When you look at breach data in today’s cloud-dominated IT environment, you can find multiple situations where a tiny mistake made by the DevOps or CloudOps team has had a huge influence on the reputations of enterprises or, in some cases, their very survival. Misconfigured AWS S3 buckets, poor password management on publicly accessible databases, and information accidentally revealed by developers on GitHub are just a few instances. Misconfigurations and unpatched vulnerabilities are common, allowing attackers to gain access.

Researchers exploited a server-side request forgery vulnerability in a web application in development during one of IBM X- Force’s AWS cloud penetration testing engagements, allowing them to access the EC2 instance metadata service and steal the access keys used by the webserver EC2 instance. The CloudOps team had accidentally granted full access to an S3 bucket via this instance profile, thereby giving researchers complete access to the sensitive data stored in that bucket.

Since the cloud’s introduction, solutions supplied by cloud service providers (CSPs) have enabled organizations to innovate quicker and decrease the time it takes to create and deploy production applications, but this process is accompanied by an added element of security risk. While cloud service providers are responsible for safeguarding their platforms, companies are responsible for securing the data stored on those platforms, which may be a difficult undertaking.

The Struggles of Cloud Adoption

Many businesses began their cloud journey by using CSPs’ Infrastructure-as-a-Service solutions, with the advantage that they had complete control over the infrastructure. With time, adopters realized that maintaining their cloud infrastructure was becoming too difficult and time-consuming, leading to a transition to PaaS solutions. CSPs improved their PaaS services along the road to make them more dependable, feature-rich, and easier to manage and interface with, making them more appealing to their clients.

Businesses have not outsourced the responsibility for data security to the CSP by employing a PaaS product. CloudOps and DevOps teams are responsible for securely setting all parts of any cloud service so that their company’s data is not exposed to attackers. That is where firms are now having difficulty.

“Have I set up the security tools offered by my CSP correctly?” companies are wondering. “Do my identity and access management methods have any flaws?” “Are my cloud-based storage containers configured correctly to enable only authorized access? ” “Do I have security appropriately integrated into my continuous integration/continuous delivery pipelines?” If security best practices are not included in every phase of the development life cycle, these questions might be difficult to address.

Furthermore, competent experts with cross-industry knowledge are hard to come by and keep, making it difficult to run, secure, and maintain vital cloud assets. During the past year, we’ve seen attackers target supply chains that are outside the control of enterprises. Many companies struggle to keep track of who is using their cloud infrastructure, what rights individuals have, and what misconfigurations exist.

Cloud Operations: Threats and Trends

While it is simple to comprehend the advantages of cloud computing, it is more difficult to comprehend and handle the risks related to today’s hybrid multi-cloud installations.

Attackers use a variety of tactics to gain access to the cloud infrastructure, including credential hunting (such as scanning for accidentally exposed credentials in code hosting platforms, phishing, and social engineering), exploiting vulnerabilities and misconfigurations in public-facing cloud-based assets (web applications, storage, and so on), and pivoting from on-premises victims to the cloud infrastructure.

Developers might be profitable targets as well. The public cloud is the ideal platform for them because it gives them access to all of the tools they need to build, execute, and debug code, communicate with other developers and serve as a centralized platform for code testing and deployment to production. Developers, on the other hand, are typically under pressure to get their code into production as soon as possible. When this happens, people are more likely to make mistakes and miss security. For example, improper handling of secrets (application programming interface keys, passwords, certificates, and so on) might result in the disclosure of a production database administrator password, which can spell doom for many businesses. As a ‘temporary’ or ‘quick’ test, CloudOps administrators may utilize overprivileged users or roles, but they frequently neglect to apply the principle of least privilege after successful testing, allowing privilege abuse and data leakage.

These are the kinds of things attackers are searching for, and once they’ve gotten their hands on a cloud asset, they may go on to their next target (data manipulation, exfiltration, etc.).

Securing the Cloud: Recommendations

IBM Security X-Force believes that firms should focus on three factors when it comes to cloud security:

• For your DevOps process, invest in building a security mindset. ‘Start left’ instead of shift left.’ Early in the development life cycle (shift left), testing your code for security issues should be paired with creating secure code (start left). Developers should also take security awareness training to learn how to recognize the hallmarks of a social engineering hoax. Developers are the new target in serverless setups.
• Use cloud-based security technologies. (given by CSP and available commercially) to improve your threat detection and response capabilities.
• Perform regular cloud security assessments. (configuration reviews and penetration testing), which will disclose the possibility of attackers breaking into your cloud infrastructure and how they would exploit any vulnerabilities uncovered. The evaluations should finish with prioritized suggestions for you to adopt in order to lower your risk of a security breach and incorporate best security practices into your cloud workloads, personnel, and entire infrastructure.

Learn how green computing minimizes energy usage and carbon emissions from technology product design, use, and disposal.

Green computing (also known as green IT or sustainable IT) is the process of designing, manufacturing, using, and disposing of computers, chips, other technology components, and peripherals in a way that has a low environmental impact, such as lowering carbon emissions and reducing energy consumption by manufacturers, data centers, and end-users. Choosing sustainably produced raw materials, eliminating electronic waste, and encouraging sustainability through the use of renewable resources are all part of green computing.

Green computing has the potential to have a significant beneficial influence on the environment. Between 1.8 and 3.9 percent of worldwide greenhouse gas emissions are attributed to the information and communication technology (ICT) industry. Furthermore, data centers consume 3% of total yearly energy consumption, an increase of 100% in the previous decade.

According to a paper issued by the Association for Computers Machinery, “the energy consumption and carbon output of computing and the overall ICT industry must be drastically controlled if climate change is to be delayed in time to avert catastrophic environmental harm.”

Every part of contemporary information technology has a carbon price tag, from the tiniest chip to the biggest data center, and green computing aims to lower that price tag. Green computing involves both technology producers and the enterprises, organizations, governments, and individuals that utilize it. Green IT is multi-faceted and requires several decisions at every level, from big data centers implementing rules to cut energy use to people deciding not to use screen savers.

What manufacturers can do

Long before items reach customers, decisions about being green are made. Product design and production, for example, are key areas for reducing technology’s environmental effect.

Chips that are more energy-efficient, such as the IBM-Samsung vertically stackable chip or the IBM 2nm chip, are instances of creative design that increase computer sustainability. Although the energy usage of a single computer chip may appear insignificant, when multiplied by millions, considerable savings may be achieved.

IBM has also discovered technologies that help save energy. For example, heterogeneous architectures combine frameworks such as CPUs and graphics processing units (GPUs) to improve power and energy efficiency.

AiMOS (Artificial Intelligence Multiprocessing Optimized System) is an example of a computer built as part of a cooperation between IBM, Empire State Development, and NY CREATEs. AiMOS is one of the world’s most energy-efficient computers, and it’s being used to produce more advanced and efficient computing processors, among other things.

The carbon price tag of computers is reduced when designers take measures to limit the amount of energy each device consumes in operation and the amount of heat those items create. For example, one of the earliest examples of designers using the notion of green computing to save energy in sleep mode.

Material selection is also critical. Hazardous materials are avoided in the design process, which keeps them out of landfills afterward. Producing less waste in the manufacture of gadgets and components also reduces the environmental impact of technology. Green manufacturing is a distinct but related category of green technology that regulates the factory’s operations.

Other green computing measures include boosting consumers’ capacity to reuse items and making equipment recyclable when they do need to be replaced.

What organizations can do

Corporations, governments, and other major organizations may make the most progress in making IT more sustainable. Data centers, server rooms, and data storage spaces all have a substantial possibility for improvement.

Setting up hot and cold aisles in such locations is a crucial step toward greener computing since it lowers energy usage and improves heating, ventilation, and cooling. Emissions are further reduced when automated devices meant to manage temperature and comparable conditions are integrated with hot and cold aisles. Cost savings from reduced energy usage may be recognized in the future.

Making ensuring everything is switched off is a basic step toward efficiency. When not in use, central processing units (CPUs) and peripheral devices such as printers should be turned off. When certain tasks, like printing, are scheduled in blocks of time, peripherals are only used when they are required.

Purchasing departments can also help with green computing. IT’s carbon footprint may be reduced by selecting equipment that will endure and utilizes the least amount of energy required for the work at hand. Notebooks consume less energy than laptops, which in turn consume less energy than desktop PCs.

What you can do

Green computing isn’t just for major corporations; you can help improve IT sustainability as well. When a large number of people choose to utilize hibernate or sleep mode, the impact can be enormous.

Using the power management functions, as well as altering the screen brightness, minimizes energy usage on any device. Turning off computers at the end of the day and keeping accessories like speakers and printers turned off unless they are in use are other methods to save electricity.

Refilling printer cartridges instead of buying new ones creates less trash, and buying remanufactured equipment instead of new equipment has a lower environmental effect. Electronic waste management promotes sustainability and provides security benefits.

You, too, should pick the most efficient equipment for the work at hand, just as buying departments do. Choose the most efficient device if a notebook or laptop can execute critical tasks just as effectively as a desktop computer. Individuals purchasing new equipment might use the Energy Star ratings as a reference.

The evolution of green computing

In the United States, the Environmental Protection Agency (EPA) launched the Energy Star Program in 1992 with the goal of promoting and recognizing energy efficiency. That program sparked widespread adoption of the sleep mode capability in the IT sector, as well as a slew of additional initiatives to boost green computing efforts. Energy Star-certified items must fulfill specific performance requirements and have power management features that non-certified devices may not have.

An EPA funding to the Global Electronics Council aided the effort, resulting in the Electronic Product Environmental Assessment Tool (EPEAT). EPEAT is a product register for items that must meet particular performance requirements, such as materials used, transportation-related greenhouse gas emissions, product lifetime, energy usage, and end-of-life cycle management.

Prior to green computing, the IT sector was more concerned with making smaller and quicker devices than with enhancing sustainability or lowering emissions. Traditional computing is connected with on-premises physical servers and technology, whereas cloud computing signifies a shift toward a more environmentally friendly approach with a higher focus on efficiency.

Various projects and certifications exist to increase green computing standards through the production of industry metrics linked to sustainability. The Green500 is a subset of the Top500, which includes supercomputers as well as the applications for which they are utilized. The Green500 rates supercomputers according to their energy efficiency. The Transaction Processing Performance Council (TCP) is a non-profit that creates performance criteria for the transaction processing industry. SPECPower sets benchmarks for single- and multi-node servers’ power and performance characteristics, with the purpose of increasing efficiency.

Challenges to implementing green computing

One of the most significant obstacles to green computing advancement is a lack of care. When it comes to climate change, few people think about the IT business. Along with a widespread lack of care, the IT industry has evolved in such a way that the creation of smaller and quicker components and devices has taken precedence over environmentally friendly ones.

Because technology advances and changes so quickly, extending the lives of products is difficult, and technology creators must guarantee that each iteration continues to fulfill environmentally friendly requirements. Switching from a traditional setup in a factory, data center, or corporate office to a green setup necessitates a significant upfront financial expenditure, which can be a significant obstacle.

Decision-making is tough for IT end-users due to fragmented data and different demands. Speed and performance, for example, have a different value in a large data center than they do for a home user.

Users must consider several issues during the lifespan of a computer device. When it comes to servers, security may be more important to a large corporation than the environmental effect. A smaller gadget that is easy to carry may be more significant to a college student than having one that is totally recyclable.

Green computing and IBM

Green computing offers the potential to reduce the environmental effect of computers. The ICT sector, on the other hand, has a huge chance to help the environment by developing programs and systems that minimize power usage, enhance water management, and embrace virtualization as a strategy to save energy.

Wherever you are on the journey to green IT and sustainability within your business, ensuring that your apps only utilize the resources they require is a realistic first step that may have a significant and immediate impact on energy usage. In data centers and the public cloud, this significantly decreases waste (cost and carbon impact). A solution like IBM Turbonomic Application Resource Management may help you achieve that objective by continually analyzing application resource use and ensuring that apps have what they need to execute while conforming to business requirements. You can automate operations and minimize resource congestion across a hybrid cloud environment with a thorough awareness of the application and infrastructure stack, while also launching larger sustainability investments.

API testing is critical. It aids with the detection of code problems, improves code quality, and allows developers to make changes more quickly while remaining confident that they will not break existing functionality. API testing can benefit greatly from automation and artificial intelligence. Many products use API testing automation, but the majority of firms have yet to realize the benefits of AI and machine learning in testing. As the future of API testing involves more AI and automation, IBM believes there are a few critical skills to keep an eye on.

Adding Intelligence to Automation

A developer might use code to create random inputs for each field in basic automated testing. Many of those tests will be ineffective because they are repetitious or do not correspond to the application’s intended business purpose. Manually developed tests are more valuable in these situations because the developer has a greater understanding of how the API is used.

Adding intelligence to automated testing allows it to integrate with business logic – for example, customers will add an item to their online shopping basket before being directed to the page that requires an address, so testing an API with an address but no items is a waste of time. Intelligent automated testing could generate a dynamic set of input values that make sense and are a broader test of the API’s design with more confident results.

Semantic and Syntactic Awareness

Manually creating new API test cases might be time-consuming. Test generation can help, but developers can only rely on it if the created tests are of good quality.

One way to improve the quality of generated tests is semantic and syntactic awareness – that is, training an intelligent algorithm to understand key business or domain entities such as a ‘customer’, ’email’, or ‘invoice’ – and how to generate data from them. It should be able to ‘learn’ from current tests, APIs, and business rules, and become better at creating tests with less developer input in the future.

Automating Setup and Teardown

Identifying and automating typical operations can drastically reduce a tester’s burden. The machine may perform routine setup and takedown chores by using an algorithm to examine an API definition and determine the dependencies. If a bookshop has an API for ordering, for example, the AI can set up the scaffolding and create the test prerequisites. If a tester wants to generate a book and a customer before placing an order, the AI does those chores, which are then cleaned up and removed after the test is completed. As an algorithm becomes more familiar with the company’s API structures, it will be able to produce additional setup and teardown jobs.

Mining real-world data

Identifying and automating common tasks can significantly reduce a tester’s workload. The computer may conduct routine setup and takedown tasks by examining an API definition and determining dependencies via an algorithm. If a bookshop has an API for ordering, for example, the AI can set up the scaffolding and create the test prerequisites. If a tester wants to create a book and a customer before placing a purchase, the AI will take care of those tasks, which will then be cleaned up and removed after the test is through. As an algorithm gains a better understanding of the company’s API structures, it will be able to generate more setup and teardown jobs.

Using AI to identify gaps in test coverage

A new feature in IBM Cloud Pak for Integration Test and Monitor employs artificial intelligence to analyze API workloads in both production and test settings, detecting how APIs are used in each. This analysis enables it to discover real-world production API scenarios that aren’t effectively replicated in the existing test suite and produce automated tests to fill the gap.

Allowing an algorithm to efficiently examine millions of production API calls means that all that’s left for production personnel to do is review and approve the tests smartly. This is a highly effective method of boosting test coverage in the most impactful way possible, as it prioritizes resolving testing gaps based on how users interact with APIs in the real world.

It goes without saying that retail has been severely disrupted in recent years. Even before Covid turned the world on its head, the media was awash in stories about the so-called “retail apocalypse.”

Since then, we’ve seen lockdowns, inconsistent openings and closings, some businesses going out of business entirely, celebrations of vital retail workers, and a spike in internet shopping that delivered record profits to some but left others with more uncertain consequences. With continued supply chain disruption, inflation, and a tight labor market, the retail sector is clearly facing significant headwinds.

However, these obstacles also bring opportunities, and every business leader serious about prospering in the post-Covid world will need to leverage the power of digital transformation. Retail isn’t just big, it’s massive: the National Retail Federation predicts that sales in the United States would increase by 13.5% to $4.56 trillion by 2021.

While we may not yet be in the post-Covid era, the contours of what that “new normal” might entail are beginning to emerge. According to recent data by NielssenIQ, the abundant availability of vaccines is fuelling a “cautious confidence renewal” among customers, even as the epidemic continues to affect priorities and buying habits.

But, in the middle of all this uncertainty, what trends should business executives pay attention to?

The rise of hybrid shopping experiences

Each year analysts pay close attention to retail spending around the holidays, and this year the news was upbeat. Despite a 1.9% decline in December sales, the overall solid Q4 rise of 17.9% over the same period last year was offset.

People appear to have gone from a “just in time” to a “just in case” approach to shopping, as IBM CEO Arvind Krishna noted in a recent keynote speech at the National Retail Federation (RTF), however, whether this tendency will last is unknown.

Consumer buying patterns, like their relationship to the shopping experience, are changing. While there has been a significant movement toward internet purchasing, this does not mean that physical shopping is dead. Consumers demand more than just opening the door and picking up a package, according to new research from IBM’s Institute for Business Value (IBV) and the RTF.

In fact, over three-quarters of consumers (72%) say that retailers are still their primary source of purchases. Hybrid retail, which includes experiences like curbside shopping or ordering online and picking up in-store, is now the preferred mode of purchase for 27% of consumers.

Surprisingly, Gen Z consumers, sometimes known as “digital natives,” appreciate this hybrid approach of buying the most of any age group.

Future-proofing retail through AI

But, while trends show us where we are now and where we might be going, what can retailers do to ensure that their digital transformation programs are future-proofed?

With IBM’s Krishna informing the RTF audience that we have only tapped 10% of the technology’s potential, AI represents a powerful opportunity to raise profitability and deliver new and improved experiences.

AI is already being utilized to power virtual assistants and automated checkouts. By analyzing historical and location data, AI-powered logistics management can estimate product demand and get the appropriate products in front of customers at the right moment.

However, it’s also crucial to understand AI’s broader implications. More efficient, automated procedures have a human impact in addition to greater profits. The more that we can get machines to shoulder repetitive time-consuming work, lead to less stressed, more engaged employees and satisfied customers.

The importance of the purpose-driven consumer

Another increasingly significant topic for company executives to consider when pursuing their digital transformation strategy is what impact our actions will have on the environment and society.

This isn’t just about meeting the growing number of regulatory requirements. According to the IBV, 62% of consumers are willing to adjust their purchase patterns in order to lessen their environmental effects. Meanwhile, “purpose-driven customers,” who seek products and brands that correspond with their values, are on the rise and already account for nearly half of all buyers (44%). Digital transformation also plays a key role here: for example, Heineken has partnered with IBM to modernize its integration capabilities while also supporting the company’s environmental and social responsibility goals.

The good news is that profit and purpose do not have to be mutually exclusive. In fact, a recent analysis of business sustainability strategies by the IBV found that between 2018 and the first half of 2021 a select group of “transformational trailblazers” saw estimated cumulative revenue growth of 51% — a difference of nine percentage points over their next best-performing peers.

Meanwhile, according to Gallup, Gen Z and Millennials now make up nearly half (46%) of the full-time workforce in the U.S., and these age groups want to work for companies with ethical leadership. Indeed, according to PwC data, 65% of employees throughout the world desire to work for a socially conscious organization.

Conclusion

Thinking thoroughly and holistically about how to implement new technologies like intelligent automation can help retailers not only increase profits but also improve customer and employee experiences, resulting in greater experiences for all.

Create hybrid cloud, on-premises, and at-the-edge applications.

In just a few years, digital transformation has experienced a 10-year metamorphosis, such as the requirement to offer remote access to services and link individuals from their homes.

How can businesses keep up with the shift to “digital-first” thinking and offer new business value faster and more efficiently while lowering costs?

More specifically, how can IT leaders:

• To make it easier to design and maintain applications, why not modernize them?
• Improve IT infrastructure to more effectively share resources?
• To preserve investments, should workloads be portable across several clouds?
• Is it possible to automate and manage workloads from the core to the cloud to the edge?

Cloud-native applications on hybrid cloud

The way apps are designed, deployed, and managed is undergoing a change. In the public cloud, cloud-native development has been adopted as a faster, more agile, and more dependable method of developing the next generation of apps.

To provide flexibility, cloud-native apps are built on three core technologies:

1. Containers: to organize software with their needs so that they can run on any platform

2. Microservices: loosely connected services to create applications

3. Orchestration: to deploy and manage containerized applications at scale.

Cloud-native applications can also be designed and deployed in the data center, on private clouds, and at the edge, which is not widely mentioned. Or that these new applications will be able to use existing data to develop tomorrow’s mission-critical systems.

When this technology is combined with consistent development tools, portability across platforms, and common operational skills, it enables a new approach to develop workloads across the hybrid cloud.

Cloud-native workloads can be optimized for hardware architectures, including IBM zSystems and LinuxONE, IBM Power, x86, and Arm. They can also be co-located with data to maximize performance and application management and to support data residency requirements.

Building a hybrid cloud platform

A hybrid cloud platform that spans all conceivable deployments is the first step in developing cloud-native apps that can operate anywhere. This platform spans the whole hybrid cloud — from core to cloud to edge — and provides the foundation for developing and deploying apps and services.

They believe that having an open-source basis is critical for future flexibility, community creativity, and consistency across client development teams. That’s why many cloud platforms are built on open-source components like Linux, containers, and Kubernetes. The open-source components must be combined, hardened for enterprise workloads, and made simple to use and maintain.

Red Hat OpenShift serves as the foundation for IBM’s hybrid cloud platform. Red Hat OpenShift is the industry’s premier enterprise Kubernetes platform, providing a consistent foundation for developing, deploying and managing hybrid cloud applications. In March 2022, Red Hat OpenShift 4.10 was released, which enhances installer flexibility, automated operations, and workload extensibility.

Choosing a hybrid cloud infrastructure

The infrastructure on which the hybrid cloud platform works — public or private cloud, traditional infrastructure, and edge — is at its foundation. It’s critical that the hybrid cloud platform runs on all of the company’s IT infrastructure, not just a single public cloud or on-premises server. This enables existing data and applications to be part of the hybrid cloud alongside new cloud-native applications. It also eliminates the possibility of vendor lock-in by allowing workload placement flexibility to best match the infrastructure.

Red Hat OpenShift is available on the most popular public clouds, including IBM Cloud, which offers fully automated container hosting. With IBM Cloud Satellite, it can be expanded to on-premises, edge, and public cloud settings.

Red Hat OpenShift can also run on-premises, close to current data and applications, on IBM Power, IBM zSystems, and IBM LinuxONE. The IBM Cloud Infrastructure Center then offers IaaS for Linux on IBM zSystems, which might make the Red Hat OpenShift installation process easier. IBM has also just launched IBM zCX Foundation for Red Hat OpenShift, which allows Red Hat OpenShift apps to run in the z/OS address space while still being supported by IBM.

With a container-native hybrid cloud data platform for Red Hat OpenShift applications, IBM Spectrum Fusion and Red Hat OpenShift Data Foundation provide persistent data support for Red Hat OpenShift.

Deploying hybrid cloud software

The business value is then delivered by the hybrid cloud software workloads, which can be containerized to run on top of the hybrid cloud platform. Databases and automation software, as well as ISV and commercial applications, are examples of these workloads. Once containerized, they can take advantage of the scalability and orchestration provided by tools like Kubernetes.

IBM has containerized its core software to run on Red Hat OpenShift across a variety of hardware architectures and packaged it into a set of AI-powered IBM Cloud® Paks.

The Red Hat Marketplace is an open software marketplace where ISVs can sell hybrid cloud apps.

Running cloud-native applications everywhere

Cloud-native applications are no longer just for the public cloud. The availability of a hybrid cloud platform that runs across public cloud, private cloud, and traditional infrastructure has opened the possibility of a common approach to developing applications across the hybrid cloud — helping enable faster delivery of new value to businesses and their customers.

The demand for “real-time” money movement, posting, clearing, and settlement is one that many individuals share. Money and data flow together swiftly in their optimal form.

Faster Payments in the UK (2008) started the trend from some day’ to ‘faster’ to ‘immediate’ and now ‘instant,’ where transactions that took days to weeks to move and settle were reduced to roughly a day. Many other nations, including G3, PIX, NPP, UAEFTS, RTP, and, most recently, RTR and FedNow, followed suit (often as a result of government or central bank mandates) (to name a few).

Real-time is not visible to most customers; it just happens. Real-time is simply offered to the user in various regions of the world. In some cases, it’s an opt-in service; in others, it’s a paid, value-added service for businesses.

The end goal

Both sides of “real-time” payment transactions and the associated data move, clear, post, and settle within seconds (payor and receiver debits and credits) — 24x7x365 — and are irreversible. Only a few people have made it this far. Some systems allow payments to move in seconds, others in minutes, and some appear to move in real-time, 24 hours a day, seven days a week, but are credit and/or collateral-backed transactions after business hours or on weekends (the TCH model).

We’re not there yet

Moving to a 24x7x365 system where payments flow, clear, and settle in seconds with certainty poses issues (i.e., irrevocable). Despite the fact that some systems appear to be fully real-time, very few are. Consider the following scenario:

• Some demand deposit account systems at financial institutions (i.e., DDA, current or core systems)’memo-post’ same-day transactions — such as debit-card transactions or a paper check deposit — so it appears as if the transaction has occurred, but it doesn’t post until overnight processing, and availability can be deferred. You can reverse or challenge transactions in many circumstances if the payment type and account regulations allow it.
• When you use a credit card to buy groceries, your card seems to be charged and you have made a transaction in real-time. However, the credit card payment system takes up to three days to complete the transaction among all parties involved, including the retailer.
• Most consumer transactions appear to be in real-time until you can’t write a check on Sunday to buy a car, or when a check is deposited and the funds are put on hold until they’re “cleared.”

Accounts payable, accounts receivable, treasury management, and/or enterprise resource planning (ERP) systems in many firms only handle transactions during business hours on business days. These systems aren’t built to process and provide positions in real-time, and they frequently have to make changes for transactions made by financial institutions outside of business hours. There may not be enough data related to the payments, restricting real-time insight into payables and receivables positions.

The DDA, lending, card, and other systems used by financial institutions are frequently outdated, expensive to operate, cumbersome, and multi-layered. Fixes, tweaks, and ‘stacked-on’ incremental additions and functionalities to satisfy developing demands and accommodate innovations or new market requirements might be the reason for this. Even if core systems are capable of processing in real-time, upstream and downstream systems frequently are not.

The Clearing House’s RTP rails guarantee “real-time, both sides, in seconds with irrevocability,” however real-time posting and settlement between participating institutions take place only during normal business hours, with banks committing to respect transactions done within such hours.

Are real-time payments made with cash or cryptocurrencies?

Although cash is arguably instantaneous, it is an incredibly costly non-earning asset for a financial organization. It is neither traceable nor readily replaceable, and merchants must deal with significant handling expenses and security issues. Currencies differ over the world, and retailers may not always accept them.

Cryptocurrencies do allow for real-time exchange and payments, and some believe that this is why they were created in 2008 – to enable real-time P2P transactions utilizing blockchain technology. There are already over 4,000 cryptocurrencies in use across the world, with varying degrees of acceptability. Central institutions, notably the Federal Reserve Board of the United States, have expressed opinions on the feasibility of cryptocurrencies and other types of digital money but have not yet reached a conclusion (a question that has been debated since 2012 and will continue to be so for the foreseeable future).

The possibilities

What if, in this new real-time world, every transaction took place in real-time? What if employees who are now paid every month were paid daily? What if we could make all of the payment systems run in real-time, with payroll files produced every day and delivered in real-time for clearing and posting? Instead of net 10/30, what if bills were paid “on receipt/order”? Will there be an increase in Buy Now Pay Later (BNPL) sales? How will the worlds of real-time and credit conflict, compete, merge, or complement?

In contrast to the rest of the world, membership in these programs is optional in the United States; otherwise, a mandate would need an Act of Congress. RTP was freely adapted by the Clearing House banks, and it became operational in 2017. The Federal Reserve Bank of New York (FRB) is now conducting a trial with financial institutions and platform suppliers, with a launch date set for 2023.

In the lack of a complete real-time rail system in the United States, a group of American banks organized a consortium and developed Zelle, a real-time P2P mobile app built on the Early Warning Systems platform, adding another layer. In any case, we need all platforms engaged (both nationally and worldwide), not just financial institutions, to participate end-to-end. All of this will contribute to the world’s larger aims of increased corporate efficiency and client loyalty.

The IBM Payments Center™ can get you there

The influence on liquidity management, cash, cash flow, cash positioning, and cash forecasting for corporations is enormous. As a result, the IBM Payments Center offers our bank clients and their corporate clients both experience and solutions in Payments, Liquidity Management, Treasury, and Cash Management capabilities.

With an analytics layer above the ERP, the ability to “see” incoming and outgoing transactions in real-time offers real-time insight into what is about to happen, as well as the capacity to plan for future investment/debit/cash needs based on current views. In an increasingly global, real-time environment, that degree of openness has become a daily request and a crucial necessity for businesses.

The IBM Payments Center brings a long history of financial markets consulting knowledge, talent, skills, and platforms to bear on payments, including legacy, fintech, mobiles, multichannel, internet, and real-time transactions. We can help with strategic partnerships, local-markets-served insights, regulatory needs, and more cost-effective payments modernization choices, as well as utilizing technology, AI, and cloud to construct either end-to-end or snap-in components to payments ecosystems.

Managing hybrid cloud IT infrastructures may be difficult, and businesses require the correct tools to meet the difficulties.

Organizations want to use current corporate systems to provide novel features faster. However, sustaining old programs consumes a considerable percentage of the IT budget. When it comes to application and data modernization, most companies opt for a hybrid cloud strategy, balancing application and data workloads across public and private clouds. To keep all systems functioning properly, organizations want a powerful management solution that works across infrastructure and application suites.

What is hybrid cloud management?

The administration of an organization’s infrastructure installations and services, whether on-premises or off-premises, is referred to as hybrid cloud management (HCM). Organizations may use hybrid cloud management to plan, implement, and manage IT infrastructure and services spanning clouds, data centers, containers, virtual machines (VMs), and physical platforms. Software that combines on- and off-prem infrastructure into a management platform that allows managers to observe and control resources is commonly used.

Why utilize hybrid cloud management?

With all of the numerous platforms and operating systems that enterprises are adopting a hybrid cloud IT architecture, managing that environment becomes increasingly complicated. Businesses want consistent virtual machine administration across on-premises, off-premises, and platforms, preferably under a single pane of glass.

Organizations also lack a standardized approach to automating throughout the company and a simple way to track cloud spending.

Organizations must address the following issues to meet these challenges:

1. Support and manage environments across hybrid multi-cloud landscapes with efficiency.
2. Ensure corporate automation is consistent across hybrid apps and infrastructure.
3. To expedite digital transformation and increase operational efficiency, modernize cloud-native apps and enhance legacy systems.

Hybrid cloud management capabilities

IBM software capabilities that are intimately integrated with IBM Power Systems and IBM Power Systems Virtual Server can handle these difficulties. We’ll look at some of these features and how they help to provide a consistent experience across on-premises, cloud providers, conventional apps, and new cloud-native apps.

Managing virtual infrastructure

Across the hybrid cloud landscape, organizations want a simple method to access and manage existing apps. Because most businesses rely significantly on virtual machines to operate their business applications, they require a powerful platform management solution.

The IBM Cloud Pak for Watson AIOps unifies a virtual landscape into a unified user experience, substantially simplifying hybrid cloud resource management. Cloud Pak for Watson AIOps not only makes it easy for teams to evaluate the health and performance of their applications and infrastructure, but it also provides insights that can be used to propose and use automation, resulting in increased efficiency and outcomes for your company.

The Cloud Pak for Watson AIOps offers the following advantages:

• Diagnose problems faster: Correlate a massive quantity of unstructured and structured data in real-time using AIOps technologies.
• Build and manage securely: Create policy at the microservices level, then automate it across all application components.
• Automate provisioning: Keep track of the current status of your environment and provide your IT personnel self-service.

Enterprise observability

Having insight into what’s going on within the environment from infrastructure and application perspectives can be tough as enterprises increasingly adopt a hybrid cloud architecture and its numerous benefits. Enterprises need a complete observability platform that includes not just what’s in their data centers, but also what’s in public cloud providers’ data centers and across all platforms.

Enterprise observability and monitoring are provided by IBM Observability by Instana. Let’s take a closer look at these capabilities:

• Enterprise observability: Monitors, traces, and profiles all applications and services automatically. Intelligent steps are taken to reduce troubleshooting and improve performance.
• Automatic application performance monitoring: Observe, monitor, and correct any application, service, or request automatically.
• Hybrid and multi-cloud monitoring: Visualize and monitor containers, hosts, middleware, and Kubernetes in real-time.

Across all hardware platforms, Instana offers a diverse range of use cases and tight interaction with key corporate applications, language runtimes, and software. From a VM viewpoint, Instana supports AIX, IBM I, and Linux; from a cloud-native perspective, OpenShift on Power (as well as x86); and from an infrastructure layer, the IBM Power Hardware Management Console (HMC).

Resource optimization

There is a huge potential in the new IT environment to accurately identify and assign the necessary resources to a workload or application. When workload performance, compliance, and cost are accurately and consistently matched against the best-fit infrastructure in real-time, companies realize efficiency. However, because the environments are too huge and complicated at an enterprise size, this cannot be done manually. IT operators require an analytics engine that can plug into multiple infrastructures and apps to continually improve.

IBM Turbonomic Application Resource Management delivers constant resource optimization across any cloud environment. The program makes resourcing choices in real-time to guarantee that applications have the computing, storage, and network resources they require while accounting for business restrictions.

Turbonomic offers the following services:

• Continuously assure performance with AI-powered software: 60% of clients said Turbonomic helped them increase application performance.
• Increase IT productivity: When Turbonomic ensures application performance, 90% of clients believe they can focus on more strategic tasks.
• Unite application and infrastructure teams: Customers can enjoy true full-stack visibility.

Turbonomic collects data from virtualization management software, Kubernetes, and OpenShift clusters, among other places. Turbonomic offers AI-powered automation solutions that may be used on Red Hat OpenShift and in any hybrid cloud environment. Turbonomic also has a close interaction with Instana, which allows it to see how apps perform and how their actions affect performance.

Application modernization at an enterprise scale

To achieve a competitive advantage, businesses are aiming to augment their existing apps with new cloud-native technologies. Applications are shifting away from monolithic design and toward cloud-native architecture, which is comprised of numerous components distributed over many clusters and cloud providers. Operating in a multi-cluster environment has various problems, such as managing the lifespan of numerous clusters using a single control plane, regardless of where they are located (on-premises or across public clouds).

Red Hat Advanced Cluster Management for Kubernetes can help with this. Red Hat Advanced Cluster Administration for Kubernetes integrates the management of numerous Kubernetes or OCP clusters into a single interface. From there, you can see all of your clusters and applications in one convenient location. You may also install new apps and set policies to ensure that each cluster follows the organization’s standards and best practices.

The following are some of the advantages of Red Hat Advanced Cluster Management for Kubernetes:

• Accelerate development to production: Self-service provisioning accelerates application development processes.
• Increase application availability: Deploy legacy and cloud-native apps across dispersed clusters in a matter of minutes.
• Central management automatically: Self-service cluster deployment that automatically distributes apps frees up IT teams.

Conclusion

Many businesses continue to operate in hybrid cloud settings, hosting many of their core business workloads on-premises while developing or transferring additional workloads to public cloud platforms. Managing various settings may be difficult, and companies require the necessary tools to overcome the hurdles offered by these diverse contexts in order to achieve their goals.

The capabilities discussed in this article work together extremely well to create a consistent management platform between client data centers, public cloud providers, and multiple hardware platforms (including IBM Power) — providing all of the necessary elements for a comprehensive hybrid cloud platform.