The original article can be obtained here

Developing digital assets is rapidly becoming one of the most expensive undertakings for businesses in 2016. Skilled developers command high salaries, which can add up to millions of dollars over the life of your startup. After all, it often takes a team of project managers and developers to create and maintain unique, high quality software.

This leads many businesses down the road of outsourcing development work to remote developers, often in countries like India or Ukraine. It allows businesses to maintain low costs, and can even reduce product development time by allowing for larger number of developers to be hired.

Is there a problem with this? Not really, just differences, which you can manage if you know what to expect. You can’t directly manage these remote workers in-person, so getting the work done remotely will be a different experience than with your in-house employees. This doesn’t have to be a difficult experience though! Here are three ways to have your cake and eat it too with outsourcing your software development.

Hire an Experienced Project Manager

Having an in-house project manager is a great way to maintain control over your development without bringing on expensive in-house workers. It’s up to the project manager to oversee the remote workers and keep you up to date on the progress being made. This may even be a part-time position, because it’s rare that remote workers need full time management, unless there are large numbers of people working abroad.

This means you and your team can concentrate on business, while still being completely informed of the ups and downs related to the project. Your manager can brief you daily or weekly, translating the technical jargon to English, as well as providing demonstrations as projects are fleshed out.

For those who thrive on control, this may be a great solution to reduce costs of in-house developers and reduce the risk that comes from outsourcing.

Bring on a Firm to Handle it All

If your company doesn’t want the extra overhead of a project manager, you could bring on a firm like DevTeam.Space or Toptal.com to handle the entire process.

These types of companies have their own developers, or source from a pool of hundreds of manually vetted development teams in the case of DevTeam.Space, so that you don’t have to worry about recruiting or managing developers. This solution also allows your business to scale up projects if necessary, as these firms can more easily bring in additional developers than you could in-house.

The goal of these businesses is to be a safe solution for outsourcing software, as there’s more organization and peace of mind than working with individual remote developers.

“The software outsourcing market will continue its rapid growth, serving more and more companies every year. To actively participate in this growth, every company should focus on providing a higher level of quality and communication,” says Alexey Semeney, CEO of DevTeam.Space, a firm that helps companies build software using elite remote development teams. “We provide every client with two project managers, vetted and trained senior level developers with at least 5 years of dev experience, and a reporting dashboard with daily written updates and roadblocks tracking. This not only allows us to build precise project estimates and deliver products faster, but makes our clients feel safe and in control of the situation.”

Find a Rockstar as Your Lead Developer

If you already have someone to oversee the project, a rockstar in-house developer can be an extremely valuable asset to maintain quality standards and handle the most complex parts of the development process.

Having access to someone in-house can also make getting through roadblocks easier, as well as keep you even more informed of the ups and downs associated with developing digital products, especially enterprise-level software.

Yes, you’ll end up paying this person more than even an average in-house developer; however, it’s more than worth it if this rockstar is providing the value of 2 or 3 mediocre developers. Like hiring an in-house project manager, this option is for companies looking to save money through outsourcing without sacrificing control–and potentially quality–in the process.

To find the right fit for your company, consider using a service like CyberCoders to get the most exposure for this type of position. You’ll want to make sure you get the right person in for the job, as they could make or break a project.

Drew Hendricks is a tech, social media, and environmental addict. He’s written for many major publications, such as Forbes and Entrepreneur.

The powerful Sunway TaihuLight supercomputer makes some telling trade-offs in pursuit of efficiency

By Rachel Courtland

Posted 20 Jul 2016 | 15:00 GMT

In June, the ranks of the Top500 list were rearranged, and the title of world’s most powerful supercomputer was handed off to a new machine—China’s Sunway TaihuLight.

The Wuxi-based machine can perform the Linpack Benchmark—a long-standing arbiter of supercomputer prowess—at a rate of 93 petaflops, or 93 quadrillion floating-point operations per second. This performance is more than twice that of the previous record holder, China’s Tianhe-2. What’s more, TaihuLight achieves this capacity while consuming 2.4 megawatts less power than Tianhe-2.

Such efficiency gains are important if supercomputer designers hope to reach exascale operation, somewhere in the realm of 1,000 Pflops. Computers with that capability could be a boon for advanced manufacturing and national security, among many other applications. China, Europe, Japan, and the United States are all pushing toward the exascale range. Some countries are reportedly setting their sights on doing so by 2020; the United States is targeting the early 2020s. But two questions loom over those efforts: How capable will those computers be? And can we make them energy efficient enough to be economical?

We can get to the exascale now “if you’re willing to pay the power bill,” saysPeter Kogge, a professor at the University of Notre Dame. Scaling up a supercomputer with today’s technology to create one that is 10 times as big would demand at least 10 times as much power, Kogge explains. And the difference between 20 MW and 200 MW, he says, “is the difference [between having] a substation or a nuclear power plant next to you.”

Kogge, who led a 2008 study on reaching the exascale, is updating power projections to cover the three categories of supercomputers built today: those with “heavyweight” high-performance CPUs; those that use “lightweight” microprocessors that are slower but cooler, and so can be packed more densely; and those that take advantage of graphics processing units to accelerate computation.

TaihuLight follows the lightweight approach, and it has made some sacrifices in pursuit of energy efficiency. Based on its hardware specs, TaihuLight can, in theory, crunch numbers at a rate of 125 Pflops. The machine reaches 74 percent of this peak theoretical capacity when running Linpack. But it does not fare as well on a new alternative benchmark, High Performance Conjugate Gradients(HPCG), which is designed to reflect how well a computer can perform more memory- and communications-intensive, real-world applications. When it runs HPCG, TaihuLight utilizes just 0.3 percent of its theoretical peak abilities, which means that only 3 out of every 1,000 possible floating-point operations are actually used by the computer. By comparison, Tianhe-2 and the United States’ Titan, the second- and third-fastest supercomputers in the Top500 rankings, respectively, can take advantage of just over 1 percent of their computing capacity. Japan’s K computer, currently ranked fifth on the list, achieved 4.9 percent with the HPCG metric.

“Everything is a balancing act,” says Jack Dongarra, a professor at the University of Tennessee, Knoxville, and one of the organizers of the Top500. “They produced a processor that can deliver high-arithmetic performance but is very weak in terms of data movement.” But he notes that the TaihuLight team has developed applications that take advantage of the architecture; he says that three projects that were finalists for this year’s ACM Gordon Bell Prize, a prestigious supercomputing award, were designed to run on the machine.

TaihuLight uses DDR3, an older, slower memory, to save on power. Its architecture also uses small amounts of local memory near each core instead of a more traditional memory hierarchy, explains John Goodacre, a professor of computer architectures at the University of Manchester, in England. He says that while today’s applications can execute between 1 and 10 floating-point operations for every byte of main memory accessed, that ratio needs to be far higher for applications to run efficiently on TaihuLight. The design cuts down on a big expense in a supercomputer’s power budget: the amount of energy consumed shuttling data back and forth.

“I think what they’ve done is build a machine that changes some of the design rules that people have assumed are part of the requirements” for moving toward the exascale, Goodacre says. Further progress will depend, as the TaihuLight team has shown, on end-to-end design, he says. That includes looking not only at changes to hardware—a number of experts point to 3D stacking of logic and memory—but also to the fundamental programming paradigms we use to take advantage of the machines.

This article appears in the August 2016 print issue as “China Inches Toward the Exascale.”

Source from here

by Robert J. Hall

AT&T Labs Research

The safe operation of drones for commercial and public use presents communication and computational challenges. This article overviews these challenges and describes a prototype system (the Geocast Air Operations Framework, or GAOF) that addresses them using novel network and software architectures.

The full article can be accessed by subscribers at https://www.computer.org/cms/Computer.org/ComputingNow/issues/2016/07/mic2016030068.pdf

Drones, flying devices lacking a human pilot on-board, have attracted major public attention. Retailers would love to be able to deliver goods using drones to save the costs of trucks and drivers; people want to video themselves doing all sorts of athletic and adventuresome activities; and news agencies would like to send drones to capture video of traffic and other news situations, saving the costs of helicopters and pilots.

Today, both technological and legal factors restrict what can be achieved and what can be allowed safely. For example, the US Federal Aviation Administration (FAA) requires drones to operate within line-of-sight (LOS) of a pilot who’s in control, and also requires drones to be registered.

In this article, I will briefly overview some of the opportunities available to improve public and commercial drone operation. I will also discuss a solution approach embodied in a research prototype, the Geocast Air Operations Framework (GAOF), I am working on in AT&T Laboratories Research. This prototype system has been implemented and tested using simulated drones; aerial field testing with real drones is being planned and will be conducted in accordance with the FAA guidelines. The underlying communications platform, the AT&T Labs Geocast System, 1-3 has been extensively field tested in other (non-drone) domains with Earth bound assets, such as people and cars. The goal of the work is to demonstrate a path toward an improved system for the operation of drones, with the necessary secure command and control among all legitimate stakeholders, including drone operator, FAA, law enforcement, and private property owners and citizens. While today there are drones and drone capabilities that work well with one drone operating in an area using a good communication link, there will be increased challenges when there are tens or hundreds of drones in an area.

Note that some classes of drone use are beyond the scope of this discussion:

• Military drones. The US military has been operating drones for many years and are the acknowledged world experts in the field. However, its usage scenarios are quite different, and many of its technical approaches are out of scope for this discussion, because they have resources and authority that are unavailable (such as military frequency bands) or impractical (high-cost drone designs and components) to use in the public/commercial setting. Instead, we seek solutions whose costs are within reason for public and commercial users and which do not require access to resources unavailable to the public.

• Non-compliant drones. It will always be possible for someone to build and fly drones that do not obey the protocols of our system. For example, we will not discuss defense against drones, such as electromagnetic pulse (EMP) weapons, jamming, or trained birds-of-prey.4 However, we hope to work toward a framework for safe and secure large-scale drone use, analogous to establishing traffic laws for cars.

• Drone application-layer issues. Obviously, drones should actually do something useful once we have gone to the trouble to operate them safely. Often, this takes the form of capturing video or gathering other sensor data. This article does not address the issues involved in transferring large data sets from drone to ground or drone to cloud.

The rest of this article will give background on the communications system underlying the GAOF, the challenges of safe and scalable air operations, and how the GAOF addresses these challenges.

by:

Ali Basiri, Niosha Behnam, Ruud de Rooij, Lorin Hochstein, Luke Kosewski, Justin Reynolds, and Casey Rosenthal,

Netflix

This is an excerpt of the article published in the July 2016 edition of Computing Now at  https://www.computer.org/cms/Computer.org/ComputingNow/issues/2016/07/mso2016030035.pdf

Modern software-based services are implemented as distributed systems with complex behavior and failure modes. Chaos engineering uses experimentation to ensure system availability. Netflix engineers have developed principles of chaos engineering that describe how to design and run experiments.

THIRTY YEARS AGO, Jim Gray noted that “A way to improve availability is to install proven hardware and software, and then leave it alone.”1 For companies that provide services over the Internet, “leaving it alone” isn’t an option. Such service providers must continually make changes to increase the service’s value, such as adding features and improving performance. At Netflix, engineers push new code into production and modify runtime configuration parameters hundreds of times a day. (For a look at Netflix and its system architecture, see the sidebar.) Availability is still important; a customer who can’t watch a video because of a service outage might not be a customer for long.

But to achieve high availability, we need to apply a different approach than what Gray advocated. For years, Netflix has been running Chaos Monkey, an internal service that randomly selects virtualmachine instances that host our production services and terminates them.2 Chaos Monkey aims to encourage Netflix engineers to design software services that can withstand failures of individual instances. It’s active only during normal working hours so that engineers can respond quickly if a service fails owing to an instance termination.

Chaos Monkey has proven successful; today all Netflix engineers design their services to handle instance failures as a matter of course.

That success encouraged us to extend the approach of injecting failures into the production system to improve reliability. For example, we perform Chaos Kong exercises that simulate the failure of an entire Amazon EC2 (Elastic Compute Cloud) region. We also run Failure Injection Testing (FIT) exercises in which we cause requests between Netflix services to fail and verify that the system degrades gracefully.3 Over time, we realized that these activities share underlying themes that are subtler than simply “break things in production.” We also noticed that organizations such as Amazon,4 Google,4 Microsoft,5 and Facebook6 were applying similar techniques to test their systems’ resilience. We believe that these activities form part of a discipline that’s emerging in our industry; we call this discipline chaos engineering. Specifically, chaos engineering involves experimenting on a distributed system to build confidence in its capability to withstand turbulent conditions in production. These conditions could be anything from a hardware failure, to an unexpected surge in client requests, to a malformed value in a runtime configuration parameter. Our experience has led us to determine principles of chaos engineering (for an overview, see http://principlesofchaos .org), which we elaborate on here.