ML Algorithms that Dynamically Allocate CPU, Memory, and I/O Resources

Abstract

The cloud and edge platforms of the present time need to distribute CPU, memory, and I/O under unsteady, multi-tenanted workloads without violating service-level aims (SLOs), finances, and energy restrictions. This paper introduces a safety-first architecture that integrates the telemetry-based prediction and policy-based decision making. We have instrumented hosts and services with fine-grained signals CPU runnable depth and throttling, page-fault rate and cache residency, disk/NVMe queue depth and tail latency, NIC retransmits and engineer cross-resource features to measure the coupling effects. Developing these contributions, we consider three categories of the methods: (i) the supervised forecasters (gradient boosting, temporal CNN/Transformer) to predict demand in the short-horizon and SLO-risk, (ii) the constrained reinforcement learning to predict joint and continuous changes in the shares/quota of CPU, memory constraints/placement, and I/O priorities, (iii) the hybrid predict-then-optimize controllers with model-predictive guardrails. Automatic rollback and limited step sizes of canary-first rollout guarantee stability of production. Our solution to trace-driven experiments on OLTP microservices, streaming analytics, storage-heavy phases, and ML inference pipelines results in 20-45% p99 and SLO misses over well-tuned rule/PID baselines, and higher throughput and slightly reduced cost. We talk about design options such as design uncertainty-conscious headroom, equity rights, and oscillation parameters that transform raw profits into high quality results. Lastly, we present boundaries (domain shift, data heterogeneity) and future prospects in causal, carbon-sensitive and geo-distributed allocation. The findings suggest that ML-driven, policy-bound allocators have the potential to be useful control planes of dynamically managed, multi-resource management