Operating System - FCFS and Priority Scheduling Algorithm and Code Tamir Azrab
Presentation contains brief explanation of first come first serve algorithm it's code and some sample solvable example, Also includes priority scheduling it's algorithm, implementation and example.
Great time saver if you got presentation on same topics.
Evolution of Lead Generation and Marketinggrosocial
Lead generation has come a long way in the world of marketing. From the first newspaper ad to the first billboard to the first Superbowl commercial to the first social network. This is our step by step tribute to marketing and lead generation. Please enjoy The Evolution of Lead Gen.
Operating System - FCFS and Priority Scheduling Algorithm and Code Tamir Azrab
Presentation contains brief explanation of first come first serve algorithm it's code and some sample solvable example, Also includes priority scheduling it's algorithm, implementation and example.
Great time saver if you got presentation on same topics.
Evolution of Lead Generation and Marketinggrosocial
Lead generation has come a long way in the world of marketing. From the first newspaper ad to the first billboard to the first Superbowl commercial to the first social network. This is our step by step tribute to marketing and lead generation. Please enjoy The Evolution of Lead Gen.
Have you ever wondered how to speed up your code in Python? This presentation will show you how to start. I will begin with a guide how to locate performance bottlenecks and then give you some tips how to speed up your code. Also I would like to discuss how to avoid premature optimization as it may be ‘the root of all evil’ (at least according to D. Knuth).
Improving GStreamer performance on large pipelines: from profiling to optimiz...Luis Lopez
When using GStreamer for creating media middleware and media infrastructures performance becomes critical for achieving the appropriate scalability without degrading end-user QoE. However, GStreamer does not provide off-the-shelf tools for that objective.
In this talk, we present efforts carried out for improving the performance of the Kurento Media Server during the last year. We present our main principle: “you cannot improve what you cannot measure”. Developing on it, we introduce different techniques for benchmarking large GStreamer pipelines including callgrind, time profiling, gst-meta profiling, chain-profiling, etc. We present results for different pipeline configurations and topologies. After that, we introduce some evolutions for GStreamer which could be helpful for optimizing performance such as the pervasive use of buffer-lists, the introduction of thread-pools or the appropriate management of queues.
To conclude, we present some preliminary work carried out in the GStreamer community for implementing such optimization and we discuss their advantages and drawbacks.
Pragmatic Optimization in Modern Programming - Ordering Optimization ApproachesMarina Kolpakova
The slides give an idea about how to look pragmatically at software optimization and order optimization approaches according to this pragmatic point of view
Building of systems of automatic C/C++ code loggingPVS-Studio
Sometimes logging of an application's events is the only debugging method. The logging method's disadvantage is the large size of the code which you have to write manually to save the whole necessary information. The article touches upon the method allowing you to build a system of automatic logging of C/C++ code.
Data Structures for High Resolution, Real-time Telemetry at ScaleScyllaDB
The challenge within telemetry in real-time systems is that you need as many sources of telemetry as possible (Throughput, latency, Errors, CPU, and many more... ) but you can't pay for extra overhead when our users are expecting sub-ms ops that scale to millions of transactions per second.
In this talk, we'll describe how we're using and improving several OSS data structures to incorporate telemetry features at scale, and showcase why they do matter on scenarios in which we have Performance/Security/Ops issues.
This talk shows how to extract (structured) value from the huge amount of (unstructured) information that logs contain using InfluxData technologies.
Particularly the task is achieved using two pieces of code I wrote: the influxdata/go-syslog library and the Telegraf Syslog Input Plugin.
The slides demonstrate how to parse logs and to store consequent time-series data into InfluxDB.
At this point it is possible to visualize them via the new Chronograf's Log Viewer, eliciting new meaningful metrics to plot (eg., number of process OOM killed) processing them via a Kapacitor UDF.
The stack used to achieve this is:
- Telegraf with the syslog input plugin, which uses this blazing fast go-syslog parser
- Chronograf with its new Log Viewer
- InfluxDB
- Kapacitor
Companion source code and repository is at http://bit.ly/logs-2-metrics-influx-code
When things get messy, you need to use proper tools to solve your problems. We'll demonstrate how to use traceview and dmtracedump for debugging those nasty little bugs and errors.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Have you ever wondered how to speed up your code in Python? This presentation will show you how to start. I will begin with a guide how to locate performance bottlenecks and then give you some tips how to speed up your code. Also I would like to discuss how to avoid premature optimization as it may be ‘the root of all evil’ (at least according to D. Knuth).
Improving GStreamer performance on large pipelines: from profiling to optimiz...Luis Lopez
When using GStreamer for creating media middleware and media infrastructures performance becomes critical for achieving the appropriate scalability without degrading end-user QoE. However, GStreamer does not provide off-the-shelf tools for that objective.
In this talk, we present efforts carried out for improving the performance of the Kurento Media Server during the last year. We present our main principle: “you cannot improve what you cannot measure”. Developing on it, we introduce different techniques for benchmarking large GStreamer pipelines including callgrind, time profiling, gst-meta profiling, chain-profiling, etc. We present results for different pipeline configurations and topologies. After that, we introduce some evolutions for GStreamer which could be helpful for optimizing performance such as the pervasive use of buffer-lists, the introduction of thread-pools or the appropriate management of queues.
To conclude, we present some preliminary work carried out in the GStreamer community for implementing such optimization and we discuss their advantages and drawbacks.
Pragmatic Optimization in Modern Programming - Ordering Optimization ApproachesMarina Kolpakova
The slides give an idea about how to look pragmatically at software optimization and order optimization approaches according to this pragmatic point of view
Building of systems of automatic C/C++ code loggingPVS-Studio
Sometimes logging of an application's events is the only debugging method. The logging method's disadvantage is the large size of the code which you have to write manually to save the whole necessary information. The article touches upon the method allowing you to build a system of automatic logging of C/C++ code.
Data Structures for High Resolution, Real-time Telemetry at ScaleScyllaDB
The challenge within telemetry in real-time systems is that you need as many sources of telemetry as possible (Throughput, latency, Errors, CPU, and many more... ) but you can't pay for extra overhead when our users are expecting sub-ms ops that scale to millions of transactions per second.
In this talk, we'll describe how we're using and improving several OSS data structures to incorporate telemetry features at scale, and showcase why they do matter on scenarios in which we have Performance/Security/Ops issues.
This talk shows how to extract (structured) value from the huge amount of (unstructured) information that logs contain using InfluxData technologies.
Particularly the task is achieved using two pieces of code I wrote: the influxdata/go-syslog library and the Telegraf Syslog Input Plugin.
The slides demonstrate how to parse logs and to store consequent time-series data into InfluxDB.
At this point it is possible to visualize them via the new Chronograf's Log Viewer, eliciting new meaningful metrics to plot (eg., number of process OOM killed) processing them via a Kapacitor UDF.
The stack used to achieve this is:
- Telegraf with the syslog input plugin, which uses this blazing fast go-syslog parser
- Chronograf with its new Log Viewer
- InfluxDB
- Kapacitor
Companion source code and repository is at http://bit.ly/logs-2-metrics-influx-code
When things get messy, you need to use proper tools to solve your problems. We'll demonstrate how to use traceview and dmtracedump for debugging those nasty little bugs and errors.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
2. Goal
1) Ensure that developers can quickly get comprehensive log and
performance data without operations intervention.
2) Avoid a complete rewrite of code.
3) Avoid brittle regexp parsing that may change often.
2
3. Solution – DTM
●
- DTM stands for nothing
●
- Just a short string that rarely appears in the languages that I support
●
- Good intermediate step
3
5. Example Usage
Calls to some 3rd party API are failing intermittently and/or taking too
long
How do I figure out what is going on when there are several hundred
requests a second and there are 40 app servers?
I don't have the time to re-write all of the logging code to use JSON
5
12. DTM_STATS
- JSON blobs
Required fields
{"DTM_STATS":"outside_api.fetch",
1) DTM_STATS – stat name
"value":0.0001437664031982422,
"stat_type":"timer"}
2) value number
3) stat_type
timer, counter, gauge
12
13. DTM_STATS types
Counter Timer Timed events
Statsd provides the following
data count_ps count lower
mean_90 mean median std
sum_90 sum upper_90 upper
Rate events eg: number of
HTTP 200's a second
Gauge Number during a given time
period eg: 7 200's in the past
minute
13
15. Can be found in graphite
stats.timers.dtm_stats.application.heroku.staging.outside_api.fetch.mean_90
stats.guage.dtm_stats.application.heroku.staging.STAT
stats.counter.dtm_stats.application.heroku.staging.STAT
15
17. More Information
Graphite on EC2
http://shokunin.co/blog/2013/03/16/graphite_on_ec2.html
Elasticsearch for Logstash
http://shokunin.co/blog/2013/06/24/elasticsearch.html
Map visualizations
http://shokunin.co/blog/2013/05/10/maptail_plus_logstash.html
17