A comprehensive analysis of omics data can require vast computational resources and access to varied data sources that must be integrated into complex, multi-step analysis pipelines. Execution of many such analyses can be accelerated by applying the cloud computing paradigm, which provides scalable resources for storing data of different types and parallelizing data analysis computations. Moreover, these resources can be reused for different multi-omics analysis scenarios. Traditionally, developers are required to manage a cloud platform’s underlying infrastructure, configuration, maintenance and capacity planning. The serverless computing paradigm simplifies these operations by automatically allocating and maintaining both servers and virtual machines, as required for analysis tasks. This paradigm offers highly parallel execution and high scalability without manual management of the underlying infrastructure, freeing developers to focus on operational logic. This paper reviews serverless solutions in bioinformatics and evaluates their usage in omics data analysis and integration. We start by reviewing the application of the cloud computing model to a multi-omics data analysis and exposing some shortcomings of the early approaches. We then introduce the serverless computing paradigm and show its applicability for performing an integrative analysis of multiple omics data sources in the context of the COVID-19 pandemic.

In the context of scaling a business-critical medical service that involves electronic medical record storage deployed in Kubernetes clusters, this research addresses the need to optimize the configuration parameters of horizontal pod autoscalers for maintaining the required performance and system load constraints. The maximum entropy principle was used for calculating a load profile to satisfy workload constraints. By observing the fluctuations in the existing workload and applying a kernel estimator to smooth its trends, we propose a methodology for calculating the threshold parameter of a maximum number of pods managed by individual autoscalers. The results obtained indicate significant computing resource savings compared to autoscalers operating without predefined constraints. The proposed optimization method enables significant savings in computational resource utilization during peak loads in systems managed by Kubernetes. For the investigated case study, applying the calculated vector of maximum pod count parameter values for individual autoscalers resulted in about a 15% reduction in the number of instantiated nodes. The findings of this study provide valuable insights for efficiently scaling services while meeting performance demands, thus minimizing resource consumption when deploying to computing clouds. The results enhance our comprehension of resource optimization strategies within cloud-based microservice architectures, transcending the confines of specific domains or geographical locations.

Algorithms of queue management in IP routers determine which packet should be deleted when necessary. The article investigates the influence of the self-similarity on the optimal packet rejection probability function in a special case of NLRED queues. This paper describes another approach to the non-linear packet dropping function. We propose to use the solutions based on the polynomials with degree equals to 3. The process of obtaining the optimal dropping packets function has been presented. Our researches were carried out using the Discrete Event Simulator OMNET++. The AQM model was early verified using the discrete-time Markov chain. The obtained results show that the traffic characteristic has the great impact on the network node behavior, but self-similarity of network traffic has no influence on the choosing of the optimal dropping packet function.

Analysis of eye movement has attracted a lot of attention recently in terms of exploring areas of people’s interest, cognitive ability, and skills. The basis for eye movement usage in these applications is the detection of its main components—namely, fixations and saccades, which facilitate understanding of the spatiotemporal processing of a visual scene. In the presented research, a novel approach for the detection of eye movement events is proposed, based on the concept of approximate entropy. By using the multiresolution time-domain scheme, a structure entitled the Multilevel Entropy Map was developed for this purpose. The dataset was collected during an experiment utilizing the “jumping point” paradigm. Eye positions were registered with a 1000 Hz sampling rate. For event detection, the knn classifier was applied. The best classification efficiency in recognizing the saccadic period ranged from 83% to 94%, depending on the sample size used. These promising outcomes suggest that the proposed solution may be used as a potential method for describing eye movement dynamics.