Rule-Based Autoscalers: The Underdog Thrashing Deep Reinforcement Learning

In the area of adaptive resource control, a surprising contender is outperforming the heavyweights. Rule-based autoscalers have shown they can best deep reinforcement learning (DRL) algorithms on cost efficiency across all tested workloads. This isn't just a one-off anomaly. It's a consistent pattern observed in comprehensive benchmarking.

DRL on the Ropes?

Six mainstream DRL algorithms, including PPO, DQN, A2C, SAC, TD3, and DDPG, faced off against a calibrated rule-based baseline in RLScale-Bench, a rigorous benchmarking setup. The setting? Kubernetes Horizontal Pod Autoscaling, where the agent manages resource allocation under cost and service-level constraints.

The results are eye-opening. The rule-based controller delivered lower costs across six diverse workload patterns. Even against flash and bursty traffic, where DRL typically shines, the rule-based approach held its ground. It's a wake-up call for those putting faith in the sophistication of DRL.

Action Space: The Achilles' Heel

Another intriguing takeaway is the performance disparity between discrete and continuous-action algorithms. Discrete-action models lead by a wide margin, outperforming continuous-action ones by one to two orders of magnitude constraint violations. Why? An action-space mismatch that's proving hard to counter.

This highlights a critical flaw in DRL's approach to resource control. The algorithms aren't as universally adaptable as some might hope. They're not a silver bullet. So, what's the real bottleneck here?

Calibration Over Complexity

The study suggests a provocative conclusion: the real challenge isn't choosing the right algorithm. It's mastering the fundamentals, baseline calibration, reward function engineering, and establishing realistic evaluation protocols.

Why does this matter? Because it challenges the prevailing wisdom that more complex solutions are inherently superior. If a simple calibrated controller can outperform advanced DRL models, maybe it's time to rethink our strategies.

Is the industry too focused on algorithmic complexity at the expense of practical effectiveness? Perhaps. The results urge us to prioritize the basics, ensuring our benchmarks and protocols reflect real-world scenarios. It's a reminder that sometimes, the simplest solutions are the most effective.

For developers and engineers, this means questioning the assumption that more data and more sophisticated algorithms will always yield better outcomes. Clone the repo. Run the test. Then form an opinion.