Cloud computing is a system that allows data to be saved in the cloud on a virtual worker. Outsiders and virtual machines in the cloud worker supplier played a critical part in efficiently storing and accessing information. Security, access control, and load balancing are critical challenges in cloud engineering. In the past, various solutions for adjusting cloud load have been proposed. Operator-based burden adjustment calculation surpassed all other offered CPU use, cost, and idle time strategies. The productivity of the specialist-based load adjustment computation decreased when any of the client hubs changed regions. Experimental outcomes show that Modified-HBB-LB performs better than the existing load balancing strategies such as HBB-LB, DLB, FCFS, WRR, HDLB, and FIFO by achieving the load balance of the complete system. The Modified-HBB-LB technique reduces the number of migrations tasks (30%, 25% and 20%) as compared to HDLB, DLB, and HBB-LB. The proposed Modified-HBB-LB technique maintains the 3-5% higher performance levels on makespan, completion, and response time as compared to existing comparative techniques.

Information about healthcare is derived from healthcare data. Healthcare data sharing helps make healthcare systems more efficient as well as improving healthcare quality. Patients should own and control healthcare information, one of their most valuable assets, instead of letting data be spread out among health care providers differ. This protects data from being shared between healthcare systems and privacy. Public ledger accompanied by a decentralized network of peer's compromises patient has been demonstrated to be able to achieve trusted, auditable computing by blockchain. The use of access control and cryptographic primitives are insufficient in addressing modern cyber threats all privacy and security concerns associated with a cloud-based environment. In this paper, the authors proposed a lightweight blockchain technique based on privacy and security for healthcare data for the cloud system. The cost-effectiveness of our system's smart contracts is evaluated, as well as the procedures used for data processing in order to encrypt and pseudonymize patient data.

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