pull latest mainline changes, address pull request comments

README.md

@@ -79,6 +79,11 @@ Currently, the `--step-function` mode is designed to optimize step functions tha
 2. `src/step_function/step_function.py`: Update the `_create_state` function to parse the configuration files and create the state types.
 
 
+## Support for 2D optimization for both CPU and memory configurations in GCP Cloud Functions V2
+
+GCP Cloud Functions V2 allows decoupled configuration for CPU and memory allocations. Parrotfish can optimize the CPU and memory configuration of the V2 functions. To enable this feature, use `GCPv2` as the vendor in the config file.
+
+
 ## Acknowledgments
 
 This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), Mitacs, and The

benchmarks/aws_step_functions/ImageProcessingStateMachine/parrotfish.json

@@ -1,5 +1,5 @@
 {
-    "arn": "arn:aws:states:us-west-2:898429789601:stateMachine:ImageProcessing",
-    "region": "us-west-2",
-    "payload": {}
+  "arn": "arn:aws:states:us-west-2:898429789601:stateMachine:ImageProcessing",
+  "region": "us-west-2",
+  "payload": {}
 }

src/configuration/README.md

@@ -5,7 +5,7 @@
 ```
 {
     "function_name": The serverless function's name,
-    "vendor": The cloud provider ("AWS" or "GCP"),
+    "vendor": The cloud provider ("AWS" or "GCP" or "GCPv2"),
     "region": The serverless function's region,
     "payload": Payload to invoke the serverless function with (Required if "payloads" attribute is not provided),
     "payloads": [
@@ -112,6 +112,9 @@ dynamic_sampling_params.max_sample_count=5
                                               and when the calculated coefficient of variation reaches this threshold we terminate the dynamic sampling (Default is 0.05),
     } (Optional),
     "max_number_of_invocation_attempts": The maximum number of attempts per invocation when this number is reached an error is raised. (Optional, Default is 5)
+    "constraint_execution_time_threshold": The step function execution time threshold constraint. We leverages the execution time model and step function workflow structure 
+                                to recommend a configuration that minimizes cost while adhering to the specified execution time constraint. (Optional, Default is +infinity)
+    "memory_size_increment": The step size by which memory size is increased to meet execution time threshold. (Optional, Default is 10)
 }
 ```
 
@@ -121,7 +124,22 @@ dynamic_sampling_params.max_sample_count=5
 {
   "arn": "example_step_function_arn",
   "region": "example_region",
-  "payload": "payload"
+  "payload": "payload",
+  "constraint_execution_time_threshold": 5000
 }
 ```
 
+# Parrotfish for GCP Cloud Functions V2
+
+Parrotfish now supports optimization for both CPU and memory configurations in GCP Cloud Functions V2. To enable this feature, use `GCPv2` as vendor.
+
+## Example:
+
+```json
+{
+    "function_name": "example_function_name",
+    "region": "example_region",
+    "vendor": "GCPv2",
+    "payload": "example_payload"
+}
+```

src/configuration/defaults.py

@@ -5,8 +5,9 @@
     "coefficient_of_variation_threshold": 0.05,
 }
 MAX_NUMBER_OF_INVOCATION_ATTEMPTS = 5
-MAX_TOTAL_SAMPLE_COUNT = 250
-MIN_SAMPLE_PER_CONFIG = 5
-TERMINATION_THRESHOLD = 4
+MAX_TOTAL_SAMPLE_COUNT = 20
+MIN_SAMPLE_PER_CONFIG = 4
+TERMINATION_THRESHOLD = 3
+MEMORY_SIZE_INCREMENT = 10
 
 LOG_LEVEL = logging.WARNING

src/configuration/step_function_configuration.py

@@ -7,7 +7,7 @@
 
 
 class StepFunctionConfiguration:
-    def __init__(self, config_file: TextIO):
+    def __init__(self, config_file: Union[TextIO, dict]):
         self._load_config_schema()
 
         # Setup default values
@@ -16,6 +16,8 @@ def __init__(self, config_file: TextIO):
         self.max_total_sample_count = MAX_TOTAL_SAMPLE_COUNT
         self.min_sample_per_config = MIN_SAMPLE_PER_CONFIG
         self.max_number_of_invocation_attempts = MAX_NUMBER_OF_INVOCATION_ATTEMPTS
+        self.memory_size_increment = MEMORY_SIZE_INCREMENT
+        self.constraint_execution_time_threshold = None
 
         # Parse the configuration file
         self._deserialize(config_file)
@@ -58,6 +60,8 @@ def _load_config_schema(self):
                     },
                 },
                 "max_number_of_invocation_attempts": {"type": "integer", "minimum": 0},
+                "constraint_execution_time_threshold": {"type": "integer", "minimum": 1},
+                "memory_size_increment": {"type": "integer", "minimum": 1},
             },
             "required": ["arn", "region", "payload"],
             "if": {"not": {"required": ["payload"]}},

src/exploration/explorer.py

@@ -31,7 +31,6 @@ def __init__(
         self.payload = payload
 
         self.memory_space = np.array(list(memory_space), dtype=int)
-        # self.cpu_memory_space = np.array()
         if memory_bounds:
             self.memory_space = np.array(
                 list(

src/exploration/explorer_2d.py

@@ -92,22 +92,12 @@ def explore_parallel(
                     logger.debug(e)
                     if error is None:
                         error = e
-                    # self.cost += self.price_calculator.calculate_price(
-                    #     self._memory_config_mb, e.duration_ms, self._cpu_config
-                    # )
                     continue
 
         # If one thread raises an invocation error we raise it.
         if error:
             raise error
 
-        # Calculate the cost
-        # self.cost += np.sum(
-        #     self.price_calculator.calculate_price(
-        #         self._memory_config_mb, np.array(results), self._cpu_config
-        #     )
-        # )
-
         return results
 
     def explore(self, cpu: float = None, memory_mb: int = None, enable_cost_calculation=False) -> int:

src/main.py

@@ -53,7 +53,10 @@ def main():
         exit(1)
 
     if args.step_function:
+        # Create step function
         step_function = StepFunction(configuration)
+
+        # Run cost and execution time optimization
         step_function.optimize()
 
     else:

src/objective/parametric_function.py

@@ -18,7 +18,7 @@ class ParametricFunction:
         bounds (tuple): Lower and upper bounds on parameters.
     """
 
-    function: callable = lambda x, a0, a1, a2: a0 + a1 * np.exp(-x / a2)
+    function: callable = lambda x, a0, a1, a2: (a0 + a1 * np.exp(-x / a2)) if a2 != 0 else a0
     bounds: tuple = ([-np.inf, -np.inf, -np.inf], [np.inf, np.inf, np.inf])
     params: any = None
 

src/step_function/execution_time_optimizer.py

@@ -0,0 +1,99 @@
+from src.exception.step_function_error import StepFunctionError
+from src.logger import logger
+
+
+class ExecutionTimeOptimizer:
+    def __init__(self, workflow, function_tasks_dict, config):
+        self.workflow = workflow
+        self.function_tasks_dict = function_tasks_dict
+        self.memory_increment = config.memory_size_increment
+        self.execution_time_threshold = config.constraint_execution_time_threshold
+
+    def optimize_for_execution_time_constraint(self):
+        """Optimize the step function for execution time constraints."""
+        if self.execution_time_threshold is None:
+            logger.warning("No execution time threshold.")
+            return
+
+        critical_path_tasks, critical_path_time = self.workflow.get_critical_path()
+        logger.info(
+            f"Start optimizing step function for execution time, time: {critical_path_time}ms, threshold: {self.execution_time_threshold}ms, cost: {self.workflow.get_cost()}."
+        )
+
+        cost_increases = self._initialize_cost_increases()
+
+        while critical_path_time > self.execution_time_threshold:
+            time_reductions = self._calculate_time_reductions(critical_path_tasks)
+            best_function = self._find_best_function_to_optimize(cost_increases, time_reductions)
+
+            if best_function:
+                self._update_memory_size_and_cost(best_function, cost_increases)
+            else:
+                raise StepFunctionError("Execution time threshold too low.")
+
+            critical_path_tasks, critical_path_time = self.workflow.get_critical_path()
+            logger.debug(
+                f"Optimized function {best_function}, time: {critical_path_time}ms, cost: {self.workflow.get_cost()}.\n"
+            )
+
+        logger.info(
+            f"Finish optimizing step function for execution time, time: {critical_path_time}ms, threshold: {self.execution_time_threshold}ms, cost: {self.workflow.get_cost()}.\n"
+        )
+        self._print_memory_sizes()
+
+    def _initialize_cost_increases(self):
+        """Initialize the cost increases for each function."""
+        cost_increases = {}
+        for function in self.function_tasks_dict:
+            cost_increases[function] = 0.0
+            for task in self.function_tasks_dict[function]:
+                original_cost = task.get_cost(task.memory_size)
+                new_cost = task.get_cost(task.memory_size + self.memory_increment)
+                cost_increases[function] += new_cost - original_cost
+        return cost_increases
+
+    def _calculate_time_reductions(self, critical_path_tasks):
+        """Calculate time reductions for tasks on the critical path."""
+        time_reductions = {}
+        for task in critical_path_tasks:
+            if task.memory_size + self.memory_increment > task.max_memory_size:
+                continue
+
+            original_time = task.get_execution_time()
+            new_time = task.get_execution_time(task.memory_size + self.memory_increment)
+
+            if task.function_name not in time_reductions:
+                time_reductions[task.function_name] = 0.0
+            time_reductions[task.function_name] += original_time - new_time
+        return time_reductions
+
+    def _find_best_function_to_optimize(self, cost_increases, time_reductions):
+        """Find the function with the lowest cost to time reduction ratio."""
+        best_function = None
+        lowest_ratio = float('inf')
+        for function_name in time_reductions:
+            if time_reductions[function_name] > 0:
+                ratio = cost_increases[function_name] / time_reductions[function_name]
+                logger.debug(
+                    f"ratio: {ratio}, {function_name}, {self.function_tasks_dict[function_name][0].memory_size}MB, {cost_increases[function_name]}, {time_reductions[function_name]}"
+                )
+
+                if ratio < lowest_ratio:
+                    lowest_ratio = ratio
+                    best_function = function_name
+        return best_function
+
+    def _update_memory_size_and_cost(self, best_function, cost_increases):
+        """Increase memory size of the best function and update cost increases."""
+        cost_increases[best_function] = 0.0
+        for task in self.function_tasks_dict[best_function]:
+            task.increase_memory_size(self.memory_increment)
+            original_cost = task.get_cost()
+            new_cost = task.get_cost(task.memory_size + self.memory_increment)
+            cost_increases[best_function] += new_cost - original_cost
+
+    def _print_memory_sizes(self):
+        """Print memory sizes after optimization."""
+        print("Finish optimizing step function for execution time, optimized memory sizes:")
+        for function in self.function_tasks_dict:
+            print(f"{function}: {self.function_tasks_dict[function][0].memory_size}MB")

src/step_function/states.py

@@ -1,4 +1,5 @@
-from abc import ABC
+from abc import ABC, abstractmethod
+from typing import Tuple
 
 import boto3
 
@@ -12,14 +13,22 @@ class State(ABC):
     def __init__(self, name: str):
         self.name = name
 
+    @abstractmethod
+    def get_cost(self) -> float:
+        pass
+
 
 class Task(State):
     """Task: a single Lambda function """
 
     def __init__(self, name: str, function_name: str):
         super().__init__(name)
         self.function_name = function_name
+        self.input = None
         self.param_function = None
+        self.memory_size = None
+        self.initial_memory_size = None
+        self.max_memory_size = None
 
     def set_input(self, input: str):
         self.input = input
@@ -37,6 +46,29 @@ def get_output(self, aws_session: boto3.Session) -> str:
         logger.debug(f"Finish invoking {self.function_name}, output: {output}")
         return output
 
+    def increase_memory_size(self, increment: int):
+        self.memory_size += increment
+
+    def decrease_memory_size(self, decrement: int):
+        self.memory_size -= decrement
+
+    def reset_memory_size(self):
+        self.memory_size = self.initial_memory_size
+
+    def get_execution_time(self, memory_size: int = None):
+        if memory_size is not None:
+            execution_time = self.param_function(memory_size)
+        else:
+            execution_time = self.param_function(self.memory_size)
+        return execution_time
+
+    def get_cost(self, memory_size: int = None):
+        if memory_size is not None:
+            execution_time = memory_size * self.param_function(memory_size)
+        else:
+            execution_time = self.memory_size * self.param_function(self.memory_size)
+        return execution_time
+
 
 class Parallel(State):
     """Parallel: parallel workflows (branches) with same input."""
@@ -48,19 +80,46 @@ def __init__(self, name: str):
     def add_branch(self, workflow: "Workflow"):
         self.branches.append(workflow)
 
+    def get_critical_path(self) -> Tuple[list[Task], float]:
+        """Get tasks on critical path and execution time."""
+        max_time = 0.0
+        critical_path = None
+        for workflow in self.branches:
+            states, time = workflow.get_critical_path()
+            if time > max_time:
+                max_time = time
+                critical_path = states
+        return critical_path, max_time
+
+    def get_cost(self) -> float:
+        return sum(workflow.get_cost() for workflow in self.branches)
+
 
 class Map(State):
-    """Map: multiple same workflows with different inputs"""
+    """Map: multiple same workflows with different inputs."""
 
     def __init__(self, name: str):
         super().__init__(name)
-        self.iterations: list[Workflow] = []
+        self.items_path = None
         self.workflow_def = None
-        self.items_path = ""
+        self.iterations: list[Workflow] = []
 
     def add_iteration(self, workflow: "Workflow"):
         self.iterations.append(workflow)
 
+    def get_critical_path(self) -> Tuple[list[Task], float]:
+        max_time = 0.0
+        critical_path = None
+        for workflow in self.iterations:
+            states, time = workflow.get_critical_path()
+            if time > max_time:
+                max_time = time
+                critical_path = states
+        return critical_path, max_time
+
+    def get_cost(self) -> float:
+        return sum(workflow.get_cost() for workflow in self.iterations)
+
 
 class Workflow:
     """A workflow, containing a sequence of states."""
@@ -70,3 +129,22 @@ def __init__(self):
 
     def add_state(self, state: State):
         self.states.append(state)
+
+    def get_critical_path(self) -> Tuple[list[Task], float]:
+        critical_path: list[Task] = []
+        total_time = 0.0
+
+        for state in self.states:
+            if isinstance(state, Task):
+                critical_path.append(state)
+                time = state.get_execution_time()
+                total_time += time
+            elif isinstance(state, (Parallel, Map)):
+                states, time = state.get_critical_path()
+                critical_path.extend(states)
+                total_time += time
+
+        return critical_path, total_time
+
+    def get_cost(self) -> float:
+        return sum(state.get_cost() for state in self.states)