Five Ways Cloud Computing Helps Companies Optimize Operations and Lower IT Costs
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There is no one way to recover. The past year has seen unprecedented challenges for business across the world, with social distancing and quarantine measures forcing many organizations to quickly adapt to remote working. A new report from BCG Platinion, titled “Finding a New Normal in the Cloud”, points out that while companies have done well to survive so far, the overall business environment is still a tough one with contracted economies and lowered revenues. CIOs have to find the right balance between technological innovation and lowering their cash burn rate.
The report identifies five key ways in which companies can use cloud computing to optimize their operations and reduce overall IT costs by as much as 10%.
1. Go beyond VPN
The BCG Platinion report states that companies need “rapid, efficient, highly scalable, and device agnostic solutions”. Traditionally, when employees had to work offsite, companies provided them with VPNs. But the sheer scale of the COVID-19 pandemic has shown these solutions to be expensive, slow, inconvenient, and hard to manage when entire workforces are working remotely.
Instead, BCG Platinion emphasizes the need for cloud-based solutions such as BeyondCorp Enterprise for more secure, more effective, lower cost remote access at scale. BeyondCorp Enterprise offers customers a zero trust platform for simple and secure access with continuous end-to-end protection that can be used on any device at any time. Deliveroo, a global food delivery company headquartered in the UK, uses BeyondCorp Enterprise to bring the zero trust model to its distributed workforce.
“Having secure access to applications and associated data is critical for our business,” says Vaughn Washington, VP of Engineering at Deliveroo. “With BeyondCorp Enterprise, we manage security at the app level, which removes the need for traditional VPNs and associated risks.”1
With a low cost cloud subscription model, BeyondCorp Enterprise eliminates the need for hardware, operating, and maintenance costs that come with VPN solutions and can also enable organizations to offer protection to the extended workforce at a fraction of the cost. BCG Platinion estimates that solutions like BeyondCorp Enterprise can save companies as much as 50% versus the cost of a traditional VPN.
2. Use SaaS to keep productivity up
With so much of the workforce fragmented due to social distancing measures, the need for seamless, efficient collaboration is greater than ever. According to BCG Platinion, a fully functional Software-as-a-Service productivity solution such as Google Workspace helps to “alleviate the traditional costs and burdens around availability, backup, and maintenance of on-premise collaboration infrastructure.” BCG Platinion analysts estimate that adopting a SaaS solution can lower computing costs for end users by up to 35%.
But working in the cloud does more than lower costs. The BCG Platinion report cites a 2020 study from Forrester that found adopting Google Workspace boosted revenue growth by 1.5%, reduced the need for on-demand tech support by 20%, and cut the risk of data breaches by more than 95%. Over the last year, companies of all sizes have looked on the conditions of the pandemic as an opportunity to change the way they work.
“Airbus has spent the past year thinking about what it actually means to return to work and we’re looking to support greater flexibility with Google Workspace in a leading role,” says Andrew Plunkett, Airbus Vice President Digital Workplace. “In 2020, we held 5.6M Google Meet sessions and we now have more than 70,000 shared Drives where people collaborate. Google Workspace has changed the way people work at Airbus and that will continue as the solution empowers the hybrid work reality.”2
3. Reduce IT overhead and management costs with cloud-first devices
Working in the cloud can be made even more effective with devices specifically designed for the cloud, says the BCG Platinion report. “Cloud-native devices such as Google’s Chromebooks and Chromeboxes are cost-effective, easy to deploy, simple to use, and highly secure,” says BCG Platinion. Additionally, with “thin client” devices, companies can save on hardware costs compared with traditional laptops and desktops.The report suggests that a thin client approach can produce savings of up to 25% in end-user technology and support.
Organizations and businesses of all sizes have found that Chrome OS and devices have greatly enhanced their capacity to work together in even the most challenging circumstances. Chrome OS provides employees with a modern experience and devices that stay fast, have built-in security, deploy quickly, and reduce the total cost of ownership. “Chromebooks are simple-to-use and cost-effective devices that do everything that our staff need them to do, which is mainly accessing Google Workspace online,” says Henry Lewis, Head of Platform for the London Borough of Hackney. “As soon as the Grab and Go Chromebooks were available, they were well used every day.”3
4. Lift and shift for easier transitions
While migrating to the cloud is a priority for many organizations to succeed, it does not have to happen all at once and in the same way. A total cloud migration can be a huge undertaking, involving redesigns of architecture and refactoring of applications. For many organizations, this process can take several months, even years.
But in times like these, CIOs need to make decisions quickly and reduce cash burn as much as possible. When resources are stretched and time is tight, BCG Platinion recommends a “lift-and-shift” approach for minimal disruption. With Google Compute Engine, for example, organizations can simply rehost their existing workloads on virtual machines without transformation. BCG Platinion reports that moving non-critical workloads to the cloud with lift-and-shift approaches can reduce IT spend by as much as 4% in just three months. Additionally, a quick, effective migration paves the way for more advanced IT infrastructure changes, creating effects that ripple long into the future.
5. Make data work for you with the cloud
Data is central to any industry and making the most out of it is more important now than ever before. With business as usual upended by the pandemic, organizations have turned to data for urgent tasks like demand forecasting or predicting supply-chain disruptions. A McKinsey report from last year points out that while many organisations had already started to engage with analytics and AI technologies, their progress was dramatically accelerated by the urgency of the pandemic: “Analytics capabilities that once might have taken these organizations months or years to build came to life in a matter of weeks.”
With the right setup a company can use data to drive efficiencies, respond quickly to its customers and open new markets. But big wins require huge amounts of information and powerful analytics, which means that effective data handling can be very difficult and expensive to do with on-premises architecture.
Moving to a cloud-based infrastructure minimizes infrastructure costs while opening up cutting edge techniques like machine learning for greater insight. The BCG Platinion report found that using a cloud-based data platform was not only cheaper and more efficient for businesses, but could result in a 70% increase in “effectiveness” which it defined as increased sales, lower costs of procured goods, and reduced inventory holding costs. “Moving to cloud provides competitive advantage as you scale innovation, accelerating the creation of new services to keep ahead of the competition,” explains Norbert Faure, Managing Director, Platinion Western Europe at Boston Consulting Group (BCG).
The key mission of any data platform is to make sure that the right people have access to the right information at the right time. Google Cloud helps to unify data across the entire business, increase agility, and innovate faster with a range of products. BigQuery runs complex analytics at infinite scale while helping organizations save up to 34% on the total cost of ownership compared with alternative cloud-based data warehouse solutions.4 Cloud Spanner provides a fully managed relational database that operates at 99.999% availability. Meanwhile, with Vertex AI, businesses can access the same groundbreaking machine learning tools that Google uses itself for unprecedented insight on a unified AI platform. “The whole Google Cloud suite of products accelerates the process of getting established and up and running, so we can perform the ‘bread-and-butter’ work of data science,” says David Herberich, VP, Head of Data at fintech startup Branch.5
Minimize costs today, innovate for tomorrow
As the world recovers from the pandemic, continued success depends on staying nimble and adaptive, argues the BCG Platinion report. Cloud computing can make companies more resilient by helping them to keep costs low, reducing overall IT spend by as much as 10% overall according to BCG Platinion. “For CIOs, cloud migrations can offer important near-term savings and benefits, while also reinvigorating progress toward the long-term goal of digital transformation.“
To learn more, read the full report from BCG Platinion.
1. BeyondCorp Enterprise: Introducing a safer era of computing
2. Building the future of work with Google Workspace
3. Hackney Council: Empowering 4,000 staff to keep serving their community from home
4. The Economic Advantages of Google BigQuery versus Alternative Cloud-based EDW Solutions
5. Fintech startup, Branch makes data analytics easy with BigQuery
Three Benefits of VPC Network Peering for SAP Managed Apps
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Over the past year, RISE with SAP has emerged as a valuable solution for SAP customers seeking a faster, simpler, and more affordable path to the cloud. RISE with SAP is a subscription-based offering that typically includes a number of fully managed cloud application options, including SAP S/4HANA Cloud and elements of the SAP Business Technology Platform. It’s a great way to migrate your business to modern cloud ERP applications, while handing off the implementation and management to SAP and freeing your own IT staff to focus on other projects.
As a strategic partner for RISE with SAP, Google Cloud works closely with SAP to ensure reliable, secure, high-performance connectivity between your SAP managed applications and your other Google Cloud applications and services. Today, we’re focusing on a Google Cloud capability that plays a big part in achieving this goal: VPC network peering. If your company uses Google Cloud to host its RISE with SAP managed applications (or any other SAP-managed offering, such as SAP Enterprise Cloud Services), it’s worth learning more about why VPC network peering is an important capability and how your network admin can implement it.
VPC network peering: The critical link for your SAP managed applications
We recently co-authored a white paper with SAP that goes into technical detail about how VPC network peering works and includes step-by-step configuration instructions. But it is useful to understand, at a higher level, why it matters for RISE with SAP subscribers.
When SAP implements your RISE with SAP managed applications on Google Cloud, it also provisions a virtual private cloud (VPC) — a virtualized network that provides the same functionality, performance, and security benefits as a dedicated physical network — for this SAP environment. In fact, a VPC actually gives you some capabilities that a physical network can’t match, such as the ability to increase IP space without downtime.
Using VPC network peering to connect your SAP managed environment with your Google Cloud applications and services gives you three key benefits:
- Network latency: Traffic that remains inside Google’s network enjoys lower latency than connectivity that uses external addresses.
- Network security: Service owners do not need to have their services exposed to the public internet and deal with its associated risks.
- Network cost: Rather than pay egress bandwidth costs for networks using external IPs to communicate, peered networks can use internal IPs to communicate, saving you money on those egress costs. Regular network pricing still applies to all traffic.
Many customers also appreciate the flexibility they get from VPC network peering: It supports the ability to peer your own VPC networks with other VPC networks that reside in different projects and even in different organizations.
Using VPC network peering to strengthen your SAP security posture
Most Google Cloud customers find VPC network peering most useful to ensure seamless connectivity between their SAP managed applications and the rest of their Google Cloud environment. But some companies also rely on VPC network peering to gain greater control over the security of their cloud environments. This can include:
- Implementing additional network security capabilities, such as advanced firewalls, intrusion detection, and network package inspection, that go above and beyond standard SAP security measures.
- Leveraging Google Cloud Interconnect to link your on-premises and Google Cloud environments, including your RISE with SAP managed applications, without sending traffic across the public internet.
Once your company has configured VPC network peering, things get even more interesting. Google Cloud offers plenty of ways to complement and enhance the value of your SAP applications: using Google BigQuery to consolidate and enrich your SAP application data; relying on Google AI/ML capabilities to sharpen your predictive analytics and real-time decision-making; or leveraging Google Kubernetes Engine to jump-start your own cloud-native application development efforts, among many other examples.
Some of the world’s biggest SAP customers, including The Home Depot, and Cardinal Health, have chosen Google Cloud to migrate their business-critical SAP applications. And we’ve seen how useful VPC network peering can be for SAP customers to unlock the full value and potential of Google Cloud. Be sure to download our white paper so you can get VPC network peering configured for your RISE with SAP managed applications, and discover for yourself just what’s possible when you run SAP applications on Google Cloud.
How The New York Times Increased Speed of Delivery by Using Kubernetes
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When New York Times decided a few years ago to move out of its data centers, its first deployments on the public cloud were smaller and less critical applications that were being managed on virtual machines.
“We started building more and more tools, and at some point, we realized that we were doing a disservice by treating Amazon as another data center,” says Deep Kapadia, Executive Director, Engineering at The New York Times.
Kapadia was tapped to lead a Delivery Engineering Team that would “design for the abstractions that cloud providers offer us.”
The team decided to use Google Cloud Platform and its Kubernetes-as-a-service offering, GKE (Google Kubernetes Engine). Owing to Google Cloud solution and GKE, The New York Times was able to increase the speed of delivery.
Some of the legacy VM-based deployments took 45 minutes; with Kubernetes, that time was “just a few seconds to a couple of minutes,” says Brian Balser, Engineering Manager at The New York Times.
“Teams that used to deploy on weekly schedules or had to coordinate schedules with the infrastructure team, now deploy their updates independently, and can do it daily when necessary,” says Tony Li, Site Reliability Engineer, The New York Times.
Adopting Cloud Native Computing Foundation technologies allowed The New York Times to have a more unified approach to deployment across the engineering staff, and portability for the company.
Groupe Dauphinoise Grows it Customer Base with G Suite and Google Cloud Platform
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As a leading French agricultural cooperative, Groupe Dauphinoise places collaboration at the heart of its philosophy. Working with farmers in the Rhone-Alpes region, Groupe Dauphinoise takes on a diverse range of activities from agricultural production to research and development to running retail outlets.
As its operations expanded and strained its existing infrastructure, Groupe Dauphinoise saw the opportunity to upgrade its technology solutions and adopted G Suite, Chrome devices and ultimately Google Cloud Platform (GCP).
“We transitioned to G Suite to improve our staff’s collaboration. We quickly realized that as the company grew, we needed a new infrastructure. Based on our satisfaction with G Suite, we chose GCP. With Google, ‘any device, anytime, anywhere’ is not just a dream,” says Sylvain Claudel, Head of IT at Groupe Dauphinoise.
“We transitioned to G Suite to improve our staff’s collaboration. We quickly realized that as the company grew, we needed a new infrastructure. Based on our satisfaction with G Suite, we chose GCP. With Google, ‘any device, anytime, anywhere’ is not just a dream.”
—Sylvain Claudel, Head of IT, Groupe Dauphinoise
Flexible workforce, stable infrastructure
Many of Groupe Dauphinoise’s 1,500 employees spend their days out of the office. Five years ago, with the help of Google partner GoWizYou, the company transitioned to G Suite to improve the flexibility and collaboration of its staff.
As Groupe Dauphinoise began to expand and collect more data, the company reached the limits of its on-premise infrastructure. Adding new storage was not a simple matter. Acquiring, configuring and synchronising new servers costs Groupe Dauphinoise time as well as money. In addition, with all its servers stored in a single room, security was a concern. Groupe Dauphinoise needed a new infrastructure.
Google Cloud Platform was the only solution in mind after Groupe Dauphinoise’s experience with G Suite and Chromebooks. Disruption was kept to a minimum thanks to Google’s licensing agreements with Microsoft products, allowing the company to migrate without affecting its operations.
After migrating its infrastructure to Compute Engine, Groupe Dauphinoise can let Google look after the security and maintenance. Cloud IAM makes it easy for Groupe Dauphinoise to hand out permissions to sensitive resources across a number of sites with maximum security and minimum fuss. The company placed its archives in Cloud Storage, while Cloud SQL allows it to continue to make use of its MySQL databases without disrupting the day to day business. Meanwhile, BigQuery provides Groupe Dauphinoise with the raw power to analyse large datasets quickly.
“Our company is growing and we have more and more data to collect and analyze like sales data, weather patterns or production numbers. With GCP, we have more than a single on-premise data store, so our disaster recovery plan is much more flexible. Compute Engine allows us to add more storage quickly and easily, without having to spend days synchronizing data and installing new servers. We have improved security and maintained the stability of our infrastructure while keeping costs down,” says Sylvain.
Safeguarding the present, looking to the future
With Google Cloud Platform, Groupe Dauphinoise has expanded and secured its infrastructure without sinking costs into on-premise servers or DevOps staff. Its investment in Chrome devices and adoption of G Suite mean that its mobile workforce can fully reap the benefits of a cloud-based infrastructure while dramatically cutting the cost of hardware. Meanwhile, working with a 200 million line table of sales data in Google BigQuery, the cooperative found that queries ran ten times faster than with its previous database provider. Groupe Dauphinoise is experimenting with Google BigQuery to expand its BI capabilities. Products like Google BigQuery help Groupe Dauphinoise grow its business, safe in the knowledge that its infrastructure is stable and secure.
“The amount of data we collect is growing very quickly. We need to break our rules and evolve from the mindset that we had with on-premise infrastructure and our old databases. We can now look at collecting more customer fidelity data, or big data for our farmers. With our data and infrastructure in Google’s care, we can concentrate on growing our customer base instead of our IT department!” says Sylvain.
Cloud Bigtable Helps Fraud-detection Company Meet Scalability Demands and Secure Customer Data
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Editor’s note: Today we are hearing from Jono MacDougall , Principal Software Engineer at Ravelin. Ravelin delivers market-leading online fraud detection and payment acceptance solutions for online retailers. To help us meet the scaling, throughput, and latency demands of our growing roster of large-scale clients, we migrated to Google Cloud and its suite of managed services, including Cloud Bigtable, the scalable NoSQL database for large workloads.
As a fraud detection company for online retailers, each new client brings new data that must be kept in a secure manner and new financial transactions to analyze. This means our data infrastructure must be highly scalable and constantly maintain low latency. Our goal is to bring these new organizations on quickly without interrupting their business. We help our clients with checkout flows, so we need latencies that won’t interrupt that process—a critical concern in the booming online retail sector.
We like Cloud Bigtable because it can quickly and securely ingest and process a high volume of data. Our software accesses data in Bigtable every time it makes a fraud decision. When a client’s customer places an order, we need to process their full history and as much data as possible about that customer in order to detect fraud, all while keeping their data secure. Bigtable excels at accessing and processing that data in a short time window. With a customer key, we can quickly access data, bring it into our feature extraction process, and generate features for our models and rules. The data stays encrypted at rest in Bigtable, which keeps us and our customers safe.
Bigtable also lets us present customer profiles in our dashboard to our client, so that if we make a fraud decision, our clients can confirm the fraud using the same data source we use.
We have configured our bigtable clusters to only be accessible within our private network and have restricted our pods access to it using targeted service accounts. This way the majority of our code does not have access to bigtable and only the bits that do the reading and writing have those privileges.
We also use Bigtable for debugging, logging, and tracing, because we have spare capacity and it’s a fast, convenient location.
We conduct load testings against Bigtable. We started at a low rate of ~10 Bigtable requests per second and we peaked at ~167000 mixed read and write requests per second at absolute peak. The only intervention that was done to achieve this was pressing a single button to increase the number of nodes in the database. No other changes were made.
In terms of real traffic to our production system, we have seen ~22,000 req/s (combined read/write) on Bigtable in our live environment as a peak within the last 6 weeks.
Migrating seamlessly to Google Cloud
Like many startups, we started with Postgres, since it was easy and it was what we knew, but we quickly realized that scaling would be a challenge, and we didn’t want to manage enormous Postgres instances. We looked for a kind of key value store, because we weren’t doing crazy JOINS or complex WHERE clauses. We wanted to provide a customer ID and get everything we knew about it, and that’s where key value really shines.
I used Cassandra at a previous company, but we had to hire several people just for that chore. At Ravelin we wanted to move to managed services and save ourselves that headache. We were already heavy users and fans of BigQuery, Google Cloud’s serverless, scalable data warehouse, and we also wanted to start using Kubernetes. This was five years ago, and though quite a few providers offer Kubernetes services now, we still see Google Cloud at the top of that stack with Google Kubernetes Engine (GKE). We also like Bigtable’s versioning capability that helped with a use case involving upserts. All of these features helped us choose Bigtable.
Migrations can be intimidating, especially in retail where downtime isn’t an option. We were migrating not just from Postgres to Bigtable, but also from AWS to Google Cloud. To prepare, we ran in AWS like always, but at the same time we set up a queue at our API level to mirror every request over to Google Cloud. We looked at those requests to see if any were failing, and confirmed if the results and response times were the same as in AWS. We did that for a month, fine tuning along the way.
Then we took the big step and flipped a config flag and it was 100% over to Google Cloud. At the exact same time, we flipped the queue over to AWS so that we could still send traffic into our legacy environment. That way, if anything went wrong, we could fail back without missing data. We ran like that for about a month, and we never had to fail back. In the end, we pulled off a seamless, issue-free online migration to Google Cloud.
Flexing Bigtable’s features
For our database structure, we originally had everything spread across rows, and we’d use a hash of a customer ID as a prefix. Then we could scan each record of history, such as orders or transactions. But eventually we got customers that were too big, where the scanning wasn’t fast enough. So we switched and put all of the customer data into one row and the history into columns. Then each cell was a different record, order, payment method, or transaction. Now, we can quickly look up the one row and get all the necessary details of that customer. Some of our clients send us test customers who place an order, say, every minute, and that quickly becomes problematic if you want to pull out enormous amounts of data without any limits on your row size. The garbage collection feature makes it easy to clean up big customers.
We also use Bigtable replication to increase reliability, atomicity, and consistency. We need strong consistency guarantees within the context of a single request to our API since we make multiple bigtable requests within that scope. So within a request we always hit the same replica of Bigtable and if we have a failure, we retry the whole request. That allows us to make use of the replica and some of the consistency guarantees, a nice little trade-off where we can choose where we want our consistency to live.https://www.youtube.com/embed/0-eH5u7rrQQ?enablejsapi=1&
We also use BigQuery with Bigtable for training on customer records or queries with complicated WHERE clauses. We put the data in Bigtable, and also asynchronously in BigQuery using streaming inserts, which allows our data scientists to query it in every way you can imagine, build models, and investigate patterns and not worry about query engine limitations. Since our Bigtable production cluster is completely separate, doing a query on BigQuery has no impact on our response times. When we were on Postgres many years ago, it was used for both analysis and real time traffic and it was not the optimal solution for us. We also use Elasticsearch for powering text searches for our dashboard.
If you’re using Bigtable, we recommend three features:
- Key visualizer. If we get latency or errors coming back from Bigtable, we look at the key visualizer first. We may have a hotkey or a wide row, and the visualizer will alert us and provide the exact key range where the key lives, or the row in question. Then we can go in and fix it at that level. It’s useful to know how your data is hitting Bigtable and if you’re using any anti-patterns or if your clients have changed their traffic pattern that exacerbated some issue.
- Garbage collection. We can prevent big row issues by putting size limits in place with the garbage collection policies.
- Cell versioning. Bigtable has a 3d array, with rows, columns, and cells, which are all the different versions. You can make use of the versioning to get history of a particular value or to build a time series within one row. Getting a single row is very fast in Bigtable so as long as you can keep the data volume in check for that row, making use of cell versions is a very powerful and fast option. There are patterns in the docs that are quite useful and not immediately obvious. For example, one trick is to reverse your timestamps (MAXINT64 – now) so instead of the latest version, you can get the oldest version effectively reversing the cell version sorting if you need it.
Google Cloud and Bigtable help us meet the low-latency demands of the growing online retail sector, with speed and easy integration with other Google Cloud services like BigQuery. With their managed services, we freed up time to focus on innovations and meet the needs of bigger and bigger customers.
Learn more about Ravelin and Bigtable, and check out our recent blog, How BIG is Cloud Bigtable?
Go Green with Google’s Latest Tool and Pick the Most Sustainable Cloud Region
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As a Google Cloud customer, your carbon footprint is already carbon neutral: Google first achieved carbon neutrality in 2007, and has been purchasing enough solar and wind energy to match 100% of its global electricity consumption since 2017. Now, Google is targeting a new sustainability goal: operating on carbon-free energy (CFE) 24/7, everywhere, by 2030.
We want to empower you to make more sustainable decisions and progress with us towards this 24/7 carbon-free future. Earlier this year, we published the carbon characteristics of our Google Cloud regions. Later, we introduced a simple tool to help you pick a Google Cloud region, taking variables like price, latency and sustainability into account. Our next question was: what’s the best way to surface that sustainability info when you’re actually picking a region for your cloud resources?
Starting today, we are indicating regions with the lowest carbon impact inside Cloud Console location selectors. Available today for Cloud Run and Datastream, you’ll see it roll out to more Google Cloud offerings over time:
Regions that feature the “Lowest CO2” and the leaf have a CFE% of at least 75%, or, if the information is not available for this region yet, a grid carbon intensity of maximum 200 gCO2eq/kWh. You can read more about how we calculate these metrics in our documentation.
Before releasing this feature, we ran experiments to measure its impact: Users who were exposed to the enhanced region picker were 19% more likely to select a “low carbon” region for their Cloud Run service—a significant lift. These results show that by displaying carbon information in context of when you make the decision of picking a region, we are helping you make more sustainable decisions.
By sharing and displaying carbon information of Google Cloud regions, together we’re making tangible progress towards our goal of a carbon-free future. Learn more about Carbon free energy for Google Cloud regions.
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