A patients life can be saved if it is possible to make quicker decisions based on faster processing of real-time health care data, such as ECG processing. To achieve faster decision making, contemporary health care applications use cloud computing for such data. When cloud computing is used, data transmission deferrals may cause delays in the decision-making process. To overcome this, Fog Computing is used. Fog Computing saves energy, bandwidth and prevents transmission latencies but, lacks in computing power as compared to Cloud Computing. To enhance the computing power of the Fog node, a Cluster of Raspberry Pi having heterogeneous configurations can be used. In Health Care applications the Fog Computing performance can be assessed by measuring the time elapsed between the generation of the health care data and decision-making. In this paper, ECG signal analysis is taken as a processing job in Fog Computing. Dispy is used to facilitate the scalability and parallel data processing on a Cluster of Raspberry Pi used for Fog Computing, to enable faster decision making. Further, the performance of the Raspberry Pi cluster-systems using dispy are analyzed and optimized step by step based on different parameters. The first parameter is data transmission time which is improvised by minimizing network overheads. Other optimization parameters like CPU usage, number of cores, response time and available memory space, these parameters are considered and varied, to assess the performance of Heterogeneous Raspberry Pi cluster. Based on the results obtained, a novel optimization approach ?OptiFog? is proposed to achieve faster computation in worst-case scenarios by varying and assigning jobs to the nodes to measure performance parameters in Distributed Fog Computing. Based on the obtained results ?OptiFog? assures best possible improvement in the performance of the Distributed Fog Computing environment.
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