot responses are examined to ensure the responsible deployment of this technology. The findings of this thesis highlight the potential of intelligent chatbots to enhance user experience and provide valuable insights for future developments in conversational AI.

Unit 1 Information Storage and Retrieval

KishorMahale5

PMSM-Motor-Control : A research about FOC

itssurajthakur06

OCS Training - Rig Equipment Inspection - Advanced 5 Days_IADC.pdf

Muanisa Waras

OCS Training Institute is pleased to co-operate with a Global provider of Rig Inspection/Audits, Commission-ing, Compliance & Acceptance as well as & Engineering for Offshore Drilling Rigs, to deliver Drilling Rig Inspec-tion Workshops (RIW) which teaches the inspection & maintenance procedures required to ensure equipment integrity. Candidates learn to implement the relevant standards & understand industry requirements so that they can verify the condition of a rig’s equipment & improve safety, thus reducing the number of accidents and protecting the asset.

Phone Us ❤ X000XX000X ❤ #ℂall #gIRLS In Chennai By Chenai @ℂall @Girls Hotel ...

Miss Khusi #V08

Vernier Caliper and How to use Vernier Caliper.ppsx

Tool and Die Tech

Trends in Computer Aided Design and MFG.

Tool and Die Tech

Understanding Cybersecurity Breaches: Causes, Consequences, and Prevention

Bert Blevins

Cybersecurity breaches are a growing threat in today’s interconnected digital landscape, affecting individuals, businesses, and governments alike. These breaches compromise sensitive information and erode trust in online services and systems. Understanding the causes, consequences, and prevention strategies of cybersecurity breaches is crucial to protect against these pervasive risks. Cybersecurity breaches refer to unauthorized access, manipulation, or destruction of digital information or systems. They can occur through various means such as malware, phishing attacks, insider threats, and vulnerabilities in software or hardware. Once a breach happens, cybercriminals can exploit the compromised data for financial gain, espionage, or sabotage. Causes of breaches include software and hardware vulnerabilities, phishing attacks, insider threats, weak passwords, and a lack of security awareness. The consequences of cybersecurity breaches are severe. Financial loss is a significant impact, as organizations face theft of funds, legal fees, and repair costs. Breaches also damage reputations, leading to a loss of trust among customers, partners, and stakeholders. Regulatory penalties are another consequence, with hefty fines imposed for non-compliance with data protection regulations. Intellectual property theft undermines innovation and competitiveness, while disruptions of critical services like healthcare and utilities impact public safety and well-being.

Lecture 3 Biomass energy...............ppt

RujanTimsina1

GUIA_LEGAL_CHAPTER_4_FOREIGN TRADE CUSTOMS.pdf

ProexportColombia1

GUIA_LEGAL_CHAPTER-9_COLOMBIAN ELECTRICITY (1).pdf

ProexportColombia1

Recently uploaded (20)

Evento anual Splunk .conf24 Highlights recap

How to Manage Internal Notes in Odoo 17 POS

Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large...

SCADAmetrics Instrumentation for Sensus Water Meters - Core and Main Training...

Online music portal management system project report.pdf

Rohini @ℂall @Girls ꧁❤ 9873777170 ❤꧂VIP Yogita Mehra Top Model Safe

Rotary Intersection in traffic engineering.pptx

Best Practices of Clothing Businesses in Talavera, Nueva Ecija, A Foundation ...

Exploring Deep Learning Models for Image Recognition: A Comparative Review

Development of Chatbot Using AI/ML Technologies

Unit 1 Information Storage and Retrieval

PMSM-Motor-Control : A research about FOC

OCS Training - Rig Equipment Inspection - Advanced 5 Days_IADC.pdf

Phone Us ❤ X000XX000X ❤ #ℂall #gIRLS In Chennai By Chenai @ℂall @Girls Hotel ...

Vernier Caliper and How to use Vernier Caliper.ppsx

Trends in Computer Aided Design and MFG.

Understanding Cybersecurity Breaches: Causes, Consequences, and Prevention

Lecture 3 Biomass energy...............ppt

GUIA_LEGAL_CHAPTER_4_FOREIGN TRADE CUSTOMS.pdf

GUIA_LEGAL_CHAPTER-9_COLOMBIAN ELECTRICITY (1).pdf

PAC 2019 virtual Hemalatha Murugesan

1. Predictive Performance Modeling Using Machine Learning (ML) Algorithms By Hemalatha Murugesan & Madhu Tanikella

2. The Magical Stone

3. Predictive Modelling Need • Insights into IT systems behavior (non-functional) ahead • Better predictability of ➢ System responsiveness ➢ Infrastructure utilizations • Ability to plan for hardware in advance for Mergers & Acquisitions / Country Rollouts / New Features etc. • Minimize surprises w.r.t. performance & scalability

4. Predictive Performance Modeling 4 What? Predict Quality of Service (QoS) of IT systems (response time, throughput, server utilization) for varying conditions such as increase in # of users / transaction load / hardware configurations - by making use of available data points from Production / QA and forecast How it helps? Data-driven decision making for additional hardware procurement for future workloads Effective hardware utilization Provides leads for quicker tuning / optimization of specific layers of Application Landscape Reduces cost & time to carry out multiple trial-error performance runs KEY CONSIDERATIONS  Not a replacement for performance benchmarking / Application tuning – complements them  More the data - more accurate the predictions  Analytical models are cost effective – can be implemented in multiple ways Thumb Rules Linear Projections Analytical Models Simulation Techniques Real Environment Accuracy Cost Performance Prediction Techniques QNM ML Empirical Formula

5. Key Components ofMachine Learning for Performance Modeling 5 Trained Data Set Test Data Set ML-based Performance models Prediction results

6. Predictive Performance Modeling 6

7. Thank You!

PAC 2019 virtual Hemalatha Murugesan

More Related Content

Similar to PAC 2019 virtual Hemalatha Murugesan

Similar to PAC 2019 virtual Hemalatha Murugesan (20)

More from Neotys

More from Neotys (20)

Recently uploaded

Recently uploaded (20)

PAC 2019 virtual Hemalatha Murugesan