Serverless computing provides developers with a maintenance-free approach to resource usage, but it also transfers resource management responsibility to the cloud platform. However, the fine granularity of serverless function resources can lead to performance bottlenecks and resource fragmentation on nodes when creating many function containers. This poses challenges in effectively scaling function resources and optimizing node resource allocation, hindering overall agility. To address these challenges, we have introduced ComboFunc, an innovative resource scaling system for serverless platforms. ComboFunc associates function with heterogeneous containers of varying specifications and optimizes their resource combination and placement. This approach not only selects appropriate nodes for container creation, but also leverages the new feature of Kubernetes In-place Pod Vertical Scaling to enhance resource scaling agility and efficiency. By allowing a single function to correspond to heterogeneous containers with varying resource specifications and providing the ability to modify the resource specifications of existing containers in place, ComboFunc effectively utilizes fragmented resources on nodes. This, in turn, enhances the overall resource utilization of the entire cluster and improves scaling agility. We also model the problem of combining and placing heterogeneous containers as an NP-hard problem and design a heuristic solution based on a greedy algorithm that solves it in polynomial time. We implemented a prototype of ComboFunc on the Kubernetes platform and conducted experiments using real traces on a local cluster. The results demonstrate that, compared to existing strategies, ComboFunc achieves up to 3.01 × faster function resource scaling and reduces resource costs by up to 42.6%.

Function-as-a-Service (FaaS) offers a fine-grained resource provision model, enabling developers to build highly elastic cloud applications. User requests are handled by a series of serverless functions step by step, which forms a multi-step workflow. The developers are required to set proper configurations for functions to meet service level objectives (SLOs) and save costs. However, developing the configuration strategy is challenging. This is mainly because the execution of serverless functions often suffers from cold starts and performance fluctuation, which requires a dynamic configuration strategy to guarantee the SLOs. In this article, we present StepConf, a framework that automates the configuration as the workflow runs. StepConf optimizes memory size for each function step in the workflow and takes inter and intra-function parallelism into consideration, which has been overlooked by existing work. StepConf intelligently predicts the potential configurations for subsequent function steps, and proactively prewarms function instances in a configuration-aware manner to reduce the cold start overheads. We evaluate StepConf on AWS and Knative. Compared to existing work, StepConf improves performance by up to 5.6× under the same cost budget and achieves up to a 40% cost reduction while maintaining the same level of performance.

Function-as-a-Service (FaaS) offers a fine-grained resource provision model, enabling developers to build highly elastic cloud applications. User requests are handled by a series of serverless functions step by step, which forms a function-based workflow. The developers are required to set proper resource configuration for functions, so as to meet service level objectives (SLOs) and save cost. However, developing the resource configuration strategy is challenging. It is mainly because execution of cloud functions often suffers from cold start and performance fluctuation, which requires a dynamic configuration strategy to guarantee the SLOs. In this paper, we present StepConf, a framework that automates the resource configuration for functions as the workflow runs. StepConf optimizes memory size for each function step in the workflow and takes inter and intra-function parallelism into consideration. We evaluate StepConf on AWS Lambda. Compared with baselines, the experimental results show that StepConf can save cost up to 40.9% while ensuring the SLOs.