FaaS offers significant advantages with its infrastructure abstraction, on-demand execution, and attractive no idle resource pricing for modern cloud applications. Despite these benefits, challenges such as startup latencies, static configurations, sub-optimal resource allocation and scheduling still exist due to coupled resource offering and workload-agnostic generic scheduling behaviour. These issues often lead to inconsistent function performance and unexpected operational costs for users and service providers. This paper introduces Saarthi, a novel, end-to-end serverless framework that intelligently manages the dynamic resource needs of function workloads, representing a significant step toward self-driving serverless platforms. Unlike platforms that rely on static resource configurations, Saarthi is input-aware, allowing it to intelligently anticipate resource requirements based on the characteristics of an incoming request payload. This input-driven approach reinforces function right-sizing and enables smart request orchestration across available function configurations. Saarthi further integrates a proactive fault-tolerant redundancy mechanism and employs a multi-objective Integer Linear Programming (ILP) model to maintain an optimal function quantity. This optimisation aims to maximise system throughput while simultaneously reducing overall operational costs. We validate the effectiveness of Saarthi by implementing it as a framework atop OpenFaaS. Our results demonstrate Saarthi's ability to achieve up to 1.45x better throughput, 1.84x reduced costs, while maintaining up to 98.3% service level targets with an overhead of up to 0.2 seconds as compared to the baseline OpenFaaS.

翻译：函数即服务（FaaS）凭借其基础设施抽象、按需执行以及极具吸引力的无闲置资源定价模式，为现代云应用带来了显著优势。然而，由于资源供给与工作负载无关的通用调度行为紧密耦合，启动延迟、静态配置、次优资源分配与调度等挑战依然存在。这些问题常导致用户和服务提供商面临不一致的函数性能与意外的运营成本。本文提出Saarthi，一种新颖的端到端无服务器框架，能够智能管理函数工作负载的动态资源需求，标志着向自主式无服务器平台迈出了重要一步。与依赖静态资源配置的平台不同，Saarthi具备输入感知能力，可基于传入请求负载的特征智能预测资源需求。这种输入驱动的方法强化了函数的精准资源配比，并实现了跨可用函数配置的智能请求编排。Saarthi进一步集成了主动容错冗余机制，并采用多目标整数线性规划（ILP）模型以维持最优函数数量。该优化旨在最大化系统吞吐量，同时降低总体运营成本。我们在OpenFaaS之上将Saarthi实现为框架以验证其有效性。实验结果表明，相较于基准OpenFaaS，Saarthi能够实现高达1.45倍的吞吐量提升，降低1.84倍的成本，在仅增加最多0.2秒开销的同时，保持高达98.3%的服务等级目标达成率。