Disk scrubbing is a process aimed at resolving read errors on disks by reading data from the disk. However, scrubbing the entire storage array at once can adversely impact system performance, particularly during periods of high input/output operations. Additionally, the continuous reading of data from disks when scrubbing can result in wear and tear, especially on larger capacity disks, due to the significant time and energy consumption involved. To address these issues, we propose a selective disk scrubbing method that enhances the overall reliability and power efficiency in data centers. Our method employs a Machine Learning model based on Mondrian Conformal prediction to identify specific disks for scrubbing, by proactively predicting the health status of each disk in the storage pool, forecasting n-days in advance, and using an open-source dataset. For disks predicted as non-healthy, we mark them for replacement without further action. For healthy drives, we create a set and quantify their relative health across the entire storage pool based on the predictor's confidence. This enables us to prioritize selective scrubbing for drives with established scrubbing frequency based on the scrub cycle. The method we propose provides an efficient and dependable solution for managing enterprise disk drives. By scrubbing just 22.7% of the total storage disks, we can achieve optimized energy consumption and reduce the carbon footprint of the data center.

Google has devised a machine learning (ML) model that predicts disk failures with 98 per cent accuracy. The idea is to reduce data recovery work when disks actually fail. According to a Google blog by technical program manager Nitin Agarwal and AI engineer Rostam Dinyari, Google has millions of hard disk drives (HDDs) under management, some of which fail. "Any misses in identifying these failures at the right time can potentially cause serious outages across our many products and services." When a disk in Google's data centres encounters non-fatal problems, short of an actual crash, then data is (drained) read from the drive. The drive is then disconnected from production use, they apply diagnostics and it is fixed and returned to production.

It is an exciting time in video game world: two new consoles, the Xbox Series X and PlayStation 5, are arriving this month. With a long, lonely Covid winter ahead, it is tempting to splash out. New machines bring with them the promise of new worlds, as leaps in technology unlock creative possibilities for game developers. Throughout the 1980s, 90s and 00s, there was a transformational shift every five or so years, blowing apart people's expectations of what you could do in a game, how big a virtual world could be and how you could explore it. It is not quite like that any more.

Our thirst for data is great. Our desire to be connected, at all times, means that by 2025 an average connected person will interact with connected devices one interaction every 18 seconds. The stat includes using smart home security, smart TVs and more – nearly 4,800 times a day. But how is AI impacting surveillance data storage? As our world becomes increasingly connected, the vast amounts of data being created are also enabling us to refine and improve systems and processes. We are seeing this from security through to smart cities and AI.

One of only two surviving prototypes of the original Apple Macintosh computer will go up for auction this week – at an asking price of £155,000. The prototype, which was made in 1983, features the aborted 5.25-inch'Twiggy' disk drive, and is going under the hammer at Bonhams in New York on Wednesday. The Macintosh began as a personal project of inventor Jef Raskin before the late Apple founder Steve Jobs took it over. The original plan was to use a 5.25-inch drive to greatly expand the capacity of standard floppy discs. But they proved unreliable, so a 3.5 inch drive, which was more robust and small enough to fit in a shirt pocket, was chosen instead for mass production.

With the advent of pervasive cloud computing technologies, service reliability and availability are becoming major concerns,especially as we start to integrate cyber-physical systems with the cloud networks. A number of smart and connected community systems such as emergency response systems utilize cloud networks to analyze real-time data streams and provide context-sensitive decision support.Improving overall system reliability requires us to study all the aspects of the end-to-end of this distributed system,including the backend data servers. In this paper, we describe a bi-layered prognostic architecture for predicting the Remaining Useful Life (RUL) of components of backend servers,especially those that are subjected to degradation. We show that our architecture is especially good at predicting the remaining useful life of hard disks. A Deep LSTM Network is used as the backbone of this fast, data-driven decision framework and dynamically captures the pattern of the incoming data. In the article, we discuss the architecture of the neural network and describe the mechanisms to choose the various hyper-parameters. We describe the challenges faced in extracting effective training sets from highly unorganized and class-imbalanced big data and establish methods for online predictions with extensive data pre-processing, feature extraction and validation through test sets with unknown remaining useful lives of the hard disks. Our algorithm performs especially well in predicting RUL near the critical zone of a device approaching failure.The proposed architecture is able to predict whether a disk is going to fail in next ten days with an average precision of 0.8435.In future, we will extend this architecture to learn and predict the RUL of the edge devices in the end-to-end distributed systems of smart communities, taking into consideration context-sensitive external features such as weather.

It drains your productivity, builds frustration, and ultimately makes the broader computing experience one big mess. You can dramatically improve your PC's performance with these tools. But there's also little you can do to stop the slow descent into obsolescence. The technology world is changing at a rapid rate and as technology improves and new computers, apps, and services launch, older machines become old and less relevant. Still, there are ways to extend the longevity of your machine.

This week's milestones in the history of technology include Microsoft unleashing MS-DOS and Windows, the first Turing Test and the introduction of the Turing Machine, and IBM launching a breakthrough in computer storage technology. IBM and Microsoft sign a contract under which Microsoft will develop an operating system for IBM's upcoming personal computer (PC). To meet its obligations, Microsoft acquired an existing product developed by a Seattle company for the Intel 8086 CPU card, originally called Quick and Dirty Operating System (QDOS). IBM released the Microsoft operating system with its first PC in 1981. Within a year Microsoft licensed the software (MS-DOS) to over 70 other companies, making it the dominant PC software company for years to come.

Increase sequential processing speed enough to make it feasible on a single machine (i.e., speed hacks).

Programmers are loathe to interrupt their workflow to document their design rationale, leading to frequent errors when software is modified — often much later and by different programmers. A Programmer’s Assistant could interact with the programmer to capture and preserve design rationale, in a natural way that would make rationale capture "cost less than it's worth", and could also detect common flaws in program design. Such a programmer’s assistant was not practical when it was first proposed decades ago, but advances over the years make now the time to revisit the concept, as our prototype shows.