The serverless computing paradigm has emerged as a promising solution to address the resource underutilization and inflexible service scaling in edge environments by decoupling the monolithic application into a serverless workflow. However, existing serverless platforms are primarily designed for cloud centers, relying on heavyweight isolation mechanisms that are illsuited for resource-constrained edge computing. These limitations result in high latency, low deployment density, and restricted parallelism. In this paper, we proposeSonnet, a serverless platform tailored for edge computing, capable of rapidly responding to user requests and supporting efficient and elastic service scaling. Sonnet offers these features by (i) employing lightweight WebAssembly as the execution environment for functions, (ii) leveraging serverless workflow information to optimize function deployment on resource-constrained edge environments, and (iii) designing a function deployment algorithm that achieves dynamic load balancing within the cluster. An extensive evaluation ofSonnetwith real-world serverless workflows demonstrates its effectiveness and practical applicability. Compared with SOTA and commonly used edge computing serverless solutions, our experiments show that Sonnet can reduce end-to-end latency by 27% and improve throughput by 2.83×.

Serverless platforms typically adopt an earlybinding approach for function sizing, requiring developers to specify an immutable size for each function within a workflow beforehand. Accounting for potential runtime variability, developers must size functions for worst-case scenarios to ensure service-level objectives (SLOs), resulting in significant resource inefficiency. To address this issue, we propose Janus, a novel resource adaptation framework for serverless platforms. Janus employs a late-binding approach, allowing function sizes to be dynamically adapted based on runtime conditions. The main challenge lies in the information barrier between the developer and the provider: developers lack access to runtime information, while providers lack domain knowledge about the workflow. To bridge this gap, Janus allows developers to provide hints containing rules and options for resource adaptation. Providers then follow these hints to dynamically adjust resource allocation at runtime based on real-time function execution information, ensuring compliance with SLOs. We implement Janus and conduct extensive experiments with real-world serverless workflows. Our results demonstrate that Janus enhances resource efficiency by up to 34.7% compared to the state-of-the-art.

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.