diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..4c6bf62
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,74 @@
+{
+ "files.associations": {
+ "cctype": "cpp",
+ "clocale": "cpp",
+ "cmath": "cpp",
+ "csignal": "cpp",
+ "cstdarg": "cpp",
+ "cstddef": "cpp",
+ "cstdio": "cpp",
+ "cstdlib": "cpp",
+ "cstring": "cpp",
+ "ctime": "cpp",
+ "cwchar": "cpp",
+ "cwctype": "cpp",
+ "array": "cpp",
+ "atomic": "cpp",
+ "strstream": "cpp",
+ "bit": "cpp",
+ "*.tcc": "cpp",
+ "bitset": "cpp",
+ "chrono": "cpp",
+ "compare": "cpp",
+ "complex": "cpp",
+ "concepts": "cpp",
+ "condition_variable": "cpp",
+ "cstdint": "cpp",
+ "deque": "cpp",
+ "list": "cpp",
+ "map": "cpp",
+ "set": "cpp",
+ "unordered_map": "cpp",
+ "unordered_set": "cpp",
+ "vector": "cpp",
+ "exception": "cpp",
+ "algorithm": "cpp",
+ "functional": "cpp",
+ "iterator": "cpp",
+ "memory": "cpp",
+ "memory_resource": "cpp",
+ "numeric": "cpp",
+ "optional": "cpp",
+ "random": "cpp",
+ "ratio": "cpp",
+ "string": "cpp",
+ "string_view": "cpp",
+ "system_error": "cpp",
+ "tuple": "cpp",
+ "type_traits": "cpp",
+ "utility": "cpp",
+ "fstream": "cpp",
+ "future": "cpp",
+ "initializer_list": "cpp",
+ "iomanip": "cpp",
+ "iosfwd": "cpp",
+ "iostream": "cpp",
+ "istream": "cpp",
+ "limits": "cpp",
+ "mutex": "cpp",
+ "new": "cpp",
+ "ostream": "cpp",
+ "ranges": "cpp",
+ "shared_mutex": "cpp",
+ "sstream": "cpp",
+ "stdexcept": "cpp",
+ "stop_token": "cpp",
+ "streambuf": "cpp",
+ "thread": "cpp",
+ "cfenv": "cpp",
+ "cinttypes": "cpp",
+ "typeindex": "cpp",
+ "typeinfo": "cpp",
+ "variant": "cpp"
+ }
+}
\ No newline at end of file
diff --git a/README.md b/README.md
index 562601c..4b332e4 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,124 @@
-Experiment files for the RTIS ros_rt executor. Clone with submodules on, or add the executor to the src folder.
+# Dynamic Priority Scheduling for ROS-EDF Executor
-Run `ros2 run casestudy casestudy_example` then run the `analysis/analysis.ipynb` notebook.
\ No newline at end of file
+This repository contains experiments for the ROS-EDF executor with priority-based and deadline-based scheduling capabilities.
+
+## Usage
+
+Clone with submodules enabled:
+```
+git clone --recurse-submodules https://github.com/user/ROS-Dynamic-Executor-Experiments.git
+```
+
+Make sure you have sourced your ROS 2 environment.
+
+Run the example:
+```
+ros2 run casestudy casestudy_example
+```
+
+Then run the analysis notebook:
+```
+jupyter notebook analysis/analysis.ipynb
+```
+
+---
+
+
+## Dynamically Adjusting Scheduler Approaches
+
+The PriorityMemoryStrategy supports different scheduling policies for ROS callbacks. To extend it with dynamically adjusting scheduling that reacts to semantic events, there are several viable approaches:
+
+### Approach A: Custom PriorityExecutableComparator
+
+Replace the existing comparator with a custom implementation:
+
+**Pros:**
+- Direct control over scheduling logic
+- Clean separation of concerns
+- Can implement complex scheduling policies
+- Doesn't require modifying deadline values
+
+**Cons:**
+- Requires understanding and maintaining the comparison logic
+- May need to add new fields to PriorityExecutable to track dynamic priorities
+- Could become complex if multiple factors affect priority
+
+### Approach B: Dynamic Deadline Adjustment
+
+Keep the EDF comparator but adjust deadlines based on priority logic:
+
+**Pros:**
+- Works within the existing EDF framework
+- Conceptually simple - just manipulate deadline values
+- Doesn't require changing the core sorting mechanism
+- Easier to debug (you can log deadline changes)
+
+**Cons:**
+- Potentially confusing semantics (using deadlines to represent non-deadline priorities)
+- May interfere with actual deadline-based requirements
+- Could lead to instability if not carefully managed
+
+### Approach C: Event-Driven Priority Field
+
+Add a dynamic boost factor field to PriorityExecutable:
+
+```cpp
+int dynamic_priority_boost = 0;
+```
+
+Then modify the comparator to consider this value:
+
+```cpp
+if (p1->sched_type == DEADLINE) {
+ // First compare deadline as usual
+ // Then use dynamic_priority_boost as a tiebreaker
+ if (p1_deadline == p2_deadline) {
+ return p1->dynamic_priority_boost > p2->dynamic_priority_boost;
+ }
+ return p1_deadline < p2_deadline;
+}
+```
+
+**Pros:**
+- Clearer semantics than manipulating deadlines
+- Keeps deadline scheduling intact for real-time guarantees
+- Easy to adjust at runtime
+
+**Cons:**
+- Requires modifying both PriorityExecutable and comparison logic
+
+### Approach D: Priority Multiplier System
+
+Implement a system where chains can have priority multipliers applied:
+
+```cpp
+float priority_multiplier = 1.0f;
+```
+
+And then in the comparator:
+
+```cpp
+// For priority-based scheduling
+int effective_p1 = p1->priority * p1->priority_multiplier;
+int effective_p2 = p2->priority * p2->priority_multiplier;
+return effective_p1 < effective_p2;
+```
+
+**Pros:**
+- Scales existing priorities rather than replacing them
+- Preserves relative importance within chains
+- Intuitive model for temporary priority boosts
+
+**Cons:**
+- May need to handle overflow/boundary cases
+- Requires careful tuning of multiplier ranges
+
+## Recommendation
+
+Approach C (Event-Driven Priority Field) offers the best balance of:
+1. Clean semantics
+2. Minimal interference with existing scheduling logic
+3. Clear separation between baseline priorities and dynamic adjustments
+4. Straightforward implementation
+
+This approach maintains real-time guarantees while enabling dynamic behaviors based on semantic events.
diff --git a/analysis/analysis.ipynb b/analysis/analysis.ipynb
index 9fe4dbf..5c7b860 100644
--- a/analysis/analysis.ipynb
+++ b/analysis/analysis.ipynb
@@ -58,9 +58,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Number of records: 11755\n",
- "First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'node_0', 'count': 500, 'next_release_us': 99884}, 'time': 0.0001}\n",
- "Operation types: ['wait_for_work', 'next_deadline', 'end_work', 'get_next_executable', 'start_work']\n"
+ "Number of records: 12118\n",
+ "First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'node_0', 'count': 500, 'next_release_us': 99886}, 'time': 0.0001}\n",
+ "Operation types: ['get_next_executable', 'next_deadline', 'wait_for_work', 'end_work', 'start_work']\n"
]
}
],
@@ -164,18 +164,18 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f4ac2c553514434b9f1980bd9bc391d7",
+ "model_id": "3c172c35015c4ffd92a176ce80ffd18d",
"version_major": 2,
"version_minor": 0
},
- "image/png": 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",
+ "image/png": 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",
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