In the competitive and dynamic world of financial technology (fintech), high performance and low latency are crucial for real-time transactions and data processing. Microservices architecture, combined with efficient caching mechanisms, significantly enhances scalability and responsiveness. VMware GemFire, an in-memory data grid, provides robust caching solutions that, when paired with Java 17’s Z Garbage Collector (ZGC) and heap Least Recently Used (LRU) eviction strategies, can maximize cache performance. This paper proposes a system integrating GemFire with ZGC to optimize memory management, reduce (...) latency, and maintain high throughput. The heap LRU eviction strategy ensures that the least recently used data is evicted first when memory limits are reached, maintaining high cache hit rates and reducing the risk of the JVM running out of memory. By adjusting ZGC and using heap LRU eviction, this combined method makes it easier for microservices to handle changing workloads, grow horizontally, and speed up data access, which improves the overall performance and reliability of the system. Different settings for SoftMaxHeapSize have different effects on heap usage, operation throughput, and garbage collection performance. The results of the experiments show how to tune GemFire cache performance to work best in a microservices environment. (shrink)

As part of their digital transformation, financial service companies can greatly benefit from the implementation of a microservice architecture. We can build a service-oriented architecture (SOA) application using the architecture to enhance its overall performance and maintainability. This enables the application to consist of several smaller components that operate independently and simultaneously. In the financial services industry, the accuracy of artifact states holds immense significance. Given that an inaccurate artifact state or anomalous artifact operation(s) could potentially ruin the entire application, (...) it is crucial to conduct an analysis of the artifact operations within each microservice during the design process. In this study, we present a technique for identifying anomalies through the characteristics of artifacts associated with the microservice architecture. Following this, we identify the properties of artifacts. As a result of technological improvements and shifting client expectations, the financial sector is currently going through a tremendous upheaval. Among these advancements, microservices architecture has emerged as a key enabler of agility, scalability, and innovation in banking systems. This paper delves into the relationship between microservices and banking security, emphasizing the use of microservices to strengthen financial systems in the face of growing cyber security risks. The microservices architecture simplifies the development, deployment, and scalability of big financial applications by separating them into smaller, self-contained services. This modular approach not only makes the system more reliable and helps separate problems, but it also makes continuous integration and delivery (CI/CD) easier, which lets you respond quickly to changes in the market and new rules. However, the implementation of microservices introduces new security challenges, such as controlling inter-service communication, ensuring data integrity, and safeguarding against attack detection and protection. (shrink)

With the increasing popularity of microservice architecture, there is a growing need to deploy service-based applications efficiently in cloud environments. Traditional cluster schedulers often fail to optimize service placement adequately, as they only consider resource constraints and overlook traffic demands between services. This oversight can lead to performance issues such as high response times and jitter. To address this challenge, we propose a novel approach to optimize the placement of service-based applications in clouds. Our approach involves partitioning the application into (...) segments while minimizing overall traffic between them, and then strategically allocating these segments to machines based on their resource and traffic demands. We have developed a prototype scheduler and conducted extensive experiments on test bed clusters to evaluate its performance. The results demonstrate that our approach surpasses existing container cluster schedulers and heuristic methods, significantly reducing overall intermachine traffic and improving application performance. (shrink)

Driven by the use cases of PubChemRDF and SCAIView, we have developed a first community-based clinical trial ontology (CTO) by following the OBO Foundry principles. CTO uses the Basic Formal Ontology (BFO) as the top level ontology and reuses many terms from existing ontologies. CTO has also defined many clinical trial-specific terms. The general CTO design pattern is based on the PICO framework together with two applications. First, the PubChemRDF use case demonstrates how a drug Gleevec is linked to multiple (...) clinical trials investigating Gleevec’s related chemical compounds. Second, the SCAIView text mining engine shows how the use of CTO terms in its search algorithm can identify publications referring to COVID-19-related clinical trials. Future opportunities and challenges are discussed. (shrink)