All About Hybrid Cloud Environment Before Workload Migration

Sayantani Bhattacharya | June 11, 2021

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Modern organizations need fast-paced solutions to scale their services and integrate their applications for a digital foothold, resulting in a demand to shift their ecosystem from on-premise to a cloud environment. Therefore, selecting the exemplary cloud architecture based on the organizational ecosystem is the need of the hour; otherwise, it may directly impact their business growth. Hence, companies are interested in opting for a hybrid cloud solution that will allow gradual migration of their workloads without affecting the daily operations, providing enhanced security by allowing segregation of the critical and general workloads to private and public clouds, respectively.

Many businesses across the globe are raising concerns on whether to adopt a public, private or hybrid cloud platform. According to Gartner, over 75% of midsize and large organizations will adopt a hybrid or multi-cloud strategy by 2021. Today, organizations realize that a hybrid platform is a good-to-go solution after managing IT resources and workloads in public and private cloud environments.

The hybrid cloud definition says that it is a combination of storage, computing, and services environments of an on-premise, private and public cloud. It creates a single platform to operate both in on-premises, private resources, and public cloud resources, such as those offered by Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). It allows seamless sharing of data and applications when computing and processing requests vary within the organization. A hybrid ecosystem establishes interconnectivity initially through data virtualization, following via connection tools and protocols such as application programming interfaces (APIs), virtual private networks (VPNs), and/or wide area networks (WANs). The goal is to create a collaborative, automated, secured, and organized environment, where integrating these applications among one another and with on-premises systems is easy and fast. 

How is Hybrid Platform Different from Others?

Deployment of a hybrid infrastructure describes the organization's ecosystem where on-premise, public, and private cloud environment conglomerate to provide a secure and unified platform. Hybrid cloud platforms allow you to leverage the utilities of the private and public cloud ecosystem.

The private cloud is an infrastructure that can isolate all your vital business data of an organization behind firewalls on a single server, which are non-sharable and thus is a perfect fit for businesses dealing with confidential and sensitive information.Whereas public cloud platforms are shared spaces that offer massive amounts of storage capacity and other resources, they are highly scalable. In addition, due to the shared architecture, public clouds are affordable and allow organizations to control the security and backups provided by the server's data center.

Hybrid platforms are secured, scalable, and cost-effective, allowing you to have a flexible option to separate the workloads and benefit private and public cloud platforms.

Benefits of Hybrid Cloud

According to the Mordor Intelligence research report, Hybrid Cloud Market was valued at USD 52.16 billion in 2020 and projects to record USD 145 billion by 2026, at a CAGR of 18.73% for the forecast period 2021 to 2026. The vital factors such as flexibility, reliability, scalability, cost-effectiveness, security, and rapidity drive its progress.

Flexibility and Reliability

One of the prime benefits of a hybrid environment is its flexibility. A typical hybrid cloud solutions flexibility refers to the service that enables you to utilize the ecosystem as per their needs. Hybrid cloud architecture can use traditional on-premise systems and the latest cloud technology, with/without registering to a third-party host. Organizations with hybrid infrastructure can migrate workloads and access information to and from their traditional systems and to the public/private cloud whenever necessary, without any service interruption. 


As we know, a hybrid cloud infrastructure allows us to utilize both a public cloud and a private cloud. It permits more resources via the public cloud services that enable the organizations to expand their storage capacity and computing resources. Thus, workload migration to a hybrid ecosystem makes it easier to facilitate, implement and scale the resources whenever demand exceeds the ability of the on-premise infrastructure.


Typically comprised of private cloud and public cloud architectures, hybrid cloud management allows you to own and operate the data center infrastructure with a significant capital expense. On the other hand, it also enables the public cloud infrastructure to offer resources and services accountable as variable and operational expenses. Thus, hybrid cloud users can select any infrastructures that make it affordable to run the workloads. Moreover, it can accommodate the business demand upsurge and increase its capacity without additional cost. As cost-saving is one of the prime aims of organizations, a hybrid infrastructure leverages a cost-effective approach with no compromise on your ecosystem's scalability, flexibility, security, and agility. According to a study by IT management solution provider Flexera, 76 % of organizations use cost efficiency and savings to measure cloud progress.


Securing business-critical information is always a challenge in any network-enabled ecosystem. Typically for a public cloud, susceptibility to data breaches and data leakages are common. While cloud service providers make exhaustive efforts to ensure data protection for their clients, yet public cloud infrastructure remains at high risk because of its open environment framework. Whereas, for the private cloud ecosystem, companies hold direct control over the stored data. They can manage and establish strict protocols for accesses because the data stored in the private ecosystem are generally highly critical and confidential. However, with a hybrid service, companies can leverage the security of a private cloud with the flexibility and benefits of a public cloud. You can migrate the business-critical data from the private ecosystem to a public cloud for different operations, analytics, and applications. You can also implement extensive encoding methods to ensure data security as much as possible. Hence, Hybrid cloud security is simply protecting the data, applications, and infrastructure that incorporates a certain degree of workload sharing, consistency, and management across multiple cloud environments.


Speed/Rapidity is one of the noteworthy attributes of network-enabled applications. Although a hybrid platform is not fundamentally quicker than a public cloud platform, it allows network optimization to curtail latency and simplify data migration. In addition, hybrid cloud storage transfers non-critical workloads to the public cloud and critical workload on the private cloud and optimizes the network to streamline the traffic. As a result, you can work faster and increase their productivity.

Few Setbacks to Keep in Mind

Despite several benefits, you should consider some of the hybrid cloud challenges before workload migration. Flexera's 2020 State of the Cloud Report says that the complexity and dynamic nature, the hybrid/multi-cloud environment brings many challenges, such as assessing the suitability of on-premises apps for migrating to the cloud. However, analyzing and noting the limitations can make it easy for organizations to utilize their hybrid ecosystem optimally.

Latency and Compatibility

As we know that a hybrid cloud strategy is derived by combining private and public cloud platforms, there can be compatibility issues between them because of their distinct properties. Moreover, analyzing the compatibility of the on-premise applications before migrating to the cloud requires a lot of effort and bandwidth. For example, the private cloud component of a hybrid application cannot respond as fast as the front-end public cloud component and can cause operational latency and other complexities.

Temporary Risk Due to Data Transfers

Data transfers across a hybrid cloud platform can involve third-party (public cloud host) for many organizations, resulting in unnecessary and unacceptable security risks. In addition, data leakages are also a common concern while shifting the on-premise applications to the cloud infrastructure. Therefore, organizations must consider encryption of all traffic to protect the network and avoid temporary security risks for the data in transit.

IAM Complexities

Using Identity and Access Management (IAM) protocols steadily across private and public clouds requires a highly synchronized effort to meet security and compliance requirements. It is also an essential protocol to ensure no gaps are present in the hybrid cloud design. However, organizations dealing with confidential data, such as the healthcare or finance industries, may face few compliance setbacks. In addition, knowing the actual position of the data and who has access can be a real challenge in a hybrid environment. Hence, organizations need to adopt single sign-on applications and allocate authorizations only when critical and necessary.

Initial Implementation Cost

The on-premise or private cloud component requires substantial investment, maintenance, and operational skills for traditional hybrid cloud architecture. Thus, implementing additional software, though necessary, can further add to the initial cost for a private cloud. At the same time, proper data planning, security tools, employee training, and cloud certifications can boost the initial investment for the public cloud.

Is Hybrid Cloud Environment A Right Choice for Your Business?

Advanced companies align their business transformation strategy with the orchestration of their cloud platforms to achieve a 'GenX' business model. This model establishes an automated and agile organization, empowered by data, directed by AI insights. IBM's Voice of the Enterprise Digital Pulse report by 451 Research says that 3 out of 5 of approximately 1,000 organizations surveyed have implemented a hybrid ecosystem with integrated on-and off-premises cloud services.

Here are some hybrid cloud use cases to help you analyze whether a hybrid infrastructure fits your organizational ecosystem.

Dynamic Workloads

Hybrid cloud is particularly effective for dynamic workloads. For example, a trading company entry system that experiences significant demand surges is ideal for implementing hybrid cloud infrastructure. Using an easily scalable public cloud for your dynamic workloads while leaving more sensitive workloads to a private cloud or on-premises data center helps to increase your operational efficiency without hampering the security of your critical data.

Segregation of Critical and Non-Critical Workloads

When your company leverages several SaaS applications, it requires identifying and segregating their workloads to perform while keeping the data security aspect high priority optimally. In comparison, hybrid cloud storage diversification allows moving business-critical workloads to the private cloud with access control mechanisms for security. You can shift the non-critical applications to a public environment and utilize them for business analytics. Under a hybrid ecosystem, both these platforms share information under the same data management yet remain distinct. 

Periodical Migration to Cloud Environment

Suppose you are planning to upgrade your operational infrastructure but not sure about its operability. In that case, you can migrate a portion of your critical workload to the private cloud and general applications to the public environment of the hybrid cloud platform and analyze the performance. You can continue expanding your cloud presence as needed by periodical migration of the workload, utilizing the hybrid structure. It enables you to assess the platform in terms of your current requirements and potential future growth.

Big Data Management

Hence, opting for a more brilliant choice by implementing a hybrid cloud strategy will allow you to run a portion of the big data analytics using a highly scalable public cloud platform. Furthermore, you can ensure data security and retain your confidential big data behind the firewall while using the private cloud.

Capacity Management

A hybrid cloud allows you to assign public cloud resources for short-term projects at a much lower cost than the on-premise data center. Thus, you can maintain efficient investment by controlling over-expenditures on the equipment that you may require temporarily.

Additionally, utilizing a hybrid environment to supplement your on-premise infrastructure is a better choice for projects that require multi-user collaboration or significant data storage and may hinder your current network performance or surpass your network capacity.

Multiple Business Requirements

Assume your organization needs to fulfill several business requirements. You can cater to some of your critical business needs by only a private cloud and other essentials through a public cloud. Under such circumstances, the hybrid solution is perfect for you. Hybrid cloud services help in many ways to provide benefits of both solutions for a business.


A well-integrated and balanced hybrid approach gives your business the best of both worlds. It allows you to leverage public cloud and private cloud services without completely offloading your data to a third-party data center.  Hybrid Cloud Computing can be an ideal solution for your high-focussed businesses.

With the extensive features and benefits of the hybrid ecosystem, your organization will be able to take a step ahead to the world of modern technology and get to see the advantage of combining the security and control of private infrastructure with the scalability and versatility of public cloud computing. 


How do hybrid clouds work?

The hybrid cloud creates a single platform for the on-premise, private, and public cloud data, allowing gradual migration of the workloads without disturbing the daily transactions by establishing interconnectivity between different platforms. In addition, the hybrid cloud provides the security of the private ecosystem and the flexibility of the public infrastructure. 

Are hybrid clouds secure?

Hybrid clouds allow the migration of sensitive workload to its private infrastructure that you can protect behind the company's firewall. Thus, you can secure all the confidential data within the hybrid cloud infrastructure. In addition, you can shift the remaining non-critical workloads to the public portion of the hybrid cloud ecosystem, which you can utilize for several computational purposes.

Why hybrid cloud?

Hybrid cloud provides the security of the private environment and the scalability and flexibility of the private environment, which makes it one of the most preferred infrastructures for several organizations.

What is hybrid cloud storage?

Hybrid cloud storage is a method to manage cloud storage that uses both local and off-site data. With hybrid cloud storage, businesses can shift their workloads between on-premises or private clouds. In addition, hybrid clouds help organizations to get the most out of containers, which simplifies shifting workloads among clouds.


AbleVets LLC

AbleVets LLC is a fast-growing IT consulting and engineering firm, providing advanced IT solutions to improve citizen health, security and services.


Effects of Integrating Machine Learning in Cloud Computing

Article | September 29, 2021

Nowadays, most businesses across the globe are leveraging cloud technology and its advanced natives, such as machine learning and artificial intelligence (AI), to pave their path towards success. As per Analytics Insight, Machine Learning would record revenue of US$80.3 billion by the year 2023, with a CAGR of 33.6% from 2020. This article, explains the effects of integrating machine learning in cloud computing and how it helps make future-proof decisions, create innovative solutions, and drive organizational success. Machine Learning (ML) is one of the subgroups of artificial intelligence. It helps infer effective business decisions to attain digital presence in the market by providing valuable insights from disparate organizational data. “AI works effectively because extremely high volumes of high-quality data drive it. So, identifying and collecting data, and making that data meaningful, is an integral step in the machine learning process.” Jaime Punishill, CMO at Lionbridge said during an interview with Media 7. Importance of Merging Machine Learning in the Cloud and Some of the Enterprise Case Studies Machine Learning utilizes the resources of the cloud to optimize the industrial sectors. On the other hand, cloud computing provides scalable and cost-effective resources to leverage a considerable amount of data for processing to run ML-enabled systems efficiently. As a result, combining machine learning with cloud helps to optimize the capacity of both. Let us quickly run through some of the stimulating effects of merging machine learning in cloud computing. Detect and Protect Business Ecosystem According to the IDG Survey, almost 80% of IT security leaders believe their organizations are susceptible to cyberattacks despite augmented investments made on IT security to accommodate the advanced distributed IT framework. Today, most business leaders are aware of security threats because of various cloud-native and web-based modern applications. In addition, by merging machine learning in cloud computing, companies are developing intelligent security applications to protect organizational data from vulnerable malpractices. How an Advanced Approach by McAfee Endpoint Security 10.5 Helped a Leading Insurance Company Safeguard its Sensitive Data? For an insurance company, securing their customers’ sensitive personal data without compromising customers’ experience is imperative. And there lies the primary challenge of any IT security measure, where the traditional silos approach doesn’t work anymore. In this case, you need robust and automated solutions powered by advanced technologies, such as machine learning. Eventually, to solve this, McAfee deployed McAfee Endpoint Security version 10.5, and as a result, the company’s IT help desk started receiving 80% lesser tickets. In addition, the insurance company is now enabled with cloud-based Real Protect machine learning behavioral analysis technology that helps enhance overall security capabilities and stand against the rising risk of security breaches. Cloud-Based Cognitive Technology Integration of machine learning in cloud computing makes cloud data the source of ML algorithms for cloud-enabled businesses. ML algorithms utilize the cloud data and modify the cloud archetype to cognitive computing. Cognitive computing technology is one of the emerging trends. Businesses are inclined to deploy cloud-based cognitive technology because it helps to grow their revenue, enhance operational efficiencies, and cater to real-time use cases in a cost-efficient manner. How Does IBM’s Watson Help Banking and Financial Sectors? IBM’s Watson is one of the ideal examples of cognitive computing. Assisting the banking and financial sector, IBM’s Watson is a question-answering system, which receives unstructured data in the form of questions and provides humanized answers. Furthermore, Watson can differentiate its limits and route it to respective resources whenever human intervention is required. For example, IBM Watson has helped the Royal Bank of Scotland develop an intelligent assistant proficient in handling 5000 queries in a single day. Predictive Analytics Predictive analytics uses predictive models that are typically machine learning algorithms helping to make accurate predictions of your business outcomes. Playing a crucial role in cloud computing helps to optimize cloud infrastructure, take proactive measures to envisage downtime or infrastructure performance issues. Further, predictive analytics play an intrinsic role in merging the structured and unstructured data from diverse and distributed networks in multifaceted cloud environments. How WNS Helped a Globally Acclaimed Hotel Chain to Retain its Timeshare Members by Leveraging Predictive Analytics? For hotels, it is crucial to retain their timeshare members. However, experiencing a high attrition rate among its timeshare members, followed by nonrenewal or membership cancellation, started denting the hotel chain's reputation. WNS recommended an approach based on the principles of the predictive analytics approach for a better understanding of the behavior of the members. They created complete member profiles based on demographics, duration of membership, and transactional patterns. Implementing statistical analysis to generate probable attrition scores for every member helped to divide members into high, medium, and low attrition groups. Further, identifying the attrition drivers by deploying a logistic regression model was also a part of this exercise. All these drivers helped to predict the members' behaviors in the future. As a result, the insights empowered the hotel chain to employ marketing campaigns targeted towards the specific audience with special promotional offers to arrest the attrition of their timeshare members. Internet of Things (IoT) Internet of Things is described as a network through which multiple devices (read 'Things') are interconnected via the internet. IoT is adapted by technology experts worldwide. Various industrial sectors embrace the utilities of IoT devices. Further, when hosted on a cloud platform and leveraging machine learning in the cloud, IoT provides impactful real-time insights. How Did Medium One Leverage Machine Learning to Enable Cloud-Connected Industrial Pressure Sensors? The legacy industrial sensors couldn't generate automated real-time alerts to monitor, detect irregularity, or forecast key events. Medium One’s environment helped the customer in making data sensible to unlock its hidden insights. Medium one helped develop a machine learning algorithm for real-time predictions and alerts with historic cloud data. It enabled the cloud-connected industrial pressure sensors to correlate events smartly and monitor remotely. Chatbots and Virtual Assistants Chatbots and personal assistants are innovative examples of machine learning in cloud computing collaborations that dominate personal and corporate ecosystems. Intelligent cloud-based virtual assistants like Siri, Alexa, Cortana interact with you just like any other human being and perform several operations, as per your command. These chatbots are AI-enabled, operate on machine learning algorithms in the cloud computing framework. Further, they use natural language processing technology, predictive analytics, and sentiment analysis to learn from the inputs and engage in real-time conversations. How Oracle Intelligent Bots Reduced Call Center Wait Times? During the release of senior school exam results in the summer, the inquiry center of the University of Adelaide received massive traffic from the existing and prospective students. A significant part of that traffic was to enquire about their grades and admission facilities, respectively. The university appointed Rubicon Red, an Oracle cloud consultancy, to build a chatbot based on artificial intelligence (AI), machine learning, and natural language processing to meet this surged demand. The bot is supposed to handle the first line of inquiry when calls come in, relieving human agents' load. Rubycon Red leveraged Oracle Intelligent Bots to create an intelligent chatbot. The deployment was a success that resulted in 40% reduced traffic and 97% less wait time on the university's inquiry service (call center). Final Thoughts The merger of machine learning in cloud computing enables organizations to leverage their massive organizational data in deriving valuable data-driven insights and accurate predictions by analyzing the trends and patterns of the data. Machine Learning helps businesses to understand their target audience, automate their business processes and develop advanced products per market demand. As a result, it drives the success of a company and helps them to stay competitive. Frequently Asked Questions How is Machine Learning Used in Cloud Computing? Cloud-enabled businesses can leverage the vast data and utilize advanced machine learning technology to determine, compute and predict valuable futuristic insights about their business. As a result, it helps to scale the efficiency of the cloud within a cost-effective reach. Is Machine Learning Important for Cloud Computing? Machine Learning leverages cloud data and derives insightful information out of it. It computes the data to provide future forecasts and helps the businesses to take necessary actions on it. As a result, adopting machine learning applications are crucial for cloud businesses. What are the benefits of Machine Learning on the Cloud? Businesses can experiment with their processes using machine learning on the cloud and then scale up as demand increases. 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AbleVets LLC

AbleVets LLC is a fast-growing IT consulting and engineering firm, providing advanced IT solutions to improve citizen health, security and services.