Entries tagged as cloud
Related tagsamazon apple ebook google innovation&society kindle microsoft network software ai hardware history ios iphone mobile siri technology big data algorythm artificial intelligence cloud computing data mining data visualisation privacy computing farm android api book car chrome os display glass interface internet laptop maps mirror 3d 3d printing 3d scanner ad amd ar arduino army asus augmented reality automation camera chrome advertisements API art browser computer history crowd-sourcing app app store homeos kinect open source os programming sdk windows 3g botnet data center drone facebook firefox intel law medical device pc satellite wifi piracy itunes malware music peer to peer virus DNA facial recognition genome hack health monitoring picture search security encryption idevices protocole surveillance wireless sustainability energy cpu cray
Wednesday, October 16. 2013
The world of Big Data is one of pervasive data collection and aggressive analytics. Some see the future and cheer it on; others rebel. Behind it all lurks a question most of us are asking — does it really matter? I had a chance to find out recently, as I got to see what Acxiom, a large-scale commercial data aggregator, had collected about me.
At least in theory large-scale data collection matters quite a bit. Large data sets can be used to create social network maps and can form the seeds for link analysis of connections between individuals. Some see this as a good thing; others as a bad one — but whatever your viewpoint, we live in a world which sees increasing power and utility in Big Data’s large-scale data sets.
Of course, much of the concern is about government collection. But it’s difficult to assess just how useful this sort of data collection by the government is because, of course, most governmental data collection projects are classified. The good news, however, is that we can begin to test the utility of the program in the private sector arena. A useful analog in the private sector just became publicly available and it’s both moderately amusing and instructive to use it as a lens for thinking about Big Data.
Acxiom is one of the largest commercial, private sector data aggregators around. It collects and sells large data sets about consumers — sometimes even to the government. And for years it did so quietly, behind the scene — as one writer put it “mapping the consumer genome.” Some saw this as rather ominous; others as just curious. But it was, for all of us, mysterious. Until now.
In September, the data giant made available to the public a portion of its data set. They created a new website — Abouthedata.com — where a consumer could go to see what data the company had collected about them. Of course, in order to access the data about yourself you had to first verify your own identity (I had to send in a photocopy of my driver’s license), but once you had done so, it would be possible to see, in broad terms, what the company thought it knew about you — and how close that knowledge was to reality.
I was curious, so I thought I would go explore myself and see what it was they knew and how accurate they were. The results were at times interesting, illuminating and mundane. Here are a few observations:
To begin with, the fundamental purpose of the data collection is to sell me things — that’s what potential sellers want to know about potential buyers and what, say, Amazon might want to know about me. So I first went and looked at a category called “Household Purchase Data” — in other words what I had recently bought.
It turns out that I buy … well … everything. I buy food, beverages, art, computing equipment, magazines, men’s clothing, stationary, health products, electronic products, sports and leisure products, and so forth. In other words, my purchasing habits were, to Acxiom, just an undifferentiated mass. Save for the notation that I had bought an antique in the past and that I have purchased “High Ticket Merchandise,” it seems that almost everything I bought was something that most any moderately well-to-do consumer would buy.
I do suppose that the wide variety of purchases I made is, itself, the point — by purchasing so widely I self-identify as a “good” consumer. But if that’s the point then the data set seems to miss the mark on “how good” I really am. Under the category of “total dollars spent,” for example, it said that I had spent just $1,898 in the past two years. Without disclosing too much about my spending habits in this public forum, I think it is fair to say that this is a significant underestimate of my purchasing activity.
The next data category of “Household Interests” was equally unilluminating. Acxiom correctly said I was interested in computers, arts, cooking, reading and the like. It noted that I was interested in children’s items (for my grandkids) and beauty items and gardening (both my wife’s interest, probably confused with mine). Here, as well, there was little differentiation, and I assume the breadth of my interests is what matters rather that the details. So, as a consumer, examining what was collected about me seemed to disclose only a fairly anodyne level of detail.
[Though I must object to the suggestion that I am an Apple user J. Anyone who knows me knows I prefer the Windows OS. I assume this was also the result of confusion within the household and a reflection of my wife’s Apple use. As an aside, I was invited throughout to correct any data that was in error. This I chose not to do, as I did not want to validate data for Acxiom – that’s their job not mine—and I had no real interest in enhancing their ability to sell me to other marketers. On the other hand I also did not take the opportunity they offered to completely opt-out of their data system, on the theory that a moderate amount of data in the world about me may actually lead to being offered some things I want to purchase.]
Things became a bit more intrusive (and interesting) when I started to look at my “Characteristic Data” — that is data about who I am. Some of the mistakes were a bit laughable — they pegged me as of German ethnicity (because of my last name, naturally) when, with all due respect to my many German friends, that isn’t something I’d ever say about myself. And they got my birthday wrong — lord knows why.
But some of their insights were at least moderately invasive of my privacy, and highly accurate. Acxiom “inferred” for example, that I’m married. They identified me accurately as a Republican (but notably not necessarily based on voter registration — instead it was the party I was “associated with by voter registration or as a supporter”). They knew there were no children in my household (all grown up) and that I run a small business and frequently work from home. And they knew which sorts of charities we supported (from surveys, online registrations and purchasing activity). Pretty accurate, I’d say.
Finally, it was completely unsurprising that the most accurate data about me was closely related to the most easily measurable and widely reported aspect of my life (at least in the digital world) — namely, my willingness to dive into the digital financial marketplace.
Acxiom knew that I had several credit cards and used them regularly. It had a broadly accurate understanding of my household total income range [I’m not saying!].
They also knew all about my house — which makes sense since real estate and liens are all matters of public record. They knew I was a home owner and what the assessed value was. The data showed, accurately, that I had a single family dwelling and that I’d lived there longer than 14 years. It disclosed how old my house was (though with the rather imprecise range of having been built between 1900 and 1940). And, of course, they knew what my mortgage was, and thus had a good estimate of the equity I had in my home.
So what did I learn from this exercise?
In some ways, very little. Nothing in the database surprised me, and the level of detail was only somewhat discomfiting. Indeed, I was more struck by how uninformative the database was than how detailed it was — what, after all, does anyone learn by knowing that I like to read? Perhaps Amazon will push me book ads, but they already know I like to read because I buy directly from them. If they had asserted that I like science fiction novels or romantic comedy movies, that level of detail might have demonstrated a deeper grasp of who I am — but that I read at all seems pretty trivial information about me.
I do, of course, understand that Acxiom has not completely lifted the curtains on its data holdings. All we see at About The Data is summary information. You don’t get to look at the underlying data elements. But even so, if that’s the best they can do ….
In fact, what struck me most forcefully was (to borrow a phrase from Hannah Arendt) the banality of it all. Some, like me, see great promise in big data analytics as a way of identifying terrorists or tracking disease. Others, with greater privacy concerns, look at big data and see Big Brother. But when I dove into one big data set (albeit only partially), held by one of the largest data aggregators in the world, all I really became was a bit bored.
Maybe that’s what they wanted as a way of reassuring me. If so, Acxiom succeeded, in spades.
Friday, November 23. 2012
Politics Transformed: The High Tech Battle for Your Vote is an in-depth look at how digital media is affecting elections. Mashable explores the trends changing politics in 2012 and beyond in these special reports.
Big Data. The very syntax of it is so damn imposing. It promises such relentless accuracy. It inspires so much trust –- a cohering framework in a time of chaos.
Big Data is all the buzz in consumer marketing. And the pundits are jabbering about 2012 as the year of Big Data in politics, much as social media itself was the dizzying buzz in 2008. Four years ago, Obama stunned us with his use of the web to raise money, to organize, to get out the vote. Now it’s all about Big Data’s ability to laser in with drone-like precision on small niches and individual voters, picking them off one by one.
It in its simplest form, Big Data describes the confluence of two forces — one technological, one social. The new technological reality is the amount of processing power and analytics now available, either free or at no cost. Google has helped pioneer that; as Wired puts it, one of its tools, called Dremel, makes “big data small.”
This level of mega-crunchability is what’s required to process the amount of data now available online, especially via social networks like Facebook and Twitter. Every time we Like something, it’s recorded on some cosmic abacus in the sky.
Then there’s our browsing history, captured and made available to advertisers through behavioral targeting. Add to that available public records on millions of voters — political consultants and media strategists have the ability drill down as god-like dentists.
Website TechPresident describes the conventional wisdom of Big Data as it relates to elections:
There are two sides to the use of Big Data. One is predictive — Twitter has its own sentiment index, analyzing tweets as 140-character barometers. Other companies, like GlobalPoint, aggregate social data and draw algorithmic conclusions.
But Big Data has a role beyond digital clairvoyance. It’s the role of digital genotyping in the political realm. Simply find the undecided voters and then message accordingly, based on clever connections and peeled-back insights into voter belief systems and purchase behaviors. Find the linkages and exploit them. If a swing voter in Ohio watches 30 Rock and scrubs with Mrs. Meyers Geranium hand soap, you know what sites to find her on and what issues she cares about. Tell them that your candidate supports their views, or perhaps more likely, call out your opponent’s demon views on geranium subsidies.
Central to this belief is that the election won’t be determined by big themes but by small interventions. Big Data’s governing heuristic is that shards of insight about you and your social network will lead to a new era of micro-persuasion. But there are three fallacies that undermine this shiny promise.
The atomic fallacy is the assumption that just because you can find small, Seurat-like dots in my behavior which indicate preferences, you can motivate me by appealing to those interests.
Yes, I may have searched for a Prius out of curiosity. I may follow environmental groups or advocates on Twitter. I may even have Facebook friends who actively oppose off-shore drilling and fracking. Big Data can identify those patterns, but it still doesn’t mean that Romney’s support of the Keystone pipeline will determine my vote.
There are thousands of issues like this. We care about subjects and might research them online, and those subjects that might lead us to join certain groups, but they aren’t going to change our voting behavior. Candidates can go down a rabbit hole looking for them. Give a child a hammer and everything is a nail; give a data scientist a preference and everything is a trigger.
And then when a candidate gets it wrong — and that’s inevitable — all credibility is lost. This data delinquency was memorialized in a famous Wall Street Journal story a decade ago: “If TiVo Thinks You Are Gay, Here’s How to Set It Straight.”
Big Data still hasn’t solved its over-compensation problem when it comes to recommendations.
I define the interruption fallacy as the mistaken notion that a marketer or a candidate (the difference is only the level of sanctimony) can rudely insert his message and magically recalibrate deeply ingrained passions.
So even if Big Data succeeds in identifying subjects of paramount importance to me, the interruption fallacy makes it extremely unlikely that digital marketing can overcome what behavioral psychologists call the confirmation bias and move minds. Targeting voters can reinforce positions, but that’s not what pundits are concerned about. They’re opining that Big Data has the ability to shift undecideds a few points in the swing states.
Those who haven’t made up their minds after being assaulted by locally targeted advertising, with messaging that has been excruciatingly poll-tested, are victims of media scorch. They’re burned out. They are suffering from banner blindness. Big Data will simply become a Big Annoyance.
Mobile devices pose another set of challenges for advertisers and candidates, as Randall Stross recently pointed out in The New York Times. There’s a tricky and perhaps non-negotiable tradeoff between intrusiveness and awareness, as well as that pesky privacy issue. Stross writes:
But that shiver is exactly what Big Data’s crunching is designed to produce– a jolt of hyper-awareness that can easily cross over into creepy.
And then there’s the ongoing decline in the overall effectiveness of online advertising. As Business Insider puts it, “The clickthrough rates of banner ads, email invites and many other marketing channels on the web have decayed every year since they were invented.”
No matter how much Big Data is being paid to slice and dice, we’re just not paying attention.
If Big Data got its way, elections would be decided based on a checklist that matched a candidate’s position with a voter’s belief systems. Tax the rich? Check. Get government off the back of small business? Check. Starve public radio? Check. It’s that simple.
Or is it? We know from neuromarketing and behavioral psychology that elections are more often than not determined by the way a candidate frames the issues, and the neural networks those narratives ignite. I’ve written previously for Mashable about The Political Brain, a book by Drew Westen that explains how we process stories and images and turn them into larger structures. Isolated, random messages — no matter how exquisitely relevant they are — don’t create a story. And without that psychological framework, a series of disconnected policy positions — no matter how hyper-relevant — are effectively individual ingredients lacking a recipe. They seem good on paper but lack combinatorial art.
This is not to say that Big Data has no role in politics. But it’s simply a part of a campaign’s strategy, not its seminal machinery. After all, segmentation has long enabled candidates to efficiently refine and target their messages, but the latest religion of reductionism takes the proposition too far.
And besides, there’s an amazing — if not embarrassing — number of Big Data revelations that are intuitively transparent and screechingly obvious. A Washington Post story explains what our browsing habits tell us about our political views. The article shared this shocking insight: “If you use Spotify to listen to music, Tumblr to consume content or Buzzfeed to keep up on the latest in social media, you are almost certainly a vote for President Obama.”
Similarly, a company called CivicScience, which offers “real-time intelligence” by gathering and organizing the world’s opinions, and that modestly describes itself as “a bunch of machines and algorithms built from brilliant engineers from Carnegie Mellon University,” recently published a list of “255 Ways to Tell an Obama Supporter from a Romney Supporter.” In case you didn’t know, Obama supporters favor George Clooney and Woody Allen, while mysteriously, Romney supporters prefer neither of those, but like Mel Gibson.
At the end of the day, Big Data can be enormously useful. But its flaw is that it is far more logical, predicable and rational than the people it measures.
Tuesday, October 30. 2012
Via Slash Gear
It isn’t exactly a secret that authorities and entertainment groups don’t like The Pirate Bay, but today the infamous site made it a little bit harder for them to bring it down. The Pirate Bay announced today that it has move its servers to the cloud. This works in a couple different ways: it helps the people who run The Pirate Bay save money, while it makes it more difficult for police to carry out a raid on the site.
“All attempts to attack The Pirate Bay from now on is an attack on everything and nothing,” a Pirate Bay blog post reads. “The site that you’re at will still be here, for as long as we want it to. Only in a higher form of being. A reality to us. A ghost to those who wish to harm us.” The site told TorrentFreak after the switch that its currently being hosted by two different cloud providers in two different countries, and what little actual hardware it still needs to use is being kept in different countries as well. The idea is not only to make it harder for authorities to bring The Pirate Bay down, but also to make it easier to bring the site back up should that ever happen.
Even if authorities do manage to get their hands on The Pirate Bay’s remaining hardware, they’ll only be taking its transit router and its load balancer – the servers are stored in several Virtual Machine instances, along with all of TPB’s vital data. The kicker is that these cloud hosting companies aren’t aware that they’re hosting The Pirate Bay, and if they discovered the site was using their service, they’d have a hard time digging up any dirt on users since the communication between the VMs and the load balancer is encrypted.
In short, it sounds like The Pirate Bay has taken a huge step in not only protecting its own rear end, but those of users as well. If all of this works out the way The Pirate Bay is claiming it will, then don’t expect to hear about the site going down anytime soon. Still, there’s nothing stopping authorities from trying to bring it down, or from putting in the work to try and figure out who the people behind The Pirate Bay are. Stay tuned
Tuesday, September 25. 2012
Via New York Times
SANTA CLARA, Calif. — Jeff Rothschild’s machines at Facebook had a problem he knew he had to solve immediately. They were about to melt.
The company had been packing a 40-by-60-foot rental space here with racks of computer servers that were needed to store and process information from members’ accounts. The electricity pouring into the computers was overheating Ethernet sockets and other crucial components.
Thinking fast, Mr. Rothschild, the company’s engineering chief, took some employees on an expedition to buy every fan they could find — “We cleaned out all of the Walgreens in the area,” he said — to blast cool air at the equipment and prevent the Web site from going down.
That was in early 2006, when Facebook had a quaint 10 million or so users and the one main server site. Today, the information generated by nearly one billion people requires outsize versions of these facilities, called data centers, with rows and rows of servers spread over hundreds of thousands of square feet, and all with industrial cooling systems.
They are a mere fraction of the tens of thousands of data centers that now exist to support the overall explosion of digital information. Stupendous amounts of data are set in motion each day as, with an innocuous click or tap, people download movies on iTunes, check credit card balances through Visa’s Web site, send Yahoo e-mail with files attached, buy products on Amazon, post on Twitter or read newspapers online.
A yearlong examination by The New York Times has revealed that this foundation of the information industry is sharply at odds with its image of sleek efficiency and environmental friendliness.
Most data centers, by design, consume vast amounts of energy in an incongruously wasteful manner, interviews and documents show. Online companies typically run their facilities at maximum capacity around the clock, whatever the demand. As a result, data centers can waste 90 percent or more of the electricity they pull off the grid, The Times found.
To guard against a power failure, they further rely on banks of generators that emit diesel exhaust. The pollution from data centers has increasingly been cited by the authorities for violating clean air regulations, documents show. In Silicon Valley, many data centers appear on the state government’s Toxic Air Contaminant Inventory, a roster of the area’s top stationary diesel polluters.
Worldwide, the digital warehouses use about 30 billion watts of electricity, roughly equivalent to the output of 30 nuclear power plants, according to estimates industry experts compiled for The Times. Data centers in the United States account for one-quarter to one-third of that load, the estimates show.
“It’s staggering for most people, even people in the industry, to understand the numbers, the sheer size of these systems,” said Peter Gross, who helped design hundreds of data centers. “A single data center can take more power than a medium-size town.”
Energy efficiency varies widely from company to company. But at the request of The Times, the consulting firm McKinsey & Company analyzed energy use by data centers and found that, on average, they were using only 6 percent to 12 percent of the electricity powering their servers to perform computations. The rest was essentially used to keep servers idling and ready in case of a surge in activity that could slow or crash their operations.
A server is a sort of bulked-up desktop computer, minus a screen and keyboard, that contains chips to process data. The study sampled about 20,000 servers in about 70 large data centers spanning the commercial gamut: drug companies, military contractors, banks, media companies and government agencies.
“This is an industry dirty secret, and no one wants to be the first to say mea culpa,” said a senior industry executive who asked not to be identified to protect his company’s reputation. “If we were a manufacturing industry, we’d be out of business straightaway.”
These physical realities of data are far from the mythology of the Internet: where lives are lived in the “virtual” world and all manner of memory is stored in “the cloud.”
The inefficient use of power is largely driven by a symbiotic relationship between users who demand an instantaneous response to the click of a mouse and companies that put their business at risk if they fail to meet that expectation.
Even running electricity at full throttle has not been enough to satisfy the industry. In addition to generators, most large data centers contain banks of huge, spinning flywheels or thousands of lead-acid batteries — many of them similar to automobile batteries — to power the computers in case of a grid failure as brief as a few hundredths of a second, an interruption that could crash the servers.
“It’s a waste,” said Dennis P. Symanski, a senior researcher at the Electric Power Research Institute, a nonprofit industry group. “It’s too many insurance policies.”
Friday, May 11. 2012
The cloud storage scene has heated up recently, with a long-awaited entry by Google and a revamped SkyDrive from Microsoft. Dropbox has gone unchallenged by the major players for a long time, but that’s changed – both Google and Microsoft are now challenging Dropbox on its own turf, and all three services have their own compelling features. One thing’s for sure – Dropbox is no longer the one-size-fits-all solution.
These three aren’t the only cloud storage services – the cloud storage arena is full of services with different features and priorities, including privacy-protecting encryption and the ability to synchronize any folder on your system.
Dropbox introduced cloud storage to the masses, with its simple approach to cloud storage and synchronization – a single magic folder that follows you everywhere. Dropbox deserves credit for being a pioneer in this space and the new Google Drive and SkyDrive both build on the foundation that Dropbox laid.
Dropbox doesn’t have strong integration with any ecosystems – which can be a good thing, as it is an ecosystem-agnostic approach that isn’t tied to Google, Microsoft, Apple, or any other company’s platform.
Dropbox today is a compelling and mature offering supporting a wide variety of platforms. Dropbox offers less free storage than the other services (unless you get involved in their referral scheme) and its prices are significantly higher than those of competing services – for example, an extra 100GB is four times more expensive with Dropbox compared to Google Drive.
Google Drive is the evolution of Google Docs, which already allowed you to upload any file – Google Drive bumps the storage space up from 1 GB to 5 GB, offers desktop sync clients, and provides a new web interface and APIs for web app developers.
Google Drive is a serious entry from Google, not just an afterthought like the upload-any-file option was in Google Docs.
Its integration with third-party web apps – you can install apps and associate them with file types in Google Drive – shows Google’s vision of Google Drive being a web-based hard drive that eventually replaces the need for desktop sync clients entirely.
You can actually purchase up to 16 TB of storage space with Google Drive – for $800/month!
Microsoft released a revamped SkyDrive the day before Google Drive launched, but Google Drive stole its thunder. Nevertheless, SkyDrive is now a compelling product, particularly for people into Microsoft’s ecosystem of Office web apps, Windows Phone, and Windows 8, where it’s built into Metro by default.
Like Google with Google Drive, Microsoft’s new SkyDrive product imitates the magic folder pioneered by Dropbox.
Microsoft offers the most free storage space at 7 GB – although this is down from the original 25 GB. Microsoft also offers good prices for additional storage.
SugarSync is a popular alternative to Dropbox. It offers a free 5 GB of storage and it lets you choose the folders you want to synchronize – a feature missing in the above services, although you can use some tricks to synchronize other folders. SugarSync also has clients for mobile platforms that don’t get a lot of love, including Symbian, Windows Mobile, and Blackberry (Dropbox also has a Blackberry client).
Amazon also offers their own cloud storage service, known as Amazon Cloud Drive. There’s one big problem, though – there’s no official desktop sync client. Expect Amazon to launch their own desktop sync program if they’re serious about competing in this space. If you really want to use Amazon Cloud Drive, you can use a third-party application to access it from your desktop.
Box is popular, but its 25 MB file size limit is extremely low. It also offers no desktop sync client (except for businesses). While Box may be a good fit for the enterprise, it can’t stand toe-to-toe with the other services here for consumer cloud storage and syncing.
If you’re worried about the privacy of your data, you can use an encrypted service, such as SpiderOak or Wuala, instead. Or, if you prefer one of these services, use an app like BoxCryptor to encrypt files and store them on any cloud storage service.
Tuesday, April 03. 2012
Via ars technica
It's nice to imagine the cloud as an idyllic server room—with faux grass, no less!—but there's actually far more going on than you'd think.
Maybe you're a Dropbox devotee. Or perhaps you really like streaming Sherlock on Netflix. For that, you can thank the cloud.
In fact, it's safe to say that Amazon Web Services (AWS) has become synonymous with cloud computing; it's the platform on which some of the Internet's most popular sites and services are built. But just as cloud computing is used as a simplistic catchall term for a variety of online services, the same can be said for AWS—there's a lot more going on behind the scenes than you might think.
If you've ever wanted to drop terms like EC2 and S3 into casual conversation (and really, who doesn't?) we're going to demystify the most important parts of AWS and show you how Amazon's cloud really works.
Elastic Cloud Compute (EC2)
Think of EC2 as the computational brain behind an online application or service. EC2 is made up of myriad instances, which is really just Amazon's way of saying virtual machines. Each server can run multiple instances at a time, in either Linux or Windows configurations, and developers can harness multiple instances—hundreds, even thousands—to handle computational tasks of varying degrees. This is what the elastic in Elastic Cloud Compute refers to; EC2 will scale based on a user's unique needs.
Instances can be configured as either Windows machines, or with various flavors of Linux. Again, each instance comes in different sizes, depending on a developer's needs. Micro instances, for example, only come with 613 MB of RAM, while Extra Large instances can go up to 15GB. There are also other configurations for various CPU or GPU processing needs.
Finally, EC2 instances can be deployed across multiple regions—which is really just a fancy way of referring to the geographic location of Amazon's data centers. Multiple instances can be deployed within the same region (on separate blocks of infrastructure called availability zones, such as US East-1, US East-2, etc.), or across more than one region if increased redundancy and reduced latency is desired
Elastic Load Balance (ELB)
Another reason why a developer might deploy EC2 instances across multiple availability zones and regions is for the purpose of load balancing. Netflix, for example, uses a number of EC2 instances across multiple geographic location. If there was a problem with Amazon's US East center, for example, users would hopefully be able to connect to Netflix via the service's US West instances instead.
But what if there is no problem, and a higher number of users are connecting via instances on the East Coast than on the West? Or what if something goes wrong with a particular instance in a given availability zone? Amazon's Elastic Load Balance allows developers to create multiple EC2 instances and set rules that allow traffic to be distributed between them. That way, no one instance is needlessly burdened while others idle—and when combined with the ability for EC2 to scale, more instances can also be added for balance where required.
Elastic Block Storage (EBS)
Think of EBS as a hard drive in your computer—it's where an EC2 instance stores persistent files and applications that can be accessed again over time. An EBS volume can only be attached to one EC2 instance at a time, but multiple volumes can be attached to the same instance. An EBS volume can range from 1GB to 1TB in size, but must be located in the same availability zone as the instance you'd like to attach to.
Because EC2 instances by default don't include a great deal of local storage, it's possible to boot from an EBS volume instead. That way, when you shut down an EC2 instance and want to re-launch it at a later date, it's not just files and application data that persist, but the operating system itself.
Simple Storage Service (S3)
Unlike EBS volumes, which are used to store operating system and application data for use with an EC2 instance, Amazon's Simple Storage Service is where publicly facing data is usually stored instead. In other words, when you upload a new profile picture to Twitter, it's not being stored on an EBS volume, but with S3.
S3 is often used for static content, such as videos, images or music, though virtually anything can be uploaded and stored. Files uploaded to S3 are referred to as objects, which are then stored in buckets. As with EC2, S3 storage is scalable, which means that the only limit on storage is the amount of money you have to pay for it.
Buckets are also stored in regions, and within that region “are redundantly stored on multiple devices across multiple facilities.” However, this can cause latency issues if a user in Europe is trying to access files stored in a bucket within the US West region, for example. As a result, Amazon also offers a service called CloudFront, which allows objects to be mirrored across other regions.
While these are the core features that make up Amazon Web Services, this is far from a comprehensive list. For example, on the AWS landing page alone, you'll find things such as DynamoDB, Route53, Elastic Beanstalk, and other features that would take much longer to detail here.
However, if you've ever been confused about how the basics of AWS work—specifically, how computational data and storage is provisioned and scaled—we hope this gives you a better sense of how Amazon's brand of cloud works.
Correction: Initially, we confused regions in AWS with availability zones. As Mhj.work explains in the comments of this article, "availability Zones are actually "discrete" blocks of infrastructure ... at a single geographical location, whereas the geographical units are called Regions. So for example, EU-West is the Region, whilst EU-West-1, EU-West-2, and EU-West-3 are Availability Zones in that Region." We have updated the text to make this point clearer.
Monday, February 06. 2012
Monday, January 23. 2012
Researchers have succeeded in combining the power of quantum computing with the security of quantum cryptography and have shown that perfectly secure cloud computing can be achieved using the principles of quantum mechanics. They have performed an experimental demonstration of quantum computation in which the input, the data processing, and the output remain unknown to the quantum computer. The international team of scientists will publish the results of the experiment, carried out at the Vienna Center for Quantum Science and Technology (VCQ) at the University of Vienna and the Institute for Quantum Optics and Quantum Information (IQOQI), in the forthcoming issue of Science.
Quantum computers are expected to play an important role in future information processing since they can outperform classical computers at many tasks. Considering the challenges inherent in building quantum devices, it is conceivable that future quantum computing capabilities will exist only in a few specialized facilities around the world – much like today's supercomputers. Users would then interact with those specialized facilities in order to outsource their quantum computations. The scenario follows the current trend of cloud computing: central remote servers are used to store and process data – everything is done in the "cloud." The obvious challenge is to make globalized computing safe and ensure that users' data stays private.
The latest research, to appear in Science, reveals that quantum computers can provide an answer to that challenge. "Quantum physics solves one of the key challenges in distributed computing. It can preserve data privacy when users interact with remote computing centers," says Stefanie Barz, lead author of the study. This newly established fundamental advantage of quantum computers enables the delegation of a quantum computation from a user who does not hold any quantum computational power to a quantum server, while guaranteeing that the user's data remain perfectly private. The quantum server performs calculations, but has no means to find out what it is doing – a functionality not known to be achievable in the classical world.
The scientists in the Vienna research group have demonstrated the concept of "blind quantum computing" in an experiment: they performed the first known quantum computation during which the user's data stayed perfectly encrypted. The experimental demonstration uses photons, or "light particles" to encode the data. Photonic systems are well-suited to the task because quantum computation operations can be performed on them, and they can be transmitted over long distances.
The process works in the following manner. The user prepares qubits – the fundamental units of quantum computers – in a state known only to himself and sends these qubits to the quantum computer. The quantum computer entangles the qubits according to a standard scheme. The actual computation is measurement-based: the processing of quantum information is implemented by simple measurements on qubits. The user tailors measurement instructions to the particular state of each qubit and sends them to the quantum server. Finally, the results of the computation are sent back to the user who can interpret and utilize the results of the computation. Even if the quantum computer or an eavesdropper tries to read the qubits, they gain no useful information, without knowing the initial state; they are "blind."
The research at the Vienna Center for Quantum Science and Technology (VCQ) at the University of Vienna and at the Institute for Quantum Optics and Quantum Information (IQOQI) of the Austrian Academy of Sciences was undertaken in collaboration with the scientists who originally invented the protocol, based at the University of Edinburgh, the Institute for Quantum Computing (University of Waterloo), the Centre for Quantum Technologies (National University of Singapore), and University College Dublin.
Publication: "Demonstration of Blind Quantum Computing" Stefanie Barz, Elham Kashefi, Anne Broadbent, Joseph Fitzsimons, Anton Zeilinger, Philip Walther. DOI: 10.1126/science.1214707
Tuesday, December 20. 2011
Everyone likes personal cloud services, like Apple’s iCloud, Google Music, and Dropbox. But, many of aren’t crazy about the fact that our files, music, and whatever are sitting on someone else’s servers without our control. That’s where ownCloud comes in.
OwnCloud is an open-source cloud program. You use it to set up your own cloud server for file-sharing, music-streaming, and calendar, contact, and bookmark sharing project. As a server program it’s not that easy to set up. OpenSUSE, with its Mirall installation program and desktop client makes it easier to set up your own personal ownCloud, but it’s still not a simple operation. That’s going to change.
According to ownCloud’s business crew, “OwnCloud offers the ease-of-use and cost effectiveness of Dropbox and box.net with a more secure, better managed offering that, because it’s open source, offers greater flexibility and no vendor lock in. This makes it perfect for business use. OwnCloud users can run file sync and share services on their own hardware and storage or use popular public hosting and storage offerings.” I’ve tried it myself and while setting it up is still mildly painful, once up ownCloud works well.
OwnCloud enables universal access to files through a Web browser or WebDAV. It also provides a platform to easily view and sync contacts, calendars and bookmarks across all devices and enables basic editing right on the Web. Programmers will be able to add features to it via its open application programming interface (API).
OwnCloud is going to become an easy to run and use personal, private cloud thanks to a new commercial company that’s going to take ownCloud from interesting open-source project to end-user friendly program. This new company will be headed by former SUSE/Novell executive Markus Rex. Rex, who I’ve known for years and is both a business and technology wizard, will serve as both CEO and CTO. Frank Karlitschek, founder of the ownCloud project, will be staying.
To make this happen, this popular–350,000 users-program’s commercial side is being funded by Boston-based General Catalyst, a high-tech. venture capital firm. In the past, General Catalyst has helped fund such companies as online travel company Kayak and online video platform leader Brightcove.
General Catalyst came on board, said John Simon, Managing Director at General Catalyst in a statement, because, “With the explosion of unstructured data in the enterprise and increasingly mobile (and insecure) ways to access it, many companies have been forced to lock down their data–sometimes forcing employees to find less than secure means of access, or, if security is too restrictive, risk having all that unavailable When we saw the ease-of-use, security and flexibility of ownCloud, we were sold.”
“In a cloud-oriented world, ownCloud is the only tool based on a ubiquitous open-source platform,” said Rex, in a statement. “This differentiator enables businesses complete, transparent, compliant control over their data and data storage costs, while also allowing employees simple and easy data access from anywhere.”
As a Linux geek, I already liked ownCloud. At the company releases
mass-market ownCloud products and service in 2012, I think many of you
are going to like it as well. I’m really looking forward to seeing where
this program goes from here.
Friday, September 09. 2011
We’ve worked hard to reduce the amount of energy our services use. In fact, to provide you with Google products for a month—not just search, but Google+, Gmail, YouTube and everything else we have to offer—our servers use less energy per user than a light left on for three hours. And, because we’ve been a carbon-neutral company since 2007, even that small amount of energy is offset completely, so the carbon footprint of your life on Google is zero.
(Page 1 of 2, totaling 12 entries) » next page
Show tagged entries