# Centralized vs. Decentralized Cloud Computing in Healthcare
## 6 Decentralized Cloud Computing
The framework empowers patients to manage their health data by keeping the mPHR on their personal devices, enabling them to control data sharing with healthcare providers. This ensures better privacy and security through secure communication protocols and techniques such as encryption and authentication. The distributed nature of the framework reduces implementation and maintenance costs. It can be deployed cost-effectively by leveraging smartphones and using standardized interfaces to minimize integration costs for healthcare providers. Yet, the architecture relies on the capabilities of smartphones, which may be constrained by restrictions in storage, computing power, and battery capacity, particularly for managing large volumes of medical data, which will affect the performance of the system. It also relies on Near-Field Communication (NFC) to facilitate data transmission between end-user interfaces and peripheral hardware. However, implementing NFC data exchange can be challenging as it requires the patient's physical presence. If data are missing, the patient must return to the provider to collect it. In contrast, ref. [67] presented MedShare, a hybrid cloud architecture developed to share medical resources among healthcare providers who operate autonomously and may be disconnected. This architecture allows for quicker data retrieval and exchange among healthcare providers, improving system response time, enhancing computational efficiency through localized data processing, and eliminating centralized information relay requirements. The private clouds of healthcare providers securely hold patient data, while only indexes and pointers are saved on the public cloud. Data sharing requires a two-way authorization process and patient consent to address privacy concerns. The MedShare system uses existing legacy EHR systems and data repositories of healthcare providers without the need for a complete infrastructure overhaul. Its hybrid cloud architecture efficiently utilizes resources, with private clouds managing data retention and computational operations of sensitive patient medical records and the public cloud handling indexing and patient information exchange services. The modular and scalable design of the MedShare system allows healthcare providers to join the network without significant upfront investments, making it a cost-effective solution. Nevertheless, performance analysis revealed computational constraints, with data retrieval processes demonstrating less-than-ideal response metrics and elevated failure rates. This was attributed to the transfer of medical information between private hospital clouds. Meanwhile, ref. [68] introduced the retrieval and storage-based indexing framework (RSIF), designed for distributed cloud computing environments, to improve healthcare data access and concurrency. The RSIF uses a replication-free and continuous indexing approach to decrease the duration needed for simultaneously accessing and retrieving data. Utilizing deep learning techniques to classify data restrictions for augmentation and updates helps approximate the optimal indexing and ordering, further reducing retrieval time. To assess the efficacy of the RSIF, the study authors performed a comparative investigation of the method of retrieval using four distinct metrics: ICNNDLA, IPO-PAS, BAS-LS, and RSIF. The mean retrieval time and cost factor were evaluated for each metric, and an analysis of the data yielded evidence suggesting that the RSIF metric performed better than the other metrics, exhibiting the lowest average retrieval time of 0.066 s and a cost factor of 0.153. The simulation analysis conducted also showed that the framework proposed was reliable. Yet, the paper does not explicitly discuss patient data privacy mechanisms in the RSIF model. However, continuous indexing can slow down other processes, especially for large datasets like patient data, and can consume significant computing resources such as CPU, memory, and I/O. In contrast, ref.