Efficient resource utilization and perfect user experience usually conflict with each other in cloud computing platforms. Great efforts have been invested in increasing resource utilization but trying not to affect users' experience for cloud computing platforms. In order to better utilize the remaining pieces of computing resources spread over the whole platform, deferrable jobs are provided with a discounted price to users. For this type of deferrable jobs, users are allowed to submit jobs that will run for a specific uninterrupted duration in a flexible range of time in the future with a great discount. With these deferrable jobs to be scheduled under the remaining capacity after deploying those on-demand jobs, it remains a challenge to achieve high resource utilization and meanwhile shorten the waiting time for users as much as possible in an online manner. In this paper, we propose an online deferrable job scheduling method called \textit{Online Scheduling for DEferrable jobs in Cloud} (\OSDEC{}), where a deep reinforcement learning model is adopted to learn the scheduling policy, and several auxiliary tasks are utilized to provide better state representations and improve the performance of the model. With the integrated reinforcement learning framework, the proposed method can well plan the deployment schedule and achieve a short waiting time for users while maintaining a high resource utilization for the platform. The proposed method is validated on a public dataset and shows superior performance.

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.

Cloud adoption is accelerating fast in enterprises surging towards modernity. But are there better ways of utilizing the full potential of cloud computing? Leaving behind the constraints of a single cloud computing platform, you will find various other arrangements like hybrid and multi-cloud computing. The annual RightScale State of the Cloud Report suggests, 90% of respondents believe that multi-cloud is already the most common pattern with businesses and enterprises. So, let's delve into understanding more about multi-cloud for modern enterprises.

The crucial component making smart technologies possible – from something as small as a ring to as large as an entire city – is the IoT. Although there are varying definitions, the term IoT is mainly used for previously'dumb' devices that didn't have an Internet connection, but that now communicate with the network independently of human action. For this reason, a smartphone isn't explicitly defined as an IoT device – although it's crammed with sensors. A connected refrigerator or microwave oven however is. Nowadays, these smart technology devices devices include billions of objects of all shapes and sizes – coffee machines, lightbulbs, driver-less trucks, wearable fitness devices, jet engines and children's smart toys – all equipped with sensors and communicating data through the Internet.

Rapid advancement in Artificial Intelligence is reforming almost every industry in the world. Internet of Things and Edge Computing became dominant in recent years. Lets explore how the combination of Artificial Intelligence and Internet of Things is making a new branch of emerging technology called Artificial Intelligence of Things(AIoT). The application can range from smart home speakers to self driving cars. Both terms, Artificial Intelligence and Internet of Things, are buzzwords. This means we did already hear about them and have a notion of what each terms means independently, While Artificial Intelligence is the use of machine intelligence to infer things, Internet of Things, on the other hand, is the interconnected collection of sensor devices which collects useful data.

Alphabet's (NASDAQ: GOOG) (NASDAQ: GOOGL) Google gets lots of attention for its artificial intelligence (AI) pursuits, mainly because the company is so good at applying AI to a variety of businesses. Consider that Google's core advertising business uses AI to determine which ads to serve online users, and the company's AI assistant, Google Assistant, is one of the best voice-search services available. Or that Waymo, formerly Google's self-driving car project, has driven 11 billion autonomous miles on public roads and even has its own commercial autonomous-vehicle ride-hailing service. But aside from Google's impressive AI moves, plenty of other players are applying AI to key parts of their businesses, and should benefit from what is projected to be a $15.7 trillion market by 2030. The tech giant is one of the biggest cloud computing companies in the world right now, but it also has its sights set on AI.

NEW DELHi: The NITI Aayog has drawn up a plan for creating an institutional framework for artificial intelligence (AI) in the country. It has circulated a cabinet note to provide Rs 7,500 crore in funding for creation of cloud computing platform called AIRAWAT and research institutes. A senior government official told ET that the Aayog has already circulated the note for consideration by the Expenditure Finance Committee, which is expected to take it up soon. The note proposes that the new government pump in Rs 7,500 crore initially over a three-year period and set up a high-level taskforce to oversee roll-out and implementation of AI, the official said on condition of anonymity. "A cabinet note is ready... We would present it to the new government as we want an institutional framework as well as a transparent policy in place for AI," said the official.

Anomaly detecting as an important technical in cloud computing is applied to support smooth running of the cloud platform. Traditional detecting methods based on statistic, analysis, etc. lead to the high false-alarm rate due to non-adaptive and sensitive parameters setting. We presented an online model for anomaly detecting using machine learning theory. However, most existing methods based on machine learning linked all features from difference sub-systems into a long feature vector directly, which is difficult to both exploit the complement information between sub-systems and ignore multi-view features enhancing the classification performance. Aiming to this problem, the proposed method automatic fuses multi-view features and optimize the discriminative model to enhance the accuracy. This model takes advantage of extreme learning machine (ELM) to improve detection efficiency. ELM is the single hidden layer neural network, which is transforming iterative solution the output weights to solution of linear equations and avoiding the local optimal solution. Moreover, we rank anomies according to the relationship between samples and the classification boundary, and then assigning weights for ranked anomalies, retraining the classification model finally. Our method exploits the complement information between sub-systems sufficiently, and avoids the influence from imbalance dataset, therefore, deal with various challenges from the cloud computing platform. We deploy the privately cloud platform by Openstack, verifying the proposed model and comparing results to the state-of-the-art methods with better efficiency and simplicity.

At Microsoft's Build Developers Conference, The Company Is Announcing A Preview Of Project Brainwave Integrated With Azure Machine Learning, Which The Company Says Will Make Azure The Most Efficient Cloud Computing Platform For AI Project Brainwave is a hardware architecture designed to accelerate real-time AI calculations. The Project Brainwave architecture is deployed on a type of computer chip from Intel called a field programmable gate array, or FPGA, to make real-time AI calculations at competitive cost and with the industry's lowest latency, or lag time. This is based on internal performance measurements and comparisons to other organization's publicly posted information. At Microsoft's Build developers conference in Seattle this week, the company is announcing a preview of Project Brainwave integrated with Azure Machine Learning, which the company says will make Azure the most efficient cloud computing platform for AI. According to Allison Linn, senior content manager at Microsoft – in her blog – Mark Russinovich, chief technical officer for Microsoft's Azure cloud computing platform, said the preview of Project Brainwave marks the start of Microsoft's efforts to bring the power of FPGAs to customers for a variety of purposes.

NVIDIA (NASDAQ:NVDA) and Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL) have turned out to be the unlikeliest of rivals in a slugfest for a greater share of the artificial intelligence (AI) market. So far, Alphabet has been using NVIDIA's GPUs (graphics processing units) to power AI applications on the Google Cloud Platform, though it looks like the search giant has now decided to go it alone in this lucrative space. Let's take a closer look at NVIDIA and Google's AI feud and the potential implications for both companies. Alphabet revealed its plans for its own AI chip -- the Tensor Processing Unit (TPU) -- at last year's Google I/O conference. The TPU chip was already deployed across a variety of applications, including optimizing search results and speech recognition, and in Alphabet's data centers.