The serverless pay-as-you-go model in the cloud enables payment of services during execution and resources used at the smallest, most granular level, as was the initial idea when setting the foundations and concepts of the pay-as-you-go model in the cloud. The disadvantage of this method of payment during execution and the resources used is that it is subject to financial damage if we have an attack on serverless services. This paper defines notions for three types of attacks that can cause financial damage to the serverless pay-as-you-go model and are experimentally validated. The first attack is Blast DDoW - Distributed Denial of Wallet, the second attack is Continual Inconspicuous DDoW, and the third one is Background Chained DDoW. We discussed financial damages and the consequences of each type of attack.

We analyze a heart monitoring center for patients wearing electrocardiogram sensors outside hospitals. This prevents serious heart damages and increases life expectancy and health-care efficiency. In this paper, we address a problem to provide a scalable infrastructure for the real-time processing scenario for at least 10,000 patients simultaneously, and efficient fast processing architecture for the postponed scenario when patients upload data after realized measurements. CardioHPC is a project to realize a simulation of these two scenarios using digital signal processing algorithms and artificial intelligence-based detection and classification software for automated reporting and alerting. We elaborate the challenges we met in experimenting with different serverless implementations: 1) container-based on Google Cloud Run, and 2) Function-as-a-Service (FaaS) on AWS Lambda. Experimental results present the effect of overhead in the request and transfer time, and speedup achieved by analyzing the response time and throughput on both container-based and FaaS implementations as serverless workflows.

In this article, we explore a novel architecture for distributing health monitoring computations over distributed cloud regions, both for constantly online patients and offline for several hours daily. We propose a conceptual architecture for a use-case example capable of processing thousands of simultaneous incoming streams with electrocardiogram signals.

In this paper, we conduct experiments to deploy a scalable serverless computing solution for real-time monitoring of thousands of patients with streaming electrocardiograms as an example of embarrassingly parallel tasks originally executed on two virtual machines. The research question is to find the speedup of such solution versus classical virtual machine approaches with sequential or parallel threads.The challenge of migrating an existing service to a serverless solution is to adapt and reconfigure the code for serverless platform, to write the code to invoke the service in parallel and asynchronously, and to use other services in the cloud that are needed for the whole solution to be functional and scalable. Evaluation of developing various solutions matching migration challenges to Google Cloud Run, Google Cloud Compute Engine, and Google Cloud Storage (customization of code, the configuration of services) shows that greater speedups can be achieved by dividing the Embarrassingly Parallel tasks into sub-tasks executed as a serverless service. We achieved highest speedup of almost 40 for Serverless solution compared to a sequential execution on a virtual machine solution, and speedup of 23 for Serverless solution compared to a Parallel execution using virtual machines.