Information Processing in the Cloud : Resource Allocation and Security Perspective
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- PhD theses (TN-IDE) 
Original versionInformation Processing in the Cloud : Resource Allocation and Security Perspective by Jayachandra Surbiryala, Stavanger : University of Stavanger, 2019 (PhD thesis UiS, no. 487)
Cloud computing has been adopted at faster rates due to its advantages, that are brought to the customers over the Internet. Usage of various services over the Internet is quite high. Users are storing large amounts of data in the Cloud environment, because of On-demand self-service, resource pooling, transparency, pay per-use, and access over the Internet. As the usage of services over the Internet are increasing, the data generated from various applications and customers is also increasing. Growth in the data creation also leads to the rapid adoption of cloud services. This adoption can either be for storage of the information, processing of the data, or analysis of the information to get actionable intelligence. Information has become the central part of many organizations and the analysis of such information is required for decision making. Due to existence of Big Data with different volume, velocity, variety, veracity, and value, customers need dynamic solutions to meet their requirements. Large-scale data management and analysis can be easily accomplished dynamically in the Cloud environment without worrying about underlying infrastructure. As cloud usage is increasing, customers are using more than one cloud service provider depending on their needs and requirements. They have the liberty to compare and choose from the various options from multiple cloud service providers available to store, process, and analyze their data in the cloud environment. As the customer's data is being distributed across multiple cloud service providers, they will not have complete control over their data that is stored and processed on remote servers, where they only have limited access to the actual underlying infrastructure. Data security plays an important role in the Cloud computing environment. Therefore, to protect the customer's data, which gets distributed across the cloud, we need to have proper security mechanisms to protect user data in the cloud environment. This thesis addresses the problem for resource allocation with cost-effective solution for customers to choose the cloud services from different cloud service providers using nash bargaining principles for distributed resource allocation. Distributed resource allocation helps the customers to reduce the cost of using the cloud services, for the same amount of resources, across various cloud service providers. Further, we have identified the problems associated with usage of the Cloud for the data distribution across the various cloud service providers such as data recovery, security and privacy aspects for customers. We have proposed several methods to protect the users' information in the Cloud while the customers are still using the Cloud services and we introduced some approaches to protect the information in the Cloud after deletion of their data.
Has partsPaper 1: Agrawal, B., Surbiryala, J., Rong, C. (2017) Resource Allocation in Cloud-Based Distributed Cameras. 2017 IEEE International Congress on Big Data (BigData Congress), Honolulu, HI, pp. 153-160, doi: 10.1109/BigDataCongress.2017.29.
Paper 2: Surbiryala, J., Rong, C. (2018) Data Recovery and Security in Cloud. 9th International Conference Information, Intelligence, Systems and Applications (IISA), Zakynthos, Greece. DOI: 10.1109/IISA.2018.8633640
Paper 3: Surbiryala, J., Rong, C. (2018) Secure Customer Data over Cloud Forensic Reconstruction. 2018 IEEE International Conference on Consumer Electronics (ICCE). Las Vegas, NV, USA. DOI: 10.1109/ICCE.2018.8326324
Paper 4: Agrawal, B., Surbiryala, J., Rong, C. (2018) Improve Security over Multiple Cloud Service Providers for Resource Allocation. 1st International Conference on Data Intelligence and Security (ICDIS). South Padre Island, TX, USA. DOI: 10.1109/ICDIS.2018.00031
Paper 5: Surbiryala, J., Chung, R. (2020) Method to Solve a Privacy and Security Issue in Cloud for Energy Informatics, In: J.P. Liyanage, J. Amadi-Echendu, J. Mathew (Eds.) Engineering Assets and Public Infrastructures in the Age of Digitalization: Proceedings of the 13th World Congress on Engineering Asset Management. This paper is not in Brage due to copyright.
Paper 6: Surbiryala, J., Li, C. & Rong, C. (2017). A framework for improving security in cloud computing. pp. 260-264. IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). Chengdu, China. DOI: 10.1109/ICCCBDA.2017.7951921.