Sourav Kanti Addya

Serverless computing emerges as a new standard to build cloud applications, where developers write compact functions that respond to events in the cloud infrastructure. Several cloud service industries started adopting serverless for running their applications. With increased popularity of serverless computing, the demand for an orchestration service has also become important. Popular serverless service providers have come up with various orchestration services such as Amazon's Step Functions and Azure's Logic App. But these orchestration services have their own limitations. One key limitation of serverless computing is that it disregards the significance of data. In the age of big data, when applications run around a huge volume, transferring data for processing across regions, leads to high communication latency. Again, popular serverless service providers such as Amanzon Web Services(AWS) and Microsoft etc do not provide any orchestration service where the service can access to resources from other regions. In this paper, we proposed an orchestration framework InterFlux. InterFlux completely eradicates the need of region wise deployment of serverless applications. Also, InterFlux allows orchestration services to access resources from cross regions with a highly restricted data transmission latency. We evaluated the performance of proposed InterFlux on AWS's orchestration services, AWS Step Functions. InterFlux performs 82.6% better compared to its data pulling implementation.

Serverless computing abstracts server management, enabling developers to focus on application logic while benefiting from automatic scaling and pay-per-use pricing. However, dynamic workloads pose challenges in resource allocation and response time optimization. Response time is a critical performance metric in serverless environments, especially for latency-sensitive applications, where inefficient scheduling can degrade user experience and system efficiency. This paper proposes RUSH (Rule-based Scheduling for Low-Latency Serverless Computing), a lightweight and adaptive scheduling framework designed to reduce cold starts and execution delays. RUSH employs a set of predefined rules that consider system state, resource availability, and timeout thresholds to make proactive, latency-aware scheduling decisions. We implement and evaluate RUSH on a real-world serverless application that generates emoji meanings. Experimental results demonstrate that RUSH consistently outperforms First-Come-First-Served (FCFS), Random Scheduling, and Profaastinate, achieving ≈ 33% reduction in average execution time.