Machine Learning Techniques and Analytics for Cloud Security. Группа авторов

in the industry. Special use of AI makes Nutanix very attractive for building secure hybrid cloud.

      The amalgamation of AI into cloud has bring an evolution as AI was still complex, expensive, and high-end technology which was out of the reach of the general masses [25]. But now services of AI can be utilized by general masses without actually knowing the background technology. Several mobile apps and IoT work in this regard in an effective way. Human like interfaces, self-service, and customer-oriented application are made possible through the use of hybrid cloud and AI.

      Use of AI made hybrid cloud more intelligence by playing a key part in cost analysis, real-time decision-making, policy optimization, and workload distribution leaving the IT experts to work on complex things rather than doing trivial tasks. AI-as-a-service is heading toward next level. Cloud giants AWS, Microsoft, IBM, and Google all are doing investments on this to make their service better as this is a competitive world. Whoever will provide better service in less cost will be more popular.

Quantifi [28] is one such solution. They are using AI/ML to perform risk analysis, data analytics, portfolio management, and trading predictions. Some major banking sectors, asset management companies, pension funds, and financial institutions are their customer. By the use of AI, big data, Lamda architecture, and in-memory computing, Quantifi is one of the front benchers in risk assessment. Cross platform which supports Windows, Linux, MacOS, and Adaptation of ELT layer provides various external data sources to communicate bi-directionally at ease; rich API helps to provide tools and functionalities to clients so that they can extend their business without knowing much of the technical details; real-time business analysis and predictions are just one click away. Quantifi uses Microsoft Azure for its cross platform application development.

      IBM Watson [29, 30] is another pioneer in this regard. It a tool where can give their instructions using natural language. This AI-powered search engine can answer complex business queries on demand. The natural language understanding capability makes it more interesting as otherwise for getting high-end business insight may be lines of code has to be written. Watson is also used with other AI-based tools for providing a platform which provides better customer services. This allows client to run all Watson products, IBM’s own AI products on IBM cloud or any cloud from other vendors. It can club private cloud services also.

      Lots of other tools are also available which use AI and hybrid cloud for better customer services.

      1.8 Future Research Direction

      The adaptation of hybrid cloud still is in nurture state. It is not exhaust in nature. High possibility of amalgamation of different technologies makes it useful and difficult to handle also in some cases. Here is a list of future research directions:

       Standardization is a vital part of cloud architecture. No uniform standard is there for setting up the hybrid cloud architecture. Research can be done in this direction.

       Orchestration of hybrid cloud often requires huge power consumption and high-end networking. Use of green energy sources can be kept as an option.

       Security is always a concert. Intrusion detection and encryption techniques should be able to handle advanced threats. The pattern of attacks is changing every day so to cope up with that security measures also need to be updated also. Thus, it a subject of research all the time.

       Use of AI and data analytics can do wonders. Many researches are already going on in this direction but still scopes are there.

       Reliability still is a major issue in hybrid cloud computing, some portion of the network may remain unavailable for some period of time. How failover plans can be made stronger so that it can handle these issues in transparent way is a matter of consideration.

       SLA is a portion where more research should be done. Miscommunication, misinterpretation, and lack of information often cause problem. In multi-cloud system, it is more prominent.

      1.9 Conclusion

      Hybrid cloud offer several benefits but complex large and distributed nature makes it hard to handle. It keeps business sensitive data in private data centers for their protection and use public cloud for additional services. But still, management and monitoring of all in bound data is not always possible. As a result, security issues always happen. IT security is always challenging, and in case of hybrid platform it seems more complicated. Use of AI can do miracles in cloud computing. Adaptation of AI and hybrid cloud is new trend in the industry. So, by taking reasonable thoughts and with judiciously designed automation system in middleware hybrid cloud can offer exciting features in cost-effective way. Leading organizations are moving forward the adaptation of hybrid cloud and AI as it can provide scalable, cost-effective, flexible, user friendly, and secure solution.

      References

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      8. Hybrid cloud: https://azure.microsoft.com/en-in/overview/what-is-hybrid-cloud-computing/, Accessed on 9th Oct, 2020.

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      10. Hybrid Cloud Architecture: https://www.vxchnge.com/blog/building-hybrid-cloud-architecture, Accessed on 10^th October, 2020.

      11. Solution brief: What Is hybrid cloud. A crash course on combining private and public cloud Infrastructure, Intel, https://cdw-prod.adobecqms.net/content/dam/cdw/on-domain-cdw/brands/intel/intel-hybrid-cloud-brief.pdf, 2016.

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