Understanding Policy Groups: A New Approach to Memory Management in Linux
In this Q&A, we explore Chris Li's proposed 'policy groups' for memory management, presented at the 2026 Linux Storage, Filesystem, Memory Management, and BPF Summit. Learn about the limitations of control groups and how policy groups aim to address them.
What are policy groups and who proposed them?
Policy groups are a conceptual enhancement to Linux memory management, introduced by kernel developer Chris Li during a memory-management track at the 2026 Linux Storage, Filesystem, Memory Management, and BPF Summit. They are designed to complement—and in some cases replace—the traditional control group (cgroup) subsystem for certain resource management scenarios. While cgroups excel at isolating and limiting resources like memory and CPU for groups of processes, Li argued that they fall short for more nuanced policy-driven use cases. Policy groups aim to provide a flexible, rule-based framework that allows administrators to define memory management policies based on application behavior, system state, or user-defined criteria. This proposal emerged from Li's practical experience with cgroup limitations in production environments, where fine-grained control beyond simple limits is often needed. The session sparked lively discussion, though a unified vision for how policy groups should work remains elusive.
Why are control groups insufficient for some use cases?
Control groups (cgroups) are a core Linux feature for resource management, widely used for memory, CPU, and I/O isolation. However, as Chris Li pointed out, they work well only for specific scenarios—like containerization or workload partitioning—where the goal is to enforce hard limits or guarantees. For other use cases, such as adaptive memory sharing, priority-based allocation, or dynamic policy adjustments based on system load, cgroups become cumbersome. For instance, cgroups cannot easily express 'this application should get more memory when idle memory is abundant, but less under pressure' without complex user-space scripts. They also lack built-in support for policies that cross group boundaries, such as memory stealing among workloads with different priorities. Li argued that these shortcomings limit innovation in memory management and create unnecessary overhead for administrators trying to implement sophisticated resource strategies. Policy groups address these gaps by offering a declarative, kernel-level mechanism for defining and enforcing memory policies.
What specific problem does Chris Li aim to solve with policy groups?
The primary problem Chris Li aims to solve is the inflexibility of control groups when applied to non-isolation use cases. In his talk at the summit, Li highlighted that cgroups are essentially a 'one-size-fits-all' tool designed primarily for resource accounting and enforcement of limits. Yet modern systems often require more nuanced memory management—such as differentiated service levels, application-aware pressure handling, or reclaim strategies that adapt to workload patterns. For example, an administrator might want a background batch job to yield memory aggressively to a latency-sensitive web server, but only when memory pressure is high. Achieving this with cgroups alone requires complex user-space monitoring and manual adjustments, which are error-prone and inefficient. Li's policy groups proposal aims to embed such intelligence directly into the kernel, allowing policies to be defined declaratively and enforced automatically. This would reduce administrative overhead and enable new capabilities like memory prioritization, adaptive quotas, and cross-group cooperation.
How do policy groups differ from traditional control groups?
Policy groups differ from traditional control groups (cgroups) in both philosophy and mechanics. While cgroups are hierarchical, process-based containers that manage resource limits and accounting, policy groups are rule-driven entities that define how memory should be allocated, reclaimed, or shared across processes. In Li's vision, policy groups are not a replacement for cgroups but an optional layer that can sit atop or alongside them. For example, a cgroup might set a maximum memory limit, while a policy group could specify that within that limit, certain processes get higher reclaim priority. Policy groups also support dynamic conditions—such as 'when memory pressure exceeds a threshold, reduce allocation to non-critical workloads'—which cgroups cannot natively express. Furthermore, policy groups can span multiple cgroups, enabling coordinated policies across different resource control domains. This flexibility makes them better suited for complex, policy-driven environments like cloud computing, mixed-criticality systems, or user-facing services with varying demands.
What was the reaction or consensus at the summit regarding policy groups?
According to the original report, a consensus on how policy groups should look still seems distant. While Chris Li's presentation received attention and sparked discussion among attendees, opinions varied on the exact design and implementation approach. Some kernel developers expressed support for the concept of more flexible memory policies, but raised concerns about complexity, integration with existing cgroup infrastructure, and potential performance overhead. Others suggested that the problem could be solved with improved user-space tools rather than kernel changes. The lack of a clear agreement means that policy groups remain a proposal under active debate rather than a committed feature. Li acknowledged that further refinement is needed and encouraged community experimentation. The summit served as a platform to gather feedback, with the hope that a more concrete design might emerge from ongoing discussions. Until then, the memory management subsystem continues to rely on cgroups for most resource control tasks.
What improvements could policy groups bring to memory management?
If implemented, policy groups could bring several improvements to Linux memory management. First, they would allow administrators to define fine-grained, adaptive policies that respond to real-time system conditions—such as memory pressure, workload importance, or energy constraints. This would enable more efficient memory utilization, as idle memory could be dynamically reassigned to active tasks without manual intervention. Second, policy groups could simplify the creation of differentiated service tiers, where high-priority applications receive preferential memory treatment during contention. Third, they could support cooperative memory sharing across processes, reducing overall memory footprint in multi-tenant environments. Fourth, by embedding policy logic in the kernel, policy groups could reduce the need for complex user-space daemons, lowering latency and improving reliability. Finally, they could provide better accounting and observability, making it easier to audit memory usage decisions. These improvements align with modern workload demands, such as those in cloud computing, real-time systems, and AI/ML inference serving.
What are the challenges in implementing policy groups?
Implementing policy groups faces several significant challenges, as highlighted during the summit discussion. One major hurdle is maintaining backward compatibility with the existing cgroup subsystem, which is deeply integrated into the kernel and heavily used in containerization (e.g., Docker, Kubernetes). Any new feature must not break existing workflows. Another challenge is performance: adding policy evaluation logic to memory management paths could introduce overhead, especially for latency-sensitive workloads. Kernel developers also worry about complexity—policy groups could become a sprawling mechanism with many configuration options, leading to maintenance burdens and potential bugs. Additionally, there is debate over whether the kernel should even be responsible for such high-level policies, since many administrators prefer to manage these decisions in user space using tools like systemd or custom controllers. Finally, reaching consensus on a design that satisfies diverse use cases—from embedded systems to massive data centers—is difficult. Li's proposal is a starting point, but significant engineering and community alignment will be required before policy groups become a reality.