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Modern datacenter servers increasingly deploy heterogeneous, multi-tier memory hierarchies. For these new architectures, OSes depend on measurements of memory usage to make intelligent placement and control decisions. However, existing hardware and software mechanisms for tracking memory usage on these systems require difficult tradeoffs between coverage, timeliness, granularity, flexibility, and overhead. We present NEMO, a nimble and expressive hardware memory telemetry engine for server memory controllers (MCs) that gives OS subsystems policy-specific views of memory behavior. NEMO enables flexible telemetry rules that filter memory operations, map accesses to counters, and apply simple updates to per-counter state. We prototype NEMO on an FPGA-based CXL-attached memory expander. Evaluating three diverse use cases, we show that NEMO provides higher-fidelity signals at substantially lower CPU overhead across a range of state-of-the-art memory management systems: it speeds up HeMem’s reaction to hot-set changes by 5×, accelerates THP splitting in MEMTIS by 10.4×, and detects noisy neighbors in Linux with 350× lower overhead. These telemetry improvements yield up to 1.7× higher throughput and 23% lower latency across key-value stores and databases. 

Modern software inevitably encounters periods of resource overload, during which it must still sustain high servicelevel objective (SLO) attainment while minimizing request loss. However, achieving this balance is challenging due to subtle and unpredictable internal resource contention among concurrently executing requests. Traditional overload control mechanisms, which rely on global signals, such as queuing delays, fail to handle application resource overload effectively because they cannot accurately predict which requests will monopolize critical resources. In this paper, we propose Atropos, an overload control framework that proactively cancels the culprit request that cause severe resource contention rather than the victim requests that are blocked by it. Atropos continuously monitors the resource usage of executing requests, identifies the requests contributing most significantly to resource overload, and selectively cancels them. We integrate Atropos into six large-scale applications and evaluate it against 16 real-world overload scenarios. Our results show that Atropos maintains the performance goals while achieving minimal request drop, significantly outperforming state-of-the-art solutions.