Working set size estimation (WSS) is of great significance to improve the efficiency of program executing and memory arrangement in modern operating systems. Previous work proposed several methods to estimate WSS, including self-balloning, Zballoning and so on. However, these methods which are based on virtual machine usually cause a large overhead. Thus, using those methods to estimate WSS is impractical. In this paper, we propose a novel framework to efficiently estimate WSS with eBPF (extended Berkeley Packet Filter), a cutting-edge technology which monitors and filters data by being attached to the kernel. With an eBPF program pinned into the kernel, we get the times of page fault and other information of memory allocation. Moreover, we collect WSS via vanilla tool to train a predictive model to complete estimation work with LightGBM, a useful tool which performs well on generating decision trees over continuous value. The experimental results illustrate that our framework can estimate WSS precisely with 98.5\% reduction in overhead compared to traditional methods.

You'll be responsible for successful delivery, reporting, and use of budget across the account. In the course of this work, you'll manage relationships across your key client's organization, as well as within Myplanet, and with our partners. Is This Role a Fit for You? The Sr. CSM, Program Lead role emphasizes exceptional interpersonal skills, adept task planning and management, and a thorough understanding of the retail space. You'll find fulfillment in the role if you: Our environment is not your average, hierarchical software studio: we emphasize accountability for outcomes, but autonomy in activities.

A global panel will consider how to define ethical leadership and the particular challenges posed by emerging technologies in a virtual event from 1-1:45 p.m. ET on Oct. 28. "Defining Ethical Leadership" is free and open to the public. Those who wish to participate may register online. The event is made possible by a grant from Lilly Endowment Inc. to support Leading Ethically in the Age of AI and Big Data, an initiative designed to develop curricula to foster character and ethical values in future leaders, preparing them to respond appropriately to the challenges posed by rapidly evolving technologies, such as artificial intelligence and Big Data management. "As we embark upon the work of our Lilly Endowment grant, a thoughtful conversation about how we define ethical leadership offers an appropriate starting point," said David Reingold, the Justin S. Morrill Dean of Liberal Arts and professor of sociology at Purdue, principal investigator for the grant.

For the past couple of decades, the trend in technological development has been toward maximizing the capacities of computers and machines to do tasks that people would rather not do, or at least ones that machines could do cheaper. In an interview with Raconteur, chief strategy and innovation officer for The Future Laboratory Tracey Fellows predicts that 35 percent of jobs currently done by humans could be taken over by robots one day, and those would include jobs that are either tedious or dangerous, saving innumerable hours and lives. The landmark achievement of the 21st century is, arguably, artificial intelligence (AI). With the writing of some strings of code, machines can now perceive their environment, process relevant information, and execute actions that provide the highest probability of success. Further innovations in artificial intelligence are often driven by the elimination of human error in day-to-day tasks (e.g., self-driving cars).

Computers are getting pretty good at predicting the future. In many cases they do it better than people. That's why Amazon uses them to figure out what you're likely to buy, how Netflix knows what you might want to watch, the way meteorologists come up with accurate 10-day forecasts. Now a team of scientists has demonstrated that a computer can outperform human judges in predicting who will commit a violent crime. In a paper published last month, they described how they built a system that started with people already arrested for domestic violence, then figured out which of them would be most likely to commit the same crime again.