BalanceFaaS: a decentralized load and energy-aware scheduling framework using federated resource modeling and distributed function orchestration in serverless edge computing

Abstract The rapid proliferation of serverless computing in heterogeneous edge environments has introduced critical challenges in task scheduling, load balancing, energy efficiency, and orchestration, particularly due to the absence of centralized control and the resource-constrained nature of edge nodes. Existing approaches partially address these issues but often suffer from high latency, inefficient energy utilization, suboptimal load distribution, and limited scalability. To overcome these limitations, we propose BalanceFaaS, a decentralized, load- and energy-aware serverless scheduling framework that integrates key components, including Federated Multi-Objective Gaussian Process Regression (FMOGPR) jointly predicts latency and energy consumption, Ant Colony Optimization with Reinforcement Feedback (ACO-RF) for adaptive load-aware scheduling, Combinatorial Auction-Based Bidding (CAB-B) for dynamic cost-efficient task allocation, a Hierarchical Context-Aware Orchestration Engine (H-CAOE) for reinforcement learning-based orchestration, and a Multi-Agent Byzantine Fault-Tolerant protocol (MA-BFT) with Load-Aware Weighted Voting (LA-WV) for robust and reliable consensus in decentralized environments. BalanceFaaS enables edge nodes to collaboratively estimate execution latency and energy consumption, adaptively allocate tasks based on node status and resource availability, and ensure reliable, context-aware function orchestration without reliance on a central controller. The framework is evaluated using multiple real-world edge workloads, including Alibaba, GOOGLE CLUSTER, AZURE, and SmartCIoT, demonstrating consistent improvements across key performance metrics such as latency, energy consumption, Task Success Rate (TSR), Load Balancing Efficiency (LBE), resource utilization, and federated learning accuracy. Experimental results show that BalanceFaaS achieves up to 4% improvement across key performance metrics compared to baseline methods, highlighting its effectiveness in optimizing both operational efficiency and energy-aware performance in dynamic edge environments.