Google

Google Announces An Impressive Array Of Cloud Price Cuts And Enhancements

At Google Cloud Platform Live, Google just announced a range of enhancements to its Infrastructure as a Service, Platform as a Service and Big Data analytics platforms. For starters, Google announced price cuts to its Google Compute Engine platform ranging from 30-85%. Prices for Google’s Infrastructure as a Service offering will be slashed by 32% for all “sizes, regions and classes.” Meanwhile, Google Cloud Storage and Google BigQuery experienced price reductions of 68% and 85% respectively. Google simplified the pricing of its platform as a service, Google App Engine, and reduced it by roughly 30%. In addition to price cuts, Google unveiled an analogue to the Amazon Web Services product reserved instances which provides deep discounts on VM pricing in the event they are used for one or three year time periods. Branded “Sustained-Use Discounts,” Google offers price cuts on top of its already announced reduction for customers who use a VM for more than 25% of a given month. Customers who use a VM for an entire month can see additional discounts of up to 30%, resulting in price cuts of over 50% compared to original prices given today’s other price reductions. Google is also launching BigQuery Streaming, an enhancement that enables the BigQuery platform to consume 100,000 rows of data per second and render the data available for real-time analytics in ways comparable to products such as Amazon Kinesis and Treasure Data. Moreover, Google announced a Managed Virtual Machines service that allows users to configure a virtual machine to their own specifications and subsequently deploy the VM to the Google App Engine infrastructure, thereby giving developers more flexibility vis-à-vis the type of machine managed that can take advantage of App Engine’s auto-scaling and management functionality. For developers, Google announced integration with Git featuring automated build and unit testing of changes committed as well as aggregated logs of testing results. Finally, Google revealed the general availability of Red Hat Enterprise Linux and SUSE Linux Enterprise Server and Windows Server 2008 R2 in limited preview for VMs.

All told, today’s price cuts and news of functionality represent much more than a price war with Amazon Web Services. Just a day before the AWS Summit in San Francisco, Google confirmed the seriousness of its intent to increase traction for its development-related cloud-based products. The variety of today’s enhancements to Google Compute Engine, Google App Engine, BigQuery and the introduction of its Managed Virtual Machines service indicate that Google is systematically preparing to service the cloud computing needs of enterprise customers. Despite all the media hype over the last two years about companies gearing up “take on Amazon,” no other cloud vendor has even been close to the depth of IaaS features and functionality possessed by Amazon Web Services with the exception of Google as it revealed itself today. All this means that we now have a two horse race in the Infrastructure as a Service space until the commercial OpenStack community convincingly demonstrates the value of OpenStack-based cloud inter-operability in conjunction with richness of features and competitive pricing.

Categories: Google | Tags: , , , , , , , , ,

Google’s Cloud Storage Price Cut Indicates Subtle Move To Revamp Its Market Perception

On Thursday, Google slashed prices on its cloud storage platform by lowering the price of 100GB storage from $4.99/month to $1.99/month, and 1TB from $49.99 to $9.99/month. Meanwhile, the price for 10TB is only $99.99/month. In comparison, Dropbox charges $10/month for 100GB and Microsoft OneDrive charges $50/year, or roughly $4.17/month. Google’s decision to cut Google Drive prices is likely to precipitate Dropbox and competitors to respond with similar cuts to stay competitive. More importantly, however, the price cut sends a subtle signal on Google’s part that it is gearing up to go after business customers both for cloud storage, and for its cloud offerings more generally via its Google Compute Engine platform. By increasing market share in the cloud storage space, Google affirms and underscores the reliability and cost-effectiveness of its cloud-based storage offering, and thereby continues to demonstrate its competency in verticals other than keyword search. Thursday’s aggressive price cut positions Google as a leader in the cloud storage space and stands to continue the transformation of Google’s market perception to a leader in cloud-based infrastructures more generally.

Categories: Dropbox, Google | Tags:

Introducing Google Compute Engine in General Availability Mode

Categories: Google | Tags:

Given The General Availability Of Google Compute Engine, Is Amazon Web Services Destined To Meet Its Match?

On Monday, Google announced the general availability of Google Compute Engine, the Infrastructure as a Service public cloud platform that Google first announced in June 2012. Unlike many of Google’s product offerings, which are not targeted toward enterprise customers, Google Compute Engine comes with 24/7 customer support and a 99.95% SLA. Moreover, the platform boasts encryption of data at rest in an effort to respond to customer concerns about data security, particularly given Google’s vaunted reputation for mining every piece of data touched by its hardware and range of software applications. Monday’s general availability release features a 10% price reduction on standard, server instances and a 60% price reduction in storage pricing per gigabyte for its persistent disk service.

At the level of functionality, the GA release of Google Compute Engine claims the following three notable features:

Expanded Support for Operating Systems

Whereas Google Compute Engine supported the Linux distributions Debian and Centos in preview mode, the GA version supports a range of Linux distributions including SELinux, CoreOS, SUSE and Red Hat Enterprise Linux (limited preview). This release also features support for Docker containers that enable users to spin up containers instead of virtual machines to accelerate automated testing, continuous integration and deployment.

Transparent, automated maintenance and live migration

Google Compute Engine is now the beneficiary of ongoing, transparent maintenance routines and processes in order to ensure the effective functioning of the GCE infrastructure. Transparent maintenance operates by working on “only a small piece of the infrastructure in a given zone” such that “Google Compute Engine automatically moves your instances elsewhere in the zone, out of the way of the maintenance work” with the help of live migration technology. Customer instances continue to operate as usual while maintenance is performed.

Three New 16 Core Instances

In order to serve the needs of customers that require greater computational power, Google Compute Engine now boasts three 16 core instances for the standard, high memory and high CPU instance types. Use cases for the computing power delivered by these instances include advanced simulations and NoSQL platforms that require high degrees of scalability and performance.

Gartner analyst Lydia Leong reflected on a comparison between GCE and Amazon Web Services in a blog post and concluded:

GCE still lags AWS tremendously in terms of breadth and depth of feature set, of course, but it also has aspects that are immediately more attractive for some workloads. However, it’s now at the point where it’s a viable alternative to AWS for organizations who are looking to do cloud-native applications, whether they’re start-ups or long-established companies. I think the GA of GCE is a demarcation of market eras — we’re now moving into a second phase of this market, and things only get more interesting from here onwards.

Leong sees the general availability of Google Compute Engine as the “second phase” of the IaaS market, whereby Google and AWS stand poised to out-innovate each other and subsequently push each other to new technological heights. The challenge for Google, however, as Leong rightly suggests elsewhere in her blog post, is that it will need to earn the trust of enterprise customers. The industry will not expect Google to deliver the “fanatical support” which became the hallmark and differentiator of Rackspace, for example, but it will expect degrees of white glove support and professional services that are not familiar parts of the Google apparatus, just yet.

Moreover, as part of the project of gaining the support of the enterprise, Google will need to deliver more explicit guarantees of the safety of data hosted within its IaaS platform from the prying eyes of its repertoire of tools for analyzing structured and unstructured data stored in every conceivable format and structure. Finally, Google will ultimately need an outward facing CTO comparable to Amazon’s Werner Vogels that can evangelize the platform and sell customers on a roadmap that ultimately achieves feature parity, if not superiority, as compared to Amazon Web Services. Technology and innovation has never been Google’s problem. Capturing the confidence of the enterprise, however, has been a different story entirely for Google, although as Leong notes, Monday’s announcement may signal a fork in the road for the IaaS space and the Mountain View-based, search engine and technology behemoth. Current GCE customers include Snapchat, Evite and Wix.

Categories: Amazon Web Services, Google | Tags: , , , , , ,

Amazon Web Services Continues To Increase IaaS/PaaS Market Share According To Synergy Research Group

A recent article by the Synergy Research Group (Synergy) claims that Amazon Web Services continues to dominate the IaaS and PaaS space in terms of revenue. According to Synergy, Amazon Web Services increased its quarterly revenue by 55% to over $700M in Q3 of 2013, whereas the aggregate of revenue for Salesforce, IBM, Windows Azure and Google was less than $400M for the same time period. Worldwide, total IaaS and PaaS revenues exceeded $2.5 billion for the quarter, with IaaS accounting for 64% of cloud revenues, a surprisingly small proportion given the limited penetration of platform as a service within the enterprise. Synergy Research’s John Dinsdale remarked on the company’s findings as follows:

We’ve been analyzing the IaaS/PaaS markets for quite a few quarters now and creating these leadership metrics, and the relative positioning of the leaders really hasn’t changed much. While Amazon dwarfs all competition, the race is on to see if any of the big four followers can distance themselves from their peers. The good news for these companies and for the long tail of operators with relatively small cloud infrastructure service operations, is that IaaS/PaaS will be growing strongly long into the future, providing plenty of opportunity for robust revenue growth.

Here, Dinsdale remarks that the “race is on to see if” Salesforce, IBM, Microsoft and Google can decisively secure second place in the battle for IaaS/PaaS market share. Strikingly, Microsoft, Google and IBM have revenues that are very close to one another, even though one might reasonably expect Microsoft’s Azure platform to edge out its competition given its earlier entry into the market than IBM and Google’s Compute Engine (GCE). That said, IBM’s sizeable IaaS revenue derives largely from its acquisition of SoftLayer, which itself had a rich and venerable history that predated IBM.

Synergy’s chart illustrating Q3 IaaS and PaaS revenues is given below:

Notable omissions from the findings include Rackspace, HP, Oracle, Pivotal One and Red Hat, the middle three of which (HP, Oracle and Pivotal One) are still relatively nascent, and hence justifiably excluded from the present calculation. As Dinsdale notes above, however, “the good news for these companies” and for remainder of the space is that revenues are set to increase significantly in the near term. Going forward, one of the key questions for subsequent IaaS market share analyses will be whether OpenStack’s momentum and gradual maturation propels disproportionate growth amongst OpenStack-based cloud platforms for vendors such as HP, IBM, Oracle, Rackspace and Red Hat.

Categories: Amazon Web Services, Google, IBM, Miscellaneous, OpenStack, Oracle, Red Hat | Tags: , ,

New Relic Partners With Google To Support Google Compute Engine For Application Monitoring

On Monday, New Relic announced that it will join the Google Cloud Platform Partner program as a monitoring and application analytics application for Google Compute Engine. As a result of the partnership, Google Compute Engine customers will have free access to New Relic for “large-scale computing workloads on Linux virtual machines in Google’s data centers hosted on Google Cloud Platform.” Existing Google Compute Engine customers that are already using New Relic will receive the Google Cloud Platform Starter Pack, which features $2,000 toward the use of either Google Compute Engine or Google App Engine. Google Compute Engine customers can now use New Relic to keep track of the performance and health of applications as well as their larger IT environment. Meanwhile, Google will participate prominently at New Relic’s first technology conference, FutureStack13, from October 24-25.

New Relic’s partnership with the Google Compute Engine platform builds upon another recent partnership, namely, its integration into the Cloud Foundry Java “buildpack” that Cloud Foundry uses for application deployment. With just a few clicks of the mouse, developers can now use New Relic to monitor the performance of Java applications in Cloud Foundry development, testing and production environments.

Categories: Google, New Relic | Tags: , | 1 Comment

Iterative Computation Between Vertices In Pregel and Apache Giraph

As a follow-up to our post on Facebook’s use of Apache Giraph, I wanted to return to Pregel, the graphing technology on which Giraph was based. Alongside, MapReduce, Pregel is used by Google to mine relationships between richly associative data sets in which the data points have multi-valent, highly dynamic relationships that morph, proliferate, aggregate, disperse, emerge and vanish with a velocity that renders any schema-based data model untenable. In a well known blog post, Grzegorz Czajkowski of Google’s Systems Infrastructure Team elaborated on the importance of graph theory and Pregel’s structure as follows:

Despite differences in structure and origin, many graphs out there have two things in common: each of them keeps growing in size, and there is a seemingly endless number of facts and details people would like to know about each one. Take, for example, geographic locations. A relatively simple analysis of a standard map (a graph!) can provide the shortest route between two cities. But progressively more sophisticated analysis could be applied to richer information such as speed limits, expected traffic jams, roadworks and even weather conditions. In addition to the shortest route, measured as sheer distance, you could learn about the most scenic route, or the most fuel-efficient one, or the one which has the most rest areas. All these options, and more, can all be extracted from the graph and made useful — provided you have the right tools and inputs. The web graph is similar. The web contains billions of documents, and that number increases daily. To help you find what you need from that vast amount of information, Google extracts more than 200 signals from the web graph, ranging from the language of a webpage to the number and quality of other pages pointing to it.

In order to achieve that, we have created scalable infrastructure, named Pregel, to mine a wide range of graphs. In Pregel, programs are expressed as a sequence of iterations. In each iteration, a vertex can, independently of other vertices, receive messages sent to it in the previous iteration, send messages to other vertices, modify its own and its outgoing edges’ states, and mutate the graph’s topology (experts in parallel processing will recognize that the Bulk Synchronous Parallel Model inspired Pregel).

The key point worth noting here is that Pregel computation is marked by a “sequence of iterations” whereby the relationship between vertices is iteratively refined and recalibrated with each computation. In other words, Pregel computation begins with an input step, followed by a series of supersteps that successively lead to the algorithm’s termination and finally, an output. During each of the supersteps, the vertices send and receive messages to other vertices in parallel. The algorithm terminates when the vertices collectively stop transmitting messages to each other, or, to put things in another lexicon, vote to halt. As Malewizc, Czajkowsk et.al note in a paper on Pregel, “The algorithm as a whole terminates when all vertices are simultaneously inactive and there are no messages in transit.” Like Pregel, Apache Giraph uses a computation structure whereby computation proceeds iteratively until the relationships between vertices in a graph stabilize.

Categories: Big Data, Facebook, Google | Tags: , , , | Leave a comment

Blog at WordPress.com. Customized Adventure Journal Theme.