Distributed replica allocation and load balancing for Edge–Cloud FaaS: A cooperative multi-agent orchestration approach

Abstract

The Function-as-a-Service (FaaS) paradigm supports many Cloud-native applications, but rising demand for low-latency services exceeds what the Cloud alone can deliver. Edge computing addresses this limitation; however, its heterogeneity and fragmented administrative domains, together with the workload dynamicity, greatly complicate resource coordination and function replica allocation across the Edge–Cloud continuum. This paper addresses these challenges through two complementary contributions. First, we formalize the Function Replica Allocation and Load Balancing (FRALB) problem in the Edge–Cloud continuum as a Distributed orchestration problem referred to as DiFRALB. The formulation jointly optimizes function placement, horizontal offloading among Edge nodes, and vertical offloading toward Cloud resources. Computation offloading plays a central role in this setting, as it enables workloads to be distributed across heterogeneous Edge and Cloud infrastructures while meeting performance requirements. Second, recognizing that centralized orchestration approaches suffer from scalability limitations and raise privacy concerns in multi-stakeholder environments, we propose FaaS-MACrO, a distributed multi-agent orchestration architecture designed to solve the DiFRALB problem. In FaaS-MACrO, each Edge node operates as an independent agent making local decisions, while a lightweight coordinator ensures global consistency by iteratively updating offloading prices to resolve conflicts among neighboring nodes. Crucially, coordination requires only minimal information exchange, thereby preserving operational privacy. Our solution approach jointly optimizes processing and offloading decisions by capturing the trade-offs among local execution efficiency, horizontal offloading latency, and vertical offloading costs. Extensive experiments across heterogeneous node configurations, diverse network topologies, and varying function characteristics demonstrate that FaaS-MACrO achieves solutions within 0.03–12.14% of the centralized optimum on average while significantly improving scalability, reducing solution times by up to three orders of magnitude in large-scale deployments with 200 nodes.