Online task scheduling serves an integral role for task-intensive applications in cloud computing and crowdsourcing. Optimal scheduling can enhance system performance, typically measured by the reward-to-cost ratio, under some task arrival distribution. On one hand, both reward and cost are dependent on task context (e.g., evaluation metric) and remain black-box in practice. These render reward and cost hard to model thus unknown before decision making. On the other hand, task arrival behaviors remain sensitive to factors like unpredictable system fluctuation whereby a prior estimation or the conventional assumption of arrival distribution (e.g., Poisson) may fail. This implies another practical yet often neglected challenge, i.e., uncertain task arrival distribution. Towards effective scheduling under a stationary environment with various uncertainties, we propose a double-optimistic learning based Robbins-Monro (DOL-RM) algorithm. Specifically, DOL-RM integrates a learning module that incorporates optimistic estimation for reward-to-cost ratio and a decision module that utilizes the Robbins-Monro method to implicitly learn task arrival distribution while making scheduling decisions. Theoretically, DOL-RM achieves convergence gap and no regret learning with a sub-linear regret of $O(T^{3/4})$, which is the first result for online task scheduling under uncertain task arrival distribution and unknown reward and cost. Our numerical results in a synthetic experiment and a real-world application demonstrate the effectiveness of DOL-RM in achieving the best cumulative reward-to-cost ratio compared with other state-of-the-art baselines.

Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership. Effective resource sharing, however, remains an open challenge due to the adverse effects that resource contention can have on high-priority, user-facing workloads with strict Quality of Service (QoS) requirements. Although recent approaches have demonstrated promising results, those works remain largely impractical in public cloud environments since workloads are not known in advance and may only run for a brief period, thus prohibiting offline learning and significantly hindering online learning. In this paper, we propose RAPID, a novel framework for fast, fully-online resource allocation policy learning in highly dynamic operating environments. RAPID leverages lightweight QoS predictions, enabled by domain-knowledge-inspired techniques for sample efficiency and bias reduction, to decouple control from conventional feedback sources and guide policy learning at a rate orders of magnitude faster than prior work. Evaluation on a real-world server platform with representative cloud workloads confirms that RAPID can learn stable resource allocation policies in minutes, as compared with hours in prior state-of-the-art, while improving QoS by 9.0x and increasing best-effort workload performance by 19-43%.

We're excited to announce that the Microsoft Azure Connected Learning Experience (CLX) program now has three new Data and AI tracks designed for data professionals. Personalized, self-paced, and culminating in a certificate of completion, these courses help you boost your data and AI skills your way--allowing you to maximize your learning in minimal time. CLX is a four-step learning program that helps aspiring learners and IT professionals build skills on the latest topics in cloud services by providing learners with a mix of self-paced, interactive labs and virtual sessions led by Microsoft tech experts. At the start of the program, you'll take a 20-question Knowledge Assessment to test your skills. Based on your results, you'll receive customized course content that fits your experience--so you can focus only on the information that's useful for you.

This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

Academic Earth More than 1,500 video lectures by professors from Harvard, Yale, broken down into single classes on topics like art, architecture, and astronomy.

Are you looking for the Best Pluralsight Courses in 2023? This Pluralsight Learning paths list contains the Best & Free Pluralsight Tutorials, Classes, and Certifications. Today's world needs people who are technologically advanced. Pluralsight gives you the opportunity to be skillful through the Pluralsight Specialization Courses. You can also get Free Pluralsight Online Courses. By enrolling in Pluralsight Learning Path courses everyone can have the opportunity to create progress through technology and develop the skills of tomorrow. With assessment, learning paths, and courses authorized by industry experts, this platform helps businesses and individuals benchmark expertise across roles, speed up release cycles and build reliable, secure products. Choose from a number of batches as per your convenience if you got something urgent to do, reschedule your batch for a later time. If you want to get started with top Pluralsight free courses check out the Pluralsight course catalog from ...

Are you looking for the Best Pluralsight Courses in 2022? This Pluralsight Learning paths list contains the Best & Free Pluralsight Tutorials, Classes, and Certifications. Today's world needs people who are technologically advanced. Pluralsight gives you the opportunity to be skillful through the Pluralsight Specialization Courses. You can also get Free Pluralsight Online Courses. By enrolling in Pluralsight Learning Path courses everyone can have the opportunity to create progress through technology and develop the skills of tomorrow. With assessment, learning paths, and courses authorized by industry experts, this platform helps businesses and individuals benchmark expertise across roles, speed up release cycles and build reliable, secure products. Choose from a number of batches as per your convenience if you got something urgent to do, reschedule your batch for a later time. If you want to get started with top Pluralsight free courses check out the Pluralsight course catalog from ...

In this paper we discuss our experience in teaching the Robotic Applications Programming course at ZHAW combining the use of a Kubernetes (k8s) cluster and real, heterogeneous, robotic hardware. We discuss the main advantages of our solutions in terms of seamless simulation-to-real experience for students and the main shortcomings we encountered with networking and sharing GPUs to support deep learning workloads. We describe the current and foreseen alternatives to avoid these drawbacks in future course editions and propose a more cloud-native approach to deploying multiple robotics applications on a k8s cluster.

With more companies leveraging software that runs on the Cloud, there is a growing need to find and hire individuals with the skills needed to build solutions on a variety of Cloud platforms. Employers agree: Cloud talent is hard to find. This Specialization is designed to address the Cloud talent gap by providing training to anyone interested in developing the job-ready, pragmatic skills needed for careers that leverage Cloud-native technologies. In the first course, you will learn how to build foundational Cloud computing infrastructure, including websites involving serverless technology and virtual machines, using the best practices of DevOps. The second course will teach you how to build effective Microservices using technologies like Flask and Kubernetes that are continuously deployed to a Cloud platform: Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP).

The first day of the course is free, so register today!