Data centers are set to grow and become more complex, survey finds

Companies will invest more in data centers in the coming years, but it won’t necessarily be around compute. That’s according to a new survey by AFCOM, the data center and IT management education company.

This is AFCOM’s first study on the subject in two years, and it found that ownership, renovations, and building of new data centers were on the increase. It found 58 percent of survey respondents currently own between two and nine data centers and that on average, 5.3 data centers will be renovated per organization. That number increases to 7.8 data centers over the course of 12 months.

Once again, we see the notion of people shutting down their data centers and moving everything to the cloud is evaporating.

To read this article in full, please click here

Source: Network World

BrandPost: Discover a New Way to Save: Green Aps

pat

Patrick LaPorteBlog Contributor Full bio

Over the last decade, mobile has fueled unprecedented innovation. The economy is booming, and productivity has reached new heights. But the reality is that technology we use every day – mobile devices and applications included – consume an organization’s finite resources. With technology fueling customer experience and employee productivity across offices, schools and stores, organizations are always looking for ways to conserve energy and reduce costs.

To read this article in full, please click here

Source: Network World

Security updates for Tuesday

Security updates have been issued by Debian (gnuplot and samba), Fedora (flatpak, kernel-headers, kernel-tools, mariadb-connector-c, php-PHPMailer, php-phpmailer6, and xml-security-c), Gentoo (binutils, libav, mupdf, spice-gtk, strongswan, and tablib), Mageia (libpng(12), mariadb, and openssl), Oracle (ghostscript), Red Hat (.NET Core, ghostscript, java-1.7.1-ibm, kernel, kernel-alt, kernel-rt, NetworkManager, rh-nginx112-nginx, rh-nginx114-nginx, and sos-collector), Scientific Linux (389-ds-base, binutils, curl and nss-pem, fuse, git, glibc, glusterfs, GNOME, gnutls, jasper, java-1.7.0-openjdk, java-11-openjdk, kernel, krb5, libcdio, libkdcraw, libmspack, libreoffice, libvirt, openssl, ovmf, python, python-paramiko, samba, setup, sssd, thunderbird, wget, wpa_supplicant, X.org X11, xerces-c, xorg-x11-server, zsh, and zziplib), SUSE (dom4j, glib2, java-1_7_0-ibm, java-1_7_1-ibm, openssh, postgresql94, procps, qemu, and tiff), and Ubuntu (samba).

Source: LWN

The Linux Foundation and RISC-V Foundation Announce Joint Collaboration to Enable a New Era of Open Architecture

RISC-V Foundation to leverage the Linux Foundation’s tools infrastructure, services and training programs

SAN FRANCISCO and BERKELEY, CA – Nov. 27, 2018 –The Linux Foundation, the nonprofit organization enabling mass innovation through open source, and the RISC-V Foundation, a non-profit corporation controlled by its members to drive the adoption and implementation of the free and open RISC-V instruction set architecture (ISA), today announced a joint collaboration agreement to accelerate open source development and adoption of the RISC-V ISA.

The RISC-V Foundation includes over 210 institutional, academic and individual members from around the world and has realized 100 percent year-over-year membership growth. This partnership with the Linux Foundation will enable the RISC-V Foundation to grow the RISC-V ecosystem with improved support for the development of new applications and architectures across all computing platforms.

“With the rapid international adoption of the RISC-V ISA, we need increased scale and resources to support the explosive growth of the RISC-V ecosystem. The Linux Foundation is an ideal partner given the open source nature of both organizations,” said Rick O’Connor, executive director of the non-profit RISC-V Foundation. “This joint collaboration with the Linux Foundation will enable the RISC-V Foundation to offer more robust support and educational tools for the active RISC-V community, and enable operating systems, hardware implementations and development tools to scale faster.”

“RISC-V has great traction in a number of markets with applications for AI, machine learning, IoT, augmented reality, cloud, data centers, semiconductors, networking and more.  RISC-V is a technology that has the potential to greatly advance open hardware architecture,” said Jim Zemlin, executive director at the Linux Foundation. “We look forward to collaborating with the RISC-V Foundation to advance RISC-V ISA adoption and build a strong ecosystem globally.”

Since its inception in 2015, RISC-V has quickly evolved its ecosystem to feature leading technology companies and emerging startups all working together to enable a wide range of open-source and proprietary RISC-V hardware and software solutions. Members are solving some of today’s most complex design challenges including security, performance, power, efficiency, flexibility and more.

In addition to neutral governance and best practices for open source development, The Linux Foundation will also provide an influx of resources for the RISC-V ecosystem, such as training programs, infrastructure tools, as well as community outreach, marketing and legal expertise.

The RISC-V ISA offers a number of advantages over other architectures, including its openness, simplicity, clean-slate design, modularity, extensibility and stability, delivering a new level of software and hardware freedom on architecture.

The Linux Foundation and the RISC-V communities are already collaborating on a pair of “Getting Started” guides for running the Zephyr, a small, scalable open source RTOS for connected, resource constrained devices, and Linux operating systems on RISC-V based platforms. The Zephyr and Linux guides will be unveiled at the RISC-V Summit on Dec. 3, 2018, in Santa Clara during training classes led by project contributors from RISC-V Foundation Founding Platinum Members Antmicro, Google, Microchip Technology and Western Digital, in addition to the Linux Foundation. For further details regarding the RISC-V Summit, please visit https://tmt.knect365.com/risc-v-summit/.

About RISC-V Foundation

RISC-V (pronounced “risk-five”) is a free and open ISA enabling a new era of processor innovation through open standard collaboration. Founded in 2015, the RISC-V Foundation comprises more than 200 members building the first open, collaborative community of software and hardware innovators powering a new era of processor innovation. Born in academia and research, RISC-V ISA delivers a new level of free, extensible software and hardware freedom on architecture, paving the way for the next 50 years of computing design and innovation.

The RISC-V Foundation, a non-profit corporation controlled by its members, directs the future development and drives the adoption of the RISC-V ISA. Members of the RISC-V Foundation have access to and participate in the development of the RISC-V ISA specifications and related HW / SW ecosystem. More information can be found at www.riscv.org.

About the Linux Foundation

The Linux Foundation is the organization of choice for the world’s top developers and companies to build ecosystems that accelerate open technology development and industry adoption. Together with the worldwide open source community, it is solving the hardest technology problems by creating the largest shared technology investment in history. Founded in 2000, The Linux Foundation today provides tools, training and events to scale any open source project, which together deliver an economic impact not achievable by any one company. More information can be found at www.linuxfoundation.org.

# # #

The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see our trademark usage page: https://www.linuxfoundation.org/trademark-usage. Linux is a registered trademark of Linus Torvalds.

The post The Linux Foundation and RISC-V Foundation Announce Joint Collaboration to Enable a New Era of Open Architecture appeared first on The Linux Foundation.

Source: Linux Foundation

Tor’s Strength in Numbers Campaign, New ask.krita.org Site, Kodi Announces Limited-Edition Raspberry Pi Case, IPFire 2.21 Core Update 125 Released, and Chrome and Firefox Developers Plan to End Support for FTP

News briefs for November 27, 2018.

Tor announces its Strength in
Numbers
campaign: “Stand up for the universal
human rights to privacy and freedom and help keep Tor robust and secure.”
For #GivingTuesday, another donor will match first-time
donations to the project—this is in addition to the existing matching
donations from Mozilla, so if you donate, your gift will be matched twice.
Go here to donate.

Scott Petrovic and the KDE sysadmin team have launched
ask.krita.org—the
“Krita Question and Answers Site”. The new ask.krita.org “is
a place where it’s simple to find out if your question has been asked
before, simple to ask a question, and simple to answer a question. It’s
a central place where, we hope, Krita users will get together and help each
other. Like a stackoverflow site, or like ask.libreoffice.org.

Kodi recently released a new limited-edition “Kodi
Edition” Raspberry Pi case
. This
version 2 of the case Kodi released two years ago is newly designed,
aluminum, but it’s “now gone to the dark side with a metallic, jet black
coating for that cool Vader look”. In addition, “the second-generation case
features better access to the SD card, a better built-in heatsink,
precision manufacturing, and subtle details that make a great case
amazing.” A percentage of every sale of this case goes to the USC Norris
Comprehensive Cancer Center
. You can order one from FLIRC for
$19.95.

IPFire
2.21 Core Update 125
was released yesterday. This update has various
bug and security fixes, along with new features, such as “the IPFire Access
Point add-on now supports 802.11ac WiFi if the chipset supports it” and the
dehydrated
“lightweight client to retrieve certificates from Let’s Encrypt written in
bash”. You can download it from here.

Chrome and Firefox developers plan to end support for FTP. BleepingComputer
reports
that “an upcoming change in how files stored on FTP servers are
rendered in the browser may be the first step in its ultimate removal”, and
also that “Google developers have advocated for the removal of FTP support in
Chrome for over 4 years” due to its low usage and the additional
attack surface it creates that Chrome is unable to secure properly, compared to offering the
same files over an HTTPS connection.

Source: Linux Journal

Machine Learning, Biased Models, and Finding the Truth

machine learning

Patrick Ball, Director of Research, Human Rights Data Analysis Group, offered examples of when statistics and machine learning have proved useful and when they’ve failed in this presentation from Open Source Summit Europe.

Machine learning and statistics are playing a pivotal role in finding the truth in human rights cases around the world – and serving as a voice for victims, Patrick Ball, director of Research for the Human Rights Data Analysis Group, told the audience at Open Source Summit Europe.

Ball began his keynote, “Digital Echoes: Understanding Mass Violence with Data and Statistics,” with background on his career, which started in 1991 in El Salvador, building databases. While working with truth commissions from El Salvador to South Africa to East Timor, with international criminal tribunals as well as local groups searching for lost family members, he said, “one of the things that we work with every single time is trying to figure out what the truth means.”

In the course of the work, “we’re always facing people who apologize for mass violence. They tell us grotesque lies that they use to attempt to excuse this violence. They deny that it happened. They blame the victims. This is common, of course, in our world today.”

Human rights campaigns “speak with the moral voice of the victims,’’ he said. Therefore, it is critical that statistics, including machine learning, are accurate, Ball said.

He gave three examples of when statistics and machine learning proved to be useful, and where they failed.

Finding missing prisoners

In the first example, Ball recalled his participation as an expert witness in the trial of a war criminal, the former president of Chad, Hissène Habré. Thousands of documents were presented, which had been discovered as a pile of trash in an abandoned prison and which turned out to be the operational records of the secret police.

The team honed in one type of document that detailed the number of prisoners that were held at the beginning of the day, the number held at the end of the day, and the difference between the number of prisoners who were released, new prisoners brought in, those transferred to other places, and those who had died during the course of the day. Dividing the number of people who died throughout the day by the number alive in the morning produces the crude mortality rate, he said.

The status of the prisoners of war was critical in the trial of Habré because the crude mortality rate was “extraordinarily high,” he said.

“What we’re doing in human rights data analysis is … trying to push back on apologies for mass violence. In fact, the judges in the [Chad] case saw precisely that usage and cited our evidence … to reject President Habré’s defense that conditions in the prison were nothing extraordinary.”

That’s a win, Ball stressed, since human rights advocates don’t see many wins, and the former head of state was sentenced to spend the rest of his life in prison.

Hidden graves in Mexico

In a more current case, the goal is to find hidden graves in Mexico of the bodies of people who have disappeared after being kidnapped and then murdered. Ball said they are using a machine learning model to predict where searchers are likely to find those graves in order to focus and prioritize searches.

Since they have a lot of information, his team decided to randomly split the cases into test and training sets and then train a model. “We’ll predict the test data and then we’ll iterate that split, train, test process 1,000 times,’’ he explained. “What we’ll find is that over the course of four years that we’ve been looking at, more than a third of the time we can perfectly predict the counties that have graves.”

“Machine learning models are really good at predicting things that are like the things they were trained on,” Ball said.

A machine learning model can visualize the probability of finding mass graves by county, which generates press attention and helps with the advocacy campaign to bring state authorities into the search process, he said.

That’s machine learning, contributing positively to society,” he said. Yet, that doesn’t mean that machine learning is necessarily positive for society as a whole.

Predictive Policing

Many machine learning applications “are terribly detrimental to human rights and society,’’ Ball stressed.  In his final example, he talked about predictive policing, which is the use of machine learning patterns to predict where crime is going to occur.

For example, Ball and his team looked at drug crimes in Oakland, California. He displayed a heat map of the density of drug use in Oakland, based on a public health survey, showing the highest drug use close to the University of California.

Ball and his colleagues re-implemented one of the most popular predictive policing algorithms to predict crimes based on this data. Then he showed the model running in animation, with dots on the grid representing drug arrests. Then the model made predictions in precisely the same locations as where the arrests were observed, he said.

If the underlying data turns out to be biased, then “we recycle that bias. Now, biased data leads to biased predictions.” Ball went on to clarify that he was using the term bias in a technical, not racial sense.

When bias in data occurs, he said, it “means that we’re over predicting one thing and that we’re under predicting something else. In fact, what we’re under predicting here is white crime,’’ he said. Then the machine learning model teaches police dispatchers that they should go to the places they went before. “It assumes the future is like the past,” he said.

“Machine learning in this context does not simply recycle racial disparities in policing, [it] amplifies the racial disparities in policing.” This, Ball said, “is catastrophic. Policing already facing a crisis of legitimacy in the United States as a consequence of decades, or some might argue centuries, of unfair policing. ML makes it worse.”

“In predictive policing, a false positive means that a neighborhood can be systematically over policed, contributing to the perception of the citizens in that neighborhood that they’re being harassed. That erodes trust between the police and the community. Furthermore, a false negative means that police may fail to respond quickly to real crime,” he said.

When machine learning gets it wrong

Machine learning models produce variances and random errors, Ball said, but bias is a bigger problem. “If we have data that is unrepresentative of a population to which we intend to apply the model, the model is unlikely to be correct. It is likely to reproduce whatever that bias is in the input side.”

We want to know where a crime has occurred, “but our pattern of observation is systematically distorted. It’s not that [we] simply under-observe the crime, but under-observe some crime at a much greater rate than other crimes.” In the United States, he said, that tends to be distributed by race. Biased models are the end result of that.

The cost of a machine learning being wrong can also destroy people’s lives, Ball said. It also raises the question of who bears the cost of being wrong. You can hear more from Ball and learn more about his work in the complete video presentation below.

The post Machine Learning, Biased Models, and Finding the Truth appeared first on The Linux Foundation.

Source: Linux Foundation

A Look At The Open-Source Talos II POWER9 Performance Against x86_64 Server CPUs

In the benchmarks earlier this month looking at the Talos II POWER9 dual 22-core performance its performance was compared to various AMD Threadripper and Intel Core i9 CPUs. They were used as comparison points since all of those CPUs sport four memory channels, including the Sforza POWER9 CPUs, while IBM caters the larger LaGrange/Monza POWER9 modules with eight memory channels as competition to Xeon and EPYC. But for those wondering how the POWER9 Sforza performance compares to Intel Xeon and AMD EPYC processors, here are some benchmarks.

Source: Phoronix