Dynamic Memory Optimization

• Dynamic Memory Optimization is a set of approaches that adapt memory management strategies to fluctuating capacities and varying workload demands.
• It extends classic paging and caching models by incorporating dynamic capacity changes, rigorous competitive analysis, and predictive control mechanisms.
• It has practical applications in HPC, deep learning, and virtualized environments, enabling robust, fine-grained memory management under real-time constraints.

Dynamic memory optimization encompasses a set of models, algorithms, and system techniques dedicated to maximizing the efficiency and adaptability of memory resources in environments where available capacity and workload characteristics change dynamically over time. This field addresses challenges both at the operating system/hardware level (e.g., paging and in-memory storage) and within application spaces such as deep learning, high-performance computing (HPC), virtualized container management, processing-in-memory architectures, resource-constrained embedded systems, and reinforcement learning-driven resource management. The scientific literature reveals that optimal performance under static memory conditions does not guarantee robustness when capacity varies, and that dynamic, fine-grained strategies and predictive control are essential in modern architectures.

1. Theoretical Foundations and Models

Dynamic memory optimization is rigorously modeled as the generalization of the classic paging or caching problem to settings where the system’s memory capacity varies arbitrarily over time. In the foundational framework by (Peserico, 2013), memory capacity is no longer a fixed constant $k$ but a sequence $M = m_1, m_2, \ldots$, where each $m_i$ reflects “growth” and “shrink” operations on physical memory. Each request sequence thus consists of interleaved page requests and capacity change events; a “+” increases available memory by one page, “–” decreases it (potentially forcing evictions). The competitive analysis paradigm is extended by defining a dynamic $(h,k)$-competitive ratio: for any online algorithm ALG, its cost over any request and capacity sequence is compared to an offline optimal algorithm (with possibly augmented capacities), formalized as

$$C_{\mathrm{ALG}}(T, M) \leq p \cdot C_{\mathrm{OPT}}(T, [\cdot M]) + d,$$

where $C_{\mathrm{ALG}}$ and $C_{\mathrm{OPT}}$ are the number of incurred faults for ALG and OPT, respectively, and $[\cdot M]$ denotes rounded/scaled capacities.

This competitive framework reveals that even single-page fluctuations in available memory can dramatically degrade the performance guarantees of certain algorithms and necessitate new, more robust approaches.

2. Algorithmic Approaches and Performance Guarantees

The performance of paging and caching algorithms under dynamic capacities diverges sharply from their static-setting analysis. According to (Peserico, 2013):

• The Longest Forward Distance (LFD) algorithm remains optimal in the presence of dynamic capacity, always incurring the minimum possible number of faults regardless of adversarial fluctuations.
• Classic online algorithms (such as LRU, FIFO, CLOCK) have provably bounded "dynamic" $(h, k)$-competitive ratios, characterized by expressions such as

$$\mathrm{PEL}(h, k) = \max_{k'} \left\{ \frac{k'}{k' - [h - \ldots]} \right\},$$

which in many scenarios almost matches the static ratio $k/(k-h+1)$. Notably, as $h$ approaches $k$, the bounds coincide.

• Some algorithms with optimal static guarantees (e.g. LFRU) become arbitrarily suboptimal if capacity varies, emphasizing the insufficiency of static analysis and motivating algorithmic designs specifically for dynamic environments.

Furthermore, the analysis highlights that predicting future memory capacities is much less vital than predicting access patterns. The offline LFD algorithm is “online” with respect to capacity but requires future knowledge of data accesses, underscoring that the core bottleneck lies in access pattern prediction and not capacity anticipation.

3. Practical System Implementations

The theoretical insights have been applied to a range of practical systems. Examples include:

• Dynamic Memory Controllers: DynIMS (Xuan et al., 2016) implements feedback-based runtime memory control for in-memory storage in HPC systems by continuously monitoring memory usage and dynamically reallocating storage based on a control law

$U_{i+1} = U_i - \lambda (r_i - r_0)$

where $U_i$ is the allocated capacity, $r_i$ is utilization, and $\lambda$ tunes feedback aggressiveness. DynIMS achieves up to 5× improvement in Spark performance under fluctuating HPC workloads by maintaining high in-memory hit rates and mitigating remote I/O.

• Deep Learning and GPU Scheduling: SuperNeurons (Wang et al., 2018) deploys memory-optimization primitives—liveness analysis, unified tensor pools (UTP), and cost-aware recomputation—to reduce the network-wide memory peak to the maximum per-layer memory usage, rather than the sum across all layers. This enables the training of up to $10^4$-layer networks within standard GPU memory.
• Object-Oriented HPC on GPUs: DynaSOAr (Springer et al., 2018) proposes a parallel lock-free allocator with a hierarchical block structure and structure-of-arrays (SOA) data layout for single-method multiple-object (SMMO) workloads, maximizing coalesced access and minimizing fragmentation. It leverages lock-free atomic operations and hierarchical bitmaps to emulate efficient, compact allocation and deallocation on the GPU.
• Heterogeneous/Tiered Memory: DMX (Rellermeyer et al., 2019) implements a tiered memory approach for virtualization environments, proactively migrating memory pages between DRAM and NAND flash based on predicted hotness (employing multi-queue prediction and ML) to support high-density container deployments.
• Tensor Scheduling and Swap-Based Policies: Systems such as TENSILE (Zhang et al., 2021), DELTA (Tang et al., 2022), and Chameleon (Wang et al., 14 Sep 2025) tackle the problem of dynamic GPU memory scheduling in deep learning, employing tensor-level analysis, predictive latency models, and adaptive data movement strategies (combining swap-out, prefetching, and recomputation) to optimize memory footprint under fluctuating, multi-workload scenarios—including variable operator sequences in Eager Mode LLM training.
• Memory-Efficient Adjoint Methods: In sensitivity computation for dynamic optimization, (Herrmann et al., 19 Sep 2025) proposes a superposition-based adjoint method that requires storing only a minimal number of time steps, not the full forward solution, thus reducing the memory requirement from $\mathrm{DoF} \times$ (time steps) down to $\mathrm{DoF}$.

4. Adaptive & Predictive Methods

Adaptive and predictive control plays a central role in modern dynamic memory optimization:

• Feedback Loops: Controllers such as DynIMS use continuous feedback, ensuring rapid (<1s) adaptation to workload changes and maintaining system performance under rapid memory pressure fluctuations.
• Proactive Prediction: DMX’s ML-driven prediction model anticipates “hot” and “cold” pages, prefetching the former and evicting the latter to balance performance and maximize container density without major tail latency spikes.
• Dynamic Policy Selection: Chameleon (Wang et al., 14 Sep 2025) uses lightweight online profiling and limited operator information to generate swap policies robust to dynamic changes. It employs fuzzy matching and runtime simulation to adapt policies to variational operator sequences in deep learning.
• Reinforcement Learning: RL-based methods (Lim et al., 20 Oct 2024) learn memory allocation strategies that can surpass classical heuristics (first-fit, best-fit, worst-fit) by leveraging sequential system interactions and history-aware policies that internalize recent allocation requests.

5. Applications across Architectures and Domains

Dynamic memory optimization is integral to diverse computing paradigms:

• Cloud and Virtualization: Variable VM memory necessitates robust page replacement strategies, as analyzed in (Peserico, 2013).
• In-Memory Compute and Storage: Adaptive controllers balance memory between compute and data storage, key for frameworks like Alluxio/Spark (Xuan et al., 2016).
• Model Training at Scale: Deep neural network training with massive models leverages dynamic scheduling and swap-based policies (Wang et al., 2018, Tang et al., 2022, Wang et al., 14 Sep 2025).
• GPUs and HPC: Applications in scientific simulation, agent-based modeling, and dynamic finite element analyses benefit from efficient, coalesced object allocation enabled by frameworks like DynaSOAr (Springer et al., 2018).
• Embedded and PIM Architectures: Processing-in-memory designs such as PIM-malloc (Lee et al., 19 May 2025) accelerate allocation via per-core caches and hierarchical allocation, achieving $66\times$ allocation improvements and $28\times$ throughput gains in dynamic-graph scenarios.
• Flash Storage and Tiered Memory: Multi-tier systems (DMX) increase effective capacity by two-fold or more via dynamic hot/cold page migration guided by access patterns (Rellermeyer et al., 2019).

6. Implications for System Design and Future Directions

Key implications for future system design include:

• Algorithmic Robustness: Systems must select or design paging and memory management algorithms that maintain performance under capacity variation—a static $(h,k)$-optimality guarantee is not sufficient.
• Decoupled Policy and Capacity Management: It is feasible to decouple replacement policy from capacity controls (the “RAM rental” problem), often achieving near-optimal performance using standard marking/eviction policies in an appropriately augmented memory configuration.
• Emphasis on Access-Pattern Prediction: Resource investment in detecting and exploiting access patterns is generally more impactful than predicting capacity variation.
• Real-World Integration: Efficient dynamic memory optimization is critical not just for statistical performance but for SLOs (service-level objectives) in latency-sensitive applications and in enabling new data-intensive and deep learning workloads to run on hardware originally considered too resource-constrained.

Potential research avenues include expanding adaptive control strategies, integrating RL-based and ML-driven prediction with memory management, optimizing coordination in tiered heterogeneous memory, and automating the generation of custom dynamic memory managers using evolutionary/meta-programming frameworks for specialized embedded and consumer systems.

7. Summary Table of Representative Approaches

System/Algorithm	Domain	Dynamic Aspect
LFD, LRU, FIFO	OS paging	Online vs. offline, static/dynamic capacity
DynIMS	HPC/storage	Feedback controller adapts storage size
SuperNeurons	DNN training (GPU)	Liveness, offload, recompute at runtime
DynaSOAr	GPU OOP/highly parallel	SOA layout plus block-based allocation
DMX	Containerized servers	Proactive DRAM↔NAND migration
DELTA, TENSILE	Deep learning	Tensor-level swap/recompute, multi-job
PIM-malloc	Processing-in-memory	Hierarchical per-core allocation/caching
RL Allocator	OS/simulation	Policy learned from sequential experience

The persistent theme is that effective dynamic memory optimization requires both algorithmic refinements tailored to fluctuating environments and system-level architectures that are capable of adapting policies at low overhead, often favoring adaptability and predictive control over static configuration.