Multi Cloud Environment Research Topics

This Multi Cloud method describes taking help from several cloud providers for an organization to complete a work by splitting them with participants. This research paper will help you learn more about this paper. If you want to gain more knowledge about this method you can go on reading this paper.

  1. Define Multi Cloud Environment

Making use of Multi Cloud Environment, an organization can take services from several cloud providers. This technique can increase the performance, minimize vendor lock-in and enhance redundancy by distributing work to multiple cloud platforms. This can help split the works like networking, computing power and storage with other cloud resources that can complete that certain need in a better way. When working with integrating several cloud services, it has to be managed in a way, which should provide enhanced operation and efficiency, so they need to work with more coordination.

  1. What is Multi Cloud Environment?

This method follows receiving services from various service providers in cloud in order to split their work loads and to spread their apps to different cloud providers. It can also help organization to choose the service depending upon their needs and allows following different pricing techniques. For better functioning and enhanced security of the services, it needs to be managed coordinately.

  1. Where Multi Cloud Environment is used?

In this section we are going to discuss about the uses of Multi Cloud method. This technique is used in order to maximize resiliency and minimize vendor lock-in. it is used by organizations to comply, increase performance and to produce cost-effective solutions. This kind of setup improves innovation, redundancy and uses hybrid architecture to enhance scalability.

  1. Why Multi Cloud Environment is proposed? Previous Technology Issues

Moving on to the next section, here we are going to discuss about the challenges faced by the previous technology in this field. There is no any end-to-end security available without any limitation till date, so the technology used previously for cloud security like federated learning and homographic encryption also have some drawbacks. They are listed here:

Increased Primary Vulnerabilities and Data Poisoning – The legitimacy of any user should be verified with their ID and password. The work which doesn’t consider legitimacy will lead to malicious traffic and vulnerability threats. The work which lack user data analyzing, have to face attacks like data poisoning.

Enlarged Noise Budget – The earlier methods for this technique uses “Homomorphic encryption methodologies”, by not considering operations of the encrypted data which will result in increased noise and cost. In some cases it is used “Fully Homomorphic encryption” along with Machine learning foe reducing the noise but it results in high communication and computation overhead.

Poor FL Performance – The previous works uses “Global model aggregation” to centralize a cloud server which will produce problems or issues at a single point and in earlier stages model poisoning attacks were not much popular among users. FL performance may be reduced because of not carefully verifying and monitoring FL rounds, even when the aggregation is safely done with homographic encryption.

  1. Algorithms / Protocols

After knowing about the technology, uses of it and the issues faced by them in the earlier stage, now we are going to learn about the algorithms used for this technology. The algorithms provided for Multi Cloud Environment to overcome the previous issues faced by it are: “Homomorphic Encryption Responsive Lightweight Residual Network with Energy Valley Optimizer” (HER-LResNet-EVO), “Improved Key Generation Protocol” (IKGP), “Improved Artificial Neural Networks” (IANN), “Lightweight Factorized Pyramidal Networks” (LFPN) and “Trading based Evolutionary Game Theory” (TEGT).

  1. Simulation results / Parameters

The approaches which were proposed to overcome the issues faced by Multi Cloud Environment in the above section are tested using different methodologies to analyze its performance. The comparison is done by using metrics like “Number of Epochs vs. Accuracy”, “Number of HE Operation vs. Noise Budget”, “Number of Communication Rounds vs. Accuracy”, “Attack Rate vs. Privacy threats”, “Number of Cloud-IoT Users vs. Malicious Traffic Rate” and “Size of the Data vs. HE encryption Time”.

  1. Dataset LINKS / Important URL

Here are some of the links provided for you below to gain more knowledge about Multi Cloud Environment which can be useful for you:

  1. Multi Cloud Environment Applications

In this next section we are going to discuss about the applications of Multi Cloud technology. This method is used in many places based on their requirements for increasing their availability and to enhance the performance. The Multi Cloud method protects from vendor lock-in, helpful economically and brings more innovation. With the help of special services and workload distribution, this method can provide disaster recovery, security and scalability.

  1. Topology

Here you are going to learn about the different choices of topologies which can be used in Multi Cloud method. This technique has connections with SMAs, TAS, SA, Cloud-IoT users, MDCS and Edge Servers. The major study in this area includes novel authentication, encryption used and data exchange based on MLFL.

  1. Environment

The environment in which the operation of Multi Cloud method is functioning includes Contract Agents, Cloud-IoT users, Decentralized Servers of Cloud, Authenticated Servers and Edge Servers.

  1. Simulation Tools

Here we provide some simulation software for WBAN, which is established with the usage of tool Network simulator version 3.26 or above to improve its performance.

  1. Results

After going through this paper you might now clearly understand about this technique with the help of various sections provided in this paper like its definition, used, applications, algorithms used in it, issues face by this method previously and many more.

Multi Cloud Environment Research Ideas

  1. A Research on Various Security Aware Mechanisms in Multi-Cloud Environment for Improving Data Security
  2. Multi-Objective Optimization Task Scheduling Method Based on Dynamic Programming for Multi-Cloud Environment
  3. Trust Management Frameworks in Multi-Cloud Environment: A Review
  4. Multi-Objective Workflow Scheduling to Server less Architecture in a Multi-Cloud Environment
  5. Analysis and Evaluation of Bio-Inspired Algorithmic Framework, Potential Application in Cloud/Multi-Cloud Environment
  6. Cost-Effective and Latency-Minimized Data Placement Strategy for Spatial Crowdsourcing in Multi-Cloud Environment
  7. Turbo Powered Symmetric and Asymmetric Traffic Encryption Methods in Multi-Cloud Environment
  8. A Scheduling Method for Tasks and Services in IIoT Multi-Cloud Environments
  9. Service Level Agreement Violation Detection in Multi-cloud Environment using Ethereum Blockchain
  10. CoMCLOUD: Virtual Machine Coalition for Multi-Tier Applications Over Multi-Cloud Environments
  11. A QoS Based Resource Allocation and Optimization in a Multi-Layer Cloud Computing Environment
  12. A Resource Estimation Method in Multi-Cloud Environment with a Model Based on a Repairable-Item Inventory System
  13. Computationally Efficient Neural Rendering for Generator Adversarial Networks Using a Multi-GPU Cluster in a Cloud Environment
  14. Indexing legacy data-sets for global access and processing in multi-cloud environments
  15. Handling security issues by using Homomorphic encryption in multi-cloud environment
  16. GDPR compliance verification through a user-centric blockchain approach in multi-cloud environment
  17. Meteorological data layout and task scheduling in a multi-cloud environment
  18. A Solution for A Disaster Recovery Service System in Multi-cloud Environment
  19. Blockchain-based Disaster Recovery Data Storage and Security Auditing Solution in Multi-cloud Environment
  20. Towards an approach for cloud service composition in Multi-Cloud environment based QoS using deep Q-learning
  21. Semantic-Based Cognitive Service Discovery in Multi-Cloud Environments
  22. Secure Deduplication with Dynamic Updates in Multi-Tenant Cloud Environment
  23. An Efficient Multi-Keyword Search Scheme over Encrypted Data in Multi-Cloud Environment
  24. A Genetic Programming-Based Hyper-Heuristic Approach for Multi-Objective Dynamic Workflow Scheduling in Cloud Environment
  25. Cost-Effective Web Application Replication and Deployment in Multi-Cloud Environment
  26. Towards a Federated Learning Framework on a Multi-Cloud Environment
  27. Machine Learning vs. Deep Learning for Anomaly Detection and Categorization in Multi-cloud Environments
  28. 5G architecture for hybrid and multi-cloud environments
  29. A Multi-Cloud and Zero-Trust based Approach for Secure and Redundant Data Storage
  30. Adaptive Data Placement in Multi-Cloud Storage: A Non-Stationary Combinatorial Bandit Approach
  31. Multi-Key Clustering Method for Cloud Environments’ Privacy-Preserving
  32. A Secure and Efficient Blockchain-based Multi-Cloud Medical File Sharing
  33. A multi-cloud service mesh approach applied to Internet of Things
  34. Single-cloud over Multi-Cloud Model for Latency- and Capacity-Aware VNFs Provisioning
  35. Data Leakage Free ABAC Policy Construction in Multi-Cloud Collaboration
  36. Label-affinity-Scheduler: Considering Business Requirements in Container Scheduling for Multi-Cloud and Multi-Tenant Environments
  37. Reliability-Aware Multi-Objective Memetic Algorithm for Workflow Scheduling Problem in Multi-Cloud System
  38. Analyzing Security and Privacy issues for Multi-Cloud Service Providers Using Nessus
  39. Managing Multi-Cloud Deployments on Kubernetes with Istio, Prometheus and Grafana
  40. SLA Framework and Management under Multi-Cloud Infrastructure
  41. A Blockchain-Based Multi-Cloud Storage Data Auditing Scheme to Locate Faults
  42. Multi-Cloud based Task Scheduling using Many Objective Intelligent Technique in IoT
  43. Network Automation Python-based Application: The performance of a Multi-Layer Cloud Based Solution
  44. Multi-cloud service provision based on decision tree and two-layer Restricted Monte Carlo Tree Search
  45. Secure transmission of medical images in multi-cloud e-healthcare applications using data hiding scheme
  46. An efficient and secure identity-based integrity auditing scheme for sensitive data with anti-replacement attack on multi-cloud storage
  47. RDIMM: Revocable and dynamic identity-based multi-copy data auditing for multi-cloud storage
  48. Scientific workflow scheduling in multi-cloud computing using a hybrid multi-objective optimization algorithm
  49. Corrigendum to “Region aware dynamic task scheduling and resource virtualization for load balancing in IoT–fog multi-cloud environment” [Future Gener. Comput Syst. 137C (2022) 70–86]
  50. Energy-aware service composition in multi-Cloud