Why Rule-Based Autoscalers Still Hold Their Ground Against DRL

In the ongoing battle for efficient compute resource allocation, the numbers tell a different story than you might expect. It's easy to get caught up in the excitement of deep reinforcement learning (DRL) algorithms, but cost efficiency, a properly calibrated rule-based autoscaler often beats DRL to the punch. Let's break this down.

Benchmark Revelations

Recent findings from RLScale-Bench, a benchmark for adaptive resource control, challenge the assumptions surrounding DRL's supremacy. The study evaluated six mainstream DRL algorithms, PPO, DQN, A2C, SAC, TD3, and DDPG, against a well-tuned rule-based baseline. Tested across six workload patterns with five different seeds (240 runs in total), the results were eye-opening.

The calibrated rule-based controller managed to achieve the lowest cost across all tested workloads. That's not to say it's flawless. it lagged behind the best-performing RL agents during intense bursty and flash traffic. But across steady workloads, the old-school autoscaler held its ground.

Action Space Mismatch

Here’s another surprising twist: discrete-action algorithms outperformed their continuous-action counterparts by one to two orders of magnitude in avoiding constraint violations. The crux of the matter lies in action-space mismatch, which can trip up the algorithms when faced with real-world complexities. It’s a reminder that sometimes, simplicity wins.

So, does this mean DRL is all hype? Not exactly. While no single DRL algorithm came out on top across all workloads, their potential lies in how well we can calibrate baselines, craft reward functions, and establish solid evaluation protocols.

The Takeaway

Why should this matter to you? As industries increasingly rely on adaptive resource allocation to optimize costs and performance, understanding the limitations and strengths of different approaches is key. Is DRL overrated? When stripped of hype, the reality is that architecture and proper calibration matter more than merely choosing the latest algorithm.

Here's a question worth pondering: In the pursuit of new technology, are we sometimes overlooking tried-and-true methods that deliver just as well, if not better? The architecture matters more than the parameter count. As we push forward, it might be time to reassess our priorities.