With the continuous expansion of the scale of cloud computing applications, artificial intelligence technologies such as Deep Learning and Reinforcement Learning have gradually become the key tools to solve the automated task scheduling of large-scale cloud computing systems. Aiming at the complexity and real-time requirement of task scheduling in large-scale cloud computing system, this paper proposes an automatic task scheduling scheme based on deep learning and reinforcement learning. Firstly, the deep learning technology is used to monitor and predict the parameters in the cloud computing system in real time to obtain the system status information. Then, combined with reinforcement learning algorithm, the task scheduling strategy is dynamically adjusted according to the real-time system state and task characteristics to achieve the optimal utilization of system resources and the maximum of task execution efficiency. This paper verifies the effectiveness and performance advantages of the proposed scheme in experiments, and proves the potential and application prospect of deep learning and reinforcement learning in automatic task scheduling in large-scale cloud computing systems.

Generative AI can generate new content, and it's been heralded as a groundbreaking technology with the potential to transform various industries. However, those working in the cloudops world who will be charged with running generative AI systems long term are beginning to voice their concerns. Although generative AI has many benefits, it also has the potential to cause harm to cloud computing operations. Today these are theoretical problems, but they will soon become a reality. Thus, it's helpful to talk about some of the more concerning issues before we fall in love with this technology--or at least prepare to tackle some of these issues before they cause real problems.

When legendary computer scientist Jim Gray accepted the Turing Award in 1999, he laid out a dozen long-range information technology research goals. One of those goals called for the creation of trouble-free server systems or, in Gray's words, to "build a system used by millions of people each day and yet administered and managed by a single part-time person." Gray envisioned a self-organizing "server in the sky" that would store massive amounts of data, and refresh or download data as needed. Today, with the emergence and rapid advancement of artificial intelligence (AI), machine learning (ML) and cloud computing, and Microsoft's development of Cloud Intelligence/AIOps, we are closer than we have ever been to realizing that vision--and moving beyond it. Over the past fifteen years, the most significant paradigm shift in the computing industry has been the migration to cloud computing, which has created unprecedented digital transformation opportunities and benefits for business, society, and human life.

Federal agencies that want to successfully scale and implement cloud computing systems into existing infrastructure can do so through several key practices, including designating expert teams, two-factor authentication, and enhanced education opportunities among users. Outlined in a White House report published earlier this month, officials documented how cloud computing systems can support further federal research and development in artificial intelligence, a goal within the broader Biden administration. Authored by the Machine Learning and Artificial Intelligence Subcommittee within the National Science and Technology Council, the report notes that leveraging cloud computing technology can enable better on-demand resources for researchers working with AI technologies. It went on to highlight opportunities for public agencies looking to bolster AI research efforts with advanced computing systems. "Agencies that have undertaken early efforts to leverage commercial cloud computing resources to advance AI R&D have commonly experienced benefits to their investments in terms of providing internal and external researchers persistent, on-demand access to cutting-edge capabilities, accelerating experimentation and the use of AI in new domains, and enabling reproducibility and scalability of the research activities and results," the report explains.

Modern cloud computing systems contain hundreds to thousands of computing and storage servers. Such a scale, combined with ever-growing system complexity, is causing a key challenge to failure and resource management for dependable cloud computing. Autonomic failure detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system-level dependability assurance. To detect failures, we need to monitor the cloud execution and collect runtime performance data. These data are usually unlabeled, and thus a prior failure history is not always available in production clouds. In this paper, we present a \emph{self-evolving anomaly detection} (SEAD) framework for cloud dependability assurance. Our framework self-evolves by recursively exploring newly verified anomaly records and continuously updating the anomaly detector online. As a distinct advantage of our framework, cloud system administrators only need to check a small number of detected anomalies, and their decisions are leveraged to update the detector. Thus, the detector evolves following the upgrade of system hardware, update of the software stack, and change of user workloads. Moreover, we design two types of detectors, one for general anomaly detection and the other for type-specific anomaly detection. With the help of self-evolving techniques, our detectors can achieve 88.94\% in sensitivity and 94.60\% in specificity on average, which makes them suitable for real-world deployment.

Identifying the failure modes of cloud computing systems is a difficult and time-consuming task, due to the growing complexity of such systems, and the large volume and noisiness of failure data. This paper presents a novel approach for analyzing failure data from cloud systems, in order to relieve human analysts from manually fine-tuning the data for feature engineering. The approach leverages Deep Embedded Clustering (DEC), a family of unsupervised clustering algorithms based on deep learning, which uses an autoencoder to optimize data dimensionality and inter-cluster variance. We applied the approach in the context of the OpenStack cloud computing platform, both on the raw failure data and in combination with an anomaly detection pre-processing algorithm. The results show that the performance of the proposed approach, in terms of purity of clusters, is comparable to, or in some cases even better than manually fine-tuned clustering, thus avoiding the need for deep domain knowledge and reducing the effort to perform the analysis. In all cases, the proposed approach provides better performance than unsupervised clustering when no feature engineering is applied to the data. Moreover, the distribution of failure modes from the proposed approach is closer to the actual frequency of the failure modes.

Intel plays a lot of roles in the IT business besides making processors with microscopic transistors for servers, PCs, the internet of things, and mobile devices. It also makes security hardware and software, memory and programmable enterprise solutions, 5G connectivity hardware and software and a list of others too long to note here. But one of the greenest fields coming into the venerable chipmaker's view here in mid-2018 has to do with what's called "the edge"--that mysterious, nebulous and more distributed area outside the data center where a lot of computing is starting to happen and will be happening more and more as time goes on. We're hearing a lot about this lately, largely because our devices (smartphones, laptops, tablets, IoT devices) on the fringes of centralized systems can hold much more information and do more with it than in years past. Intel wants to make more and more of the infrastructure for these devices and systems.

Artificial intelligence is set to take a key role in the design and engineering of new cars, dreaming up lighter, stronger and more complex structures than humans can envision. Just as computing power exceeds the mathematical capability of the human mind, smart software capable of innovation and problem solving is set to push product development into new territory. Hack Rod, a team of designers, engineers, geeks, Hollywood insiders and stunt drivers is working on a way to harness the power of artificial intelligence in tandem with powerful design software produced by Autodesk. Experimenting with connectivity surrounding the emerging "internet of things", the Hack Rod crew built a basic sports car, fitted it with dozens of race car-like sensors, and set about testing, racing and crashing the vehicle. They then fed millions of data points into a computer powered by NVidia processors capable of machine learning, and asked Autodesk's "Dreamcatcher" software to take that information and use it to design a better car.

Artificial intelligence is set to take a key role in the design and engineering of new cars, dreaming up lighter, stronger and more complex structures than humans can envision. Just as computing power exceeds the mathematical capability of the human mind, smart software capable of innovation and problem solving is set to push product development into new territory. Hack Rod, a team of designers, engineers, geeks, Hollywood insiders and stunt drivers is working on a way to harness the power of artificial intelligence in tandem with powerful design software produced by Autodesk. Experimenting with connectivity surrounding the emerging "internet of things", the Hack Rod crew built a basic sports car, fitted it with dozens of race car-like sensors, and set about testing, racing and crashing the vehicle. They then fed millions of data points into a computer powered by NVidia processors capable of machine learning, and asked Autodesk's "Dreamcatcher" software to take that information and use it to design a better car.