Machine Learning’s Impact on Cloud Computing

Machine learnings impact on cloudcomputing

People’s overall increasing dependency on AI and IoT (IoT Announcements from AWS re:Invent 2017) has given new goals to the cloud computing infrastructure holders. The premises enfolding within this newly emerging subfield of Information and Technology are indeed very vast in which all the domains ranging from minimal smartphones to high end robots are coming together to share the common resource. Firms are developing machineries that require the least dependency on human resource. The attempts are rather to give the man-made machinery autonomy to a great extent over the resources it needs to utilize in order to become fully functional. Clearly, the intention of eliminating the human intervention is also on run.

In order to gain that autonomy over the soft resources, the inventors will have to depend on a mediator which will assist the “smart machines” gain their functional ability, and, cloud computing seems to be the only and the last resort. The “study material” for the machines will be store retrieved and used by them through cloud means. As the cloud computing is already taking over major domains of human efforts such as data storage, this technological advancement will result in unprecedented impacts on global economy, business and world in general.

The integrated cloud services would be even more beneficial than the current cloud services. The current usage of cloud involves computing, storage and networking but the intelligent cloud will multiply the capabilities of the cloud by rendering a capability of learning from the vast amount of data stored in the cloud, to build up predictions and analyze situations. This will result in a smart advancement in the IT field where tasks are performed much efficiently.

Cognitive computing

The large amounts of data stored in the cloud serves as a source of information for the machines to gain their functional state. The millions of functions happening daily on cloud computing will all provide a source of information for the machine to learn from. The whole process will equip the machine applications with sensory capabilities. The applications will be able to perform cognitive functions and make decisions best suited for them for the desired goal.

Even though the intelligent cloud is in its infantile age, the premises are predicted to be largely increasing in the coming years and revolutionize the world in the same way the internet did. The expected are that would utilize the cognitive computing will spread over the systemsof healthcare and hospitality, business and even personal lives.

Changing Artificial Intelligence infrastructure

with the help of intelligent cloud, AI as a platform service makes the process of intelligent automation easier for users by taking control of the complexities of the process. This will further increase the capabilities of cloud computing, in return increasing demand for the cloud. The intelligent will define the future. The interdependency of cloud computing and artificial intelligence will somewhat become the essence soaring realities.

New dimensions for IoT

AS we are aware how IoT has overtaken our lives and created an undeniable dependency over gadgets, cloud that will assist the machine leaning of these gadgets are increasing rapidly too. The smart sensors that take cars into cruise control will apprehend their source of data from the cloud only. In short, cloud computing will become the long term memory for IoT from where they can retrieve the data for solving in-time problems. The massive web of interconnectivity of various machines will generate and operate on a massive amount of data saved in that very cloud. This will expand the horizons of cloud computing and it will become as needful to machines as water is for humans, in coming years.

Personal assistance with minute ease

As we have already seen the likes of Jarvis, Siri, Cortana and Google doing great in the market, it is not absurd to think of a personal assistant existing in every metropolitan home by the next decade. These assistants make life easier for individuals through pre-coded voice recognition that also gives a feel of human touch to machines. The responses are generally very common and operate on a limited set of fed information. However, these assistants are very likely to be developed in a finer way so that their capabilities will not remain limited. Through increasing use of autonomous cognition, personal assistance will attain a state of reliability where it can replace human interaction. The role or cloud computing will be supremely vital in this regard, as it will become the heart and the brain of these machines.

Business intelligence

During extensive application, the task of the Intelligent cloud will be to make the tech world even smarter—autonomous learning coupled with the capabilities of figuring and rectifying real time anomalies. In the same way, the business Intelligence will also become smarter wherein along with identifying and rectifying faults, it will be able to predict future strategies in advance. Armed with proactive analytics and real-time dashboards, the businesses will operate upon predictive analytics that processes previously collected data, makes real time suggestions or even future predictions. These predictions from current trends and suggestions for actions would make things easier on leaders.

Revolutionizing the world

Fields like banking, education, hospital, and general services etc. would be able to make use of the intelligent cloud enhancing the precision and efficient of services they provide. Consider for example having an assistant in hospital which reduces the doctor’s customary load of decision making by analyzing the case, making comparisons and prompting new approaches to the treatment.

With the rapid development of both machine learning and the cloud, it seems in the future, cloud computing will become much easier to handle, scale and protect with machine learning. Along with that, the wider the business initiatives harping on the cloud will need machine learning to be implemented, to make it more efficient, to the point that we will have no cloud service that operates as it operates today.

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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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Agile & DevOps Conference 2018

Agile & DevOps Conference
Date : 29 Jan, 2018
Location : Dallas-TX, United States
Venue: Homewood Suites by Hilton

Event Details
The conference targets to feature presentation and discussion sessions by recognized thought-leaders addressing the actual developments and trends in Agile & DevOps highlighting implementation challenges and their solutions. The conference presentations by expert speakers will make it easier to understand how Agile & DevOps can successfully bring cross-functional business units together for delivering business results speedily in the Agile environment.

Why Attend
A full day event for professionals to meet their industry peers, exchange knowledge and take away ideas for making best use of Agile & DevOps practice. Based on the conference theme ‘Let’s switch it on’, this conference provides an opportunity to learn from industry experts the concept of Agile & DevOps and how to implement it in your organizations. Get to know critical challenges faced during implementation, and their solutions. This is a great platform to meet top solution providers and industry players in this domain.

[Know more about the Conference]

About Idexcel: Idexcel is a global business that supports Commercial & Public Sector organizations as they Modernize their Information Technology using DevOps methodology and Cloud infrastructure. Idexcel provides Professional Services for the AWS Cloud that includes Program Management, Cloud Strategy, Training, Applications Development, Managed Service, Integration, Migration, DevOps, AWS Optimization and Analytics. As we help our customers modernize their IT, our clients should expect a positive return on their investment in Idexcel, increased IT agility, reduced risk on development projects and improved organizational efficiency.

Allolankandy Anand Sr. Director Technical Sales & Delivery will be attending this event. For further queries, please write to anand@idexcel.com

Everything you need to Know about Serverless Microservices in AWS

Everything you need to Know about Serverless Microservices in AWS
It’s a well-known fact that handling multiple servers can be a painful experience, especially in the short run. Multiple servers mean multiple developers will need to work on the same code, making the code repository difficult to handle in the long run. One of the biggest disadvantages in the long run is the resiliency, which causes the whole back end to get bogged down, making the website crash and slow down eventually.

What are AWS Microservices?
The microservices architecture has been designed to solve all forms of front end and back end issues. The back end is wired to communicate with various small services through a network of HTTP or other messaging systems. Since the setup is rather elaborate, the whole procedure is time consuming and can take considerable time to setup. Post the setup formalities, a developer can benefit immensely by optimizing work through work parallelization and improved resiliency. Each developer can access and develop their own microservice, without worrying about code conflicts.

What does going Serverless mean?
The concept of going serverless is relatively new and has seen the day of light just recently. In an ideal situation, the traditional back end was deployed on a group of servers. Such an approach had its own set of advantages. It allowed the developers to control their own servers along with the infrastructure behind it. However, like everything else, it contributed a lot towards the cost, making it an inefficient solution for companies. Add a set of engineers to build, maintain and run the infrastructure, and your budget will increase manifold.

With the introduction of the serverless technology, all these problems can be solved considerably. You can make use of a service which will run your code, as well as take care of all your maintenance issues. What you do end up paying for is the time it usually takes to process each request thrown at the code. For this purpose, AWS offers the AWS Lambda service, which is somewhat similar to the functionality of Microsoft’s Azure Function and Google’s Cloud Functions.

What Services aid the Serverless Microservices?
Amazon API Gateway: API is a gateway service that offers the option to use a configurable REST API in the form of a service. You get to author your needs and create it in the form of a code. Say, for example, you decided what would happen if a particular HTTP Method is implemented and called on a certain HTTP Resource. In this case, say you want to execute and implement a Lambda function if the HTTP request comes through. API Gateway helps in mapping input and output data through a series of formats. Thankfully API Gateway is a fully fledged service, which is managed extensively, allowing you to pay for only what you use.

AWS Lambda Services: Being a pay as you go service, AWS Lambda is a well-managed service hub. It allows you to get rid of over provisioning costs, as well as avoid the need of any boot time, patching, as well as load balancing.

Amazon DynamoDB: Amazon DynamoDB is a document store wherein you can look up values through their key values multiple Availability Zones or data centers to bring about a subtle consistency. Like Lambda, it too is a 99% managed service, while the remaining 1% is free to perform reading and writing of code.

The Request Flow and how it Works with Microservices
In an ideal situation, it’s imperative to understand how the data flows through serverless microservices. The user’s HTTP hits the API Gateway; the API Gateway checks the HTTP request and figures if the request is valid or not. Through this approach, it makes multiple requests within the database and executes the business logic.

Another system which aids the processing of information within the serverless environment is the AWS CloudWatch. The AWS CloudWatch stores metrics in the form of numbers and text information in the form of logs. It also allows you to define your alarms over your metrics. At any given point of time, if your system begins to default, you can get an instant notification of the default using AWS SNS, making the process seamless and streamlined.

Summary
AWS Microservices are well balanced and fully managed, thereby allowing you to concentrate on performing multiple forms of other operational tasks. Through the concentration on other important tasks, the functionality of the code can be improved manifold, as it is performed through a series of automated tasks.

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Advantages of Cloud Analytics over On-Premise Analytics

Advantages of Cloud Analytics over On-Premise Analytics
Majority of the organizations now agree that data science is a great tool to scale-up, build and streamline their businesses. But, with this huge amount of data they are collecting, are the organizations really coping up to analyze and implement the decisions in time? Most of them, in-spite of having on-premise analytics teams are in disconnection with their operations part.

Having the in-house analytics teams linked to your Enterprise Resource Planning(ERP) systems can be sometimes be irresponsive due to data loads, might cause your sales teams to lose the real-time data, also can cause delay in response to the queries. Collection of data from various internal applications, devices, online media networks, consumer data and converting them into actionable insights can be a cost consuming (both time and capital costs) process for the organizations.

Is there any better way of utilizing your Company’s data towards reaping benefits?
Yes, most of your valuable data from modes of communication to collecting track-able data of consumer behavior lies in the cloud. Cloud computing allows you to easily consolidate information from all your communication channels and resources, and helps you to do it in a wider scale.

Cloud, basically helps the business’ data teams to re-establish the connection with their operations. And hence the business will be able to minimize the time and capital costs incurred, from the research and development of the product, marketing and sales to increasing the efficiency of your consumer support teams.

How does Cloud Analytics serve as a better and real-time mode of efficient data management?

Agile Computing Resources
Instead of handling speed and delivery time related hassles from your on-premise servers, cloud computing resources are high-powered and can deliver your queries and reports in no-time.

Ad hoc Deployment of Resources for Better Performance
If you are having an in-house analytics team, you should be concerned about an efficient warehouse, latency of your data over poor public internet, being up-to date with advanced tools and experience in handling the high demands for real-time BI or emergency queries. Employing Cloud services in data science and analytics can help your business scale-up by establishing a direct connection between them, reducing the latency and response issues to less than a millisecond.

Match, Consolidate and Clean Data Effortlessly
Real time Cloud analytics with real-time access to your online data keeps your data up-to date and organized, helping your Operations and Analytics teams function under the same roof. This makes sure of no mismatches and delays, helping you to also predict and implement finer decisions.

Accessibility
Cloud services are capable in sharing data and visualization and performing cross-organizational analysis, making the raw data more accessible and perceivable by a broader user base.

High Returns on Time Investments
Cloud services provide readily-available data models, uploads, application servers, advanced tools and analytics. You need not spend any time in building up a separate infrastructure, unlike employing on-premise analytics teams.

Your marketing teams can forecast and segment your campaign plans, the campaign reports and leads generated are readily available to your sales teams to follow-up, insights from sales and marketing and more real time consumer data can help your strategy teams in predicting crucial decisions or your support teams being immediately notified with consumer queries. Better the collaboration, higher are your returns, and an ideal cloud service can make this possible.

Flexible and Faster Adoption
Cloud-based applications are built with self-learning models and have a consumer friendly user experience unlike the on-premise applications. Cloud technologies learn to adopt as your business grows and can expand or adjust as your data storage and applications needs increase or decrease.

Affordability
There are no upgrade costs or issues, and enabling new tools or applications require minimal IT maintenance. This keeps the business in a continuous flow without any interventions like the need for upgrading the on-premise infrastructure, and having to redo your integrations and other time consuming efforts.

Security
Robustly built, Cloud analytics are reportedly more reliable than on-premise systems in times of a data breach. Detecting a breach or a security issue can be within hours or minutes with Cloud security whereas with an in-house team, it takes weeks or even months in detecting a breach. Your data is more trusted and secure with cloud computing.

Implementing cloud services in data science can be the best and most-effective infrastructure you can give to your business. They are agile, secure and flexible and help you to streamline each of your business process as Cloud services enable all your teams function under the same data foundation.

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Why is Big Data Analytics Technology so Important

Big Data Analytics Technology

Yes! Big Data Analytics, as well as Artificial Intelligence, has truly shown its importance in today’s business activities. Corporations & Business sectors are coming up with their procedures to data analytics as the aggressive landscape modifications. Records analytics is slowly becoming entrenched in the enterprise. Today, it’s a well-known behavior and desired practice for companies to apply analytics to optimize something, whether or not it’s operational performance, false detection or purchaser reaction time.

To this point, usage has been pretty easy. Maximum agencies are still doing descriptive analytics (historic reporting) and their use of analytics is characteristic-unique. But in upcoming years more business areas will follow the leaders and boom their levels of class, the use of predictive and prescriptive analytics to optimize their operations. Moreover, extra groups will begin coupling feature-specific analytics to get new intuition & observation into client journeys, risk profiles, and marketplace opportunities.

The “leading” companies were also much more likely to have some sort of cross-purposeful analytics in vicinity enabled via a common framework that enables collaboration and statistics sharing. These pass-practical views allow agencies to recognize the effect of cross-useful dynamics consisting of supply chain effects.

Predictive and Prescriptive Analytics

Whilst descriptive analytics continues to be the maximum popular shape of analytics today, it is no longer the satisfactory manner to advantage a competitive side. Businesses that want to move beyond “doing business through the rear-view mirror” are the use of predictive and prescriptive analytics to decide what is going to possibly arise. Prescriptive analytics has the delivered advantage of recommending movement, which has been the number one gripe approximately descriptive and predictive analytics. The forward-searching abilities enabled through predictive and prescriptive analytics allow groups to plan for possible outcomes, excellent and bad.

Armed with the in all likelihood styles predictive and prescriptive analytics screen; agencies can identify fraud faster or intrude sooner when it seems that a consumer is set to churn. The mixed foresight and timelier action help corporations force extra sales, reduce risks, and improve consumer delight.

Artificial Intelligence (AI)

Artificial intelligence (AI) and gadget learning culture take analytics to new ranges, figuring out previously undiscovered patterns which can have profound outcomes on a commercial enterprise, consisting of identifying new product opportunities or hidden dangers.

Machine intelligence is already constructed into predictive and prescriptive analytics equipment, dashing insights and enabling the analysis of well-sized probabilities to determine the greatest route of movement or the first-rate set of alternatives. Over the years, extra state-of-the-art forms of AI will find their way into analytics systems, similarly enhancing the rate and accuracy of selection-making.

Governance and Security

Groups are supplementing their information with third-celebration records to optimize their operations, comprehensive of adapting useful resource degrees primarily based at the expected level of consumption. They are also sharing statistics with users and companions who necessitate robust governance and a focal point of safety to reduce information misuse and abuse. However, protection is turning into an increasing number of the complex as more ecosystems of records, analytics, and algorithms interact with every other.

Given latest excessive-profile breach instances, it has emerged as clean that governance and safety have to be applied to information at some point in its lifecycle to reduce facts-associated risks.

Developing Statistics

Facts volumes are developing exponentially as agencies connect to statistics outside their internal structures and weave IoT devices into their product lines and operations. Because the records volumes continue to grow, many groups are adopting a hybrid records warehouse/cloud strategy out of necessity. The businesses maximum in all likelihood to have all their records on-premises keep it there due to the fact they’re involved in security.

Groups incorporating not gadgets into their enterprise strategies are both adding an informational element to the bodily products they produce or including sensor-based total information to their existing corpus of statistics. Depending on what is being monitored and the use case, it could be that every piece of information does no longer have value and no longer each issue calls for human intervention. While one or each of those things are authentic, aspect analytics can help identify and remedy as a minimum some common issues routinely, routing the exceptions to human decision-makers.

How Artificial Intelligence is Transforming Cloud Computing

1.	How Artificial Intelligence is transforming Cloud Computing
Everyone in touch with technology is aware of cloud computing. It has already turned out to be an important part of the current digital era. It has transformed the manner in which individuals, professionals and even companies store their essential information and data.

The market for cloud computing has tremendously made progress over the past few years which in turn has strongly affected the lifestyle and work culture in various ways. But the cloud is a new technology, due to which companies are worried about whether it will evolve over time or not. Recent trends such as the use of mobile phones instead of computers have even made petty changes in the cloud technology. Therefore, AI has come up to enhance the cloud technology (AI is the capability of a robot controlled by a computer or a digital computer that carry out tasks that are usually linked with the intelligent beings). Cloud computing and AI are bringing major changes in the corporate world and their fusion is believed and known to be the coming future of technology.

The cloud technology can help AI’s by providing the required information for the learning processes while the AI can help cloud by providing information that can offer more data. AI is capable of streamlining the immense capacities of the cloud. It equips cloud technology with enormous powers. It enables the machines to act, react, think and learn in the manner human beings do. AI assists different machines in learning and analyzing the historical data, making decisions and identifying the patterns. Such a process helps in eradicating the chances of human errors. Therefore, AI enhances the process of decision making of various organizations.

Cloud technology is spread among a number of servers in various languages with huge data storage and across various geographies. Organizations can make use of this data to make up intelligent and automated solutions for customers and clients. Cloud computing is getting more powerful with AI as its applications are extended across multiple diversified sectors in the economy. Thus, even organizations can make use of AI cloud computing to attain long-term goals for their businesses.

Another crucial aspect of the fusion of AI cloud computing is the process of machine learning. Such a process helps in making reliable and quick decisions, reducing the chances of cyber crimes and improving the experiences of the customers. In the recent years, machine learning has been able to quickly employ complicated mathematical calculations to large amounts of data. It is even capable of delivering more accurate and quick results on a huge scale which drives new business opportunities and growth strategies for organizations around the world.

The fusion of AI cloud computing has brought about a huge change in information technology and several other industries. It can potentially change the manner in which the data was stored earlier and processed among several geographies. Such an amalgamation also offers unique opportunities for the professionals of AI and cloud to look over the boundless possibilities for future.

Cloud, when stood alone, has the capability of becoming a significant computing commodity in several fields. But the AI cloud computing integration will enhance its requirement in the market. With huge strides existing in the growth of both cloud and artificial intelligence, their future seems to be highly tied together. Cloud computing gets much easy to protect, scale and handle with artificial intelligence. Above that, the more the businesses are getting on the cloud, more it needs to be integrated with AI to attain efficiency. A point will come when no cloud technology would be existing without artificial intelligence.

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Microservices: Building an Effective Business Model with AWS Architecture

Microservices: Building an Effective Business Model with AWS Architecture

One buzz-word that has been spreading across the IT industry for the last few years is ‘Microservices’. However, these are not completely new approach to the IT infrastructure, but a combination of best proven methods of concepts such as nimble software development, service related architecture and API-first design (building the API first and developing the web application on top of that).

Microservices can be simply defined as ‘a self-contained process fulfilling a unique business capability’.

Following are some characteristics of a microservice architecture:

– Redistributed data management: Microservices don’t rely on a single schema in their central database. They have different views for various data models and are unique in the ways they are developed, deployed and managed.

– Functional independence: Modules in the microservice architecture can act independently without affecting the functionality of other components. They can be changed or upgraded without affecting other microservice modules.

– Simplicity: Each component is built on a set of capabilities fulfilling a specific function. Depending on the level of complexity, it can be split up into two or more independent components.

– Flexible and heterogeneous approach: Microservice gives the teams a freedom to choose the best tools and methods for their specific problems, be it programming languages, operating systems or data stores.

– Black box design: Microservice components potentially hide the details of their complexity from other components. The internal communication between the components happen with very well defined APIs to prevent implicit data dependencies.

– DevOps: This means, when you build it, you operate it. This helps the developers to be in close contact with their consumers, precisely understanding their needs and expectations.

Benefits and challenges of Microservices:

When addressing the agility and scalability issues of traditional monolithic architecture deployments, microservices benefit consumers in various ways such as:

Microservices create a sophisticated working environment where small and independent teams take the ownership of a particular service. Hence, empowering them to work quickly and independently shortening the cycle times.

Having a Devops culture by merging the development and operational skills removes the hassles and contradictions, providing an agile deploying environment. Making it easy to test and implement new ideas faster, henceforth creating a low cost of failure.

Dividing a software into small and well defined modules can be maintained, reused and composed easily, giving out a great output in-terms of quality and reliability.

Each service can be developed and implemented with their best-suitable programming languages and frameworks, and can be finely tuned in-line with aptly performing service configurations.

Failure isolation is made easier with microservices as techniques such as health-checking, caching or circuit breakers allow you to reduce the blast radius of a failing component.

Despite all these advantages we have discussed above, there are some disadvantages of these microservice approaches as diverse systems invite more complexity.

Determining the right boundaries for a microservice architecture is crucial when you migrate from a traditional monolithic architecture.

Versioning for a microservice architecture can be challenging.

Developing an effective team structure, transforming the organization to follow a devops approach and streamlining an effective communication between them can be challenging.

The more the number of microservice modules, the more is its complexity in interactions.

In a microservice approach, we no longer run a single service, but a combination from dozens to even hundreds of services. This increases operational complexity to a greater level.

AWS, one of the most-preferred cloud service platforms has number of offerings those address the challenges of a microservice architecture.

Effective Scaling and Provisioning of resources:

AWS microservice architecture employ on-demand resources that are readily available and provisioned when needed. Multiple environments can co-exist correspondingly, so that you need not employ difficult forecasting methods to guess the storage capacity of the microservices.

You only pay for what you use:

You can potentially experiment the new features or services, and roll them out if they aren’t successful for your business goals in AWS microservice architecture. This helps you find the innovation best suiting your business goals and also fulfills a microservice’s goal of achieving high agility.

Versatile programmability:

AWS microservices come with a specific API, Command Line Interface (CLI) and SDKs for different programming languages. Even complete architectures can be cloned, scaled and monitored through custom codes and programming languages. And, in-case of any failure, they are capable in healing themselves automatically.

AWS microservices provide you with a flexible environment to programmatically build custom tools and deploy the suitable resources, thereby reducing the operational costs and efforts.

Infrastructure as a Code:

AWS microservice architecture lets you to describe the whole infrastructure as a code and allows you to manage it in a version-control environment. You can redeploy any specific version of an infrastructure at any time, and compare the quality and performance to any application version to ensure they are in sync.

Reduce operational complexity with Continuous deployment and delivery:

Managing multiple application cycles in parallel can lead to operational complexity. AWS microservices offer automation of the provisioning and deployment process, enabling the adoption of continuous integration. This ‘continuous integration’ of the development part of the life-cycle can be further extended to the operations part of the life-cycle.

Managed services with AWS microservice architecture:

One of the key benefits of cloud infrastructures is it relieves you of the hassles of provisioning virtual servers, installing and configuring the softwares, and dealing with scaling and reliable backups. Monitoring, scaling and security are already built into the AWS microservices, helping you to further reduce the operational complexity of running microservice based architectures.

Service-oriented and Polyglot approach:

Each AWS microservice focuses on solving a specific and well-defined problem by communicating with other services using clearly defined APIs. This approach breaks down the complex infrastructure into simpler bricks or modules, preventing the need of duplication of the processes.

With microservices definitely helping to break down the complex business process into simpler modules, AWS cloud microservices further reduces the operational and interactional complexity of the microservices, helping you to define and use the most ‘appropriate’ solution for your specific business problem.

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Top Roles of Cloud Computing in IoT

Top Roles of Cloud Computing in IoT
Transformation is an ever going trend, which is becoming an absolute need of the hour in today’s fast paced world. With technology churning every bit of information in a refined new format, there is a lot of scope when it comes to data storage and manipulation.

As smart phones and social media begin to rule the roost, there is a lot of conversation happening around what’s coming next. The evident answer of the hour is, the Internet of Things or IoT. With the Internet churning out huge chunks of data every second, there is a pending strain on the data infrastructure, making it necessary to look for solutions to ease the use of data storage.

Since the rise of the Cloud, there is a massive shift towards using it as a means of storage for people and businesses alike. Given the scalability and the data dynamics, there is a lot of stress being given on the use of Cloud computing to make data available remotely.

Putting this scalability to use, the Cloud has proved to be an efficient tool for transferring data through the traditional Internet channels as well as through a dedicated direct link. The traditional method is not preferred extensively; however, at the same time, many businesses prefer to the use the direct link to transfer data to the Cloud, given the quality of the data and the security it ensures during the transfer phase.

This is not all; the Cloud has become an integral part of the Internet world. Simply put, the cloud can be termed as an enabler when it comes to IoT. The Cloud is undoubtedly an ideal solution to meet all data driven needs of businesses. As this technology is developing, it is providing an agile platform for developers to create meaningful apps to establish better data devices over the Internet.

How Cloud Computing Aids IoT?

The underlying idea behind IoT and the Cloud computing is increase efficiency in the day to day tasks, without disturbing the quality of the data being stored or transferred. Since the relationship is mutual, both the services complement each other effectively. The IoT becomes the source of the data, while the Cloud becomes the ultimate destination for it to be stored.

As we progress through the years, we will see a lot of changes happening; some of these changes will be gradual while others will be more rapid. Companies likes Amazon AWS, Google and Microsoft will become the undisputed leaders of Cloud IoT Services, making the challenge even more worthwhile.

As the Cloud gathers more attention and speed slowly, there are a multitude of Cloud service providers which are beginning to offer pay per use models to businesses. This way, businesses only need to pay for what the computer resources they use.

Some more Reasons which Highlight the Importance of the Cloud in the World of IoT are:

Reduced cost of ownership: Inflation is a never ending menace which every business has to face sooner or later. The Cloud technology provides ample resources to businesses so that they do not have to spend through the nose on setting up their infrastructure. In the absence of on-site systems, hardware and software, the IT department is more focused on their day to day up keeping activities, which are often an evident benefit with the Cloud.

Business continuity programs: The Cloud computing is capable of running businesses even in the midst of sudden disasters. Since the data is maintained on additional separate servers, there is no imminent danger to the private data, making the Cloud an indispensable part of Internet based firms.

How will the IoT and the Cloud Expand?

Startups: As more and more Cloud vendors pop up, startups will continue to evolve and become more efficient, making the technology flow stronger yet smoother. The transition from one source to another will become a cinch, making the Cloud a strong place to function.

Developing countries: The strongest and biggest source of revenue for the Cloud comes from the developing countries, as they are trying to play catch up with the times. However, this revenue will drastically dip, once these countries are able to adopt their technology to the Cloud, marking the adaptation as complete.

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