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As a result of the proliferation of digital and network technologies in all facets of modern society, including the healthcare systems, the widespread adoption of Electronic Healthcare Records (EHRs) has become the norm. At the same time, Blockchain has been widely accepted as a potent solution for addressing security issues in any untrusted, distributed, decentralized application and has thus seen a slew of works on Blockchain-enabled EHRs. However, most such prototypes ignore the performance aspects of proposed designs. In this paper, a prototype for a Blockchain-based EHR has been presented that employs smart contracts with Hyperledger Fabric 2.0, which also provides a unified performance analysis with Hyperledger Caliper 0.4.2. The additional contribution of this paper lies in the use of a multi-hosted testbed for the performance analysis in addition to far more realistic Gossip-based traffic scenario analysis with Tcpdump tools. Moreover, the prototype is tested for performance with superior transaction ordering schemes such as Kafka and RAFT, unlike other literature that mostly uses SOLO for the purpose, which accounts for superior fault tolerance. All of these additional unique features make the performance evaluation presented herein much more realistic and hence adds hugely to the credibility of the results obtained. The proposed framework within the multi-host instances continues to behave more successfully with high throughput, low latency, and low utilization of resources for opening, querying, and transferring transactions into a healthcare Blockchain network. The results obtained in various rounds of evaluation demonstrate the superiority of the proposed framework.

Decentralized clustering of modern information technology is widely adopted in various fields these years. One of the main reason is the features of high availability and the failure-tolerance which can prevent the entire system form broking down by a failure of a single point. Recently, toolkits such as Akka are used by the public commonly to easily build such kind of cluster. However, clusters of such kind that use Gossip as their membership managing protocol and use link failure detecting mechanism to detect link failures cannot deal with the scenario that a node stochastically drops packets and corrupts the member status of the cluster. In this paper, we formulate the problem to be evaluating the link quality and finding a max clique (NP-Complete) in the connectivity graph. We then proposed an algorithm that consists of two models driven by data from application layer to respectively solving these two problems. Through simulations with statistical data and a real-world product, we demonstrate that our algorithm has a good performance., Competing Interests: The authors declare no conflict of interest.