How Companies can Improve Scalability, Flexibility, and Reliability While Reducing Costs: Tips from Route4Me - Build What's Next
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How Companies can Improve Scalability, Flexibility, and Reliability While Reducing Costs: Tips from Route4Me

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Route4Me improved scalability, flexibility, reliability, and reduced costs by moving its core routing optimization algorithms and clusters to Google Cloud.

Google Cloud Results

  • Improves application performance by 8x to 12x; customers can create increasingly complex optimized driving routes in single-digit seconds
  • Improves customer satisfaction via increased reliability and greater application performance
  • Focuses on adding value to customers by improving software and algorithms, not infrastructure management
  • Saves 5x in infrastructure costs

In 2009, Dan Khasis needed to rent an apartment. His search had him driving around the greater New York City area in unfamiliar areas, scattershot-style, often ending up where he started. The frustrating experience led the serial entrepreneur to launch Route4Me, a smartphone navigation app to help consumers create driving routes optimized for multiple stops.

Soon, business users recognized Route4Me’s value and requested enhancements specifically for them. While route optimization apps for big businesses already existed, they were almost exclusively offline desktop programs that were expensive to purchase, deploy, and get trained on. Recognizing the opportunity, Route4Me developed an affordable route optimization solution across various devices, such as smartphones, smartwatches, and telematics devices. The software was tailored to logistics-intensive businesses such as last-mile delivery services and business units conducting field sales, field service, and field marketing functions.

As Route4Me grew its user base, it became clear that its infrastructure of rented, dedicated servers from various providers wasn’t sustainable. “The hardware costs seemed low, but there were many risks and hidden costs,” says Dan Khasis, Co-founder and CEO at Route4Me. For example, “Multi-zone disaster recovery, high availability, automated failover, and on-demand surging of many nodes was simply impossible,“ he adds.

Because under the hood Route4Me’s routing optimization platform requires complex computations, the company needed a globally scalable infrastructure capable of delivering low latency and high throughput. Route4Me also needed to stay competitive by developing and delivering new services as quickly and efficiently as possible.

For these and other reasons, Route4Me moved 100% into the cloud. “Like many entrepreneurial software companies, we test all the latest technologies we can find before upgrading. Typically we go with the fastest technology, with a strong bias towards open source and open standards,” Khasis says. Based on extensive testing, Route4Me selected Google Cloud Platform (GCP). Along with the scalability, flexibility, reliability, and low-cost structure of GCP, Route4Me had already migrated its entire platform to containerized microservices, which Khasis says “are extremely stable and reliable” on Google Kubernetes Engine. While Route4Me has proprietary routing and route optimization engines, it uses Google Maps for high-precision geocoding and as the frontend.

With GCP, Route4Me has reduced its IT infrastructure costs while delivering faster route optimizations and more reliable service to customers. Because of GCP, the company is also planning to add services that will deliver the fastest possible routing simulations and calculations to customers at a price that Khasis says is “impossible without a mature cloud-based platform like GCP.”

Unexpected savings, pleasant surprises

The migration to GCP and Kubernetes Engine required Route4Me to revamp its Service-Oriented Architecture (SOA) and convert millions of lines of code into containerized microservices running on Kubernetes Engine. With more than 150 microservices and thousands of add-on modules and features offered on the Route4Me platform, the migration took several months. But the transition, which began in May 2017 and concluded toward year’s end, went smoothly. “Thanks to the reliability and open source portability of Google Kubernetes Engine, Route4Me experienced one-tenth of the problems that we’ve had when onboarding to other cloud providers,” says Khasis.

Halfway into the migration, Route4Me engineers discovered an unexpected cost savings. The ability to run preemptible virtual machine (VM) instances with Kubernetes Engine resulted in a 90% savings in infrastructure costs, according to Khasis.

The engineering team was also pleasantly surprised by the improved intra-system latency and performance between the Google network and those of third-party systems and other data centers that Route4Me connects to. Overall latency dropped from 8x to 12x. “Where it used to take 8 to 14 seconds to plan a complicated route, now it takes as little as 2 seconds,” Khasis says. Route4Me is also running most of its transactional and operational data through Google BigQuery for a variety of business use cases, including complex machine learning tasks such as geospatial analytics, geospatial pattern detection, and synthetic density.

Scaling while delivering great performance

Route4Me algorithms take into account such data as driving distance, driving time, who’s driving, the day of the week, the vehicle being used, weather conditions, and dozens of other attributes. “All those scenarios and data have to be run in near real time,” Khasis explains. The Route4Me system must access multiple internal and external databases, aggregate all the information in parallel, and deliver it using a high-speed infrastructure platform.

“Our core services and algorithms work much faster on a Google architecture, bringing the total time to solve a complex route problem down to single-digit seconds.” “Many of those steps are resource-intensive,” Khasis adds. “With Kubernetes Engine clusters, we can do much more, scaling up and down as needed, and still deliver great performance to customers around the world.”

Because of its scale, Route4Me built its own automation system for marketing, support, and communications with its customers. “Since we moved our proprietary marketing automation system to GCP, we began delivering our omni-channel marketing communications more reliably, and the correct message reached customers faster and at just the right moment,” says Khasis. “That’s translated to happier customers and increased revenue.”

Customer satisfaction has increased, too, because Route4Me’s users experience far fewer slowdowns than before due to the reliability of GCP. The reliability also means the company spends less time worrying about certain clusters or servers going down for extended periods of time. “We have zero sysadmins, which was the Achilles heel of some of my previous startups,” says Khasis. “So we can focus on software development rather than infrastructure management.”

In order to scale as needed and develop new features, Khasis had expected the company would need to hire more SysAdmin, DevOps, and SecOps staff. “But once we migrated to the modern GCP environment, we didn’t have to make those hires. We saved a lot of money by not having to hire, train, and manage more people,” explains Khasis.

Flexible GCP pricing, in which customers only pay for what they use, has saved Route4Me money on its IT infrastructure. “Preemptible server pricing on GCP is so aggressive,” Khasis says. “If servers are automatically shut off for a certain time period, we don’t pay for them for that period. And if servers are on for a certain amount of time, we get an automatic 30% discount. We’re saving money on the platform with fixed and dynamic workloads.”

Per-second billing with GCP also helps Route4Me cut costs. “If it only takes 25 seconds to do something, we only pay for those 25 seconds,” Khasis says. For the same 25 seconds, other cloud providers might charge for 10 minutes usage or even an hour.”

Road map for the future

In the coming year, Route4Me plans to offer additional add-ons as part of its self-service marketplace, providing customers with transparent pricing on highly complex route optimizations. The service will be extremely valuable to heavy users. For instance, if an organization has to visit 50,000 locations by a certain time, it might wonder if it needs to add 20 people to make that happen and how much it’s going to cost. “Because we’re on GCP, our customer can run a variety of complicated routing scenarios to see which one is the most efficient in seconds instead of minutes,” says Khasis. “As far as I know, none of our competitors can offer that kind of service, giving us an edge as well as a new revenue stream.”

Going forward, Route4Me will begin migrating a huge portion of its core routing optimization platform to Google Google Cloud Spanner. “We want to take further advantage of Cloud Spanner, which comes closest to the CAP theorem and permits us to operate an infinitely scalable and nearly indestructible platform,” Khasis says.

As one example, Route4Me receives telematics data, such as GPS coordinates, from Internet of Things (IoT) devices in smartphones and vehicles, and performs complex algorithmic analysis running on Cloud Spanner. This provides real-time return on investment (ROI) information, so customers can see how much money they’re saving by using Route4Me routing optimization services.

“In order to help as many logistics-intensive businesses as possible, we intend to migrate our proprietary mapping, routing, and route optimization services to Cloud Spanner to take advantage of its extreme reliability and redundancy, and the multi-availability zones of Google Cloud Platform,” says Khasis.

Route4Me also plans to leverage Google machine learning technology, in part to make its routing solution available for use in autonomous and drone vehicles, as well as decentralized edge computing deployments. In addition, Google security and encryption technology will help the company expand its offerings to the heavily regulated medical industry.

Over 60 Route4Me team members use G Suite for almost everything. ”We’re interested in using everything possible with G Suite. We get inspiration from G Suite, too. A lot of thinking and effort went into improving G Suite, and we use that as inspiration to improve own products.”

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Case Study

The Inside Story of How PayPal Became an Innovator in a Competitive Market

PayPal is an American company operating a worldwide online payment system that supports online money transfers and serves as an electronic alternative to traditional paper methods like checks and money orders. The company enables over 29 million payments or transactions on a peak day. in over 200 markets around the world.

PayPal wanted to scale its business. It had achieved a 25% payment growth across the world. It wanted to ensure it’s available wherever its customers are and enable seamless transactions. PayPal also wanted to ensure flexibility. “There are huge variations in the amounts of payment that happen every day of the week or every week of the year. We wanted to ensure our systems are capable of keeping up with these variations,” says Sri Shivananda, SVP and CTO, PayPal.

The company also had to meet regulatory compliance needs across its 200 markets and increase its efficiency. It wanted to get rid of hardware on-premises which it wasn’t using on a regular basis. And above all, the company wanted to innovate quickly to beat the competition.

This is why PayPal turned to Google Cloud. Watch how it made the transition and reaped the benefits.



Blog

Italian Utility Company Deploys its SAP Workloads on Google Cloud to Meet Sustainability Goals

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A2A, Italian utility company needed a highly flexible, hybrid environment and robust data management and analytics to be more data-drive and customer-centric. Moving SAP data to Google Cloud is the key to incorporate 'circular economy' principle!

With more than 2.5 million customers, Italian utility company A2A is committed to delivering electricity, gas, clean water, and waste collection every day. More recently, the company made another significant commitment: To incorporate the principles of the “circular economy” into its way of doing business — part of the UN 2030 Agenda’s Sustainable-Development Goals — all while also aiming to double its client base by 2030. Growing rapidly but sustainably requires operating as efficiently as possible at every level of the organization, from operating smart meters to generating accurate demand projections. That’s why A2A chose to deploy its SAP S/4HANA ERP and the SAP BW/4HANA data warehouse on Google Cloud.

Roadblocks to innovation


Instead of a linear consumption model that starts with raw materials and ends with use and disposal, the circular economy is a continuous cycle that emphasizes repair, recycling, and the creation of materials rather than their disposal. To take an example from A2A’s own success story: The company keeps 99.7 percent of collected waste out of landfills.1 Of the UN’s sustainability goals, A2A is committing to the three most relevant to its industries:

  • Ensuring availability and sustainable management of water and sanitation for all
  • Ensuring sustainable consumption and production patterns
  • Protecting, restoring, and promoting sustainable use of terrestrial ecosystems

Achieving A2A’s sustainability and customer-first strategies requires high scalability, rapid data ingestion, and rich, accurate analytics. None of this could be reliably supported with the company’s legacy on-premises SAP and Data Warehouse, especially given A2A’s projected growth and the increasing complexity of the data landscape, including IoT deployments and energy market liberalization.

Provisioning data infrastructure was also slow and complex. Simply adding a new metric could require increasing capacity by an order of magnitude. And analytical and transactional data lived in siloes, which created a fragmented and out-of-date view of each customer across sales and customer support teams. A2A’s fragmented data also made it difficult to take proactive action when changing priorities or processes required shifting focus from one data source to another.

With a data warehouse that refreshed only once every 24 hours, simple processes such as responding to a customer calling because their power has been cut off due to an unpaid bill became cumbersome.

Scalability was also a concern. With the on-premises solution, A2A needed to define the budget for its data warehouse over a two-year timeframe, but the rollout of new electricity meters — each sending data every 10 minutes — across Italy made those data requirements hard to predict.

The move to the cloud: From monolith to microservices


The move has been a giant step forward in A2A’s goal of meeting its data-driven, customer-centric strategy. In deploying its SAP systems to Google Cloud, A2A can take advantage of a highly flexible hybrid environment and powerful data management and analytics. It can replicate data from Salesforce, SAP, and other systems in BigQuery, which operates as a data lake with Google Cloud SQL, connected directly to Google Analytics and Google Ads for data-driven customer service, decision-making, and marketing.

From BigQuery we can feed relevant information directly to the people who need it. Our customer operators work on Salesforce, so we use an OData protocol to embed real-time data in that platform. Elsewhere, we present the information through a dashboard, or with a BI component delivering one-page reports.” —Vito Martino, Head of CRM, Marketing and Sales B2C & B2B, A2A

By running SAP on Google Cloud, A2A can also count on an infrastructure platform that provides:

  • Scalability. The robust data architecture on Google Cloud adapts to shifting and increasing demands without compromising on speed or availability, so A2A doesn’t have to worry about over- or under-provisioning as the rollout of smart meters proceeds.
  • Speed. The new A2A data solution refreshes every five minutes instead of 24 hours, so the company can respond to its customers’ needs without delays. Customer operators working in Salesforce now receive real-time data from Google BigQuery so that, when a customer calls, operators can see accurate information in seconds. They can now offer value-added services and sustainable options tailored to the customer’s needs, from energy consumption to their preferred method of communication.
  • Availability. With microservices orchestrated by Google Kubernetes Engine, the team can update the solution through continuous integration and delivery (CI/CD), eliminating the need for downtime when changes are required.
  • Security and control. The A2A IT team uses Google Kubernetes Engine to orchestrate clusters of instances on Google Compute Engine, with Google Cloud Load Balancing and backups on Google Cloud Persistent Disk. Google Cloud Anthos ensures operational consistency across on-premises and cloud platforms.

Ready to grow the sustainable way


By moving to Google Cloud — the industry’s cleanest cloud, with zero net emissions — A2A is ready to grow quickly while locking down the efficiency it will need to meet its ambitious sustainability goals. “To bring sustainable utilities to market, we need to be both responsive to our customers and responsive to the internal needs of A2A,” explains Davide Rizzo, Head of IT Governance and Strategy at A2A. “Understanding what customers need in detail means we can improve their services and reduce their environmental impact at the same time.”

Learn more about the ways Google Cloud can transform your organization’s SAP solutions with scalability, speed, and advanced analytics capabilities.

1.  Circular Economy: one of the four founding pillars of A2A’s 2030 sustainability policy | Drupal

Blog

How AI-powered ML Models Helps Run Unemployment Claims Verification at Scale

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Unemployment claims surged during the previous year, giving the U.S. local government agencies a tough time to manage and validate high volume claims on weekly basis through their inefficient systems. This led to many bad actors taking thorough advantage of the system's vulnerabilities. SpringML using Google Cloud products developed a framework that helps the departments differentiate potentially fraudulent claims from legitimate unemployment claims at scale and security by detecting anomalous patterns in large datasets. Learn how.

With unemployment application submissions reaching record numbers over the past year, state and local agencies in the United States have faced the challenge of processing unprecedented numbers of claims per week. The digital infrastructure most agencies have in place is unable to handle this volume, resulting in constituents waiting longer, and bad actors taking advantage of vulnerable systems. The Department of Labor Inspector General estimates that $63 billion in claims distributed is either an improper payment or fraud

Validating claims also requires secure data sharing with other agencies for document and identity verification. Government leaders need a way to allow case adjudicators to quickly and confidently release backlogged claims, integrate with existing systems, and segment legitimate claims from potentially fraudulent ones — all within limited government budgets — securely and at scale.

Implementing a fraud detection solution on Google Cloud

States were under pressure to release payments, while also filtering out potentially fraudulent claims. SpringML and Google Cloud developed a framework to give adjudicators a reliable verification process that quickly filters potentially fraudulent claims, while processing the remaining claims so benefits reach citizens in a timely manner. SpringML and Google Cloud, applied AI-powered machine learning models to detect anomalous patterns in large datasets. Using Google Cloud tools, SpringML implemented a solution to streamline workflows, improve efficiencies, automate processes and identify potentially fraudulent claims.

SpringML used a variety of Google Cloud products to deliver a fraud detection solution, including:

  • Google Cloud Storage to store and manage data
  • BigQuery to store tabular data and BigQuery Machine Learning (BQML) to conduct machine learning on that data
  • AutoML solutions to build predictive models and risk scoring
  • Visualization tools such as Looker and Data Studio to present data and help government leaders make informed decisions.

Implementing machine learning to detect improper payments allows agencies to classify claims as “fraud” or “not fraud” based on the number of flags, as well as prioritize the most urgent claims. Deploying intelligent virtual agents to handle frequently asked questions meant that live agents could focus their time on more challenging cases. 

Even once the pandemic is behind us, there will be bad actors trying to take advantage of overwhelmed or legacy systems. We’ve identified a few best practices for agencies managing enormous case loads and looking to improve improper payment analytics: 

  • Move your systems to the cloud. Many on-premises legacy systems can’t update their applications and scale to meet the volume of claims. Moving to a cloud environment enables rapid solution deployment and ingestion of large amounts of data without fear of overloading the system. The cloud scales with you–cost-effectively and securely. 
  • Understand patterns in the data. The answer is always in the data — we used deep analysis to help uncover suspicious patterns in large data sets. We implemented unsupervised machine learning to learn behaviors and create configurable rules that adjust to new information that comes into the system. We can uncover patterns that are likely associated with fraud – ones that a human might have missed. 
  • Use AI/ML tools to automate your existing systems and teams. These tools enable humans to work smarter and more efficiently. We automate anomaly detection and create dashboards for adjudicators to rapidly process claims. We are enabling the Wisconsin Department of Workforce Development by implementing automatic calculations and processing of recharge amounts, resulting in faster processing times and fewer human errors. Proactive fraud detection and timely calculation of recharge payment allowed DWD to ensure the benefits reached the right individuals.
  • Build flexibility into your systems. We discovered that fraud patterns change over time. For instance,flags for fraud during March-May 2020 were vastly different from those we found in June-July 2020. Google Cloud tools make it easy to continually update algorithms to detect patterns and integrate external data sources.

Using Google Cloud tools, we can update digital infrastructure and incorporate machine learning best practices to help organizations efficiently process large volumes of claims and identify high probability fraudulent ones. SpringML provides consulting and implementation services and industry-specific analytics solutions that deliver high-impact business value to accelerate data-driven digital transformation. Learn more about fraud detection and how to improve improper payments analytics by watching our webinar

Blog

Transforming Canadian Healthcare and Medical Research with Google Cloud

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Explore the transformative journey of Canadian healthcare's adoption of Google Cloud. Learn about key challenges, mitigation strategies, and the critical role of security assessments in ensuring a safe, modernization process.

Is Cloud an option for Canadian Healthcare healthcare and medical research organizations?

Yes, Canadian healthcare and medical research organizations are moving to the cloud. The cloud market is expected to grow in Canada significantly through 2027.

There are several reasons why Canadian healthcare and medical research organizations are moving to the cloud. 

  • Reduce costs by eliminating the need to invest in and maintain on-premises infrastructure. 
  • Enable the healthcare research community to drive their research more expediently to clinical outcomes
  • Improve patient satisfaction by making it easier for patients to access their health information and communicate with their providers.
  • Improve the quality of care by providing access to patient data and records from anywhere in the country. 

Overall, the transition to the cloud is a positive development for Canadian healthcare and medical research organizations. 

Canadian healthcare providers face many challenges before they can move to the cloud, such as addressing security and privacy concerns, data sovereignty issues, and ensuring interoperability. To help them overcome these challenges, it is important to provide Healthcare Data Custodians, Infrastructure Architects, and Research Leads with clear guidance on how the cloud can align with Canadian Healthcare Regulations. This will allow them to have a practical understanding of what is required to enhance their cloud journey and facilitate a smoother transition to the cloud.

iSecurity and MD+A Health are actively assisting Canadian healthcare and medical research organizations in comprehending the risks and exploring pathways to embrace the cloud. Through extensive research and analysis, iSecurity and MD+A Health have evaluated Google Cloud as a suitable platform for healthcare. Their diligent efforts have resulted in the production of comprehensive documents that detail their findings via a Threat Risk Assessment (TRA) and a Privacy Impact Report (PIA).

Why a Threat Risk Assessment? 

A threat risk assessment is a process of identifying and evaluating threats to an organization and then determining the likelihood and impact of those threats. The goal of a threat risk assessment is to identify the most serious threats and develop mitigation strategies to reduce the likelihood and impact of those threats.

A threat risk assessment typically involves the following steps:

  1. Identify threats: The first step is to identify all potential threats to the organization. This can be done by brainstorming, interviewing experts, or reviewing historical data.
  2. Evaluate threats: Once the threats have been identified, they need to be evaluated in terms of their likelihood and impact. The likelihood of a threat is the probability that it will occur, while the impact of a threat is the severity of the consequences if it does occur.
  3. Prioritize threats: The threats need to be prioritized based on their likelihood and impact. The most serious threats should be addressed first.
  4. Develop mitigation strategies: Once the threats have been prioritized, mitigation strategies need to be developed to reduce the likelihood and impact of those threats. Mitigation strategies can include things like implementing security controls, training employees, and developing contingency plans.
  5. Implement mitigation strategies: The mitigation strategies need to be implemented and tested to ensure that they are effective.
  6. Monitor and review: The threat risk assessment should be monitored and reviewed regularly to ensure that it is still effective.

Why a Privacy Impact Assessment?

A Privacy Impact Assessment (PIA) is a process that organizations use to identify and assess the privacy risks associated with a new or changed information technology (IT) system or project. The goal of a PIA is to help organizations protect the privacy of individuals whose personal information is collected, used, or disclosed by the IT system or project.

PIAs typically include the following steps:

  1. Identifying the purpose of the IT system or project and the types of personal information that will be collected, used, or disclosed.
  2. Identifying the privacy risks associated with the IT system or project.
  3. Assessing the likelihood and severity of the risks.
  4. Developing and implementing controls to mitigate the risks.
  5. Monitoring the effectiveness of the controls.

PIAs are an important tool for organizations to help them follow privacy laws and regulations. They can also help organizations build trust with their patients, employees and the research community by demonstrating their commitment to protecting privacy.

The benefits of conducting a PIA:

  • Helps organizations identify and assess privacy risks
  • Helps organizations develop and implement controls to mitigate privacy risks
  • Helps organizations comply with privacy laws and regulations
  • Helps organizations build trust with customers and employees

Why Google Cloud?

Google Cloud is committed to providing Canadian healthcare organisations with an environment to expand both their clinical and research environments. Google Cloud has invested significant resources into building out a cloud environment based on best practices coming from Google’s experience running some of the world’s largest platforms.  

Some highlights include:

  • Built-in security features that help protect your data and applications from unauthorized access, use, disclosure, disruption, modification, or destruction.
  • A comprehensive security management platform that helps you assess, prioritize, and address security risks across your organization.
  • A team of security experts who can help you design, implement, and manage your security solutions.
  • A wide range of security training and resources to help you learn about and stay up-to-date on the latest security threats and best practices.
iSecurity’s thorough, independent PIA and TRA assessments of Google Cloud will help Canadian healthcare organisations, such as ours, review the effectiveness of Google Cloud’s security and privacy controls. These assessments provide additional confidence in the validation of Google Cloud’s critical controls, a clear understanding of customer responsibilities and ultimately will help accelerate the migration of patient and research data to the cloud.
-Kashif Parvaiz, Regional CISO, University Health Network (UHN)
Whitepaper

Why Indian Enterprises Need to Embrace The Cloud-First Imperative to Accelerate Digital Transformation

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Digital transformation is rewriting the rules of business both in India and worldwide. Digital customer experiences deliver easy, effective, and emotional touchpoints that focus operations on what the customers value. Around half of Indian decision makers prioritize the improvement of CX and the simplification of operations, as top priorities in their business agenda, according to a Forrester Consulting study of 360 business and technology decision makers of Indian enterprises.

According to the study, forward-thinking enterprises are increasingly turning to cloud to support their business as they attempt to keep pace with evolving customer needs. As a result, cloud has become a strategic priority, and ensuring its support in the marketplace will only enable digital business and accelerate innovation.

The study reveals that:

  • Public cloud is a key enabler for the transformation of digital business.
  • Security, inconsistent monitoring tools, and legacy applications are top barriers to public cloud expansion.
  • Enterprises are expanding their adoption of the public cloud and want to gain a competitive edge.

Download this study to understand why more and more organizations are moving applications to the cloud in order to take advantage of scalability, lower capital costs, ease of operations, and the resilience offered by the public cloud.

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