What are the most promising research areas in cloud computing?

Promising research areas in cloud computing include security, edge computing, serverless architectures, AI integration, energy efficiency, quantum computing, distributed systems, blockchain, performance optimization, and hybrid/multi-cloud management. These areas focus on enhancing data protection, optimizing resource utilization, improving reliability, and exploring emerging technologies to meet evolving cloud computing needs.

Edge computing is one of the most promising areas of cloud computing research. By extending cloud capabilities to the network's edge, this approach moves computing closer to data sources. Reducing latency and improving real-time processing for autonomous systems and Internet of Things (IoT) applications is the goal. Researchers are finding that investigating the possibilities of edge computing is still a tempting path forward, as we see an increasing demand for decentralized computing and a window into the future of responsive and distributed computing systems.

In cloud research, serverless computing shows promise as an efficient solution. By abstracting the infrastructure layer, this method frees developers from the burden of managing servers, so they can concentrate entirely on writing code. It has the potential for reduced costs, scalability, and easier implementation. Research in this field focuses on performance optimization, serverless architecture refinement, and extending the range of applications that can make use of this resource-efficient methodology. Scholars are enthralled by the development of serverless computing since it offers novel approaches to simplify and expand cloud services.

Decoupling the application components, allowing asynchronous event-based calls, and having the ability to handle millions of requests while scaling down when not required represent a significantly advanced approach to software management. Swift Development: Developers can deploy functions or services independently, facilitating faster development cycles and promoting agility. Automated Scaling: Serverless platforms manage scaling based on demand, automatically adapting resources to match the workload Decreased Administrative Burden: Tasks such as patching, updates, and server maintenance are handled by the serverless platform, liberating resources for development. Event-Driven Architecture: seamless integration with the event driven calls.

In cloud computing research, cloud-native development is a dominant field that emphasizes the creation and implementation of applications tailored for cloud settings. This method promotes scalability and agility by utilizing orchestration, containerization, and microservices technologies. Scholars investigate novel approaches to develop, implement, and oversee cloud-native apps, with a focus on streamlining these native procedures. To fully utilize cloud computing, cloud-native development is a study area that is essential given the increasing need for flexible and adaptable systems.

Safety for clouds is critical to the field of cloud computing research. Ensuring strong safety precautions becomes crucial as firms move important activities and sensitive data to the cloud. Researchers work on improving access controls, creating proactive threat detection systems, and strengthening encryption methods. Security frameworks must be continuously explored to protect cloud infrastructures due to the always-changing threat landscape. Research on cloud security is still essential to protecting the internet from new online dangers.

Talking about the elephant in the room, security becomes the most important aspect of having applications in the cloud. Let me give you an example: we wanted to set up a honeypot to check how quickly a hacker could obtain our tokens. We created an account and exposed its access token on GitHub without any restrictions. Surprisingly, it took about one minute for them to start creating large-sized instances, and shortly after, we received an AWS account suspension email. It's just a matter of time before everything we've worked on for years could be compromised. Start adopting best practices, use AWS-provided services, employ scanning services to ensure tokens are not exposed, and implement anomaly checks.

Speech recognition, Language Translation and chatbots are some of the features we take for granted in every app right now but these features are compute intensive thus integrating them as Cloud Services provides a big boost and reduces dev efforts by a huge margin. Most recent success story of GenAI was all possible due to Azure providing it as an easy to integrate service and very quickly every other application was GenAI enabled. Cloud has proven to be more than just an Infra as a Service provider and Cloud AI is a good example of how Cloud Computing has acted as a catalyst in the growth of Software Industry.

The fields of artificial intelligence and cloud computing are merging at the forefront of cloud AI research. Transformative possibilities arise from the merging of AI capabilities with cloud services. To better utilize cloud resources for AI model training and inference, researchers optimize AI algorithms for cloud deployment. The convergence of cloud computing with AI is a promising area for future research, with potential advancements in scalability, efficiency, and democratization of AI technologies as these technologies become more and more integrated into a wide range of applications, from healthcare to finance.

AI is having a big impact on businesses and the decisions that they are now making. What is currently being missed is the ethical and moral implications of some of the decisions that will inevitably have to be made.

In the AI racing world, AI by cloud providers is invaluable for its seamless integration with software, efficient management capabilities, and enterprise-grade security and privacy measures. Additionally, access to industry-leading foundation models, the ability to develop generative AI-powered applications, and customization options further enhance the appeal of cloud-based AI solutions for diverse industries and use cases.

It is a very concerning subject which we usually choose to ignore. For example m5.large EC2 instance on-demand price is approximately $0.096 per hour. Assuming the average power usage of this instance is around 100 watts per hour (this is just a hypothetical figure for illustration purposes), and the data center has an average Power Usage Effectiveness (PUE) of 1.2, the energy consumption would be 120 watt-hours per hour,If the instance runs continuously for a month (720 hours), the energy consumption would be 86.4 kilowatt-hours (kWh).Now, to estimate carbon emissions, Let's assume a simplified average carbon intensity of 0.4 kg CO2 per kWh. So the hypothetical carbon footprint for a month would be approximately 34.56 kg of CO2.

What we really need to talk about is how cloud accessibility is a game-changer for small organizations and startups, providing them with easy access to robust computing resources. pay-as-you-go model, allowing small organizations to avoid hefty upfront costs. Small organizations can easily scale their operations up or down as demand fluctuates, Cloud services facilitate rapid deployment of applications and services. Small organizations can focus on development without worrying about the complexities of infrastructure and go live global in a very less time makes the big difference.