Performance Analysis of Microservices Behavior in Cloud vs Containerized Domain based on CPU Utilization

Enterprise application development is rapidly moving towards a microservices-based approach. Microservices development makes application deployment more reliable and responsive based on their architecture and the way of deployment. Still, the performance of microservices is different in all environments based on resources provided by the respective cloud and services provided in the backend such as auto-scaling, load balancer, and multiple monitoring parameters. So, it is strenuous to identify Scaling and monitoring of microservice-based applications are quick as compared to monolithic applications [1]. In this paper, we deployed microservice applications in cloud and containerized environments to analyze their CPU utilization over multiple network input requests. Monolithic applications are tightly coupled while microservices applications are loosely coupled which help the API gateway to easily interact with each service module. With reference to monitoring parameters, CPU utilization is 23 percent in cloud environment. Additionally, we deployed the equivalent microservice in a containerized environment with extended resources to minimize CPU utilization to 17 percent. Furthermore, we have shown the performance of the application with “Network IN” and “Network Out” requests

A Rare Nasopharyngeal Foreign Body

Nasopharynx is an exceptionally rare anatomical location for foreign body impaction. We present a rare case of nasopharyngeal foreign body (NFB) in a 7 years old child. The diagnosis was confirmed by nasal endoscopy. Immediate removal of foreign body (FB) in the nasopharynx was performed under general anesthesia. This rare situation is potentially dangerous, since its dislodgment may cause fatal airway obstruction. Therefore, in all cases with missing foreign bodies in the aerodigestive system, nasopharyngeal impaction should be kept in mind and endoscopic examination of the region should be considere

Primary Non-Hodgkin's Malignant Lymphoma of the Sinonasal Tract

Primary non-Hodgkin’s lymphomas (NHL) of the sinonasal tract are rather uncommon entities. Morphologically and radiographically, sinonasal lymphomas are difficult to distinguish from other malignant neoplasms or non- neoplastic processes. They have a variable presentation from fulminant destructive manifestations to chronic indolent type of disease and may mimic as carcinomas and invasive fungal infection respectively. We report a case of primary NHL involving sinonasal tract in elderly female, which was clinically and radiologically mimicking as sinonasal malignany and was proven as NHL on histological examination and confirmed by immunohistochemistry. A high index of suspicion, appropriate histopathological examination and immunohistochemistry is necessary to differentiate sinonasal lymphomas from other possibilities. Failure to do so may miss the diagnosis and delay appropriate treatmen

Mutual Information in Frequency and its Application to Measure Cross-Frequency Coupling in Epilepsy

We define a metric, mutual information in frequency (MI-in-frequency), to detect and quantify the statistical dependence between different frequency components in the data, referred to as cross-frequency coupling and apply it to electrophysiological recordings from the brain to infer cross-frequency coupling. The current metrics used to quantify the cross-frequency coupling in neuroscience cannot detect if two frequency components in non-Gaussian brain recordings are statistically independent or not. Our MI-in-frequency metric, based on Shannon's mutual information between the Cramer's representation of stochastic processes, overcomes this shortcoming and can detect statistical dependence in frequency between non-Gaussian signals. We then describe two data-driven estimators of MI-in-frequency: one based on kernel density estimation and the other based on the nearest neighbor algorithm and validate their performance on simulated data. We then use MI-in-frequency to estimate mutual information between two data streams that are dependent across time, without making any parametric model assumptions. Finally, we use the MI-in- frequency metric to investigate the cross-frequency coupling in seizure onset zone from electrocorticographic recordings during seizures. The inferred cross-frequency coupling characteristics are essential to optimize the spatial and spectral parameters of electrical stimulation based treatments of epilepsy.Comment: This paper is accepted for publication in IEEE Transactions on Signal Processing and contains 15 pages, 9 figures and 1 tabl