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The new Google Cloud region in Jakarta is now open

Indonesia is one of the most creative, dynamic, and entrepreneurial countries in Southeast Asia. We’ve seen developers and enterprises embrace new cloud technology to solve their challenges and drive one of the fastest growing economies in the world. To better help our customers in Southeast Asia accelerate their growth, we’re excited to announce that our new Google Cloud Platform (GCP) region in Jakarta is officially open.Designed to support Indonesian customers and their end users, the Jakarta region is our first GCP region in Indonesia and ninth in Asia Pacific. In this time of social distancing, we’re celebrating the opening of the region with a digital event. Watch the keynote by going here.  A cloud made for IndonesiaThe launch of our new Jakarta region (asia-southeast2) brings the best of GCP closer to our customers and users. With lower latency access to data and applications, companies doing business in Indonesia can accelerate their digital transformation. It will also help customers meet specific regulatory and compliance requirements, and provide more disaster recovery options for customers across APAC. The region has three Cloud zones from the start, enabling high availability workloads. With this region, Google Cloud now offers 24 regions and 73 zones across 17 countries worldwide.Having a region in Jakarta will help new and existing customers in Indonesia leverage Google Cloud technologies to provide better experiences for local users. “Google Cloud already helps us to execute our digital banking strategy, which accelerates financial inclusion, and to provide better banking services for Indonesians,” said Indra Utoyo, Director of Digital, Information Technology and Operation at Bank Rakyat Indonesia(Persero) Tbk. “Right now, we are using Apigee, Google Maps Platform APIs, and Cloud Vision for our development purposes. Using a hybrid approach, in cloud and on-premises, both Google and BRI are putting data security as our highest priority. The Jakarta region launch reinforces Google’s commitment and helps us reach out to our customers even better.” With a target to move 70% of workloads to the cloud within the next three years, telecommunications leader PT XL Axiata Tbk has adopted Anthos to automate, manage and scale workloads across its hybrid- and multi-cloud environments in a secure, consistent manner. “XL Axiata is committed to the modernization of our infrastructure to get more business agility and increase application deployment velocity. Anthos was a natural fit as it lets us adopt containers while letting Google, a leader in Kubernetes, manage our container infrastructure for us,” said Yessie D Yosetya, Chief Information and Digital Officer of XL Axiata.Indonesia is home to many digital unicorns like  Tokopediawho are disrupting their industries by transforming the way they provide digital services. “Google Cloud has enabled us to connect with 7+ million merchants and 90+ million monthly active users in over 97% of districts across this vast country. This collaboration presents us with an exciting opportunity to help Indonesians achieve more and to further democratize commerce through technology in Indonesia.” said Tahir Hashmi, VP of Engineering of Tokopedia.Together with our ecosystem of local partners, we’re helping businesses across industries adopt new technologies to accelerate their digital transformation. “In a rapidly evolving banking landscape, we are seeing more banks and financial institutions looking for cloud-native solutions,” said Myles Bertrand, Mambu’s Managing Director APAC at Mambu. “There is a greater understanding that when they run in the cloud they can be more responsive, agile, innovative and run at a lower cost. We believe our partnership with Google Cloud Platform will have a hugely positive impact on the digital banking space right across Asia Pacific.” The Jakarta region launches with our standard set of services, including Compute Engine, Google Kubernetes Engine, Cloud SQL, Cloud Storage, Cloud Spanner, Cloud Bigtable, and BigQuery. Developers, data scientists, and data engineers can also leverage our ML and AI tools to take their projects from ideation to deployment, quickly and cost-effectively. Hybrid cloud customers can integrate new and existing deployments with help from our regional partner ecosystem, and via multiple Dedicated Interconnect locations.Visit our cloud locations page for a complete list of services available in the Jakarta region.Maju Sama-SamaMaju Sama-Sama is Google Indonesia’s motto and it means “advancing forward together.” As we all face unprecedented challenges at this time, Google Cloud is committed to partnering with Indonesians to build a resilient future. In addition to launching our new cloud region, we’re making additional commitments today to help develop the talent that’s critical to our customers’ digital transformation journey.To help build a cloud-ready workforce, we will deliver 150,000 hands-on training labs this year in Indonesia. These include access to GCP training sessions at no charge, credits and a range of career readiness initiatives like Juara GCP, our Cloud OnBoard training, and digital scholarships with Indonesia’s Ministry of Communication and Information Technology to help people become GCP certified. Since the start of the COVID-19 crisis, we’ve provisioned G Suite for Education to thousands of schools throughout Jakarta to support remote learning arrangements. To help everyone make the best of the new “classroom,” we’ve launched the Google Teach from Home central hub in Bahasa Indonesia, which provides information, training, and tools to help educators teach from home. Today, we also announced our commitment to work collaboratively with the Indonesian government to equip millions of educators and learners by provisioning access to G Suite for Education nationwide. What’s next2020 continues to be a tremendous year for Google Cloud as we expand our global infrastructure. Visit our Jakarta region page for more details about the region, and our cloud locations page for updates on the availability of additional services and regions.
Quelle: Google Cloud Platform

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How Schrödinger is advancing COVID-19 drug discovery efforts with Google Cloud

Technology has been key in helping the healthcare industry take actions in response to the coronavirus pandemic. Now, as doctors and nurses prepare for the possibility of a second wave, technology will take an even bigger role in the development of new medicines to fight COVID-19. To accelerate this drug discovery process, Schrödinger, a Google Cloud customer, has teamed up with Takeda, Novartis, Gilead Sciences, and WuXi AppTec in a philanthropic initiative to share ideas, resources, and data with the goal of developing antiviral therapeutics for coronavirus. As part of this alliance, Schrödinger, whose physics-based software platform enables discovery of high-quality, novel molecules for therapeutics and materials, is using credits from Google Cloud to enable rapid exploration and testing of potential new drugs. Schrödinger and Google Cloud first announced a strategic agreement earlier this year giving Schrödinger access to powerful computing capacity to accelerate discovery for its commercial partners and its internal pipeline. Historically, drug discovery relied more heavily on the experience and intuition of medicinal chemists, who had to synthesize and assay every potential compound by hand—a time-consuming and expensive process. Schrödinger’s computational platform–powered by Google Cloud’s high performance computing–revolutionizes this process. With this technology, the initial discovery work can be done “in silico,” enabling chemists to investigate vastly more compounds than they ever could before. As they begin to actually synthesize molecules, they do so with the knowledge that they have already explored billions of potential designs and identified the ones most likely to succeed as therapeutics. With the backing of Google Cloud, Schrödinger’s team can easily scale up their discovery work to meet the needs of the COVID-19 alliance..With these credits, Google Cloud is providing Schrödinger with 16 million hours of GPU time to enable computational drug discovery, which, if used consecutively, would equate to 1,826 years of around-the-clock computing. All of this has the potential to considerably accelerate the preclinical drug discovery process for COVID-19. By utilizing tens of thousands of GPU computing hours and hundreds of thousands of CPU computing hours on Google Cloud, Schrödinger’s computational design platform can triage and evaluate billions of molecules for two promising drug targets. That way, alliance partners can analyze the most promising compounds in the lab. Meanwhile, Schrödinger’s drug discovery team is turning to additional targets—and again scouring chemical space for molecules that could become powerful medicines. We’re honored to support Schrödinger and its alliance partners in the work they are doing to identify new medicines for the betterment of everyone.
Quelle: Google Cloud Platform

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Bayer Crop Science seeds the future with 15000-node GKE clusters

Editor’s note: Today’s post examines how GKE’s support of up to 15,000 nodes per cluster benefits a wide range of use cases, including helping Bayer Crop Science rapidly process new information arriving from its genotyping labs.At Google, scalability is a core requirement for the products we build. With more enterprises adopting Google Kubernetes Engine (GKE), we’ve been working to push the limits of a GKE cluster way beyond the supported limits—specifically, clusters with up to 15,000 nodes. This is the most supported nodes of any cloud-based Kubernetes service, and 3X the number of nodes supported by open-source Kubernetes. There are various use cases when this kind of huge scale is useful:If you’re running large, internet-scale services If you need to simplify infrastructure management by having fewer clusters to manageBatch processing — shortening the time needed to process data by temporarily using much more resourcesTo absorb large spikes in resource demand, for example during a gaming launch, or an online ecommerce campaign. Being able to resize an existing cluster rather than provisioning a new one can improve the availability and performance of your service. Having 15,000 nodes per cluster is all the more significant when you consider that the scalability of an IT system is much more than just how many nodes it supports. A scalable system needs to be able to use a significant amount of resources and still serve its purpose. In the context of a Kubernetes cluster, the number of nodes is usually a proxy for the size of a cluster and its workloads. When you take a closer look though, the situation is far more complex.The scale of a Kubernetes cluster is like a multidimensional object composed of all the cluster’s resources—and scalability is an envelope that limits how much you can stretch that cube. The number of pods and containers, the frequency of scheduling events, the number of services and endpoints in each service—these and many others are good indicators of a cluster’s scale. The control plane must also remain available and workloads must be able to execute their tasks. What makes operating at a very large scale harder is that there are dependencies between these dimensions. For more information and examples, check out this document on Kubernetes Scalability thresholds and our GKE-specific scalability guidelines.The Kubernetes scalability envelope, based on http://www.gregegan.net/APPLETS/29/29.htmlIt’s not just hyperscale services that benefit from running on highly scalable platforms—smaller services benefit too. By pushing the limits of an environment’s scalability, you also expand your comfort zone, with more freedom to make mistakes and use non-standard design patterns without jeopardizing the reliability and performance of your infrastructure. For a real-world example of highly scalable platforms, today we are hearing from the team at Bayer Crop Science and learning about a recent project they designed.Setting on a journey to run at 15,000 node scaleTo make it possible for GKE users to run workloads that need more than 5,000 nodes in one cluster, we engaged a group of design partners into a closed early access program. Precision agriculture company Bayer Crop Science (BCS) is currently one of the biggest users of GKE, with some of the largest GKE clusters in the Google Cloud fleet. Specifically, it uses GKE to help it make decisions about which seeds to advance in its Research & Development pipeline, and eventually which products (seeds) to make available to farmers. Doing this depends upon having accurate and plentiful genotype data. With 60,000 germplasm in its corn catalog alone, BCS can’t test each seed population  individually, but rather, uses other data sets, like pedigree and ancestral genotype observations, to infer the likely genotypes of each population. This way, BCS data scientists can answer questions like “will this seed be resistant to a particular pest?”, reducing how much farmland they need each year to operate the seed production pipeline.Bayer Crop Science’s 60,000 member corn “galaxy,” where every dot is an individual corn germplasm, and every line is the relationship between them. Credit: Tim WilliamsonLast year, BCS moved its on-premises calculations to GKE, where the availability of up to 5,000-node clusters allowed scientists to precalculate the data they would need for the month, and run it as a single massive multi-day batch job. Previously, scientists had to specially request the genotype data they needed for their research, often waiting several days for the results. To learn more, watch this presentation from Next ‘19 by BCS’s Jason Clark. Bayer Crop Science infrastructure/architectureTo facilitate the rapid processing of new information arriving from genotyping labs, BCS implemented an event-driven architecture. When a new set of genotype observations passes quality control, it’s written to a service, and an event is published to a Cloud Pub/Sub topic. The inference engine watches this topic, and if the incoming events match the requirements to allow inference, a job request is created and placed on another topic. The inference engine worker nodes are deployed on the largest available Kubernetes cluster using a Horizontal Pod Autoscaler that looks at the depth of work on the incoming queue. Once a worker selects a job from the topic, it stages all the required inputs, including the genotype observations that initially triggered the job, and runs the genotype inference algorithm. Results are written into a service for accessibility and an event is emitted to a genotype inference topic. Downstream processes like decision making based on inferred genotypes are wired into this event stream and begin their work as soon as they receive the event.Click to enlargePreparations and joint testsBCS’s biggest cluster used to infer the data (a.k.a. for imputation) uses up to 4,479 nodes with 94,798 CPUs and 455 TB of RAM. And because that imputation pipeline is a highly parallelizable batch job, scaling it to run on a 15,000-node cluster was straightforward.In our joint tests we used the cluster hosting the inference engine and its autoscaling capabilities to overscale the size of the workload and amount of available resources. We aimed to scale the cluster from 0 to 15,000 nodes with large machines (16CPU highmem 104GB RAM), for a total of 240,000 CPU cores and 1.48PiB of RAM.To make sure that the resources here are provided at low cost, the cluster hosting the inference engine worker pods used exclusively preemptible instances, while the supporting services hosting the input data and handling outputs ran on regular instances. With preemptible VMs, BCS gets a massive amount of compute power, while slashing the costs of compute power almost by a factor of five.With 15,000 nodes at its disposal, BCS also saves a lot of time. In the old on-prem environment with 1,000 CPUs, BCS would have been able to process ~62,500,000 genotypes per hour. With clusters up to the 5,000 node limit BCS can process 100 times faster. And with 240,000 CPUs across 15,000 nodes, BCS can process ~15,000,000,000 genotypes per hour. That gives BCS the flexibility to make model revisions and quickly reprocess the entire data backlog, or quickly add inference based on new data sets, so data scientists can continue to work rather than waiting for batch jobs to finish.Lessons learned from running at large scaleBoth Google and BCS learned a lot from running a workload across a single 15,000 node cluster. For one thing, scaling the components that interact with the cluster proved to be very important. As GKE processed data with increased throughput, we had to scale up other components of the system too, e.g., increase the number of instances on which Spanner runs.Another important takeaway was the importance of managing preemptible VMs. Preemptible VMs are highly cost efficient but only run for up to 24 hours, during which period they can be evicted. To use preemptible VMs effectively, BCS checkpointed their Google Cloud Storage environment every 15 minutes. That way, if the job is preempted before it completes, the job request falls back into the queue and is picked up and continued by the next available worker. Sowing the seeds of innovationFor Bayer Crop Science to handle large amounts of genome data, it needs significant amounts of infrastructure on-demand. When all is said and done, being able to run clusters with thousands of nodes helps BCS deliver precomputed data quickly, for example, being able to reprocess the entire data set in two weeks. Up to 15,000 node clusters will help cut that time down to four days. This way analysts don’t have to request specific batches to be processed offline. BCS also realized the value of testing hypotheses on large datasets quickly, in a non-production setting. And thanks to this collaboration, all GKE users will soon be able to access these capabilities, with support for 15,000 node clusters broadly available later this year. Stay tuned for more updates from the GKE team. In particular, be sure to join our session during NEXT OnAir on August 25th. There we’ll talk about how Google Cloud is collaborating with large Mesos and Aurora users to offer similar hyperscale experiences on GKE.
Quelle: Google Cloud Platform