Latest Trends in Cloud Computing

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Cloud computing has had a significant impact on the development of technology in various sectors. The use of cloud-based technologies, such as virtualization and distributed computing, has allowed for the evolution of many existing and new technologies. The implementation of cloud computing has enabled the creation of more efficient and effective solutions in a wide range of industries.

Cloud computing is a technology that involves the use of clients, data centers, and distributed servers to deliver flexible, cost-effective computing resources as services. It can be implemented in various geographical locations and offers a pay-per-use model. Cloud computing works in conjunction with other technologies to provide convenient and effective access to users.

Both individuals and corporations can benefit from cloud computing. On an individual level, cloud technology has had a significant impact on our daily lives. Many of the apps we use on a regular basis, such as social media, streaming services, and online banking, are hosted on the cloud and accessed over the internet, rather than being installed on our personal devices. For businesses, cloud computing offers a range of advantages, such as increased flexibility, scalability, and cost-efficiency.

In this blog, we will discuss some of the latest trends in cloud computing, including Kubernetes, the use of artificial intelligence in cloud computing, hybrid and multi-cloud strategies, and edge computing. These trends are shaping the future of the technology and will continue to evolve as the industry grows and develops.

Kubernetes

In mid-2014, Google announced Kubernetes, also known as K8s. Since then, the popularity of Kubernetes has grown among developers worldwide. It has become a widely used tool for managing and deploying applications in a scalable and reliable manner.

“Kubernetes is a portable, extensible, open source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation.”

Kubernetes is an open-source orchestration platform that was developed by Google. It is used to manage and deploy containerized applications in a variety of environments, including physical, virtual, and cloud-based systems. Kubernetes enables users to create and manage containers, which can help with the organization and efficient deployment of applications.

Let’s look at two topics before moving forward:

1. Immutable infrastructure is a method of deploying and managing servers in which any changes or modifications are not allowed on the existing servers. Instead, new servers are built from a base image that includes all necessary changes and upgrades. This allows for easy replacement of old servers without the need for additional configuration or modification. The key principle of immutable infrastructure is that servers are never modified once they are deployed, making it a more predictable and reliable approach to managing servers.
2. Containers are standalone units of software that provide a standardized way to package an application, including its code, runtime, system tools, libraries, and configuration files. Containers allow applications to run consistently across different environments, without the need for additional dependencies or configuration. Because containers are lightweight and only include the necessary components, they can be quickly and easily deployed without wasting compute resources on unnecessary background processes. As a result, containers have become a popular tool for managing and deploying applications in a consistent and efficient manner.

“If your app fits in a container, Kubernetes will deploy it”

Kubernetes Architecture:

A Kubernetes cluster consists of a master node, which is responsible for managing the overall operation of the cluster, and worker nodes, which are responsible for running containerized applications. The master node provides an API for interacting with the cluster, and it is used to schedule deployments and manage the worker nodes. The worker nodes, in turn, run container runtime environments such as Docker, and they communicate with the master node using an agent. Together, the master and worker nodes form a distributed system that can be used to efficiently manage and deploy applications at scale.

1. Etcd is a key-value store that is used by Kubernetes to store data about the cluster.
2. The Scheduler is a component of Kubernetes that monitors new Pods and assigns them to nodes for execution based on available resources, policies, and other specifications.
3. The Controller Manager is a daemon that runs in a continuous loop and is responsible for managing the state of the cluster. It collects and sends data to the API server, and it works to ensure that the current state of the cluster matches the desired state.
4. The Container Runtime is the platform, such as Docker, that is used to run containers.
5. The Kubelet Service ensures that the necessary containers are running in a Pod.
6. The Kubernetes Proxy Service is a proxy that runs on each node and helps to make services accessible to external hosts. It is responsible for distributing requests to Pods and providing load balancing. It also helps to ensure that the network environment is consistent and open, while also being secure. It controls access to secrets, volumes, nodes, and performs health checks on new containers, among other tasks.

Artificial Intelligence in Cloud

AI in the cloud refers to the use of artificial intelligence technologies and services that are delivered over the internet.

These services can be accessed and used by individuals and organizations across a network, allowing them to incorporate advanced AI capabilities into their systems and processes. AI in the cloud typically includes a range of tools and technologies, such as machine learning algorithms and natural language processing, that can be used to analyze and interpret data, make predictions, and automate decision-making. By using AI in the cloud, organizations can gain access to powerful tools and technologies that can help them improve their operations and make more informed decisions.

Understanding MKLs

One illustration of MKLs is giving in above figure where each step can be described as follows:

1. Monitoring an environment involves using sensors, measurement tools, and data collection methods to gather data over a period of time. The data is then stored in a data gathering and storage facility, such as a sensor data interface (SDI). For example, sensors and cameras placed on highways in a smart city can be used to collect data about the environment and traffic conditions.
2. In an AI context, data analysis often involves using AI engines to find solutions to specific problems or use cases. These AI engines can perform various functions, such as data processing, pattern recognition, and decision making. For example, an AI engine might be used to analyze data from sensors on a highway to identify traffic patterns and suggest ways to improve traffic flow.

• Data preparation is the process of cleaning and organizing data to make it suitable for analysis. This can include filtering out irrelevant or noisy data, normalizing the data to make it consistent, and de-normalizing the data to make it more understandable.
• There are many different types of knowledge generation engines, including classification, segmentation, association, regression, anomaly detection, and prediction. These engines use different algorithms and techniques to analyze data and generate insights.
• Decision support or decision-making engines use these insights to produce the desired solution or to optimize a particular outcome. Reinforcement learning is a type of AI that involves training an AI model to make decisions based on rewards and punishments

3. Based on the results of Step 2, planning and policy can be implemented by adjusting relevant parameters, such as traffic light patterns in a smart city or power allocation in a 5G network. Scheduling actions can also be based on the results of this step, allowing for efficient and effective implementation of the plan

4. The final step in this process is the execution of the planned action, which can be carried out by a collection of nodes in the target domain. For example, a group of 5G users may adjust their transmit power according to the plan, or a group of robots may change their states. This phase can continue until the learning process has converged, or it can be performed offline to evaluate the outputs of the AI process.

So far, we have discussed the general process of how AI on the cloud works. Now, let’s explore why and how it can be beneficial for businesses. As we have seen, the flow of AI on the cloud allows for human-like qualities such as reasoning, thinking, and learning, which can be useful for data analysis. Many companies have found success using AI on the cloud, and it is commonly used as a marketing tactic by service operators.

Benefits:

1. Transparency

2. Usability

3. Scalability

Types of AI as a Service Platform:

1. Bots

2. APIs

3. Machine Learning

4. Data Labeling

5. Data Classification

Hybrid Cloud and Multi-Cloud Infrastructure

A hybrid cloud is made up of several separate environments. Nowadays, nearly no one relies on the public cloud, leading to the widespread use of hybrid cloud technologies. Combining a public cloud like Google Cloud with a private cloud like on-premises data centers is the most typical hybrid cloud example.

The blending and integrating of various public clouds is referred to as “multi-cloud.” One public cloud may be used by a company as a database, another as PaaS, another for user authentication, and so forth. A multi-cloud deployment that includes a private cloud or an on-premises data center is referred to as a hybrid cloud. A hybrid cloud architecture combines two or more different types of clouds, whereas multi-cloud combines many types of clouds.

Therefore, integrating these two ways may give the impression that the service is being compromised between approaches, however hybrid services are about leveraging their strengths rather than compromising between approaches.

While sensitive material that has to be updated and accessed sometimes should be moved to private servers with access being monitored, data that needs frequent user access should be kept on public servers. A hybrid strategy that is well-integrated and well-balanced offers businesses the best of both worlds.

Image courtesy: https://www.spiceworks.com/tech/cloud/articles/multi-cloud-vs-hybrid-cloud/

Beneficial Areas for Hybrid and Multi-Cloud:

1. A hybrid cloud blends public and private clouds. Businesses in regions where industry standards govern data security will profit from a hybrid cloud adoption. For instance, a business may store sensitive information in a private cloud and less-sensitive information in a public cloud. This method can be used by businesses or departments that handle data-intensive operations, such those in the media and entertainment industries. Using high-speed on-premises technology, they may have quick access to large media files and store data that isn’t needed all the time, including backups and archives.
2. A method called “multi-cloud” joins two or more public clouds. Companies that want to avoid vendor lock-in or establish data redundancy in the case of a fail-over should use this technique. The backup cloud provider can maintain operations during a disruption.

How to Choose a Cloud ?

1. Low Latency requirements: A hybrid cloud solution may be the best option.
2. Geographical Consideration: The company’s multi-cloud approach may be useful given that it has offices in several different countries with different residency laws.
3. Regulatory issues: As was discussed previously, use a hybrid cloud for regulatory data.
4. Cost management: Pay close attention to price tiers at first, and ask the supplier about its resources, tools, and reports for keeping costs in check.

Edge Computing

A sort of data processing known as the “edge” maintains a limited amount of data on local networks or storage while the majority of the data is distributed among decentralized servers. Edge computing is a new paradigm in which the resources of an edge server are located at the edge of the Internet, close to end users, mobile devices, sensors, and the expanding Internet of Things. With edge computing, requests are promptly fulfilled without the need to transport data to data centers for processing. Avoiding the retention of data in a single location and instead dispersing it across a number of locations is the basic goal of cloud and edge computing. The primary distinction is that edge computing continues to use local discs to some extent while cloud computing prefers to store data on distant data centers. Local devices can distribute data offline with minimal bandwidth use.

Image courtesy: https://www.orientsoftware.com/blog/edge-computing-vs-cloud-computing/

Benefits of Using Edge Computing:

1. Decreased Latency

Cloud computing solutions are frequently too slow to handle frequent demands from AI and machine learning applications. Cloud storage will not offer quick and seamless performance if the workload includes real-time forecasting, analytics, and data processing.

The center must be notified of the existence of the data, and only with permission from the center may it be used. Contrarily, edge computing reduces the amount of data that needs to be saved remotely by processing data locally.

2. Performance in distributed events

Performing in a scattered environment The edge network of a network connects every point on the network, from one edge to the next. It is a tried-and-true technique for moving data without the use of data centers directly from one remote storage to another. The local network’s opposite ends can be reached by the data far more quickly than they could with a cloud solution.

3. Limited and Unstable Network

Edge computing enables local processors to process data stored locally. This is helpful in the transportation industry because, for instance, trains that communicate via the Internet of Things don’t always have a reliable connection when in motion. When they are not connected, they can access data from local networks and, once the connection is established, synchronize operations with data centers.

The middle ground between a fully decentralized strategy, in which nothing is saved locally, and conventional offline data storage, in which data does not leave the local network, is edge computing.

Data that needs to be retrieved promptly regardless of the speed of the Internet connection can be accessed on the network’s edges, while sensitive data can be stored away.

4. Keeps sensitive data in local storage

Some companies decide not to give third-party data storage providers access to sensitive customer information. Therefore, rather than the company itself, the security of information depends on the dependability of providers. Edge processing offers a compromise between conventional centralized and decentralized storage if you don’t have access to a trustworthy cloud storage provider.

Companies who don’t trust outside providers with their confidential information may send sensitive information to the network’s edge. Businesses now have total control over accessibility and security.

References:

1. An Overview on Edge Computing Research | IEEE Journals & Magazine | IEEE Xplore
2. Kubernetes Documentation | Kubernetes
3. What Are Containers in Cloud Computing? — Intel
4. Artificial Intelligence as a Service (AI-aaS) on Software-Defined Infrastructure | IEEE Conference Publication | IEEE Xplore
5. Multi-cloud vs. hybrid cloud: What’s the difference? | Cloudflare
6. Hybrid Cloud vs. Multi-Cloud (vmware.com)
7. Docker Swarm and Kubernetes in Cloud Computing Environment | IEEE Conference Publication | IEEE Xplore

Published by : Shrawani Shinde, Shivang Singh