Memory prefetching has long boosted CPU caches and is increasingly vital for far-memory systems, where large portions of memory are offloaded to cheaper, remote tiers. While effective prefetching requires accurate prediction of future accesses, prior ML approaches have been limited to simulation or small-scale hardware. We introduce FarSight, the first Linux-based far-memory system to leverage deep learning by decoupling application semantics from runtime memory layout. This separation enables offline-trained models to predict access patterns over a compact ordinal vocabulary, which are resolved at runtime through lightweight mappings. Across four data-intensive workloads, FarSight delivers up to 3.6x higher performance than the state-of-the-art.

This paper presents our approach to accelerate computer architecture simulation by leveraging machine learning techniques. Traditional computer architecture simulations are time-consuming, making it challenging to explore different design choices efficiently. Our proposed model utilizes a combination of application features and micro-architectural features to predict the performance of an application. These features are derived from simulations of a small portion of the application. We demonstrate the effectiveness of our approach by building and evaluating a machine learning model that offers significant speedup in architectural exploration. This model demonstrates the ability to predict IPC values for the testing data with a root mean square error of less than 0.1.

The machine learning life cycle is a cyclical process that data science initiatives must go through. Machine learning encompasses a wide range of disciplines, from business jobs to data scientists and DevOps. The life cycle specifies each step that an organization/individual should take to extract tangible commercial value from machine learning. A detailed grasp of the ML model development life cycle will allow you to properly manage resources and acquire a better idea of where you stand in the process. MLOps, an abbreviation for Machine Learning Operations, is a key stage in the design of a data science project.

Traditional APM relies on monitoring code execution to indicate problems, an approach that used to be enough for consistent application performance. However, modern apps typically consist of millions of lines of code often running in containers. Moreover, these application environments are interconnected and encompass both on-premises and multi-cloud environments. For example, research found that a single application transaction crosses an average of 35 different technology systems or components. To further complicate troubleshooting, IT teams must manage a broad spectrum of noncritical components that affect application performance as well as complex hybrid ecosystems that include Kubernetes orchestrations and innumerable containers.

IT operations teams have a lot to juggle. They manage servers, networks, cloud infrastructure, user experience, application performance, and cybersecurity, often working independently of one another. Staffers are more often than not overworked, burdened with excessive alerts, and struggling to solve problems that involve multiple domains. Enter AIOps, a burgeoning field of technologies and strategies that inject artificial intelligence into IT operations in an effort to solve challenges face by IT operations teams by reducing false positives, using machine learning to spot problems before they occur, automating remediation, and seeing a holistic view of the enterprise. According to an October survey of IT leaders conducted by ZK Research and Masergy, 65% of companies are already using AIOps, and 94% say that AIOps is "important or very important" for managing network and cloud application performance.

Edge computing is a networked information technology (IT) design in which customer data is processed as near to the original source as feasible at the network's edge. Modern businesses rely on data to provide significant business insight and real-time management over essential business operations and processes. Large volumes of data may be routinely acquired from sensors and IoT devices running in real-time from remote places and hostile working environments virtually anywhere in the globe, and today's organisations are drowned in a sea of information. It's all about the location when it comes to edge computing. Data is created at a client terminal, including a user's computer, in traditional corporate computing.

One of the most critical steps in any operational machine learning (ML) pipeline is artificial intelligence (AI) serving, a task usually performed by an AI serving engine. AI serving engines evaluate and interpret data in the knowledgebase, handle model deployment, and monitor performance. They represent a whole new world in which applications will be able to leverage AI technologies to improve operational efficiencies and solve significant business problems. I have been working with Redis Labs customers to better understand their challenges in taking AI to production and how they need to architect their AI serving engines. To help, we've developed a list of best practices: If you are supporting real-time apps, you should ensure that adding AI functionality in your stack will have little to no effect on application performance.

Carbon Relay announced this week that its Red Sky platform for configuring and optimizing container applications using machine learning algorithms now also makes it possible to scale Kubernetes clusters more efficiently. Company CTO Ofer Idan says Carbon Relay has extended the machine learning algorithms it developed for its IT operations platform based on artificial intelligence (AIOps) to now include support for the Kubernetes Horizontal Pod Autoscaler (HPA). That capability can be employed to ensure application performance is maintained consistently as applications scale or prevent the overprovisioning of infrastructure resources, he says. The Red Sky platform is available in both open source and enterprise editions. The enterprise edition includes deep reinforcement learning capabilities to continually train the artificial intelligence (AI) agent, automatic Kubernetes application configuration, data sharing and advanced automation and scheduling capabilities.

The pace of digital transformation has notably picked up in the past decade, as enterprises invest in technology to retain their competitive edge and avoid having their market share eroded by disruptive newcomers. Organisations' ability to out-innovate their competitors in this way often requires a full-scale modernisation of the IT infrastructure stack underpinning their operations so they are better positioned to respond to the changing needs of their customers. For many enterprises, this process of modernisation has seen them look to invest in making their private, virtualised datacentres and server rooms more agile, responsive and easier to manage by investing in software-defined networking (SDN) technologies and automation tools. Such investments can help enterprises make better and more efficient use of their existing compute capacity, but that alone may not be enough to stave off competitive threats, prompting some IT leaders to weigh up a move to the public cloud. The benefits of such an approach are well-documented and proven, with the public cloud offering enterprises ready access to an almost infinite supply of cloud-based compute resources that can be set to auto-scale in line with peaks and troughs in demand, meaning enterprises only pay for what they use.

AIOps: Is it worth the hype or a necessary cog in IT? AIOps may be a new buzzword, but it is an advanced version of IT Ops that deals with big data operations along with Artificial Intelligence, machine learning, and data analytics. In simple words, AIOps refers to the automation of IT operations artificial intelligence (AI), freeing enterprise IT operations by inputs of operational data to achieve the ultimate data automation goals. It aims to help IT run more efficient operations, make better decisions, and support business productivity. Also, it plays a pivotal role in determining the relationship between the thousands of alerts that all the elements of an IT environment can now generate. This is because most AIOps models offer IT teams with more context, and actionable intelligence. Due to the current pandemic infected market, the hunger for AIOps in the IT landscape has risen.