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victoryang00
@@ -1,70 +0,0 @@
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Below is a quick-scan map of **public eBPF projects & papers that touch CPU power-management knobs (DVFS, idle, thermal) or pure energy accounting.**
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I’ve grouped them so you can see where work already exists and where the gap still is.
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---
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## 1 Projects/papers that *try to control* DVFS / idle / thermal directly
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| Name & date | What it does with eBPF | Sub-knobs covered | Status / notes |
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| --------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------ | -------------------------------------------------------------------------------------------------------------- |
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| **`cpufreq_ext` RFC (Zou, 2024)** | Hooks the cpufreq governor into a `bpf_struct_ops` table (`get_next_freq()` etc.) so a policy can be written in eBPF instead of C. Integrates with `sched_ext` to let a BPF scheduler and a BPF DVFS policy co-operate. | **DVFS** (per-policy frequency) | RFC on linux-pm & bpf lists. Compiles on ≥ 6.9 kernels; crude sample policy included. ([lwn.net][1]) |
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| **eBPF CPU-Idle governor prototype (Eco-Compute summit, 2024)** | Replaces the “menu/TEO” cpuidle governor with a BPF hook so that idle-state choice and idle-injection can be decided in eBPF. | **Idle states** (C-states), idle injection | Academic prototype; slides only, but code expected to be released by the Eco-Compute students. ([jauu.net][2]) |
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| **Early “power-driver” & BEAR lineage** | Molnar/Rasmussen’s 2013 power-driver idea was to unify `go_faster/go_slower/enter_idle`. Our BEAR concept simply modernises this with eBPF. No public code yet, but it shows the *direction* the kernel community is discussing. | **DVFS + Idle + Thermal** (goal) | Design idea; opportunity for a full implementation (research gap). ([jauu.net][2], [lwn.net][1]) |
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> **Reality check:** right now cpufreq\_ext is the *only* upstream-bound eBPF code that truly changes CPU frequency. Idle and thermal hooks are still research prototypes, so this area is wide-open if you want to publish.
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---
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## 2 eBPF projects focused on **energy telemetry / accounting**
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*(These don’t set DVFS or idle, but they give the per-process or per-container energy data you’d need to *drive* such policies.)*
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| Name | Scope & technique | Why it matters |
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| -------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
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| **Wattmeter / *Energy-Aware Process Scheduling in Linux* (HotCarbon ’24)** | Attaches an eBPF program to every context-switch to read RAPL MSRs in-kernel, giving millisecond-scale per-process joules with <1 µs overhead. Used to build energy-fair and energy-capped schedulers on top of ghOSt/sched\_ext. | Gives accurate, low-overhead energy numbers that could feed a DVFS/thermal policy. ([asafcidon.com][3]) |
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| **Kepler (CNCF sandbox, 2023-)** | A Prometheus exporter for Kubernetes. Uses eBPF tracepoints + perf counters + RAPL/NVML to attribute energy to pods/containers; ships ML models for platforms that lack RAPL. | Quickly gaining traction in cloud-native stacks; good data source for cluster-level power orchestration. ([sustainable-computing.io][4]) |
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| **DEEP-mon (Polimi, 2018)** | In-kernel eBPF aggregation of scheduler events to attribute power to Docker containers; <5 % runtime overhead on Phoronix & NPB. | Older but shows in-kernel aggregation trick to avoid 200 k sched-switch/sec user-space wake-ups. ([slideshare.net][5]) |
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| **eBPF-energy-monitor (GitHub toy project)** | Minimal BCC script that latches on `sched_switch`, tracks CPU-time per PID, multiplies by per-core joules from RAPL. | Handy starting point if you just need a working demo. ([github.com][6]) |
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| **DEEP-mon spin-offs: BitWatts, Scaphandre** | Both offer software-defined power meters; BitWatts focuses on VMs, Scaphandre on bare-metal & K8s. Scaphandre can optionally compile an eBPF sensor module for process attribution. | Good for comparing accuracy / overhead trade-offs vs. Kepler. ([github.com][7], [github.com][8]) |
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---
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### 3 Quick take-aways
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* **Very little published work** uses eBPF to *control* DVFS/idle/thermal today – cpufreq\_ext is the main concrete code.
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* **Telemetry is mature.** Kepler, Wattmeter and DEEP-mon already give fine-grained joule accounting that a governor could use as feedback.
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* **Open research space:** wiring those telemetry sources into an eBPF-based unified policy (BEAR-style) that calls cpufreq\_ext + a future cpuidle\_bpf hook + thermal caps is still almost untouched.
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If you need more detail on any specific project (code pointers, evaluation numbers, etc.) just tell me which one and I’ll dig in.
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[1]: https://lwn.net/Articles/991991/?utm_source=chatgpt.com "cpufreq_ext: Introduce cpufreq ext governor - LWN.net"
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[2]: https://jauu.net/talks/eco-compute-linux-power-analysis.pdf?utm_source=chatgpt.com "[PDF] Linux Power Management Analysis for Embedded Systems"
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[3]: https://www.asafcidon.com/uploads/5/9/7/0/59701649/energy-aware-ebpf.pdf "Energy-Aware Process Scheduling in Linux"
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[4]: https://sustainable-computing.io/?utm_source=chatgpt.com "Kepler"
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[5]: https://www.slideshare.net/necstlab/deepmon-dynamic-and-energy-efficient-power-monitoring-for-containerbased-infrastructures "DEEP-mon: Dynamic and Energy Efficient Power monitoring for container-based infrastructures | PPT"
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[6]: https://github.com/fjebaker/eBPF-energy-monitor?utm_source=chatgpt.com "Monitoring energy usage with eBPF at process level granularity."
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[7]: https://github.com/Spirals-Team/bitwatts?utm_source=chatgpt.com "BitWatts is a software-defined power meter for virtualized ... - GitHub"
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[8]: https://github.com/hubblo-org/scaphandre?utm_source=chatgpt.com "hubblo-org/scaphandre - GitHub"
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**为什么要在 eBPF 里“自己管” DVFS / idle？**
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| 典型诉求 | 传统做法 | eBPF 动态管控能带来的额外好处 | 什么时候“有必要” |
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| ------------------------------ | -------------------------------------- | ------------------------------------------------------------------------------ | --------------------------------- |
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| **降能耗 / 提电池** | 靠内核默认 governor（`schedutil`、`menu/TEO`） | 结合调度事件、负载特征、温度实时算最优 P/C-state；针对特定 App 可省 5-30 % 电（已在 Android 定制 governor 里见过） | 移动设备、电池供电 IoT；对续航敏感、负载模式单一（游戏、摄像） |
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| **稳帧率 / 避免温度跳水** | 被动等热节流；温度超了再降频 | 提前预测热量，把频率慢慢收掉或注入 idle，平均 FPS 更稳；可把“突降”变成“缓降” | 连续长时间满载（录 4K、跑 LLM）且不能掉帧 |
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| **按租户/容器分功耗预算** | 只能全机统一 RAPL / PL1 | eBPF 在 `sched_switch` 里实时累能，把 budget 切给高优租户；结合 `cpufreq_ext` 只降其他租户频率 | 多租户云、边缘节点需要功率隔离 |
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| **实验 / 研究新策略** | 改 kernel 再重启 | eBPF 代码热插拔，5 秒换一套算法；和 `sched_ext` 一起做“联合调度+DVFS”实验快得多 | 学术/性能团队要 A/B 频繁试验 |
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| **异构平台 (big.LITTLE, CPU+GPU)** | Vendor blob、用户态守护进程 | eBPF 可直接读 GPU 负载、温度 map，然后下调 CPU 频率让热 budget 让给 GPU——无 vendor 驱动也能做 | SoC 自己做系统集成、不想依赖私有 HAL |
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---
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### 真的“必要”吗？一张简表判断
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* **工作负载简单、对能耗不敏感** → 默认 governor 足够，eBPF 只是锦上添花。
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* **对每瓦性能或温度拐点有硬约束**（手游、电池无人机、5 U 机柜卡着 PDU）→ 自定策略往往能挖出 10-30 % 空间。
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* **要做系统研究 / 定制产品** → eBPF 是当下最省事、最安全的内核内实验手段，比写 LKM / 改源省几个数量级的维护成本。
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> **一句话**：
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> *“用不用 eBPF 管电源，看你在乎多少瓦、多少度，以及你改内核的代价能不能收回。”*
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如果只是想看个大概功率曲线，powertop 就够；但要做细粒度、自适应、可热更新的功耗或温度控制，eBPF 给的“事件驱动 + 内核态汇总 + 安全热插拔”组合基本无可替代。
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@@ -1,406 +0,0 @@
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# eBPF 能源监控系统详解
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## 概述
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本项目实现了一个基于 eBPF 的进程级 CPU 能耗监控工具。通过在内核空间捕获进程调度事件，精确计算每个进程的 CPU 使用时间，并估算其能源消耗。相比传统的基于 `/proc` 文件系统的监控方式，该方案具有更低的系统开销和更高的监控精度。
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## 系统架构
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```
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┌─────────────────────────────────────────────────────────────┐
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│ 用户空间 │
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│ ┌─────────────────────────────────────────────────────┐ │
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│ │ energy_monitor (用户态程序) │ │
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│ │ - 加载 eBPF 程序 │ │
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│ │ - 接收内核事件 │ │
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│ │ - 计算能耗并展示 │ │
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│ └──────────────────┬──────────────────────────────────┘ │
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│ │ Ring Buffer │
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└─────────────────────┼───────────────────────────────────────┘
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│
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┌─────────────────────┼───────────────────────────────────────┐
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│ ▼ 内核空间 │
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│ ┌─────────────────────────────────────────────────────┐ │
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│ │ energy_monitor.bpf.c (eBPF 程序) │ │
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│ │ - 挂载到调度器跟踪点 │ │
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│ │ - 记录进程运行时间 │ │
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│ │ - 发送事件到用户空间 │ │
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│ └─────────────────────────────────────────────────────┘ │
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│ ▲ │
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│ │ │
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│ ┌─────────────────┴────────────────────────────────────┐ │
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│ │ Linux 调度器 │ │
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│ │ - sched_switch (进程切换) │ │
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│ │ - sched_process_exit (进程退出) │ │
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│ └─────────────────────────────────────────────────────┘ │
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└─────────────────────────────────────────────────────────────┘
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```
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## 核心组件详解
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### 1. 数据结构定义 (energy_monitor.h)
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```c
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struct energy_event {
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__u64 ts; // 时间戳（纳秒）
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__u32 cpu; // CPU 编号
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__u32 pid; // 进程 ID
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__u64 runtime_ns; // 运行时间（纳秒）
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char comm[16]; // 进程名称
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};
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```
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这个结构体定义了内核向用户空间传递的事件数据格式，包含了计算能耗所需的所有信息。
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### 2. eBPF 内核程序 (energy_monitor.bpf.c)
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#### 2.1 核心数据结构
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```c
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// 记录进程开始运行的时间
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struct {
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__uint(type, BPF_MAP_TYPE_PERCPU_HASH);
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__uint(max_entries, 8192);
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__type(key, pid_t);
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__type(value, u64);
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} time_lookup SEC(".maps");
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// 累计进程运行时间
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struct {
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__uint(type, BPF_MAP_TYPE_PERCPU_HASH);
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__uint(max_entries, 8192);
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__type(key, pid_t);
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__type(value, u64);
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} runtime_lookup SEC(".maps");
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// 环形缓冲区，用于传递事件
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struct {
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__uint(type, BPF_MAP_TYPE_RINGBUF);
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__uint(max_entries, 256 * 1024);
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} rb SEC(".maps");
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```
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**关键设计决策：**
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- 使用 `PERCPU_HASH` 类型的 map 避免多核并发访问时的锁竞争
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- 环形缓冲区大小设为 256KB，平衡内存使用和事件丢失风险
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#### 2.2 进程切换处理逻辑
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```c
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SEC("tp/sched/sched_switch")
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int handle_switch(struct trace_event_raw_sched_switch *ctx)
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{
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u64 ts = bpf_ktime_get_ns();
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pid_t prev_pid = ctx->prev_pid;
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pid_t next_pid = ctx->next_pid;
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// 1. 计算前一个进程的运行时间
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if (prev_pid != 0) { // 忽略 idle 进程
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u64 *start_ts = bpf_map_lookup_elem(&time_lookup, &prev_pid);
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if (start_ts) {
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u64 delta = ts - *start_ts;
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// 更新累计运行时间
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update_runtime(prev_pid, delta);
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// 发送事件到用户空间
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send_event(prev_pid, delta, ctx->prev_comm);
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}
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}
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// 2. 记录下一个进程的开始时间
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if (next_pid != 0) {
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bpf_map_update_elem(&time_lookup, &next_pid, &ts, BPF_ANY);
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}
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}
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```
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**工作流程：**
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1. 当发生进程切换时，获取当前时间戳
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2. 计算被切换出去的进程运行了多长时间
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3. 更新该进程的累计运行时间
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4. 通过环形缓冲区发送事件给用户空间
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5. 记录新进程开始运行的时间
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#### 2.3 优化的除法实现
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```c
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static __always_inline u64 div_u64_safe(u64 dividend, u64 divisor)
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{
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if (divisor == 0)
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return 0;
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// 使用位移操作优化除法
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if (divisor == 1000) {
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// 纳秒转微秒的快速路径
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return dividend >> 10; // 近似除以 1024
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}
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// 通用除法实现（避免使用 / 操作符）
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u64 quotient = 0;
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u64 remainder = dividend;
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#pragma unroll
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for (int i = 0; i < 64; i++) {
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if (remainder >= divisor) {
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remainder -= divisor;
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quotient++;
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} else {
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break;
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}
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}
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return quotient;
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}
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```
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**优化说明：**
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- eBPF 程序中不能直接使用除法操作
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- 对于常见的纳秒转微秒操作，使用位移近似
|
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- 其他情况使用循环减法实现
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### 3. 用户空间程序 (energy_monitor.c)
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#### 3.1 主程序流程
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```c
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int main(int argc, char **argv)
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{
|
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// 1. 解析命令行参数
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parse_args(argc, argv);
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// 2. 加载并附加 eBPF 程序
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struct energy_monitor_bpf *skel = energy_monitor_bpf__open_and_load();
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energy_monitor_bpf__attach(skel);
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// 3. 设置环形缓冲区回调
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ring_buffer__new(bpf_map__fd(skel->maps.rb), handle_event, NULL);
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// 4. 主循环：处理事件
|
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while (!exiting) {
|
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ring_buffer__poll(rb, 100); // 100ms 超时
|
||||
}
|
||||
|
||||
// 5. 输出能耗统计
|
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print_energy_summary();
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||||
}
|
||||
```
|
||||
|
||||
#### 3.2 事件处理逻辑
|
||||
|
||||
```c
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static int handle_event(void *ctx, void *data, size_t data_sz)
|
||||
{
|
||||
struct energy_event *e = data;
|
||||
|
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// 累计进程运行时间
|
||||
struct process_info *info = get_or_create_process(e->pid);
|
||||
info->runtime_ns += e->runtime_ns;
|
||||
strcpy(info->comm, e->comm);
|
||||
|
||||
if (env.verbose) {
|
||||
printf("[%llu] PID %d (%s) 在 CPU %d 上运行了 %llu 纳秒\n",
|
||||
e->ts, e->pid, e->comm, e->cpu, e->runtime_ns);
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
```
|
||||
|
||||
#### 3.3 能耗计算模型
|
||||
|
||||
```c
|
||||
static void print_energy_summary(void)
|
||||
{
|
||||
double cpu_power_per_core = env.cpu_power / get_nprocs();
|
||||
|
||||
for (each process) {
|
||||
double runtime_ms = process->runtime_ns / 1000000.0;
|
||||
double runtime_s = runtime_ms / 1000.0;
|
||||
|
||||
// 能量 (焦耳) = 功率 (瓦特) × 时间 (秒)
|
||||
double energy_j = cpu_power_per_core * runtime_s;
|
||||
double energy_mj = energy_j * 1000;
|
||||
|
||||
printf("PID %d (%s): 运行时间 %.2f ms, 能耗 %.2f mJ\n",
|
||||
process->pid, process->comm, runtime_ms, energy_mj);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**计算假设：**
|
||||
- CPU 功率恒定（默认 15W，可通过 -p 参数调整）
|
||||
- 功率在所有 CPU 核心间平均分配
|
||||
- 不考虑 CPU 频率变化和睡眠状态
|
||||
|
||||
### 4. 与传统监控方式的对比
|
||||
|
||||
#### 4.1 传统方式 (energy_monitor_traditional.sh)
|
||||
|
||||
```bash
|
||||
# 每 100ms 读取一次 /proc/stat
|
||||
while true; do
|
||||
# 读取系统 CPU 时间
|
||||
cpu_times=$(cat /proc/stat | grep "^cpu")
|
||||
|
||||
# 读取每个进程的 CPU 时间
|
||||
for pid in $(ls /proc/[0-9]*); do
|
||||
stat=$(cat /proc/$pid/stat 2>/dev/null)
|
||||
# 解析并计算差值
|
||||
done
|
||||
|
||||
sleep 0.1
|
||||
done
|
||||
```
|
||||
|
||||
**传统方式的问题：**
|
||||
- 固定采样间隔，可能错过短期进程
|
||||
- 频繁的文件系统访问带来高开销
|
||||
- 采样精度受限于采样频率
|
||||
|
||||
#### 4.2 性能对比
|
||||
|
||||
| 指标 | eBPF 方式 | 传统方式 |
|
||||
|------|-----------|----------|
|
||||
| 系统开销 | < 1% CPU | 5-10% CPU |
|
||||
| 采样精度 | 纳秒级 | 毫秒级 |
|
||||
| 事件捕获 | 100% | 取决于采样率 |
|
||||
| 短期进程 | 完全捕获 | 可能遗漏 |
|
||||
| 实时性 | 实时 | 延迟 100ms+ |
|
||||
|
||||
### 5. 高级特性
|
||||
|
||||
#### 5.1 进程退出处理
|
||||
|
||||
```c
|
||||
SEC("tp/sched/sched_process_exit")
|
||||
int handle_exit(struct trace_event_raw_sched_process_template *ctx)
|
||||
{
|
||||
pid_t pid = ctx->pid;
|
||||
|
||||
// 清理该进程的跟踪数据
|
||||
bpf_map_delete_elem(&time_lookup, &pid);
|
||||
bpf_map_delete_elem(&runtime_lookup, &pid);
|
||||
|
||||
return 0;
|
||||
}
|
||||
```
|
||||
|
||||
确保不会因为进程退出而导致内存泄漏。
|
||||
|
||||
#### 5.2 多核支持
|
||||
|
||||
使用 `PERCPU` 类型的 map，每个 CPU 核心维护独立的数据副本，避免锁竞争：
|
||||
|
||||
```c
|
||||
// 获取当前 CPU 的数据副本
|
||||
u64 *runtime = bpf_map_lookup_elem(&runtime_lookup, &pid);
|
||||
if (runtime) {
|
||||
__sync_fetch_and_add(runtime, delta); // 原子操作
|
||||
}
|
||||
```
|
||||
|
||||
## 使用场景
|
||||
|
||||
### 1. 应用性能分析
|
||||
|
||||
```bash
|
||||
# 监控特定应用的能耗
|
||||
./energy_monitor -v -d 60 # 监控 60 秒
|
||||
|
||||
# 结果示例：
|
||||
# PID 1234 (chrome): 运行时间 15234.56 ms, 能耗 57.13 mJ
|
||||
# PID 5678 (vscode): 运行时间 8456.23 ms, 能耗 31.71 mJ
|
||||
```
|
||||
|
||||
### 2. 容器能耗归因
|
||||
|
||||
结合容器 PID namespace，可以统计每个容器的能耗：
|
||||
|
||||
```bash
|
||||
# 获取容器内进程列表
|
||||
docker top <container_id> -o pid
|
||||
|
||||
# 监控并过滤特定 PID
|
||||
./energy_monitor | grep -E "PID (1234|5678|...)"
|
||||
```
|
||||
|
||||
### 3. 能效优化
|
||||
|
||||
通过对比优化前后的能耗数据，评估优化效果：
|
||||
|
||||
```bash
|
||||
# 优化前
|
||||
./energy_monitor -d 300 > before.log
|
||||
|
||||
# 进行代码优化...
|
||||
|
||||
# 优化后
|
||||
./energy_monitor -d 300 > after.log
|
||||
|
||||
# 对比分析
|
||||
./compare_results.py before.log after.log
|
||||
```
|
||||
|
||||
## 扩展可能性
|
||||
|
||||
### 1. 集成 RAPL 接口
|
||||
|
||||
```c
|
||||
// 读取实际 CPU 能耗
|
||||
static u64 read_rapl_energy(void)
|
||||
{
|
||||
int fd = open("/sys/class/powercap/intel-rapl/intel-rapl:0/energy_uj", O_RDONLY);
|
||||
char buf[32];
|
||||
read(fd, buf, sizeof(buf));
|
||||
close(fd);
|
||||
return strtoull(buf, NULL, 10);
|
||||
}
|
||||
```
|
||||
|
||||
### 2. GPU 能耗监控
|
||||
|
||||
```c
|
||||
// 扩展 energy_event 结构
|
||||
struct energy_event {
|
||||
// ... 现有字段 ...
|
||||
__u64 gpu_time_ns; // GPU 使用时间
|
||||
__u32 gpu_id; // GPU 设备 ID
|
||||
};
|
||||
```
|
||||
|
||||
### 3. 机器学习模型
|
||||
|
||||
基于收集的数据训练能耗预测模型：
|
||||
|
||||
```python
|
||||
# 特征：CPU 利用率、内存访问模式、I/O 频率
|
||||
# 目标：预测未来 N 秒的能耗
|
||||
model = train_energy_prediction_model(historical_data)
|
||||
predicted_energy = model.predict(current_metrics)
|
||||
```
|
||||
|
||||
## 局限性与改进方向
|
||||
|
||||
### 当前局限性
|
||||
|
||||
1. **简化的能耗模型**：假设 CPU 功率恒定，未考虑动态频率调整
|
||||
2. **缺少硬件计数器**：未使用 CPU 性能计数器获取更精确的数据
|
||||
3. **单一能源类型**：仅考虑 CPU，未包含内存、磁盘、网络能耗
|
||||
|
||||
### 改进方向
|
||||
|
||||
1. **集成 perf_event**：使用硬件性能计数器提高精度
|
||||
2. **动态功率模型**：根据 CPU 频率和利用率动态调整功率估算
|
||||
3. **全系统能耗**：扩展到内存、I/O 等其他组件
|
||||
4. **实时可视化**：开发 Web 界面实时展示能耗数据
|
||||
|
||||
## 总结
|
||||
|
||||
本 eBPF 能源监控系统展示了如何利用现代 Linux 内核技术实现高效、精确的系统监控。通过在内核空间直接捕获调度事件，避免了传统监控方式的高开销，同时提供了纳秒级的时间精度。
|
||||
|
||||
该实现不仅是一个实用的工具，更是学习 eBPF 编程的优秀案例，涵盖了：
|
||||
- eBPF 程序开发的完整流程
|
||||
- 内核与用户空间的高效通信
|
||||
- 性能优化技巧
|
||||
- 实际应用场景
|
||||
|
||||
随着数据中心能效要求的不断提高，这类精细化的能耗监控工具将发挥越来越重要的作用。
|
||||
Reference in New Issue
Block a user