Memory Manager: Techniques & Applications

Updated 28 August 2025

Memory Manager is a system-level component that dynamically allocates, tracks, and reclaims memory using techniques like overcommitment and LRU-based policies.
It leverages multi-tier scheduling and hardware-accelerated migration to optimize performance, energy savings, and overall application QoS.
Modern solutions integrate security measures such as out-of-band metadata and randomized allocation to prevent exploits and support multi-threaded, cross-device operations.

A memory manager is a system-level or user-space software component responsible for mediating and optimizing the allocation, use, and reclamation of memory resources during program execution. Its primary functions include dynamic memory allocation, tracking ownership and reachability of memory blocks, minimizing fragmentation, orchestrating migration and swapping between different storage tiers, and in some cases, exposing interfaces for cross-device, cross-thread, or security-conscious memory operations. Modern memory managers embody complex strategies to balance performance, security, energy efficiency, application quality of service (QoS), heterogeneity of devices, and programmability requirements.

1. Memory Management Principles: Allocation, Tracking, and Overcommitment

The foundational responsibility of a memory manager is dynamic allocation and robust tracking of memory blocks. This is achieved through mechanisms such as:

Explicit Object/Pointer Abstraction: Libraries like Rambrain (Imgrund et al., 2015) require wrapping data objects in templates (e.g., managedPtr<> and adhereTo<>) to track active usage. When no adherence context exists, data can be swapped out, enabling overcommitment well beyond physical RAM. Memory managers may maintain internal structures (e.g., cyclic doubly-linked lists in Rambrain) that mark active and least-recently-used blocks, blending advantages of LRU with forward-looking developer hints.
Allocation Strategies: Memory managers select policies (first-fit, best-fit, exact-fit, segregated bins, buddy systems, or hybrids) that trade off allocation/deallocation speed, fragmentation minimization, and compatibility. For example, the aging-aware, multi-level paging algorithm (Oren, 2017) places pages in different memory tiers based on access history: counters associated with each page (shifted per tick) enable the stratification of data across $N$ hierarchy levels. For specific usage intervals (as in deep neural net inference (Pisarchyk et al., 2020)), the problem is reduced to interval packing and the use of heuristics like “Greedy by Size” and “Greedy by Breadth.”
Overcommitment and Swapping: User-space managers such as Rambrain orchestrate data swapping out-of-process, using asynchronous IO to mask transfer latency and decouple application logic from system memory pressure, maintaining negligible overhead at scale. This avoids kernel-level OOM killers and allows applications to operate on datasets much larger than available physical RAM.

2. Multi-Level, Heterogeneous, and Hybrid Memory Architectures

Modern systems feature composite memory hierarchies spanning on-chip caches, DRAM, non-volatile memory (NVM), Storage Class Memory (SCM), persistent memory, and even device-local memories (e.g., GPU RAM).

Full-Hierarchy Scheduling: The memos kernel (Liu et al., 2017) extends traditional OS memory managers with page-coloring over cache, DRAM banks, and NVM channels. Runtime kernel-level profiling (SysMon) tracks per-page hotness, RD/WD status, and memory medium utilization, guiding migration of hot/write-dominated pages to DRAM and cold/read-dominated pages to NVM. Migration decisions leverage both CPU (for small, synchronized transfers) and DMA (for large, bulk data) for low-overhead page movement. Page-coloring and hashing (tuples [bank, cache-slab, channel]) ensure fast, conflict-aware allocation.
Hybrid Memory Policies in Hardware: Hardware-accelerated designs like HMMU (Wen et al., 2020) expose a flat unified address space over DRAM and NVM, automatically migrating “hot” pages or sub-page blocks into DRAM, using policies based on access counters and Bloom filters, with fine granularity fallback to minimize NVM write amplification. Adaptive page/block policies yield up to 39% energy savings versus DRAM-only configurations, with a ~12% reduction in throughput.
Multi-Level Algorithms: Automated Memory Allocation Managers (MAMs) (Oren, 2017) convert traditional two-level paging to $N$ -level hierarchies (e.g., DRAM+SCM+HDD), adapting algorithms such as Aging, NRU, and FIFO. For aging, the memory level $L$ for a page eviction is calculated as

$L = \left\lceil \frac{\text{initial zero bits}}{\text{total reference bits}} \cdot ML \right\rceil$

thus linking access recency directly with tier demotion.

Tiered User-Space Management: Systems like MaxMem (Raybuck et al., 2023) in big data settings monitor each process's fast memory miss ratio $(a_{\mathrm{miss}} = a_{\text{slow}} / (a_{\text{slow}} + a_{\text{fast}}))$ , dynamically adjusting fast memory allocation to maintain per-process QoS under colocation, leveraging access sampling and per-page “binning” for migration. This is managed entirely in user space for modifiability and extensibility.

3. Security- and Robustness-Driven Features

Robustness against attacks and programming errors increasingly motivates architectural choices:

Out-of-Band Metadata: SJMalloc (Bauroth, 23 Oct 2024) demonstrates that decoupling allocator metadata from heap data substantially hardens applications against heap exploitation. Metadata is stored in separate memory ranges, inaccessible via buffer overflows/underflows in application code. Reverse lookup upon free is realized by mapping the address to metadata using a formula like

$\text{Index} = \frac{\text{ptr} - \text{heap\_start}}{\text{min\_bin\_size}}$

so metadata can be securely, and efficiently, located. Ownership is tracked to detect cross-thread frees, and operations like double free are centrally managed.

Randomized Allocation: Mesh (Powers et al., 2019) randomizes object placement within spans and employs probabilistic meshing (SplitMesher algorithm) to assign aliasing virtual pages to a single physical page. Randomization at this level also hardens “use-after-free” exploits (as in (Astrakhantseva et al., 2021)) by making address reuse unpredictable.
Garbage-Free and Reversible Computing: Memory managers in reversible languages (e.g., ROOPL++ (Cservenka, 2018)) enforce that all allocation and deallocation are one-to-one reversible operations, prohibiting garbage formation. Allocations use buddy systems, and deallocation requires pre-zeroing content, so all memory can be restored or inverted perfectly.

4. Parallelism, Distribution, and Cross-Component Management

Effective memory managers are increasingly required to operate across multiple threads, tasks, devices, or even nodes in distributed environments.

Thread-Scheduler Coupling: Hierarchical memory managers for parallel functional languages (Guatto et al., 2018) assign “heaps” to tasks according to task nesting. Memory consistency and mutation safety are maintained by recursively promoting mutable objects encountered in “down-pointer” writes, preserving the invariant that a pointer from heap $h_x$ to $h_y$ only exists if $h_y$ is $h_x$ or an ancestor.
Multithreaded and MPI Compatibility: Designs such as Rambrain (Imgrund et al., 2015) ensure that memory blocks are resident and thread-safe by integrating reference-counted glue wrappers (adhereTo) and, in distributed (MPI) contexts, maintaining a local manager per node. Global memory locks and serialization are available to avoid deadlocks in overcommitted conditions.
Device and Peripheral Memory Sharing: Generalized memory managers like GMEM (Zhu et al., 2023) centralize memory management for both CPU and peripheral devices. Devices register MMU callbacks with the OS, and GMEM manages coherency and address mapping for device-local and shared memory, offloading most of the complexity from device drivers. This yields dramatic code reduction (e.g., 700 lines eliminated in FreeBSD IOMMU) and marked throughput/CPU efficiency gains (54% and 32%, respectively).

5. Application-Specific and Automated Memory Manager Synthesis

The “one-size-fits-all” approach to memory managers is increasingly replaced by design-time or runtime synthesis tailored to application characteristics.

Application Profiling and Grammatical Evolution: Recent methodologies (Risco-Martín et al., 7 Mar 2024, Risco-Martín et al., 22 Jun 2024) couple dynamic program profiling with grammar-driven design space exploration/grammatical evolution to synthesize custom dynamic memory managers (DMMs). The process involves:
- Instrumenting applications to collect allocation/deallocation behavior and block size distributions.
- Generating a grammar $G = (N, T, P, S)$ that defines the search space for allocators (node pool, allocator structure, block partitioning, splitting/merging).
- Evolving candidate allocators with genetic algorithms, simulating their execution on recorded traces using a dedicated simulator (e.g., counting allocation loop steps, memory accesses, fragmentation, energy).
- Fitness is measured as a normalized blend; e.g., $F = 0.5\cdot (T/T_\text{Kng}) + 0.5\cdot (M/M_\text{Lea})$ .
- Results show up to 62.55% better performance and 30.62% less memory than common allocators.
Performance Simulation and Rapid Evaluation: The simulation infrastructure (Risco-Martín et al., 22 Jun 2024) eliminates the need for full recompilation or execution of each DMM, enabling in silico search over object-oriented DMM compositions for embedded and multimedia systems. Key performance metrics—execution time, peak memory, and energy—are computed per candidate for direct optimization.

6. Emerging Paradigms and Specialized Strategies

Memory manager research spans diverse environments and specializations:

Approximate and Energy-Efficient Management: AXES (Maity et al., 2020) introduces runtime-tunable approximation knobs at each memory hierarchy level (supply voltage for caches, DRAM refresh period) orchestrated by TD( $\lambda$ )-based reinforcement learning. The system’s reward function,

$R = \begin{cases} 1 - \left(\frac{\text{Power}}{\text{max\_Power}}\right), & Q \leq Q_\text{threshold} \ -\frac{Q - Q_\text{threshold}}{\text{max\_Q}}, & \text{otherwise} \end{cases}$

coordinates energy savings and QoS enforcement, achieving up to 37% reduced subsystem energy and 75% fewer QoS violations without design-time burden.

Device-Centric and Peripheral Memory: Application-transparent frameworks like Mosaic (Ausavarungnirun et al., 2018) enable GPUs to maximize TLB reach and minimize paging latency by allocating pages en masse, in-place coalescing for large TLB-optimized pages, and dynamic splintering/compaction to mitigate internal and demand paging overheads.
Mobile and Embedded Isolations: OS-level memory partitioning approaches (VNODE (Lim et al., 2021)) preclude trusted/untrusted app interference. Experiments show a >90% reduction in execution time for system apps and complete elimination of LMK/OOMK events, directly enhancing responsiveness under pressure.
Domain-Specific Tasks: In streaming video entity linking (Zhao et al., 3 Mar 2024), a LLM serves as the memory controller, managing a rolling memory block updated per time-slice (Equation: $V_s^t = \text{Mem}_t = \text{LLM}(s_t, \text{Mem}_{t-1}, [E_k])$ ), further augmented by retrieval-based candidate selection. Evaluation with the RoFA metric—applying linearly decaying prediction weights—demonstrates a substantial improvement in robust, timely entity linking.

7. Metrics, Evaluation, and Comparison

A variety of metrics are consistently emphasized across the literature:

Metric	Description	Exemplifying Paper
Performance	Execution time, speedup, instruction throughput	SJMalloc (Bauroth, 23 Oct 2024), MaxMem (Raybuck et al., 2023)
Memory Usage	Peak/high-water mark, fragmentation, compaction	Mesh (Powers et al., 2019), grammatically-evolved DMMs (Risco-Martín et al., 7 Mar 2024, Risco-Martín et al., 22 Jun 2024)
QoS/Latency	Fast memory miss ratio, application stalling	MaxMem (Raybuck et al., 2023), memos (Liu et al., 2017)
Energy	Subsystem and total energy, DRAM/NVM efficiency	HMMU (Wen et al., 2020), AXES (Maity et al., 2020)
Robustness	Security, error detection, attack resilience	SJMalloc (Bauroth, 23 Oct 2024), randomization (Astrakhantseva et al., 2021)/Mesh (Powers et al., 2019)

Empirical results document improvements ranging from <5% memory overhead for hardened allocators up to 54% higher throughput (GMEM (Zhu et al., 2023)), >37% energy savings (AXES), and order-of-magnitude reductions in 99th percentile latency (MaxMem).

Memory managers have evolved into multifaceted subsystems that incorporate strategies for handling growing memory hierarchies, diverse hardware, demanding application QoS, and stringent security prerequisites. The architectural spectrum—from lightweight, object-oriented compositional simulators to kernel/hardware hybrids and user-space overcommitment libraries—reflects the need for memory managers that can be rapidly adapted, thoroughly evaluated, and optimized across a range of scientific, embedded, high-performance, and security-critical scenarios.