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Brief overview of image coding formats The JPEG format was introduced in 1992 and is widely popular. This is followed by quantization and entropy coding. With the motion extension, it was accepted as the video coding standard for digital cinema in 2004. Webp is based on intra-frame coding from the VP8 video coding format.
Infrastructure as code is a way to automate infrastructure provisioning and management. In this blog, I explore how Dynatrace has made cloud automation attainable—and repeatable—at scale by embracing the principles of infrastructure as code. Infrastructure-as-code. In response, Dynatrace introduced Monaco (Monitoring-as-code).
At Intel we've been creating a new analyzer tool to help reduce AI costs called AI Flame Graphs : a visualization that shows an AI accelerator or GPU hardware profile along with the full software stack, based on my CPU flame graphs. The gray "-" frames just help highlight the boundary between CPU and AI/GPU code.
Because container as a service doesn’t rely on a single code language or code stack, it’s platform agnostic. The emergence of Docker and other container services enabled companies to transport code quickly and easily. In FaaS environments, providers manage all the hardware. The classes of CaaS. CaaS vs. PaaS.
It enables multiple operating systems to run simultaneously on the same physical hardware and integrates closely with Windows-hosted services. Therefore, they experience how the application code functions and how the application operations depend on the underlying hardware resources and the operating system managed by Hyper-V.
Dynatrace has been building automated distributed application instrumentation—without the need to modify source code—for over 15 years already. Dynatrace PurePath technology captures and analyzes transactions end to end across every tier of your application technology stack, from the browser all the way down to the code and database level.
There are a few important details worth unpacking around monolithic observability as it relates to these qualities: The nature of a monolithic application using a single programming language can ensure all code uses the exact same logging standards, location, and internal diagnostics. Just as the code is monolithic, so is the logging.
The app automatically builds baselines, important reference points for analyzing the environmental impact of individual hardware or software instances. For ongoing optimization, carbon reduction practices can be embedded in coding principles. In other words, APM best practices are close to Green Coding best practices.
AWS Lambda is a serverless compute service that can run code in response to predetermined events or conditions and automatically manage all the computing resources required for those processes. Customizing and connecting these services requires code. What is AWS Lambda? Where does Lambda fit in the AWS ecosystem?
By leveraging Dynatrace observability on Red Hat OpenShift running on Linux, you can accelerate modernization to hybrid cloud and increase operational efficiencies with greater visibility across the full stack from hardware through application processes.
Software bugs Software bugs and bad code releases are common culprits behind tech outages. These issues can arise from errors in the code, insufficient testing, or unforeseen interactions among software components. These can be caused by hardware failures, or configuration errors, or external factors like cable cuts.
Indeed, according to one survey, DevOps practices have led to 60% of developers releasing code twice as quickly. But increased speed creates a tradeoff: According to another study, nearly half of organizations consciously deploy vulnerable code because of time pressure. Increased adoption of Infrastructure as code (IaC).
This allows teams to sidestep much of the cost and time associated with managing hardware, platforms, and operating systems on-premises, while also gaining the flexibility to scale rapidly and efficiently. Performing updates, installing software, and resolving hardware issues requires up to 17 hours of developer time every week.
Vulnerabilities or hardware failures can disrupt deployments and compromise application security. For instance, if a Kubernetes cluster experiences a hardware failure during deployment, it can lead to service disruptions and affect the user experience.
Next I started reading the Ninja source code. I wanted to find the precise code that delivers the audio data. I recognized a lot, but I started to lose the plot in the playback code and I needed help. I wanted to answer this question: where is the extra time?
CPU consumption in Unix/Linux operating systems is studied using eight different metrics: User CPU time, System CPU time, nice CPU time, Idle CPU time, Waiting CPU time, Hardware Interrupt CPU time, Software Interrupt CPU time, Stolen CPU time. User CPU time is the amount of time the processor spends in running your application code.
Sustainable memory bandwidth using multi-threaded code has closely followed the peak DRAM bandwidth, typically delivering best case throughput of 75%-85% of the peak DRAM bandwidth in each generation. Yes, but (on these Intel processors) only if the L2 hardware prefetchers are disabled. The same is true for software prefetches.)
The IBM Z platform is a range of mainframe hardware solutions that are quite frequently used in large computing shops. Typically, these shops run the z/OS operating system, but more recently, it’s not uncommon to see the Z hardware running special versions of Linux distributions.
We had some fun getting hardware figured out, and I used a 3D printer to make some cases, but the whole project was interrupted by the delivery of the iPhone by Apple in late 2007. I wonder if any of my code is still present in todays Netflixapps?) The code is still up on github.
To create a CPU core that can execute a large number of instructions in parallel, it is necessary to improve both the architecturewhich includes the overall CPU design and the instruction set architecture (ISA) designand the microarchitecture, which refers to the hardware design that optimizes instruction execution.
This gives you deep visibility into your code running in Azure Functions, and, as a result, an understanding of its impact on overall application performance and user experience. Code-level visibility continues to be supported for.NET-based functions running in an App Service plan. Optimize your code with code-level visibility.
They use the same hardware, APIs, tools, and management controls for both the public and private clouds. Amazon Web Services (AWS) Outpost : This offering provides pre-configured hardware and software for customers to run native AWS computing, networking, and services on-premises in a cloud-native manner.
Instead of worrying about infrastructure management functions, such as capacity provisioning and hardware maintenance, teams can focus on application design, deployment, and delivery. Using a low-code visual workflow approach, organizations can orchestrate key services, automate critical processes, and create new serverless applications.
Function as a service is a cloud computing model that runs code in small modular pieces, or microservices. Cloud providers then manage physical hardware, virtual machines, and web server software management. In a FaaS model, developers can write code functions on demand, without being hindered by dependencies on existing applications.
Security analytics solutions are designed to handle modern applications that rely on dynamic code and microservices. If the code doesn’t carry a known signature, it may gain access even if it contains malicious payloads. Infrastructure type In most cases, legacy SIEM tools are on-premises.
2% : of sales spent by consumer packaged goods companies on R&D (14% for tech); 272 million : metric tons of plastic are produced each year around the globe; 100+ fp s: Google's Edge TPU; 6,000 : bugs per million lines of code; 2.2 They'll learn a lot and love you forever.
Container technology is very powerful as small teams can develop and package their application on laptops and then deploy it anywhere into staging or production environments without having to worry about dependencies, configurations, OS, hardware, and so on. The time and effort saved with testing and deployment are a game-changer for DevOps.
For one Dynatrace customer, a hardware and software provider, introducing automation into DevOps processes was a game-changer. Today, with greater focus on DevOps and developer observability, engineers spend 70%-75% of their time writing code and increasing product innovation.
This gives you deep visibility into your code running in Azure Functions, and, as a result, an understanding of its impact on overall application performance and user experience. Code-level visibility continues to be supported for.NET-based functions running in an App Service plan. Optimize your code with code-level visibility.
The division by a power of two ( / (2 N )) can be implemented as a right shift if we are working with unsigned integers, which compiles to single instruction: that is possible because the underlying hardware uses a base 2. I make my benchmarking code available. uint32_t fastmod ( uint32_t n ) {. LLVM’s clang, GNU GCC).
The IBM Z platform is a range of mainframe hardware solutions that are quite frequently used in large computing shops. Typically, these shops run the z/OS operating system, but more recently, it’s not uncommon to see the Z hardware running special versions of Linux distributions.
In these modern environments, every hardware, software, and cloud infrastructure component and every container, open-source tool, and microservice generates records of every activity. Observability relies on telemetry derived from instrumentation that comes from the endpoints and services in your multi-cloud computing environments.
Limits of a lift-and-shift approach A traditional lift-and-shift approach, where teams migrate a monolithic application directly onto hardware hosted in the cloud, may seem like the logical first step toward application transformation. Likewise, refactoring and rewriting code takes a lot of time and effort.
A decade ago, while working for a large hosting provider, I led a team that was thrown into turmoil over the purchasing of server and storage hardware in preparation for a multi-million dollar super-bowl ad campaign. Take a look at the GKE cluster I instrumented in Las Vegas to see how tracing works without any special code commits.
Every hardware, software, cloud infrastructure component, container, open source tool, and microservice generates records of every activity within modern environments. Causes can run the gamut, from coding errors and database slowdowns to hosting or network performance issues.
It requires purchasing, powering, and configuring physical hardware, training and retaining the staff capable of servicing and securing the machines, operating a data center, and so on. They need enough hardware to serve their anticipated volume and keep things running smoothly without buying too much or too little. Reduced cost.
In fact, according to the recent Dynatrace survey, “ The state of AI 2024 ,” 95% of technology leaders are concerned that using generative AI to create code could result in data leakage and improper or illegal use of intellectual property. In this blog, Carolyn Ford recaps her discussion with Tracy Bannon about AI in the workplace.
This allows you to quickly see if there’s a specific hardware configuration or software version in your customer group that is affected or if the problem affects all your users. You’ll now find breakdowns for all relevant properties right on top of the crash-group page so that you can see patterns at a glance.
I’ve been playing around with an Arduino Uno recently, something new to me since I’ve always only used Raspberry Pi hardware. To develop and push code to an Arduino you need to use the Arduino Desktop IDE. In this tutorial I’m going to walk you through configuring Visual Studio Code for Arduino development.
This has not only led to AI acceleration being incorporated into common chip architectures such as CPUs, GPUs, and FPGAs but also mushroomed a class of dedicated hardware AI accelerators specifically designed to accelerate artificial neural networks and machine learning applications.
This is especially the case with microservices and applications created around multiple tiers, where cheaper hardware alternatives play a significant role in the infrastructure footprint. Here are details of the capabilities included in this release of OneAgent for Linux on the ARM platform: Deep-code monitoring.
Reducing CPU Utilization to now only consume 15% of initially provisioned hardware. In most cases, I’ve seen it’s either through bad coding, incorrect use of data access frameworks, or simply architecture that has grown over the years into something that became overly complex in terms of participating components and services.
That’s tremendous, especially when you see four of the six hours were introduced by customer code,” said Auer. First, he pointed to the infrastructure monitoring capabilities as critical to understanding the impact of hardware failures. More still, very little of that downtime was related to the SAP cloud platform.
While modern cloud systems simplify tasks — such as deploying apps and provisioning new hardware and servers — cloud environments can be surprisingly complex. “This facilitates what’s known as configuration as code or monitoring as code. ” Foundational observability paves the way for proactive cloud operations.
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