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Most reinforcement learning orchestration systems fail to demonstrate the robustness expected under real-world conditions, challenging the validity of existing benchmarks.
Controller performance in edge clusters can swing dramatically based on workload intensity, with a deep reinforcement-learning approach losing to a simple heuristic by 29 percentage points under heavy load.
Forget incentive compatibility, can you even trust the marketplace operator?
AIF-Router achieves stable online learning for adaptive AI service orchestration, even in unpredictable edge environments, showcasing the potential of Active Inference in real-world applications.
Pricings, inspired by SaaS subscription models, offer a surprisingly effective and generalizable way to represent and optimize resource allocation in complex computing environments.
Watch an agent learn to juggle the knobs of multiple stream processing services to meet latency goals in a resource-constrained edge environment.
Current service orchestration solutions fall short of achieving autonomous, resilient, and scalable performance in the Computing Continuum, highlighting the urgent need for standardized evaluation environments.
