Google recently revealed details of Maglev, its network load balancing technology that gives developers the ability to build infrastructures that can handle a million requests per second, without warning. Maglev leverages Equal-Cost Multi-Path routing technology to disperse incoming network packets across all Maglevs in conjunction with “consistent hashing techniques” that enable the accurate transmission of packets to the “correct service backend servers,” regardless of which Maglev receives a specific packet. Maglev’s use of Equal Cost Multi-Path routing technology differentiates from the common industry practice of using Active Passive load balancer configuration, wherein the secondary, or passive load balancer configuration operates passively and awaits the opportunity to assume responsibility for load balancing as required. Whereas Active Passive load balancer configurations can waste half of their load balancing resources, Maglev’s use of ECMP enables a more deeply engaged utilization of existing resources as noted in Google’s blog post, below:
All Maglevs in a cluster are active, performing useful work. Should one Maglev become unavailable, the other Maglevs can carry the extra traffic. This N+1 redundancy is more cost effective than the active-passive configuration of traditional hardware load balancers, because fewer resources are intentionally sitting idle at all times.
Borg, Google’s cluster management technology, renders it possible to migrate service workloads between different clusters as required. Similarly, Maglev facilitates the addition and removal of load balancing capacity and thereby illustrates the capability of Network Functional Virtualization technology to add and remove load balancing functionality without the addition of new hardware. Google’s deep dive into the workings of Maglev, which runs on commodity Linux servers, illustrates how its technology manages load balancing at scale and facilitates its management both of Google network traffic more generally as well as load balancing within the Google Cloud Platform. Named after the Japanese bullet qua magnetic levitation train, Google’s Maglev technology requires the addition of new Maglevs once a certain threshold has been reached with respect to the use of existing Maglevs for network load balancing purposes.
Google CEO Sundar Pichai recently announced that Gmail has surpassed 1 billion users per month and that its Google Cloud Platform is used by more than 4 million applications. Pichai also asserted that the Google Cloud Platform “is ready to be used at scale,” and that the company has reached a point where its cloud infrastructure and applications have reached a level of maturity at exactly the time when the broader, industry-wide “movement to cloud has reached a tipping point.” Pichai further noted that Catholic Health Initiatives, one of the nation’s largest non-profit health systems, announced its transition to Google Apps last quarter in what amounts to yet another example of the Google Cloud Platform’s readiness to embrace workloads from large organizations and enterprises. Unlike Microsoft and Amazon, Alphabet, Google’s parent company, failed to break out revenue run rate details about its subsidiary cloud business but the company’s appointment of VMware executive Diane Greene to head Google’s cloud services division in November constitutes ample proof of the company’s interest in building out its cloud business. The question now, however, is when and how Google plans to court the enterprise, which has traditionally been dominated by Microsoft and IBM in the enterprise software and infrastructure space. Without more details of its anticipated strategy for gaining traction for cloud products and services in the enterprise, investors and analysts alike will be hard pressed to understand how Google plans to build cloud market share, particularly given continued impressive revenue growth for Amazon Web Services and Microsoft’s growing ascendancy in the cloud products and services space under CEO Satya Nadella.
On Thursday, Google announced the second generation of its Cloud SQL database that was initially launched in 2011. Google’s Cloud SQL Second Generation BETA delivers performance improvements by a factor of seven as well as enhanced scalability by a factor of twenty. Cloud SQL Second Generation also offers users greater control over maintenance windows in addition to less frequent maintenance requirements. As a fully managed cloud offering, Google’s Cloud SQL database platform delivers the convenience of not having to manage deployment, upgrades and security in addition to the convenience of configuring automatic backup and data replication. Cloud SQL Second Generation offers users the ability to scale to up to 10 TB of data, 104 GB of RAM and throughput of 15,000 IOPS, well in excess of the performance and scalability of the first generation as noted in a blog post by Brett Hesterberg. The new version of Google’s cloud-based SQL database platform, based on MySQL, marks one of its most significant announcements regarding its cloud portfolio since the recent appointment of former VMware executive Diane Greene to the role of the head of its cloud business in November.
On December 2, Google announced the release of the Google Cloud Vision API, an application programming interface that gives developers access to its powerful and nuanced image recognition technology. Developers can use the Google Cloud Vision API to categorize images, identify faces and read words and characters within images hailing from multiple languages. Google’s face identification technology allows users to identify the appearance of faces within an image in conjunction with the probability that a face has a specific emotion such as joy or sadness. The API also enables the identification of popular landmarks and its associated latitude and longitude. Available in limited preview, the API gives users a glimpse of Google’s powerful image recognition technology and throws open the door toward the larger project of adding metadata to unstructured data. As scale-out storage platforms for structured and unstructured data proliferate, the Google Cloud Vision API represents a powerful tool that developers can use to add metadata to tag, organize and classify image files.
The obvious question raised by Google’s reorganization as Alphabet is whether Larry Page and Sergey Brin will decide to break out Google Cloud Platform into a separate business from Google, the search engine-focused subsidiary headed by CEO Sundar Pichai. Breaking out Google Cloud Platform (GCP) into a separate entity not only paves the way for more significant, dedicated investments dedicated to cloud computing on the part of Google, but also provides a means for investors to more accurately gauge the success of the Google Cloud Platform as measured by its financials and the growth of the entity more generally. The tricky part about breaking out Google Cloud Platform as a separate entity involves the depth of its inter-relationship with all of Google’s other businesses, and particularly Google Search, Google Maps, YouTube and Android. While assessing revenue generation from third party customers of the Google Cloud Platform is easy, the more challenging task involves quantifying the operational overhead of GCP, particularly given that it powers all of Google’s infrastructure already.
Google has reached a preliminary agreement qua memorandum of understanding to bring Project Loon, the initiative that delivers internet access by means of balloons that float high in the sky, to Sri Lanka, the island located just off the southern tip of India in the Indian Ocean. If an agreement is finalized, Sri Lanka will be the first country to boast universal internet access, all courtesy of Google. According to The Verge, Google plans to collaborate with Sri Lanka’s internet providers to bolster their services to collectively span every inch of the country. Project Loon aspires to bring access to all 20 million of Sri Lanka’s populace whereas currently, estimates indicate only 3 million people have internet access.