The Challenges and Benefits of Modernizing Legacy Applications in Cloud

The Challenges and Benefits of Modernizing Legacy Applications in Cloud
It was right from its inception that cloud computing displayed a revolutionizing potential—it had an unforeseen scope over diverse targets including individuals, companies and governments. The major services available in these sectors and the ever growing inventions of the modern world do indeed call for a more advanced and flexible application of cloud computing. It is seen by many as the new wave of information technology. In 2010, the World Economic Forum published a report which evaluated the impact of cloud computing technologies and signaled the large potential benefits of adoption, ranging from economic growth and potential improvements in employment to facilitating innovation and collaboration.

Need being the mother of invention, Cloud has evolved beyond basic SaaS, IaaS, and PaaS offerings, as the cloud matures to become the engine of enterprise technology innovation. It is moving towards a faster and more efficient world. However, the Information Technology is increasing its demands to solve the arising complexities. Take for example the modernizing of legacy applications in cloud. It extends both challenges and opportunities, as the facets of a coin, but in each way it moves towards a more advanced and intricate web of complexities.

Most of the large enterprises run at least some form of a legacy application, for which updates and replacements can sometimes be tricky. However, failing to modernize out-of-date systems may hinder the pace of information exchange due to slow runtime speeds and inefficient load balancing. Many organizations have, thus, begun to modernize their legacy applications which will yield long term benefits such as portability and scalability, better speed and resource management, and granular visibility.

Since the start, enterprises have run on time-consuming manual processes and tools that are involved with legacy applications also hinders modernizing efforts. Manual processes take up significant amount of time and still leave room for errors. However, at the same time, enterprises say they need to move to the cloud, but they don’t really understand why, nor do they realize how difficult it can be. This includes applying cloud services to a non-compatible old legacy application and facing challenges when trying to re-host. They must be cautious of the processes involved in migrating the valuable data. If one moves one application to cloud which has business logic or IT logic of another application that isn’t migrated to cloud, they might run into issues. Therefore, it is better to consult the professionals before landing into problems. In this league of advancement, the infrastructure might face challenges such as:

Cost adjustments: The cost of maintaining and upgrading Legacy systems renders the firm a challenge of combatting the financial balance. The challenge preparers the employees learn the skills of pulling the firm through the tight passage without de-establishing the financial pace of the organization.

Inflexible and closed architectures: There are some architectures used by organizations that hinder Web and mobile enabling and integration with contemporary platforms, therefore, they turn out to be challenging opportunities for the modern minds at work.

Limited Integration: Legacy systems might sometimes not go in cohesion with the integration to contemporary technologies like Mobile Apps/Devices, Enterprise Content Management Systems, Automated Workflow, E-Forms/E-Signatures, Geographic Information Systems, and so on, therefore pose a major obstacle for the integrators.

User Friendliness: The existing system uses command-based screens and cannot provide a contemporary Graphical User Interface (GUI), web, or mobile which have become commonplace, however, if it is in constant practice, the newer models of commanding may pose an oddity for quite some time for the old hands. Therefore, the migrators have to go an extra mile to ease the way by employing less complicated systems.

On the other hand, there are various benefits of applying this modernization. If the engineers handle the aforementioned challenges wisely and implement the newer technology with greater precision, there indeed some charming benefits await, such as:

Enhanced flexibility: Creates a flexible IT environment with new architectural paradigms such as web services; aligns IT systems to dynamic business needs.

Modern development tools: Legacy and new developers can use the same or similar tools, enabling both to develop Legacy applications.

Lower risks: Re-use of business rules where data becomes less risky than alternatives.

Shorter development times: Modernizes development tools and retrains developers which lead to shorter development times.

Reduced cost: Lowers high maintenance cost of existing old fashioned Legacy platforms and development tools, resulting in substantial savings in IT budgets.

Minimized disruption: Reduces the risk when modernizing Legacy platforms by combining two decades of development experience with contemporary platforms, a proven modernization framework and rich domain knowledge.

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Amazon ECS for Kubernetes: Bridging the Migration gaps

Amazon ECS for Kubernetes
AWS has unveiled a new container service that will allow its users to run Kubernetes on AWS server without needing to install and operate a separate Kubernetes cluster. The service can be identified as a major advancement for AWS which will allow the users migrate smoothly, who had, though, previously found Amazon ECS slightly rigid when it yielded optimum results only when operated on AWS’ own server.

Amazon Elastic Container Service for Kubernetes is a managed service that transcends this obstacle. With this cross platform achievement, AWS will certainly attract (or at least keep) its customers for it has eradicated one major obstacle of transferring clusters on personal server of AWS—inter-cloud exchange. Kubernetes is known to be an open-source system used for automating the deployment, scaling, and managing containerized applications. While Kubernetes had previously posed significant challenges to producing applications, where one was required to manage scaling and availability of Kubernetes masters and persistence layer, Amazon EKS has eased this tedious task by rendering an automatic selection of appropriate instance types. It runs them across multiple Availability Zones along with replacing unhealthy masters through constant heath monitoring. Even the patch and upgrade routines of master and worker nodes no longer need to be monitored manually, which required a lot of expertise and, above all, a tremendous amount of manpower and time. Amazon EKS automatically upgrades the nodes and prepares them for high availability. It runs three Kubernetes masters across three Availability Zones to achieve this flawless feat.

Amazon EKS, just like ECS, can be integrated with many AWS services to provide direct scalability and security for various applications, including Elastic Load Balancing for load distribution, IAM for authentication, Amazon VPC for isolation, AWS PrivateLink for private network access, and AWS CloudTrail for logging. It runs the latest version of the open-source Kubernetes software, which allows the user to have all the latest and existing plugins and tools from the Kubernetes community. Due to the absolute compatibility offered with Amazon EKS for application running on standard Kubernetes Environment, the user can easily migrate any standard Kubernetes application to Amazon EKS without any code modification.

Having stated the common properties of Amazon EKS, let’s look at the major benefits for opting it:

Secure
Security is of paramount importance in this cloud based IT world. With more advanced features, the Amazon EKS is loaded with highly advanced security features for the Kubernetes Environments of any managed cloud service. The migrated workers are launched on the user’s Amazon EC2 instances, where no compute resources are exposed to other customers.

It allows the users to manage the Kubernetes cluster using standard Kubernetes tools such as kubectl CLI for managing Kubernetes, through AWS Identity and Access Management (IAM) authenticated public endpoints or through PrivateLink.

Fully Compatible with Kubernetes Community Tools
Since Amazon EKS runs the latest version of the open-source Kubernetes software, all the existing and even newer features, plugins, and applications are supported in it. Applications that are already running in an existing Kubernetes environment will be fully compatible, and can be flawlessly moved to Amazon EKS cluster.

Fully Managed and Highly Available
Amazon EKS eradicates the need to install, manage, and scale personal Kubernetes clusters. With this development, EKS is one step ahead of the ECS. The worker and master clusters of Kubernetes are automatically made highly available which are distributed across three different Availability Zones for each cluster, due to which, worker and master servers start functioning more smoothly than ever before. Amazon EKS manages the multi Availability Zone architecture and delivers resiliency against the loss of an Availability Zone. Furthermore, it automatically detects and replaces unhealthy masters and provides automated version upgrades and patching for the masters.

Amazon EKS integrates IAM with Kubernetes which enables the user to register IAM entities with the native authentication system in Kubernetes. The user no longer has to worry about manually setting up credentials for authenticating with the Kubernetes masters which also allows IAM to directly authenticate with the master itself as well as granularly control access to the public endpoint with regards to the targeted Kubernetes masters.

Besides that, it also gives the option of using PrivateLink to access Kubernetes masters directly from personal Amazon VPC. With PrivateLink, Kubernetes masters and Amazon EKS service endpoint appear as an elastic network interface with private IP addresses in Amazon VPC, which opens the threshold for accessing the Kubernetes masters and the Amazon EKS service directly from Amazon VPC, without using public IP addresses or requiring the traffic to traverse the internet.

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Amazon ECS: Another Feather in AWS’ Cap

Amazon ECS: Another Feather in AWS’ Cap

Amazon ECS Another Feather in AWS’ Cap
Amazon Elastic Container Service (ECS) is a newly developed, highly scalable and high-performance container orchestration service that supports Docker and allows users to effortlessly run and scale containerized applications on the Amazon Web Services (AWS) platform. ECS removes the need for users to install and operate container orchestration software, manage and scale clusters of virtual machines, or schedule containers on said virtual machines.

ECS is a service that introduces simplicity while running application containers in an accessible manner across multiple availability zones within a region. Users can create Amazon ECS clusters within new or existing virtual PCs. After building a cluster, users can define task definitions and services that specify running Docker container images have to across selected clusters. Container images are stored in and pulled from container registries, which exist within or outside the existing AWS infrastructure.

For vaster control, users can host tasks on a cluster of Amazon Elastic Compute Cloud (EC2) instances; this enables users to schedule the placement of containers across clusters based on resource needs, isolation policies, and availability requirements. ECS is a useful option when creating consistent deployment and build experiences, along with managing Extract-Transform-Load (ETL) workloads. Users can also develop sophisticated application architectures on a micro-services model if desired.

ECS allows users to launch and stop Docker-enabled applications with simple API calls. Perform a query about the state of an application or access additional features such as Identity and Access Management (IAM) roles, security groups, load balancers, CloudWatch Events, CloudFormation templates, and CloudTrail logs.

Recent IT developments have signaled an increasing dependency over smart cloud containers, and that is where Amazon ECS has become an essential pick. Firms are seeking more efficient and ready-to-go solutions that do not add any additional obstacle to an organizational pace. Amazon ECS offers various advantages and customization options including:

Containers Without Infrastructure Management
Amazon ECS features AWS Fargate, which enables users to deploy and manage containers without having to maintain any of the embedded underlying infrastructures. Utilizing AWS Fargate technology, users no longer need to select Amazon EC2 instance types, provision, or scale clusters of virtual machines to run containers. Fargate gives ample time for users to focus on building and running applications without having to worry about the underlying infrastructure.

Containerize Everything
Amazon ECS lets users quickly build various types of containerized applications, from long-running applications and micro-services to batch jobs and machine learning applications. ECS can migrate legacy Linux or Windows applications from on-premise solutions to the cloud and run them as containerized applications.

Secure Infrastructure
Amazon ECS provides the option of launching containers in one’s own Amazon VPC, allowing them to use the VPC security groups and network ACLs. None of the available resources expose themselves to other customers, which makes data all the more secure; it also enables users to assign granular access permissions for each of the containers using IAM to exhibit restriction on access to each service and accessible resources that a container has. This intricate level of isolation permits users to use Amazon ECS to build highly secure and reliable applications.

Performance at Scale
Amazon ECS is a product of gradually developed engineering over a period of years. Built on technology developed from many years of experience, ECS can run highly scalable services. Users can launch various Docker containers in seconds using Amazon ECS with no further introduction of complexity.

Compliment Other AWS Services
Amazon ECS is a product that works well with other AWS services and renders a complete solution for running a wide range of containerized applications. ECS can seamlessly integrate with services such as Elastic Load Balancing, Amazon VPC, AWS RDS, AWS IAM, Amazon ECR, AWS Batch, Amazon CloudWatch, AWS CloudFormation, AWS CodeStar, and AWS CloudTrail, among others.

It is important to highlight that Amazon ECS, when integrated with other AWS Services, will provide the best solution for running a wide range of containerized applications or services instead. Other popular container services such as Kubernetes and Mesos can also be efficiently run on AWS EC2.

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Machine Learning’s Impact on Cloud Computing

Machine learnings impact on cloudcomputing
Increasing dependency on AI (Artificial Intelligence) and the (Internet of Things) have given new goals to cloud computing infrastructure administrators. The premises enfolding within this newly emerging subfield of Information and Technology are indeed very vast ranging from smartphones to robotics. Firms are developing new machinery requiring the least amount of dependency on human resources. Developments aimed at providing human-made mechanisms with levels of autonomy to become entirely independent.

To gain a level of autonomy over soft resources, developers have begun to depend on a mediator to assist ‘smart machines’ in increasing functional ability. As cloud computing is already taking over essential domains of human efforts such as data storage, this technological advancement will result in unprecedented impacts on the global economy.

Integrated cloud services can be even more beneficial than current offerings. The contemporary usage of cloud involves computing, storage, and networking; however, the intelligent cloud will multiply the capabilities of the cloud by rendering information from vast amounts of stored data. This will result in quick advancements within the IT field, where tasks are performed much efficiently.

Cognitive Computing
The large amounts of data stored in the cloud serve as a source of information for machines to gain their functional state. The millions of functions that are occurring daily in the cloud will provide vast sources of information for computers to learn. The entire process will equip the machine applications with sensory capabilities, and applications will be able to perform cognitive functions, making decisions best suited for them to achieve their desired goal.

Even though the intelligent cloud is in its infantile age, the propositions are predicted to increase in the coming years and revolutionize the world in the same way that the internet had. Expectations of those who would utilize cognitive computing including those in the healthcare, hospitality, and business fields

Changing Artificial Intelligence Infrastructure
With the aid of the intelligent cloud, AI as a platform service makes the process of smart automation more accessible for users by taking control of the complexities of a process; this will further increase the capabilities of cloud computing, in return growing the demand for the cloud. The interdependency of cloud computing and artificial intelligence will become the essence of new realities.

New Dimensions for the Internet of Things
Just as we are now aware how the IoT has overtaken our lives and created an undeniable dependency on gadgets, cloud-assisted machine learning is almost increasing rapidly. Smart sensors that allow cars to operate in cruise control will grasp their source of data from the cloud only. Cloud computing will become the long-term memory for the IoT where they can retrieve the data for solving in-time problems. The web’s massive of interconnectivity will generate and operate on an enormous amount of data saved in that very cloud; this will expand the horizons of cloud computing. In coming years, cloud-based machine learning will become as meaningful to machines as water is for humans.

Personal Assistance
We have already seen assistants such as Alexa, Siri, Cortana, and Google perform well in the consumer market; it is not absurd to think that an assistant will exist in every modern home by the next decade. These assistants make life easier for individuals through pre-coded voice recognition that also gives a feeling of human touch to machines.

Current assistant responses operate on a limited set of provided information. However, these assistants are very likely to be developed more finely so that their capabilities will not remain so confined. Through the increasing use of autonomous cognition, personal assistants will attain a state of reliability where they can replace human interaction. The role of cloud computing will be supremely vital in this regard, as it will become the heart and brain of these machines.

Business Intelligence
The tasks of a future intelligent cloud will be to make the tech world even smarter – autonomous learning coupled with the capabilities of understanding and rectifying real-time anomalies. In the same way, business intelligence will also become more intelligent wherein along with identifying faults, it will be able to predict future strategies in advance.

Armed with proactive analytics and real-time dashboards, businesses will operate upon predictive analytics that process previously collected data, making real-time suggestions and future predictions. These predictions from current trends and recommendations for actions would make things easier on leaders.

Revolutionizing the World
Fields like banking, education, and hospitality will be able to make use of the intelligent cloud, enhancing the precision and efficiency of the services they provide. Consider, for example, having an assistant in hospitals which diminishes doctors’ customary load of decision making by analyzing cases, making comparisons, and promoting new approaches to the treatment.

With the rapid development of both machine learning and the cloud, it seems in the future that cloud computing will become much easier to handle, scale, and protect with machine learning. Along with those mentioned above, more extensive businesses relying on the cloud will lead to the implementation of more machine learning. We will arrive at a point in which we will have no cloud service that operates as they do today.

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Amazon SageMaker in Machine Learning

Amazon SageMaker in machine Learning
Machine Learning (ML) has become the talk of the town, and its usage has grown inherent in virtually all spheres of the technology sector. As more applications are beginning to employ the use of ML in their functioning, there is a tremendous possible value for businesses. However, developers have still had to overcome many obstacles to harness the power of ML in their organizations.

Keeping the difficulty of deployment in mind many developers are turning to Amazon Web Services (AWS). Some of the challenges to processing include correctly collecting, cleaning, and formatting the available data. Once the dataset is available, it needs to be prepared, which is one of the most significant roadblocks. Post processing, there are many other procedures which need to be followed before the data can be utilized.

Why should developers use the AWS Sagemaker?
Developers need to visualize, transform, and prepare their data, before drawing insights from it. What’s incredible is that even simple models need a lot of power and time to train. From choosing the appropriate algorithm to tuning the parameters to measuring the accuracy of the model, everything requires plenty of resources and time in the long run.

With the use of AWS Sagemaker, data scientists provide easy to build, train and use Machine learning models, which don’t require extensive training knowledge for deployment. Being an end-to-end machine learning service, Amazon’s Sagemaker has enabled users to accelerate their machine learning efforts, thereby allowing them to set up and install production applications efficiently.

Bid farewell to heavy lifting along with guesswork, when it comes to using machine learning techniques. Amazon’s Sagemaker is trained to provide easy to handle pre-built development notebooks, while up-scaling popular machine learning algorithms aimed at handling petabyte-scale datasets. Sagemaker further simplifies the training process, which translates into shorter model tuning time. In the expressions of the AWS experts, the idea behind Sagemaker was to remove complexities, while allowing developers to use the concepts of Machine Learning more extensively and efficiently.

Visualize and Explore Stored Data
Being a fully managed environment, it’s easier for Sagemaker to help developers visualizer and explore stored data. The information can be modified with all of the available popular libraries, frameworks, and interfaces. Sagemaker has been designed to include the ten most commonly used algorithm structures, some of which include K-means clustering, linear regression, principal component analysis and factorization machines. All of these algorithms are designed to run ten times faster than their usual routines, allowing processing to reach more efficient speeds.

Increased Accessibility for Developers
Amazon SageMaker has been geared to make training all the more accessible. Developers can just select the quantity and the type of Amazon EC2 instances, along with the location of their data. Once the data processing process begins within Sagemaker, a distributed compute cluster is set up, along with the training, as the output is setup and directed towards Amazon S3. Amazon SageMaker is prepared to fine-tune models with a hyper-parameter optimization option, which helps adjust different combinations of algorithms, allowing the developers to arrive at the most precise predictions.

Faster One-Click Deployment
As mentioned before, Sagemaker takes care of all launching instances, which are used for setting up HTTPS end-points. This way, the application achieves high throughput with a combination of low latency predictions. At the same time, it auto-scales various Amazon EC2 instances across different availability zones (AZ) to accelerate the processing speeds and results. The main idea is to eliminate the need for heavy lifting within machine learning so that developers don’t have to indulge in elaborate coding and program development.

Conclusion
Amazon’s Sagemaker services are changing the way data is stored, processed, and trained. With a variety of algorithms in place, developers can wet their hands with the various concepts of Machine Learning, allowing themselves to understand what goes on behind the scenes. All this can be achieved without becoming too involved in algorithm preparations and logic creation. An ideal solution for companies looking forward to helping their developers focus on drawing more analytics from tons of data.

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IoT Announcements from AWS re:Invent 2017

IoT announcements
Amidst primitive turmoil in the IoT world, AWS unveiled its various solutions for IoT spreading over a large range of usage. The directionless forces of IoT will now meet the technologically advanced solutions through the hands of AWS which has offered a wide range of solutions in the arena.

AWS IoT Device Management
This product allows the user to securely onboard, organize, monitor, and remotely manage their IoT devices at scale throughout their lifecycle. The advanced features allow configuring, organizing the device inventory, monitoring the fleet of devices, and remotely managing devices deployed across many locations including updating device software over-the-air (OTA). This automatically results in reduction of the cost and effort of managing large IoT device infrastructure. It further lets the customer provision devices in bulk to register device information such as metadata, identity, and policies.

A new search capability has been added for querying against both the device attribute and device state for quickly finding devices in near real-time. Device logging levels for more granular control and remotely updating device software are also added in view of improving the device functionality.

AWS IoT Analytics
A new brain that will assist the IoT world in cleansing, processing, storing and analyzing IoT data at scale, IoT Analytics is also the easiest way to run analytics on IoT data and get insights that help project better resolutions for future acts.

IoT Analytics includes data preparation capabilities for common IoT use cases like predictive maintenance, asset usage patterns, and failure profiling etc. It also captures data from devices connected to AWS IoT Core, and filters, transforms, and enriches it before storing it in a time-series database for analysis.

The service can be set up to collect specific data for particular devices, apply mathematical transforms to process the data, and enrich the data with device-specific metadata such as device type and location before storing the processed data. IoT Analytics is used to run ad hoc queries using the built-in SQL query engine, or perform more complex processing and analytics like statistical inference and time series analysis.

AWS IoT Device Defender
The product is a fully managed service that allows the user to secure fleet of IoT devices on an ongoing basis. It audits your fleet to ensure it adheres to security best practices, detects abnormal device behavior, alerts you to security issues, and recommends mitigation actions for these security issues. AWS IoT Device Defender is currently not generally available.

Amazon FreeRTOS
Amazon FreeRTOS is an IoT operating system for microcontrollers that enables small, low-powered devices to be easily programed, deployed, secured, connected, and maintained. Amazon FreeRTOS provides the FreeRTOS kernel, a popular open source real-time operating system for microcontrollers, and includes various software libraries for security and connectivity. Amazon FreeRTOS enables the user to easily program connected microcontroller-based devices and collect data from them for IoT applications, along with scaling those applications across millions of devices. Amazon FreeRTOS is free of charge, open source, and available to all.

AWS Greengrass
AWS Greengrass Machine Learning (ML) Inference allows to perform ML inference locally on AWS Greengrass devices using models of machine learning. Formerly, building and training ML models and running ML inference was done almost exclusively in the cloud. Training ML models requires massive computing resources to naturally fit in the cloud. With AWS Greengrass ML Inference, AWS Greengrass devices can make smart decisions quickly as data is being generated, even when they are disconnected.

The product aims at simplifying each step of ML deployment. For example, with its help, the user can access a deep learning model built and trained in Amazon SageMaker directly from the AWS Greengrass console and then download it to the concerned device. AWS Greengrass ML Inference includes a prebuilt Apache MXNet framework to install on AWS Greengrass devices.

It also includes prebuilt AWS Lambda templates that is used to create an inference app. The Lambda blueprint shows common tasks such as loading models, importing Apache MXNet, and taking actions based on predictions.

AWS IoT Core
AWS IoT Core is providing new enhanced authentication mechanisms. Using the custom authentication feature, users will be able to utilize bearer token authentication strategies, such as OAuth, to connect to AWS without using a X.509 certificate on their devices. With this, they can reuse their existing authentication mechanism that they have already invested in.

AWS IoT Core also now makes it easier for devices to access other AWS services, such as to upload an image to S3. This feature removes the need for customers to store multiple credentials on their devices.

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