B2B marketplace IndiaMART aims to help businesses escape the restrictions of traditional supply chains. By providing access to a digital platform optimized for access from desktops and mobile devices, businesses can improve their operations and generate more revenue. IndiaMART’s suite of services includes web storefront, enquiry support, priority listings, premium number services, a lead management system, and payment facilitation.

IndiaMART also provides “behavior-based matchmaking” that identifies the supplier best equipped to meet buyer needs by product or service, category and location. IndiaMART then matches the designated suppliers with the buyers. Finally, IndiaMART operates as a “horizontal marketplace”—enabling suppliers to market to a large number of potential buyers—presenting a compelling offering for both groups.

Amarinder S. Dhaliwal, Chief Product Officer of IndiaMART, says once IndiaMART grew to a certain size, it benefited from a network effect—as more buyers used the marketplace, more suppliers came on board, prompting yet more buyers to access the service and so on. Suppliers becoming buyers and using IndiaMART to purchase products is another growth driver. “There is not just a network effect, but a community effect as well, as a supplier becomes a buyer,” says Dhaliwal. “This increases affinity, and supplier and buyer ‘lock-in’ to the marketplace increases multifold.”

Positioned to address challenges

IndiaMART’s proactive approach positioned the business well to address the challenges presented by new trends and market conditions. Traffic from mobile devices to its business-to-business marketplace has grown from about 30% to 75% over the last four years.

“Mobile traffic has grown at a compound annual growth rate of almost 100% over the same period,” says Dhaliwal. “The proliferation of smartphones and other mobile devices has brought a considerable number of new users onto the internet and these users look for value—the right price from the right supplier,” he adds. “Furthermore, they can connect at any time and from any location they can access a network.”

The mobility revolution also challenged IndiaMART to provide a user interface and experience optimized for devices of various types and sizes—and that incorporated screens much smaller than the screens incorporated in desktops. The organization also had to help users overcome issues such as inconsistent network coverage and quality—particularly in remote areas.

Becoming a mobile-first organization

IndiaMART is responding by becoming, for buyers, a “mobile-first” organization that meets the group’s technical and user experience requirements.

IndiaMART is also adapting its marketplace to support two key trends:
• Buyers using long, conversational sentences to conduct online searches rather than simply typing in keywords
• Non English-users—the vast majority of people in India—stepping up their use of the marketplace

“We expect that, within a few years, we will have more non-English users than English users on IndiaMART,” says Dhaliwal.

IndiaMART is also benefiting from Indian government measures to reform taxation and stimulate the digital economy. “There has been a huge focus on areas such as digital payments and the digitization of identity,” says Dhaliwal. “We have embraced elements of this agenda by implementing a digital payment platform and are continuing to look at ways of providing new digital services to suppliers.

“Meanwhile, the Indian government’s recent implementation of GST allows us to validate suppliers’ businesses and bring more qualified, more verified suppliers on our platform—improving the experience for buyers and suppliers.”

Speed and reliability an issue

IndiaMART had started operations with servers, storage, networking, and associated systems co-located in a data center in the United States. However, as buyers and suppliers increasingly used mobile devices—over occasionally unreliable networks—to access the marketplace, access speeds and reliability became an issue. With most requests traveling between India and the United States, network latency was unacceptably high. Furthermore, business growth meant IndiaMART needed an environment that could scale to meet demand for the next five to 10 years.

IndiaMART opted for a multi-cloud architecture and established criteria for cloud providers to win its business. “We required an infrastructure that could scale and meet our demand for fast response time without putting our business at risk,” says Dhaliwal. “This meant taking a phased rather than one-shot approach to the migration. We also needed to minimize any wasteful duplication of infrastructure and reduce latency. In addition, as we scaled, we needed to protect our systems, transactions and information, including the details of buyers and suppliers.”

Google Cloud team’s high-quality support

The organization performed proof of concept with the three largest multinational cloud services providers and found Google Cloud was best positioned to act as the cornerstone of its multi-cloud architecture. “The Google Cloud team gave us considerable support in helping us run a proof of concept of its services,” says Dhaliwal.

“The proof of concept also illustrated that Google Cloud was superior to the other cloud services we looked at.

“We could run our marketplace across multiple geo-locations under a single IP address, avoiding duplication, and users in India could connect to Google Cloud via the closest access point, with their traffic passing quickly across the Google network.

“In addition, Google Cloud’s load balancing service would enable encryption between load balancing layers and back ends to ensure security, while all communication would move across Google’s own protected network.”

Being one of India’s early users of G Suite, the organization was also familiar with Google Cloud applications and services.

IndiaMART then opted to work with the Google Cloud team and a certified partner on a step-by-step implementation that minimized any risk of disruption.

Deep engagement from Google

“We engaged very deeply with both Google and the partner to complete this migration,” says Dhaliwal. “The Google team worked very hard to understand our requirements and provide a solution that catered to our needs and could be deployed in a phased manner.” The team ran workshops and technical sessions with IndiaMART and, at a Google Summit, connected the marketplace provider to Google experts in databases and infrastructure.

“These discussions really helped us formulate a strategy moving forward,” says Dhaliwal.

Based on input from Google and its own evaluation, IndiaMART developed an architecture comprising virtual machine instances delivered through Compute Engine, Google Cloud’s infrastructure-as-a-service offering; Cloud Load Balancing to support cloud resources distributed across multiple locations; Cloud Pub/Sub to provide enterprise messaging; and Cloud Dataflow to transform and enrich data.

Cloud Armor works with Cloud Load Balancing to defend against distributed denial of service (DDoS) attacks; and Geocoding API helps the organization convert geographic coordinates into readable addresses and vice versa. Cloud AutoML allows IndiaMART to train machine learning models to meet its requirements. With Geocoding API, IndiaMART can matchmake buyers and suppliers based on location—providing a high quality experience for both parties. Finally, AutoML Translation allows the organization to create a custom machine learning model that converts product names from English into Hindi and other languages, effectively opening up new markets for buyers and suppliers.

Phase one complete

IndiaMART has completed phase one of the migration that involved moving its web properties across to Google Cloud. The organization is now experimenting with moving its APIs and databases to the service and anticipates completing the exercise over the coming year.

Average page load time down

The initial phase of the project has already delivered considerable benefits to IndiaMART. The organization has cut average page loading time from five seconds to three seconds, and Dhaliwal attributes close to one second of that reduction to the move to Google Cloud. “With Google Cloud, buyers and suppliers can access our marketplace much faster than previously,” says Dhaliwal. “This impacts positively on engagement, time spent on our marketplace, and the user’s entire journey with us.”

DDoS attack repelled

Google Cloud’s security features have already passed their first test. As IndiaMART undertook stage one of the migration, the business experienced a DDoS attack that generated request loads more than 400 times greater than normal. “Because we were on Google Cloud infrastructure, we could develop a solution to combat this severe DDoS attack,” says Sunil Parolia, Sr. VP at IndiaMART. “From a security perspective, this really justified our decision to go with Google Cloud.”

Google Cloud is also helping deliver the availability required by IndiaMART and the scalability to support growing demand. “As the number of people in India who access the internet grows from about 500 million to 700-800 million over the next couple of years, we will continue to build our traffic and be the dominant business-to-business platform,” says Dhaliwal. “On the supplier side, we expect to see more and more businesses come onto our marketplace—ranging from small-to-medium businesses up to larger brands. Google Cloud will enable us to accommodate this traffic without compromising the experience we provide.”

COVID-19 drastically changed the way consumers purchased goods and services, but these changes merely accelerated trends that were well underway. While many retailers were caught off guard with the suddenness of the transition, most are stepping up their cloud transformation initiatives in response to changes in consumer behavior and expectations — changes that are likely to be permanent. These retailers realize they need to migrate on-premises workloads to the cloud to achieve the speed and responsiveness required to better promote their products, expand customer support, predict demand levels, and meet ever-rising customer expectations. The trick will be to do so as quickly, efficiently and cost-effectively as possible while minimizing disruption.  By leveraging solutions such as Google Cloud VMware Engine, retailers can move their on-premises applications to the cloud, where they can achieve the scale, intelligence, and speed required to stay relevant and competitive. 

Gaining the cloud advantage

In a recent survey from MIT¹, 75% of retail IT leaders said the pandemic had accelerated their digital transformation projects to improve business processes, increase operational efficiency, and enhance customer experience. Cloud computing is at the heart of digital transformation. It gives retailers the scale, analytical power, and agility they need to respond to the increasing pace of change. By migrating IT resources to the cloud, retailers can develop and deploy innovative mobile apps, virtualize costly services such as call centers, automate business processes, and analyze massive volumes of data to improve the speed and accuracy of demand forecasts. Running applications in the cloud enables business managers and IT departments to replicate the functions of their on-premises system without changes, so that employees, customers, and partners can access those systems from anywhere and at any time. Operating in the cloud also allows retailers to avoid many of the limitations of legacy systems that may have been holding them back. 

These are just some of the capabilities that retailers gain when they migrate their applications and data to the cloud: 

• Build new revenue streams with omnichannel shopping that runs on the speed and reliability of cloud infrastructure.
• Leverage artificial intelligence and data analytics available in the cloud. Use Google Cloud’s BigQuery to run AI-powered forecasting models to predict demand and plan sales, orders, and other activities with greater precision. Deploy Recommendations AI, to deliver highly personalized product recommendations to your customers at scale.
• Improve operational efficiency with a highly scalable and elastic environment that lets you pay for the compute and storage you need instead of making major investments in physical infrastructure up front.
• Improve customer experience by analyzing behavior and other data to offer customers what they want, when they want it; build personalized mobile and web applications and provide real-time information to customer service and floor staff so they can address customer concerns quickly and effectively.
• Reduce costs by deploying AI-powered agents to help customers solve straightforward issues on their own and automate mundane back-office processes to let your team focus on more value-add work. 
• Safeguard customer data with Google Cloud’s multi-layer, secure-by-design infrastructure, built-in protection, and global network.
• Improve control with integrated cloud management tools that enable IT staff to oversee the whole stack — across on-premises systems and cloud in a single location.

Easy lift and shift with Google Cloud VMware Engine

Retailers do not need to deploy entirely new applications to take advantage of Google Cloud. Rather, they can move their back-office applications and other business systems into the cloud as-is, without the need to rewrite a line of code. 

Google Cloud VMware Engine enables businesses to migrate or extend their on-premises workloads and applications seamlessly to the cloud. This means that IT managers can move their existing applications into the cloud in just a few minutes without having to rebuild them. From there, retailers can run their existing applications — including point-of-sale (POS) systems, virtual desktops, and other devices — just as they did when those applications were installed in the store or office. 

Google Cloud VMware Engine creates a software-defined infrastructure that natively runs VMware workloads without any changes to current tools. That infrastructure includes computing power, storage, network connections, and security services that are dedicated to the individual customer.

Cloud infrastructure for new retail realities

COVID-19 was a wakeup call for many retailers who realized they needed to energize their transformation initiatives in response to permanent shifts in consumer behavior and expectations. Essential to this transformation is getting to the cloud as quickly, efficiently, and cost-effectively as possible, without creating costly disruptions or downtime. Google Cloud VMware Engine lets retailers do exactly that with a straightforward lift-and-shift process that takes just a few minutes. Once in the cloud, retailers can take advantage of the many capabilities Google Cloud offers, including sophisticated data analytics, improved customer experience, enterprise-grade security, and reduced cost.

Read our retail white paper to learn more about how easy it is to migrate your retail IT systems to the cloud with Google Cloud VMware Engine

^{1. MIT Technology Review Insights’ survey on COVID-19 and its impact on technology, in association with VMware; N=100 Retail Senior Technology and Business leaders Worldwide.}

Why Higher Ed Needs to Go All-in on Digital

In the wake of the COVID-19 pandemic, the majority of students within the 18-24-year-old demographic now expect hybrid learning environments–even once we are beyond the pandemic. And a vast number of adult learners are seeking options that accommodate their work and family lives now that it’s clear that effective learning can indeed occur virtually. Implementing cloud technologies and achieving digital maturity within higher education will enable institutions to be innovative and responsive to evolving student preferences, while being prepared for future disruptions.

The state of digital maturity

In February and March 2021, Boston Consulting Group (BCG), in partnership with Google, surveyed U.S. higher education leaders on their views of the state of digital maturity in the higher education sector. This survey found that institutional and technology leaders strongly agreed that moving legacy IT systems to the cloud, centralizing and integrating data, and increasing the use of advanced analytics is necessary to make a successful digital transformation, and ultimately achieve digital maturity.

But what is digital maturity? Digital maturity—a measure of an organization’s ability to create value through digital delivery—focuses on three areas of technological advancement that drive large-scale innovation:

1. Using cloud infrastructure
2. Expanding access to data
3. Using that data to improve processes through advanced analytics, such as Artificial Intelligence and Machine Learning (AI/ML)

Although university leaders agree on prioritizing digital maturity, more than 55% said they considered their schools to be “digital performers” or “digital leaders.” However, only 25% of tech leaders at these universities stated that their schools regularly use data analytics. As with corporations and governments, higher education institutions face barriers to technological innovation, such as:

• Competing priorities to meet step-change goals and decentralized decision making
• Budget constraints
• Cultural resistance to change
• Tech staff skillset gaps

Still, leaders understand that the way to overcome institutional inertia is with a strong, goal-oriented vision of what is best for the institution overall. Although only a handful of schools have reached digital maturity as we define it, others can learn a great deal from their examples. Here are the top takeaways from higher education leaders who successfully transformed their institutions:

Digital solutions can improve the student journey in many ways

As digital capabilities hold the key to dealing effectively with declining enrollment and rising costs, higher ed leaders identified four goals that are critical to improving performance:

1. Improve the student journey
2. Increase operational efficiency
3. Scale computing power in advanced research
4. Innovate education delivery

The research found that technology investments can help enhance the student journey in the recruiting and retention of students, improving digital education delivery, government funding, and donations from alumni. Digital maturity can make institutions more agile and efficient in delivering education that aligns with the changing societal norms, evolving student preferences, and future disruptions. Survey participants shared that they plan to increase the use of the cloud by more than 50% over the next three years. By shifting legacy IT systems to the cloud, institutions can increase scalability, lower the cost of ownership, and improve operational agility, while offering a more secure, long-term data storage solution.

Cloud-native software-as-a-service (SaaS) solutions provide an excellent platform for centralizing data. However, institutions that attempt to “lift and shift” their legacy systems to the cloud may encounter challenges to achieving measurable improvements in data integration and cost reduction. Higher ed leaders must realize that centralizing data and transitioning to the cloud do not happen simultaneously.

Leaders who are able to articulate a strong vision and commitment will experience a more successful technology transformation. By linking their vision to specific needs, such as more effective recruiting, leaders will find their technology investments will have a more substantial return. University presidents should base their decisions about which systems to move, when, and how on desired performance outcomes.

Big visions become a reality with small steps. Small pilot projects are an excellent way to start the journey toward digital maturity. Small steps toward a significant transformation can reduce resistance to change, build positive momentum, and produce better student outcomes. Read the full report here. If you’d like to talk to a Google Cloud expert, get in touch. 

Communication services providers (CSPs) are experiencing a period of disruption. Overall revenue growth is decelerating and is projected to remain below 1 percent per year, following a trend that started even before the pandemic.¹ At the same time, driven by the pandemic, data consumption in 2020 increased by 30 percent relative to 2019, with some operators even reporting increases of 60 percent.² 

The combination of pressure on revenues with rising data traffic costs is forcing operators to innovate in three fundamental ways. First, operators are looking to establish new sources of revenue. Second, increased network utilization must be met with a reduction in network cost. And third, there is an opportunity to gain new customers by improving the customer experience.

Fortunately, 5G offers a path forward across each of these three areas. Concepts such as network slicing and private networks allow CSPs to offer differentiated network services to public sector and enterprise customers. The disaggregation of hardware and software allows new vendors with unique strengths to enter the market and to enable CSPs to build, deploy, and operate networks in fundamentally new ways. And the ability to place workloads at the edge permits CSPs to offer compelling experiences to consumers and businesses alike. In this blog, we will discuss how CSPs can create a solid foundation for their cloud networks. 

Understanding telecommunications networks

First, it is useful to consider the way telecommunications networks were traditionally built. Initially, networks were built using physical network functions (PNFs) — appliances that used a tight combination of hardware and software to perform a specific function. PNFs offered the benefit of being purpose-built for a specific application, but they were inflexible and difficult to upgrade. As an example, deploying new features frequently required replacing the entire PNF, i.e., deploying a new hardware appliance.

The first step in improving deployment agility came with the concept of virtualized network functions (VNFs), software workloads designed to operate on commercial off-the-shelf (COTS) hardware. Rather than utilizing an integrated hardware and software appliance, VNFs disaggregated the hardware from the software. As such, it became possible to procure the hardware from one vendor and the software from another. It also became possible to separate the hardware and software upgrade cycles. 

However, while VNFs offered advantages over PNFs, VNFs were still an intermediate step. First, they typically needed to be run within a virtual machine (VM), and as such required a hypervisor to interface between the host operating system (OS) and the guest OS inside the VM. The hypervisor consumed CPU cycles and added inefficiency. Second, the VNF itself was frequently designed as a monolithic function. This meant that while it was possible to upgrade the VNF separately from the hardware, such an upgrade, even for a feature that affected only a portion of the VNF, required deployment of the entire large VNF. This created risk and operational complexity, which in turn meant that upgrades were delayed just as they were with PNFs.

Creating the foundation for cloud networks

The trick to establishing your cloud based network resides in the challenge of moving from VNFs to containerized network functions (CNFs) — network functions organized as containers as a collection of small programs, each of which can be independently operated. 

The concept of containers is not new. In fact, Google has been using containerized workloads for over 15 years. Kubernetes, which Google developed and open-sourced, is the world’s most popular container orchestration system, and is based on Borg, Google’s internal container management system.³ There are lots of benefits to using containers, but fundamentally, it frees developers from worrying about resource scheduling, interprocess communication, security, self-healing, load balancing, and many other tedious (but important!) tasks. 

Consider just a couple examples of benefits that containerization brings to network functions. First, when upgrading the network function to implement new features, you no longer need to re-deploy the entire network function. Instead, you only need to re-deploy the containers that are affected by the upgrade. This improves developer velocity and reduces the risk of the upgrade because, rather than infrequent upgrades that each introduce substantial changes, you can now have frequent upgrades that each deploy small changes. Small changes are less risky because they are easier to understand and to roll back in case of anomaly. Incidentally, this also improves your security posture because it reduces the time between when a security vulnerability is discovered and when a patch is deployed.

Speaking of security, another example of the benefits that containerization brings to network functions is an automatic zero-trust security posture. In Kubernetes, the communication among microservices can be handled by a service mesh, which manages mundane aspects of inter-services communication such as retries in case of failure and providing observability into communication. It can also manage other essential aspects such as security. For example, Anthos Service Mesh, which is a fully-managed implementation of the open-source Istio service mesh (also co-developed by Google), includes the ability to authenticate and encrypt all communications using mutual TLS (mTLS) and to deploy fine-grained access control for each individual microservice.

Automation and orchestration for cloud networks

CNFs bring tremendous benefits, but they also bring challenges. In place of a relatively small number of network appliances, we now have a large number of containers, each of which requires configuration, management, and maintenance. In the past, many of these processes were accomplished using manual techniques, but this is impossible to accomplish economically and reliably at the scale required by CNFs.

Fortunately, there are cloud-native approaches to solving these challenges. First, consider the problem of autonomously deploying and maintaining CNFs. The ideal way is to use the concept of Configuration as Data. Unlike imperative techniques such as Infrastructure as Code, which provide a detailed description of a sequence of steps that need to be executed to achieve an objective, Configuration as Data is a declarative method whereby the user specifies the desired end state (i.e., the actual desired configuration) and relies on automated controllers to continuously drive the infrastructure to achieve that state. Kubernetes includes such automated controllers, and the great news is that this method can be used not just for infrastructure but also for the applications residing on top of it, including CNFs. This cloud-native technique frees you from the toil and associated risk of writing detailed configuration procedures, so you can focus on the business logic of your applications.

As another example, consider the problem of understanding your network performance, including anomaly detection, root cause analysis, and resolution. The cloud-native approach starts with creating a data platform where both infrastructure and CNF monitoring data can be ingested, regularized, processed, and stored. You can then correlate data sets against each other to detect anomalies, and with AI/ML techniques, you can even anticipate anomalies before they happen. AI/ML is likewise indispensable in gaining an understanding of why the anomaly is happening, i.e. performing root cause analysis, and automated closed-loop controllers can be developed to correct the problem, ideally before it even happens.

Architecting for the edge

The transition from VNFs to CNFs is a critical piece in addressing the challenge that CSPs face today, but it alone is not enough. CNFs need infrastructure to run on, and not all infrastructure is created equal. 

Consider a typical 5G network. There are some functions, such as those associated with an access network, that need to be deployed at the edge. These functions require low latency, high throughput, or even a combination of the two. In 5G networks, examples of such functions include the radio unit (RU), distributed unit (DU), centralized unit (CU), and the user plane function (UPF). The first three are components of the radio access network (RAN), while the last is a component of the 5G core. At the same time, there are some other control plane functions such as the session management function (SMF) or the authentication and mobility management function (AMF) that do not have such tight latency and high throughput requirements and can thus be placed in a more centralized data center. Furthermore, consider an AI/ML use case where a particular model (perhaps for radio traffic steering) needs to run at the network edge because of its latency requirements. While the model itself needs to run at the edge, model training (i.e., generating the model coefficients) is frequently a compute-intensive exercise that is latency-insensitive and is thus more optimal to run in a public cloud region.

All of these use cases have one thing in common: they call for a hybrid deployment environment. Some applications must be deployed at the edge as close to the user as possible. Others can be deployed in a more centralized environment. Still others can be deployed in a public cloud region to take advantage of the large amount of compute and economies of scale available therein. Wouldn’t it be convenient — if not transformational — if you could use a single environment for deploying at the edge, in a private datacenter, and in public cloud, with a consistent set of security, lifecycle management, policy, and orchestration resources across all such locations? This is indeed what Google Distributed Cloud, enabled by Anthos, brings to the table.

With Google Distributed Cloud, you can architect a 5G network deployment such as the one shown below.

Business benefits of cloud networks

Beyond the technical benefits, consider the business benefits of such an architecture. First, by following the best practices of hardware and software disaggregation, it permits the CSP to procure the infrastructure and the network functions from different vendors, spurring competition among vendors. Second, each workload is placed in precisely the right location, enabling efficient utilization of hardware resources and offering compelling low-latency, high-throughput services to users. Third, because the architecture utilizes a common hybrid platform (Anthos), it makes it easy to move workloads across infrastructure locations. Fourth, the separation of workloads into microservices accelerates time-to-market when developing new features or applications, such as those enabling enterprise use cases. And finally, the container management platform supports the simultaneous deployment of both network functions and edge applications on the same infrastructure, allowing the operator to deploy new experiences such as AR/VR directly on bare metal as close to the user as possible.

The next generation cloud network is now

There is a lot more we could say, but perhaps the most important takeaway is that this architecture is not a future dream. It exists today, and Google is working with leading CSPs and network vendor partners to deploy it, helping them realize the promise of 5G to deliver new revenues, reduce operating costs, and enable new customer experiences.

To learn more, watch the video series on the cloudification of CSP networks.

Discover what’s happening at the edge: How CSPs Can Innovate at the Edge.

^{1.Statista, Forecast growth worldwide telecom services spending from 2019 to 2024
2 PricewaterhouseCoopers, Global entertainment and media outlook 2021-2025
3. Borg: The Predecessor to Kubernetes}