Key Performance Findings Report.
The report focuses on the key indicators of the monitored case and significantly reduces the time required to analyze collected information and identify bottlenecks.
It features the areas that influence performance of the application the most, whether it is individual execution or cumulative impact on the system due to high frequency of
use, and/or areas that create abnormally high load on the system as a whole.
System Alerts - Alerts section lists events with execution time or resource utilization exceeding predefined thresholds.
Resources Utilization - The CPU graph shows total general CPU utilization by Java Virtual Machine (JVM), as well as consumption by the most demanding individual threads within JVM, as the percentage of the total server's
CPU.
Resources Utilization - The Memory graph shows memory utilization by the Java Virtual Machine. Memory consumption is expected to grow until it is released by the garbage collector (GC). Up and down fluctuations are normal and indicate that the system is healthy. However, behavior when memory drops to increasingly higher level and oscillations grow shorter, while top line lingers around maximal available memory, may indicate memory leak in the system, or simply higher demand that JVM can
provide. In such case, the execution of the application is constrained by total available memory and JVM might eventually crash.
User Experience - Additional factors that may impact user experience are network latency and browser-side processing time. The execution time shown on the graph represents minimum user wait time which could be
achieved when user has adequately equipped workstation and negligible network delays.
Heavy Methods - The section lists twenty methods with the longest execution time, along with the details pinpointing the reasons.
For each of the listed in the table methods, report offers additional supporting information about repeatedly executed sub-methods, methods with highest net execution time, methods causing the highest CPU utilization, total time spent on database queries and the Heaviest SQL queries.
Heavy Methods by Total Net - The section lists the methods with longest total net time, which are directly responsible for the duration of the execution.
Net time is calculated as full duration time minus full duration time of methods that are called directly from the method in question. The list could contain low-level methods, including I/O methods and other
event waits.
All Queries Totals - The section highlights portion of the overall execution time that was spend on database queries.
The data could be one of the quick indicators on whether the performance issues are database related.
Heavy Queries by Total Duration - While one execution of the query doesn't take long time to execute, the queries listed in this section were executed enough times to account for the significant total execution time.
Network visibility and control using industry standard sFlow telemetrypphaal
• Find out about the sFlow instrumentation built into commodity data center network and server infrastructure.
• Understand how sFlow fits into the broader ecosystem of NetFlow, IPFIX, SNMP and DevOps monitoring technologies.
• Case studies demonstrate how sFlow telemetry combined with automation can lower costs, increase performance, and improve security of cloud infrastructure and applications.
Covert timing channels using HTTP cache headersyalegko
This document discusses using HTTP cache headers to create covert timing channels for transmitting information between hosts without detection. It provides examples of encoding data in the Accept-Language header and describes how headers like Last-Modified and ETag can be used to transmit bits by checking if the page has changed. Issues in implementation are addressed, like needing synchronization. Evaluation shows channels can transmit over 1 bit/second over local networks and around 5 bits/second over the internet. Browser-based channels in JavaScript are also proposed.
An extremely little set of rules to write good commit messages.
History (how code changes over time) become a very useful tool if associated with good commit messages.
These slides would make developers more aware about how good commit messages could improve their work.
The document discusses an instruction-based stride prefetcher that uses a reference prediction table (RPT) to track load instructions and memory addresses. It examines how the maximum prefetch degree affects prefetcher performance. The results show that increasing the maximum degree improves coverage but performance peaks at a degree of 5, providing the highest average speedup of 1.085 compared to no prefetching. While a higher degree increases coverage, prefetching too many blocks negatively impacts performance.
Improved implementation of a Deadline Monotonic algorithm for aperiodic traff...Andrea Tino
- The document describes enhancements made to an existing two-tiered network architecture that schedules aperiodic traffic using a deadline monotonic algorithm. The previous architecture suffered from a "queue overload" phenomenon during high stress simulations.
- The enhanced architecture modifies the aperiodic flow generation policy for nodes to limit how often a node can generate a new flow. This is set to the maximum response time calculated during admission control.
- Simulations of the enhanced architecture show a reduction in deadline misses and improvement in the served ratio, even under high stress scenarios. This indicates the queue overload phenomenon was reduced compared to the previous architecture.
NetScaler Web 2.0 Push technology scales Comet or Reverse Ajax applications that depend on long running idle client connections. It offloads server load by handling all the client connections on the NetScaler and publishing a REST API for the server application to send periodic updates.
The document describes a tool called TCP Congestion Avoidance Algorithm Identification (CAAI) that was proposed to identify the TCP congestion avoidance algorithm of remote web servers. CAAI works in three steps: 1) it gathers TCP window size traces from web servers in emulated network environments, 2) it extracts features like the multiplicative decrease parameter and window growth function from the traces, and 3) it uses these features to classify the TCP algorithm. Testing CAAI on over 30,000 web servers, it was able to identify the default algorithms used by major operating systems, like RENO for Windows and BIC/CUBIC for Linux, as well as some non-default algorithms.
Error tolerant resource allocation and payment minimization for cloud systemJPINFOTECH JAYAPRAKASH
This paper proposes an error-tolerant resource allocation method for cloud systems that minimizes user payments while guaranteeing task deadlines. It formulates the problem and proposes a polynomial-time solution. It also analyzes task execution lengths based on workload predictions to guarantee deadlines. The method is validated on a VM-enabled cluster and shows it can limit tasks to their deadlines with sufficient resources and keep most tasks within deadlines under competition.
Network visibility and control using industry standard sFlow telemetrypphaal
• Find out about the sFlow instrumentation built into commodity data center network and server infrastructure.
• Understand how sFlow fits into the broader ecosystem of NetFlow, IPFIX, SNMP and DevOps monitoring technologies.
• Case studies demonstrate how sFlow telemetry combined with automation can lower costs, increase performance, and improve security of cloud infrastructure and applications.
Covert timing channels using HTTP cache headersyalegko
This document discusses using HTTP cache headers to create covert timing channels for transmitting information between hosts without detection. It provides examples of encoding data in the Accept-Language header and describes how headers like Last-Modified and ETag can be used to transmit bits by checking if the page has changed. Issues in implementation are addressed, like needing synchronization. Evaluation shows channels can transmit over 1 bit/second over local networks and around 5 bits/second over the internet. Browser-based channels in JavaScript are also proposed.
An extremely little set of rules to write good commit messages.
History (how code changes over time) become a very useful tool if associated with good commit messages.
These slides would make developers more aware about how good commit messages could improve their work.
The document discusses an instruction-based stride prefetcher that uses a reference prediction table (RPT) to track load instructions and memory addresses. It examines how the maximum prefetch degree affects prefetcher performance. The results show that increasing the maximum degree improves coverage but performance peaks at a degree of 5, providing the highest average speedup of 1.085 compared to no prefetching. While a higher degree increases coverage, prefetching too many blocks negatively impacts performance.
Improved implementation of a Deadline Monotonic algorithm for aperiodic traff...Andrea Tino
- The document describes enhancements made to an existing two-tiered network architecture that schedules aperiodic traffic using a deadline monotonic algorithm. The previous architecture suffered from a "queue overload" phenomenon during high stress simulations.
- The enhanced architecture modifies the aperiodic flow generation policy for nodes to limit how often a node can generate a new flow. This is set to the maximum response time calculated during admission control.
- Simulations of the enhanced architecture show a reduction in deadline misses and improvement in the served ratio, even under high stress scenarios. This indicates the queue overload phenomenon was reduced compared to the previous architecture.
NetScaler Web 2.0 Push technology scales Comet or Reverse Ajax applications that depend on long running idle client connections. It offloads server load by handling all the client connections on the NetScaler and publishing a REST API for the server application to send periodic updates.
The document describes a tool called TCP Congestion Avoidance Algorithm Identification (CAAI) that was proposed to identify the TCP congestion avoidance algorithm of remote web servers. CAAI works in three steps: 1) it gathers TCP window size traces from web servers in emulated network environments, 2) it extracts features like the multiplicative decrease parameter and window growth function from the traces, and 3) it uses these features to classify the TCP algorithm. Testing CAAI on over 30,000 web servers, it was able to identify the default algorithms used by major operating systems, like RENO for Windows and BIC/CUBIC for Linux, as well as some non-default algorithms.
Error tolerant resource allocation and payment minimization for cloud systemJPINFOTECH JAYAPRAKASH
This paper proposes an error-tolerant resource allocation method for cloud systems that minimizes user payments while guaranteeing task deadlines. It formulates the problem and proposes a polynomial-time solution. It also analyzes task execution lengths based on workload predictions to guarantee deadlines. The method is validated on a VM-enabled cluster and shows it can limit tasks to their deadlines with sufficient resources and keep most tasks within deadlines under competition.
The document compares the Kepler GPU and Xeon Phi architectures through microbenchmarks that test memory-bound, compute-bound, and latency-bound workloads. It finds that for memory-bound workloads, vectorizing loads and using texture cache improves performance on Kepler, while gathering and aligned loads help Xeon Phi. For compute-bound workloads, vectorizing and using float4/double4 benefits Kepler, and intrinsics aid Xeon Phi. And for latency-bound workloads, loop interchange and skipping the L2 cache helps Kepler, while gathering and aligned loads assist Xeon Phi. The conclusion notes vendor performance data may differ from experiments and examples are available online.
El documento habla sobre la tecnología digital y cómo se relaciona con la comunicación inalámbrica, proporcionando acceso fácil a la información y nuevas oportunidades laborales. La tecnología digital se refiere a las herramientas y tecnologías que las personas usan para compartir, distribuir e intercambiar información de manera más frecuente a través de Internet y la informática.
Materi ini membahas tentang konsep-konsep dasar dalam suatu bidang studi seperti istilah-istilah penting, ringkasan materi, dan latihan soal untuk memperkuat pemahaman. Terdapat juga penjelasan visual berupa animasi, video, lagu, dan gambar.
The No Child Left Behind Act was established from 2001-2002 under President George W. Bush. It was proposed in January 2001 and passed by the House and Senate before being signed into law by Bush in January 2002. The act was based on the Elementary and Secondary Education Act of 1965 and aims to improve education standards and outcomes through standardized testing and measurable goals. It has been highly controversial since its inception regarding feasibility and fairness.
The No Child Left Behind Act was established from 2001-2002 under President George W. Bush. It was proposed in January 2001, passed the House of Representatives in May 2001, passed the Senate in June 2001, and was signed into law by President Bush in January 2002. The Act was based on the Elementary and Secondary Education Act of 1965 and supports standards-based education reforms through standardized testing to improve education outcomes.
HPC, Big Data & Data Center Explanation by Mert AkınMert Akın
This document discusses high performance computing (HPC), big data, and data centers. It provides details on:
- What HPC is and how it is used for scientific and engineering applications requiring large-scale processing power.
- How big data comes from both traditional and digital sources in various formats, and is used across industries like healthcare, retail, and banking.
- What a data center is and how it centralizes an organization's IT infrastructure and equipment to support applications and users. Major data center operators include telecom companies, IT firms, and cloud storage providers.
As telcos go digital, cybersecurity risks intensify by pwcMert Akın
globalaviationairospace.com
Cyber security for telecommunications companies
The rewards and risks of the cloud, devices, and data
The fastest growing sources of security incidents, increase over 2013
Security strategies for evolving technologies
Strategic initiatives to improve cybersecurity
This unofficial transcript is for Jason Hall who graduated from the University of Washington Tacoma campus in Spring 2016 with a Bachelor of Science in Computer Science and Systems. The transcript shows the courses taken at Pierce College and South Puget Sound Community College that transferred to UW Tacoma, as well as the courses completed at UW Tacoma. Jason maintained strong academic performance and earned placement on the Dean's List multiple times.
Talk from https://stackconf.eu/talks/how-to-reduce-expenses-on-monitoring-with-victoriametrics/
Given recent economic changes, cost efficiency has become a top priority for many businesses. This is especially important for monitoring because the nature of telemetry data tends to exponential growth. Many monitoring solutions are now switching their focus to optimize costs. The talk will cover open-source instruments from VictoriaMetrics ecosystem for improving monitoring cost-efficiency.
Compression optimization. While ZSTD compression and time series specific techniques like delta-encoding are great, there is still room for improvement. I’ll explain what else can be done to reduce disk footprint for long-term storage.
Extra compression on data transferring between metrics collectors and TSDB. I’ll explain how VictoriaMetrics collector reduces the traffic volume by 4 times.
Pre-computing for telemetry data on collectors, frequently referred to as edge computing. In VictoriaMetrics it is named as streaming aggregation and allows collectors to pre-compute data, reducing its resolution and cardinality before it is pushed to the database. This is especially important for Prometheus-like systems because streaming aggregation is compatible with Prometheus RemoteWrite protocol and can be used with any system which supports it.
Cardinality explorer. Interface, which provides useful insights into data stored by the TSDB. It helps to identify the most expensive metrics or labels and see how they have changed in time.
Query tracing. This feature provides details about all the stages of query execution, including time spent on index lookups, disk reads, data transfer, computation, and memory expenses. This is similar to SQL EXPLAIN feature, and helps to improve the performance of read queries.
Compute efficiency. VictoriaMetrics components for collecting and storing telemetry data consume fewer resources compared to components from Prometheus ecosystem. It may sound like competition or bragging, but this is a real reason why people migrate from Prometheus to VictoriaMetrics – to cut their infrastructure costs by 2-3 times.
All the features listed above are open-source and are available for everyone to use. The talk will be mostly concentrated on typical use cases in monitoring and elegant ways to make things more efficient.
stackconf 2023 | How to reduce expenses on monitoring with VictoriaMetrics by...NETWAYS
Given recent economic changes, cost efficiency has become a top priority for many businesses. This is especially important for monitoring because the nature of telemetry data tends to exponential growth. Many monitoring solutions are now switching their focus to optimize costs. The talk will cover open-source instruments from VictoriaMetrics ecosystem for improving monitoring cost-efficiency.
Compression optimization. While ZSTD compression and time series specific techniques like delta-encoding are great, there is still room for improvement. I’ll explain what else can be done to reduce disk footprint for long-term storage.
Extra compression on data transferring between metrics collectors and TSDB. I’ll explain how VictoriaMetrics collector reduces the traffic volume by 4 times.
Pre-computing for telemetry data on collectors, frequently referred to as edge computing. In VictoriaMetrics it is named as streaming aggregation and allows collectors to pre-compute data, reducing its resolution and cardinality before it is pushed to the database. This is especially important for Prometheus-like systems because streaming aggregation is compatible with Prometheus RemoteWrite protocol and can be used with any system which supports it.
Cardinality explorer. Interface, which provides useful insights into data stored by the TSDB. It helps to identify the most expensive metrics or labels and see how they have changed in time.
Query tracing. This feature provides details about all the stages of query execution, including time spent on index lookups, disk reads, data transfer, computation, and memory expenses. This is similar to SQL EXPLAIN feature, and helps to improve the performance of read queries.
Compute efficiency. VictoriaMetrics components for collecting and storing telemetry data consume fewer resources compared to components from Prometheus ecosystem. It may sound like competition or bragging, but this is a real reason why people migrate from Prometheus to VictoriaMetrics – to cut their infrastructure costs by 2-3 times.
All the features listed above are open-source and are available for everyone to use. The talk will be mostly concentrated on typical use cases in monitoring and elegant ways to make things more efficient.
This document discusses parameters for tuning the performance of WebLogic servers. It covers OS-level TCP parameters, JVM heap size and GC logging parameters, WebLogic server-level parameters like work managers, execute queues, and stuck threads, and JDBC and JMS pool parameters. It also provides an overview of different types of garbage collection in the HotSpot JVM.
This document discusses performance engineering for batch and web applications. It begins by outlining why performance testing is important. Key factors that influence performance testing include response time, throughput, tuning, and benchmarking. Throughput represents the number of transactions processed in a given time period and should increase linearly with load. Response time is the duration between a request and first response. Tuning improves performance by configuring parameters without changing code. The performance testing process involves test planning, creating test scripts, executing tests, monitoring tests, and analyzing results. Methods for analyzing heap dumps and thread dumps to identify bottlenecks are also provided. The document concludes with tips for optimizing PostgreSQL performance by adjusting the shared_buffers configuration parameter.
This white paper presents a solution to test performance and analyze the results for web services that are deployed on the webMethods Integration Server using Apache JMeter.
This document summarizes a major project on dynamically scaling web applications in a virtualized cloud computing environment. The project is submitted by Mallika Malhotra and Sanya Kapoor to Mr. Prakash Kumar. The project proposes an algorithm and architecture to dynamically add or remove virtual machines based on resource usage to efficiently scale the system and reduce costs. Key technologies used include the Xen hypervisor, Java, Apache, Python, and Tomcat.
This document presents a master's thesis project that proposes algorithms for energy-efficient and traffic-aware virtual machine management in cloud computing. It introduces dynamic virtual machine consolidation as a way to reduce power consumption in data centers by migrating VMs between hosts. The author develops new algorithms that improve upon existing approaches by considering both CPU utilization and communication traffic between VMs when consolidating and placing VMs on hosts. The algorithms aim to increase energy efficiency while also reducing communication latency. The performance of the new algorithms is evaluated and shown to significantly reduce energy consumption, VM migrations, and SLA violations compared to existing approaches.
The document compares the Kepler GPU and Xeon Phi architectures through microbenchmarks that test memory-bound, compute-bound, and latency-bound workloads. It finds that for memory-bound workloads, vectorizing loads and using texture cache improves performance on Kepler, while gathering and aligned loads help Xeon Phi. For compute-bound workloads, vectorizing and using float4/double4 benefits Kepler, and intrinsics aid Xeon Phi. And for latency-bound workloads, loop interchange and skipping the L2 cache helps Kepler, while gathering and aligned loads assist Xeon Phi. The conclusion notes vendor performance data may differ from experiments and examples are available online.
El documento habla sobre la tecnología digital y cómo se relaciona con la comunicación inalámbrica, proporcionando acceso fácil a la información y nuevas oportunidades laborales. La tecnología digital se refiere a las herramientas y tecnologías que las personas usan para compartir, distribuir e intercambiar información de manera más frecuente a través de Internet y la informática.
Materi ini membahas tentang konsep-konsep dasar dalam suatu bidang studi seperti istilah-istilah penting, ringkasan materi, dan latihan soal untuk memperkuat pemahaman. Terdapat juga penjelasan visual berupa animasi, video, lagu, dan gambar.
The No Child Left Behind Act was established from 2001-2002 under President George W. Bush. It was proposed in January 2001 and passed by the House and Senate before being signed into law by Bush in January 2002. The act was based on the Elementary and Secondary Education Act of 1965 and aims to improve education standards and outcomes through standardized testing and measurable goals. It has been highly controversial since its inception regarding feasibility and fairness.
The No Child Left Behind Act was established from 2001-2002 under President George W. Bush. It was proposed in January 2001, passed the House of Representatives in May 2001, passed the Senate in June 2001, and was signed into law by President Bush in January 2002. The Act was based on the Elementary and Secondary Education Act of 1965 and supports standards-based education reforms through standardized testing to improve education outcomes.
HPC, Big Data & Data Center Explanation by Mert AkınMert Akın
This document discusses high performance computing (HPC), big data, and data centers. It provides details on:
- What HPC is and how it is used for scientific and engineering applications requiring large-scale processing power.
- How big data comes from both traditional and digital sources in various formats, and is used across industries like healthcare, retail, and banking.
- What a data center is and how it centralizes an organization's IT infrastructure and equipment to support applications and users. Major data center operators include telecom companies, IT firms, and cloud storage providers.
As telcos go digital, cybersecurity risks intensify by pwcMert Akın
globalaviationairospace.com
Cyber security for telecommunications companies
The rewards and risks of the cloud, devices, and data
The fastest growing sources of security incidents, increase over 2013
Security strategies for evolving technologies
Strategic initiatives to improve cybersecurity
This unofficial transcript is for Jason Hall who graduated from the University of Washington Tacoma campus in Spring 2016 with a Bachelor of Science in Computer Science and Systems. The transcript shows the courses taken at Pierce College and South Puget Sound Community College that transferred to UW Tacoma, as well as the courses completed at UW Tacoma. Jason maintained strong academic performance and earned placement on the Dean's List multiple times.
Talk from https://stackconf.eu/talks/how-to-reduce-expenses-on-monitoring-with-victoriametrics/
Given recent economic changes, cost efficiency has become a top priority for many businesses. This is especially important for monitoring because the nature of telemetry data tends to exponential growth. Many monitoring solutions are now switching their focus to optimize costs. The talk will cover open-source instruments from VictoriaMetrics ecosystem for improving monitoring cost-efficiency.
Compression optimization. While ZSTD compression and time series specific techniques like delta-encoding are great, there is still room for improvement. I’ll explain what else can be done to reduce disk footprint for long-term storage.
Extra compression on data transferring between metrics collectors and TSDB. I’ll explain how VictoriaMetrics collector reduces the traffic volume by 4 times.
Pre-computing for telemetry data on collectors, frequently referred to as edge computing. In VictoriaMetrics it is named as streaming aggregation and allows collectors to pre-compute data, reducing its resolution and cardinality before it is pushed to the database. This is especially important for Prometheus-like systems because streaming aggregation is compatible with Prometheus RemoteWrite protocol and can be used with any system which supports it.
Cardinality explorer. Interface, which provides useful insights into data stored by the TSDB. It helps to identify the most expensive metrics or labels and see how they have changed in time.
Query tracing. This feature provides details about all the stages of query execution, including time spent on index lookups, disk reads, data transfer, computation, and memory expenses. This is similar to SQL EXPLAIN feature, and helps to improve the performance of read queries.
Compute efficiency. VictoriaMetrics components for collecting and storing telemetry data consume fewer resources compared to components from Prometheus ecosystem. It may sound like competition or bragging, but this is a real reason why people migrate from Prometheus to VictoriaMetrics – to cut their infrastructure costs by 2-3 times.
All the features listed above are open-source and are available for everyone to use. The talk will be mostly concentrated on typical use cases in monitoring and elegant ways to make things more efficient.
stackconf 2023 | How to reduce expenses on monitoring with VictoriaMetrics by...NETWAYS
Given recent economic changes, cost efficiency has become a top priority for many businesses. This is especially important for monitoring because the nature of telemetry data tends to exponential growth. Many monitoring solutions are now switching their focus to optimize costs. The talk will cover open-source instruments from VictoriaMetrics ecosystem for improving monitoring cost-efficiency.
Compression optimization. While ZSTD compression and time series specific techniques like delta-encoding are great, there is still room for improvement. I’ll explain what else can be done to reduce disk footprint for long-term storage.
Extra compression on data transferring between metrics collectors and TSDB. I’ll explain how VictoriaMetrics collector reduces the traffic volume by 4 times.
Pre-computing for telemetry data on collectors, frequently referred to as edge computing. In VictoriaMetrics it is named as streaming aggregation and allows collectors to pre-compute data, reducing its resolution and cardinality before it is pushed to the database. This is especially important for Prometheus-like systems because streaming aggregation is compatible with Prometheus RemoteWrite protocol and can be used with any system which supports it.
Cardinality explorer. Interface, which provides useful insights into data stored by the TSDB. It helps to identify the most expensive metrics or labels and see how they have changed in time.
Query tracing. This feature provides details about all the stages of query execution, including time spent on index lookups, disk reads, data transfer, computation, and memory expenses. This is similar to SQL EXPLAIN feature, and helps to improve the performance of read queries.
Compute efficiency. VictoriaMetrics components for collecting and storing telemetry data consume fewer resources compared to components from Prometheus ecosystem. It may sound like competition or bragging, but this is a real reason why people migrate from Prometheus to VictoriaMetrics – to cut their infrastructure costs by 2-3 times.
All the features listed above are open-source and are available for everyone to use. The talk will be mostly concentrated on typical use cases in monitoring and elegant ways to make things more efficient.
This document discusses parameters for tuning the performance of WebLogic servers. It covers OS-level TCP parameters, JVM heap size and GC logging parameters, WebLogic server-level parameters like work managers, execute queues, and stuck threads, and JDBC and JMS pool parameters. It also provides an overview of different types of garbage collection in the HotSpot JVM.
This document discusses performance engineering for batch and web applications. It begins by outlining why performance testing is important. Key factors that influence performance testing include response time, throughput, tuning, and benchmarking. Throughput represents the number of transactions processed in a given time period and should increase linearly with load. Response time is the duration between a request and first response. Tuning improves performance by configuring parameters without changing code. The performance testing process involves test planning, creating test scripts, executing tests, monitoring tests, and analyzing results. Methods for analyzing heap dumps and thread dumps to identify bottlenecks are also provided. The document concludes with tips for optimizing PostgreSQL performance by adjusting the shared_buffers configuration parameter.
This white paper presents a solution to test performance and analyze the results for web services that are deployed on the webMethods Integration Server using Apache JMeter.
This document summarizes a major project on dynamically scaling web applications in a virtualized cloud computing environment. The project is submitted by Mallika Malhotra and Sanya Kapoor to Mr. Prakash Kumar. The project proposes an algorithm and architecture to dynamically add or remove virtual machines based on resource usage to efficiently scale the system and reduce costs. Key technologies used include the Xen hypervisor, Java, Apache, Python, and Tomcat.
This document presents a master's thesis project that proposes algorithms for energy-efficient and traffic-aware virtual machine management in cloud computing. It introduces dynamic virtual machine consolidation as a way to reduce power consumption in data centers by migrating VMs between hosts. The author develops new algorithms that improve upon existing approaches by considering both CPU utilization and communication traffic between VMs when consolidating and placing VMs on hosts. The algorithms aim to increase energy efficiency while also reducing communication latency. The performance of the new algorithms is evaluated and shown to significantly reduce energy consumption, VM migrations, and SLA violations compared to existing approaches.
The document provides information about performance testing with JMeter including example steps to write a JMeter script for a sample performance test scenario. The summary is:
The document discusses writing a JMeter script to simulate 30 concurrent users each sending 600 orders over 3 minutes to test the performance of an order processing system. It provides details on setting up thread groups, variables, CSV data configuration, HTTP requests, assertions and includes example XML payloads and JMeter script structure. Flexibility in JMeter is discussed including using BeanShell scripting and plugins to add custom functionality.
1) The document provides tips for optimizing performance on WebSphere DataPower devices by adjusting caching, enabling persistent connections, using processing rules efficiently, optimizing MQ and XSLT configurations, and leveraging synchronous and asynchronous actions appropriately.
2) It recommends creating a "facade service" to monitor and shape requests to external services like logging servers to prevent slow responses from impacting core transactions. This facade service would use monitors and service level management policies to control latencies.
3) Using separate delegate services with monitoring is suggested to avoid direct connections to external services that could become slow and bottleneck transactions if they degrade in performance.
Joget Workflow Clustering and Performance Testing on Amazon Web Services (AWS)Joget Workflow
The document describes setting up and testing the performance of Joget Workflow on Amazon Web Services (AWS) using various configurations. Key points:
1. Joget Workflow was deployed on AWS EC2 and RDS instances in a clustered configuration with load balancing.
2. Performance tests measured throughput and response times for up to 5000 concurrent users on single and multi-node clusters.
3. The results showed that a single node could handle 500 users, scaling out linearly improved performance, and a powerful single node handled 5000 users well.
Diesel load testing software is a comprehensive tool for stress testing a website.
Diesel Test is a software designed in Delphi 5, for systems under NT environment.
It is distributed under the GNU LGPL license.
Using Diesel load testing tool you will come to know about how your website will perform in the real world when hundreds, thousands, (or potentially millions) of users would place on your website.
It is designed to test Internet web sites (HTTP and HTTPS requests), with monitoring and graphical representations.
Dmv's & Performance Monitor in SQL ServerZeba Ansari
Dynamic management views and functions in SQL Server provide information to monitor server health, diagnose issues, and tune performance. There are server-scoped and database-scoped DMVs. Common DMV categories include database, execution, I/O, index, and operating system. The Performance Monitor tool in Windows collects counters related to physical disk, memory, CPU, and network usage to identify bottlenecks. High disk queue lengths, low available memory, or high processor utilization could indicate performance issues.
Prometheus Everything, Observing Kubernetes in the CloudSneha Inguva
The document discusses using Prometheus and Alertmanager for monitoring Kubernetes clusters at DigitalOcean. It describes the company's transition from traditional monitoring tools to Kubernetes and Prometheus. Key points include setting up Prometheus scrapers to collect metrics from Kubernetes services, configuring alerts in manifest files, and addressing common issues like alert fatigue and unclear ownership of alerts. The presentation outlines next steps like automating alerts and using metrics for auto-scaling and autopilot functions.
This document discusses performance aware software defined networking (SDN) using sFlow and OpenFlow. It describes how sFlow provides visibility into network performance by exporting packet samples and interface counters. When combined with OpenFlow's programmable control plane, sFlow and OpenFlow enable feedback control applications to monitor and control network performance in real-time. Examples given include using sFlow and OpenFlow for DDoS mitigation and load balancing large flows.
The objective of this article is to describe what to monitor in and around Alfresco in order to have a good understanding of how the applications are performing and to be aware of potential issues.
This document summarizes testing done on SAP NetWeaver Gateway for throughput, scalability, and high availability. Testing showed that gateway throughput increased linearly as CPU utilization increased. Gateway processing time also scaled as the number of fetched objects increased. Additional testing demonstrated that scaling out gateway instances improved throughput and that failover worked properly when an instance was taken offline. Load was also properly distributed between instances assigned to different logon groups.
This document outlines a performance test plan for Sakai 2.5.0. It describes the objectives, approach, test types, metrics, goals, tools, and data preparation. The objectives are to validate Sakai meets minimum performance standards and test any new or changed tools. Tests include capacity, consistent load, and single function stress tests. Metrics like response time, CPU utilization, and errors will be measured. Goals include average response time under 2.5s and max under 30s, CPU under 75%, and 500 concurrent users supported. Silk Performer will be used to run tests against a Sakai/Tomcat/Oracle environment. Over 92,000 students and 1,557 instructors of data will be preloaded
The document discusses benchmarking the performance of compute and database services on the cloud. It outlines procedures to test IOPS, network performance, and CPU usage of a compute instance and database throughput, connections, and queries per second of a DB service. Tests were run varying threads, block sizes, and parallel connections. The compute instance showed optimal IOPS at specific block sizes and higher write than read performance. Database performance increased with more connections but was limited by the server. Issues noted were inability to directly measure IOPS and lack of short-term monitoring data. Finally, a WordPress application was deployed on a compute instance connected to a database service to test performance.
"Surviving highload with Node.js", Andrii Shumada Fwdays
This document discusses highload systems and strategies for scaling Node.js applications to handle increased traffic. It recommends using multiple servers for redundancy and handling spikes in load. Key metrics for monitoring include status codes, backend latency, CPU and memory utilization, and event loop lag. Batching operations to third parties and sampling logs are suggested to reduce load. Offloading heavy tasks to workers can also help optimize performance. The document emphasizes monitoring systems closely and using as few servers as possible through optimization.
Similar to DiscoveredByte - Java Performance Monitoring, Tuning and Optimization - Key Performance Findings report - Demo (20)
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
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.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
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
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
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
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.