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diff --git a/Tooling/Project_2/grading_scripts/README.md b/Tooling/Project_2/grading_scripts/README.md
index 66cd7a6..94bb6b2 100644
--- a/Tooling/Project_2/grading_scripts/README.md
+++ b/Tooling/Project_2/grading_scripts/README.md
@@ -1,6 +1,5 @@
 ### Grading Scripts
-
- - How to run the script: 
+ - How to run the script for PART-1: 
     ```
     usage: grader_script_p1.py [-h] [--access_key ACCESS_KEY] [--secret_key SECRET_KEY] [--input_bucket INPUT_BUCKET] [--lambda_name LAMBDA_NAME]
     Grading Script
@@ -18,9 +17,24 @@
                         Name of the Lambda function
 
     ```
+
+ - How to run the script for PART-2:
+    ```
+    usage: grader_script_p2_v2.py [-h] [--access_key ACCESS_KEY] [--secret_key SECRET_KEY] [--asu_id ASU_ID]
+    Grading Script
+    optional arguments:
+    -h, --help            show this help message and exit
+    --access_key ACCESS_KEY
+                          ACCCESS KEY ID of the grading IAM user
+    --secret_key SECRET_KEY
+                          SECRET KEY of the grading IAM user
+    --asu_id ASU_ID       10-digit ASU ID
+
+    ```
+    
     **Note**: We will follow the naming conventions for S3 Bucket and Lambda function names as described in the project document to grade your submission
 
-**Examples**:
+**Examples For PART-1**:
 
 We will show examples for each option below.
 
diff --git a/Tooling/Project_2/grading_scripts/grader_script_p2.py b/Tooling/Project_2/grading_scripts/grader_script_p2.py
new file mode 100644
index 0000000..a57f040
--- /dev/null
+++ b/Tooling/Project_2/grading_scripts/grader_script_p2.py
@@ -0,0 +1,315 @@
+__copyright__   = "Copyright 2024, VISA Lab"
+__license__     = "MIT"
+
+import pdb
+import time
+import botocore
+import argparse
+import textwrap
+import boto3
+from boto3 import client as boto3_client
+from botocore.exceptions import ClientError
+from datetime import datetime,timezone,timedelta
+import re
+
+class aws_grader():
+    def __init__(self, access_key, secret_key, buckets, lambda_names, region):
+
+        self.access_key = access_key
+        self.secret_key  = secret_key
+        self.region = region
+        self.s3 = boto3_client('s3', aws_access_key_id=self.access_key,
+                          aws_secret_access_key=self.secret_key, region_name=region)
+        self.cloudwatch = boto3_client('cloudwatch', aws_access_key_id=self.access_key,
+                          aws_secret_access_key=self.secret_key, region_name=region)
+        self.iam_session = boto3.Session(aws_access_key_id=self.access_key,
+                                         aws_secret_access_key=self.secret_key)
+        self.s3_resources = self.iam_session.resource('s3', region)
+        self.lambda_function = boto3_client('lambda', aws_access_key_id=self.access_key,
+                               aws_secret_access_key=self.secret_key, region_name=region)
+        self.in_bucket_name = buckets[0]
+        self.out_bucket_name = buckets[4]
+        self.buckets = buckets
+        self.lambda_names = lambda_names
+        self.test_result = {}
+
+    def validate_lambda_exists_each(self, name, TC_num):
+        try:
+            response = self.lambda_function.get_function(
+            FunctionName=name
+            )
+            print(f"Lambda function {name} HTTPStatusCode {response['ResponseMetadata']['HTTPStatusCode']}")
+            self.test_result[TC_num] = "PASS"
+        except self.lambda_function.exceptions.ResourceNotFoundException as e:
+            print(f"Error {e}")
+            self.test_result[TC_num] = "FAIL"
+        print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+    def validate_lambda_exists(self, TC_num):
+        self.validate_lambda_exists_each("video-splitting",TC_num+"_a")
+        self.validate_lambda_exists_each("motion-detection", TC_num + "_b")
+        self.validate_lambda_exists_each("face-extraction", TC_num + "_c")
+        self.validate_lambda_exists_each("face-recognition", TC_num + "_d")
+
+    def validate_s3_subfolders_each(self, buckets, in_objects, TC_num):
+        for num, bucket in enumerate(buckets[1:]):
+            print(f"\nComparing buckets {buckets[0]} and {bucket} ...")
+            TC_num_sub = TC_num+"_"+str(chr(97+num))
+            self.test_result[TC_num_sub] = "PASS"
+            for obj in in_objects['Contents']:
+                folder_name = obj['Key'].rsplit('.', 1)[0]
+                out_objects = self.s3.list_objects_v2(Bucket=bucket, Prefix=folder_name, Delimiter='/')
+                if out_objects['KeyCount'] == 1 or out_objects['KeyCount'] == 11:
+                    folder_name = out_objects['CommonPrefixes'][0]['Prefix'].rsplit("/")[0]
+                    prefix_name = out_objects['Prefix']
+                    if folder_name == prefix_name:
+                        print(f"{prefix_name} matches with {folder_name}")
+                else:
+                    prefix_name = out_objects['Prefix']
+                    self.test_result[TC_num_sub] = "FAIL"
+                    print(f"NO folder named {prefix_name}\nExiting this test case... ")
+                    # print(f"DEBUG :: {out_objects}")
+                    break
+            print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}")
+
+    def validate_s3_subfolders(self, TC_num):
+        in_objects = self.s3.list_objects_v2(Bucket=self.in_bucket_name)
+        if in_objects['KeyCount']==0:
+            print(f"Empty bucket {self.in_bucket_name}")
+            print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+            return
+        self.validate_s3_subfolders_each(buckets,in_objects,TC_num)
+
+    def check_non_empty_folders(self, bucket_num, TC_num):
+        bucket = self.s3_resources.Bucket(self.buckets[bucket_num])
+        TC_num_sub = TC_num + "_" + str(chr(96 + bucket_num))
+        self.test_result[TC_num_sub] = "FAIL"
+        try:
+            objects = list(bucket.objects.all())
+            print(f"{self.buckets[bucket_num]} contains {len(objects)} objects")
+            if bucket_num==4:
+                prefix_pattern = r"test_\d{2}/[oO]utput-\d{2}.txt"
+            else:
+                prefix_pattern = r"test_\d{2}/[oO]utput-\d{2}.(jpg|jpeg)"
+            count = self.count_values_with_prefix(objects, prefix_pattern)
+            # print(f"DEBUG :: Bucket {stage_1_bucket} has {count} objects that matches the pattern")
+            if count >= 100:
+                self.test_result[TC_num_sub] = "PASS"
+        except ClientError:
+            print(f"Couldn't get objects for bucket {bucket.name}")
+            raise
+        print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}\n")
+
+    def count_values_with_prefix(self,objects, prefix_pattern):
+        count = 0
+        for o in objects:
+            if re.match(prefix_pattern, o.key):
+                # print(f"DEBUG :: Object key '{o.key}' follows the pattern '{prefix_pattern}'")
+                count += 1
+            else:
+                print(f"Object key '{o.key}' does NOT follows the pattern '{prefix_pattern}'")
+
+        return count
+    def validate_s3_output_objects(self, TC_num):
+        in_bucket = self.s3_resources.Bucket(self.buckets[0])
+        try:
+            in_objects = list(in_bucket.objects.all())
+            print(f"{self.buckets[0]} contains {len(in_objects)} objects")
+
+            # Check if all the folders of stage-1, stage-2, stage-3, and output bucket are non-empty
+            self.check_non_empty_folders(1,TC_num=TC_num)
+            self.check_non_empty_folders(2,TC_num=TC_num)
+            self.check_non_empty_folders(3,TC_num=TC_num)
+            self.check_non_empty_folders(4,TC_num=TC_num)
+
+        except ClientError:
+            print(f"Couldn't get objects for bucket {in_bucket.name}")
+            raise
+        else:
+            return
+
+    # You have to make sure to run the workload generator and it executes within 15 mins
+    # of polling for cloudwatch metrics.
+    def check_lambda_duration(self, TC_num):
+        response = self.cloudwatch.get_metric_data(
+            MetricDataQueries=[
+                {
+                    'Id': 'testDuration',
+                    'MetricStat': {
+                        'Metric': {
+                            'Namespace': 'AWS/Lambda',
+                            'MetricName': 'Duration'
+                        },
+                        'Period': 600,
+                        'Stat': 'Average'
+                    },
+                    'ReturnData': True,
+                },
+            ],
+            StartTime=datetime.now().utcnow() - timedelta(minutes=15),
+            EndTime=datetime.now().utcnow(),
+            ScanBy='TimestampAscending'
+        )
+        print(response['MetricDataResults'][0]['Values'])
+        values = response['MetricDataResults'][0]['Values']
+        if not values:
+            self.test_result[TC_num] = "FAIL"
+            print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+            return
+        if max(values) > 10000:
+            self.test_result[TC_num] = "FAIL"
+        else:
+            self.test_result[TC_num] = "PASS"
+        print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+
+    def check_lambda_concurrency_each(self,functionName, TC_num,subcase):
+        TC_num_sub = TC_num + "_" + str(chr(96 + subcase))
+
+        response = self.cloudwatch.get_metric_data(
+            MetricDataQueries=[
+                {
+                    'Id': 'testConcurrency',
+                    'MetricStat': {
+                        'Metric': {
+                            'Namespace': 'AWS/Lambda',
+                            'MetricName': 'ConcurrentExecutions',
+                            'FunctionName': functionName
+                        },
+                        'Period': 600,
+                        'Stat': 'Maximum'
+                    },
+                    'ReturnData': True,
+                },
+            ],
+            StartTime=datetime.now().utcnow() - timedelta(minutes=15),
+            EndTime=datetime.now().utcnow(),
+            ScanBy='TimestampAscending'
+        )
+        print(response['MetricDataResults'][0]['Values'])
+        values = response['MetricDataResults'][0]['Values']
+        if not values:
+            self.test_result[TC_num_sub] = "FAIL"
+            print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}")
+            return
+        if max(values) < 5:
+            self.test_result[TC_num_sub] = "FAIL"
+        else:
+            self.test_result[TC_num_sub] = "PASS"
+        print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}")
+
+    def check_lambda_concurrency(self,TC_num):
+        self.check_lambda_concurrency_each('video-splitting', TC_num,1)
+        self.check_lambda_concurrency_each('motion-detection', TC_num,2)
+        self.check_lambda_concurrency_each('face-extraction', TC_num,3)
+        self.check_lambda_concurrency_each('face-recognition', TC_num,4)
+
+    def check_bucket_exist(self, bucket):
+        if not bucket:
+            print(f"Bucket name is empty!")
+            return False
+        try:
+            self.s3.head_bucket(Bucket=bucket)
+            print(f"Bucket {bucket} Exists!")
+            return True
+        except botocore.exceptions.ClientError as e:
+            # If a client error is thrown, then check that it was a 404 error.
+            # If it was a 404 error, then the bucket does not exist.
+            error_code = int(e.response['Error']['Code'])
+            if error_code == 403:
+                print("Private Bucket. Forbidden Access!")
+                return True
+            elif error_code == 404:
+                print(f"Bucket {bucket} does Not Exist!")
+                return False
+    def empty_s3_bucket(self, bucket_name):
+        bucket = self.s3_resources.Bucket(bucket_name)
+        bucket.objects.all().delete()
+        print(f"{bucket_name} S3 Bucket is now EMPTY !!")
+
+    def count_bucket_objects(self, bucket_name):
+        bucket = self.s3_resources.Bucket(bucket_name)
+        count = 0
+        for index in bucket.objects.all():
+            count += 1
+        #print(f"{bucket_name} S3 Bucket has {count} objects !!")
+        return count
+
+    def validate_s3_buckets_initial_each(self, bucket_num, TC_num):
+        TC_num_sub = TC_num + "_" + str(chr(97 + bucket_num))
+        isExist = self.check_bucket_exist(self.buckets[bucket_num])
+        self.test_result[TC_num_sub] = "FAIL"
+        if isExist:
+            obj_count = self.count_bucket_objects(self.buckets[bucket_num])
+            print(f"S3 Bucket:{self.buckets[bucket_num]} has {obj_count} object(s)")
+            if obj_count == 0:
+                self.test_result[TC_num_sub] = "PASS"
+            else:
+                self.test_result[TC_num_sub] = "FAIL"
+        print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}\n")
+
+    def validate_s3_buckets_initial(self, TC_num):
+        print(" - Run this BEFORE the workload generator client starts. Press Ctrl^C to exit.")
+        print(" - WARN: If there are objects in the S3 buckets; they will be deleted")
+        print(" ---------------------------------------------------------")
+
+        for i in range(len(self.buckets)):
+            self.validate_s3_buckets_initial_each(bucket_num=i, TC_num=TC_num)
+
+    def display_menu(self):
+        print("\n")
+        print("=============================================================================")
+        print("======== Welcome to CSE546 Cloud Computing AWS Console ======================")
+        print("=============================================================================")
+        print(f"IAM ACCESS KEY ID: {self.access_key}")
+        print(f"IAM SECRET ACCESS KEY: {self.secret_key}")
+        print("=============================================================================")
+        print("1 - Validate all Lambda functions")
+        print("2 - Validate S3 Buckets names and initial states")
+        print("3 - Validate S3 buckets subfolders")
+        print("4 - Validate S3 buckets objects")
+        print("5 - Check lambda average duration")
+        print("6 - Check lambda concurrency")
+        print("0 - Exit")
+        print("Enter a choice:")
+        choice = input()
+        return choice
+
+    def main(self):
+        while(1):
+            choice = self.display_menu()
+            if int(choice) == 1:
+                self.validate_lambda_exists('Test_1')
+            elif int(choice) == 2:
+                self.validate_s3_buckets_initial('Test_2')
+            elif int(choice) == 3:
+                self.validate_s3_subfolders('Test_3')
+            elif int(choice) == 4:
+                self.validate_s3_output_objects('Test_4')
+            elif int(choice) == 5:
+                self.check_lambda_duration('Test_5')
+            elif int(choice) == 6:
+                self.check_lambda_concurrency('Test_6')
+            elif int(choice) == 0:
+                break
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Grading Script')
+    parser.add_argument('--access_key', type=str, help='ACCCESS KEY ID of the grading IAM user')
+    parser.add_argument('--secret_key', type=str, help='SECRET KEY of the grading IAM user')
+    parser.add_argument('--asu_id', type=str, help='10-digit ASU ID')
+
+    args = parser.parse_args()
+
+    access_key = args.access_key
+    secret_key = args.secret_key
+    asu_id = args.asu_id
+    input_bucket = asu_id+"-input"
+    output_bucket = asu_id+"-output"
+    stage_1_bucket = asu_id+"-stage-1"
+    stage_2_bucket = asu_id + "-stage-2"
+    stage_3_bucket = asu_id + "-stage-3"
+    buckets = [input_bucket, stage_1_bucket, stage_2_bucket, stage_3_bucket, output_bucket]
+    lambda_names = ["video-splitting", "motion-detection","face-extraction", "face-recognition"]
+    region = 'us-east-1'
+
+    aws_obj = aws_grader(access_key, secret_key, buckets, lambda_names,region)
+    aws_obj.main()
diff --git a/Tooling/Project_2/grading_scripts/grader_script_p2_v2.py b/Tooling/Project_2/grading_scripts/grader_script_p2_v2.py
new file mode 100644
index 0000000..1c10b3d
--- /dev/null
+++ b/Tooling/Project_2/grading_scripts/grader_script_p2_v2.py
@@ -0,0 +1,439 @@
+__copyright__   = "Copyright 2024, VISA Lab"
+__license__     = "MIT"
+
+import pdb
+import time
+import botocore
+import argparse
+import textwrap
+import boto3
+from boto3 import client as boto3_client
+from botocore.exceptions import ClientError
+from datetime import datetime,timezone,timedelta
+import re
+import os
+import shutil
+
+class aws_grader():
+    def __init__(self, access_key, secret_key, buckets, lambda_names, region, asu_id):
+
+        self.access_key = access_key
+        self.secret_key  = secret_key
+        self.region = region
+        self.s3 = boto3_client('s3', aws_access_key_id=self.access_key,
+                          aws_secret_access_key=self.secret_key, region_name=region)
+        self.cloudwatch = boto3_client('cloudwatch', aws_access_key_id=self.access_key,
+                          aws_secret_access_key=self.secret_key, region_name=region)
+        self.iam_session = boto3.Session(aws_access_key_id=self.access_key,
+                                         aws_secret_access_key=self.secret_key)
+        self.s3_resources = self.iam_session.resource('s3', region)
+        self.lambda_function = boto3_client('lambda', aws_access_key_id=self.access_key,
+                               aws_secret_access_key=self.secret_key, region_name=region)
+        self.in_bucket_name = buckets[0]
+        self.out_bucket_name = buckets[2]
+        self.buckets = buckets
+        self.lambda_names = lambda_names
+        self.test_result = {}
+        self.end_to_end_latency = 0
+        self.output_folder = f"outputs_{asu_id}"
+        self.match = ["Trump", "Biden", "Bean", "Depp", "Diesel", "Floki", "Freeman", "Obama"]
+        self.total_points = 0
+
+    def validate_lambda_exists_each(self, name, TC_num, num):
+        TC_num_sub = TC_num + "_" + str(chr(97 + num))
+
+        try:
+            response = self.lambda_function.get_function(
+            FunctionName=name
+            )
+            print(f"Lambda function {name} HTTPStatusCode {response['ResponseMetadata']['HTTPStatusCode']}")
+            self.test_result[TC_num_sub] = "PASS"
+        except self.lambda_function.exceptions.ResourceNotFoundException as e:
+            print(f"Error {e}")
+            self.test_result[TC_num_sub] = "FAIL"
+        print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}")
+    def validate_lambda_exists(self, TC_num):
+        self.validate_lambda_exists_each("video-splitting",TC_num, num=0)
+        self.validate_lambda_exists_each("face-recognition", TC_num, num=1)
+        if self.test_result[TC_num + "_" + str(chr(97 + 0))] == "FAIL" or self.test_result[TC_num + "_" + str(chr(97 + 1))] == "FAIL":
+            self.total_points -= 10
+
+    def validate_s3_subfolders_each(self, buckets, in_objects, TC_num):
+        for num, bucket in enumerate(buckets[1:]):
+            print(f"\nComparing buckets {buckets[0]} and {bucket} ...")
+            TC_num_sub = TC_num+"_"+str(chr(97+num))
+            self.test_result[TC_num_sub] = "PASS"
+            for obj in in_objects['Contents']:
+                folder_name = obj['Key'].rsplit('.', 1)[0]
+                out_objects = self.s3.list_objects_v2(Bucket=bucket, Prefix=folder_name, Delimiter='/')
+                if out_objects['KeyCount'] == 1 or out_objects['KeyCount'] == 11:
+                    folder_name = out_objects['CommonPrefixes'][0]['Prefix'].rsplit("/")[0]
+                    prefix_name = out_objects['Prefix']
+                    if folder_name == prefix_name:
+                        print(f"{prefix_name} matches with {folder_name}")
+                else:
+                    prefix_name = out_objects['Prefix']
+                    self.test_result[TC_num_sub] = "FAIL"
+                    print(f"NO folder named {prefix_name}\nExiting this test case... ")
+                    # print(f"DEBUG :: {out_objects}")
+                    break
+            print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}")
+
+    def validate_s3_subfolders(self, TC_num):
+        in_objects = self.s3.list_objects_v2(Bucket=self.in_bucket_name)
+        if in_objects['KeyCount']==0:
+            print(f"Empty bucket {self.in_bucket_name}")
+            print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+            return
+        self.validate_s3_subfolders_each(buckets,in_objects,TC_num)
+
+    def check_non_empty_folders(self, bucket_num, TC_num):
+        bucket = self.s3_resources.Bucket(self.buckets[bucket_num])
+        TC_num_sub = TC_num + "_" + str(chr(96 + bucket_num))
+        self.test_result[TC_num_sub] = "FAIL"
+        try:
+            objects = list(bucket.objects.all())
+            print(f"{self.buckets[bucket_num]} contains {len(objects)} objects")
+            if bucket_num==4:
+                prefix_pattern = r"test_\d{2}/[oO]utput-\d{2}.txt"
+            else:
+                prefix_pattern = r"test_\d{2}/[oO]utput-\d{2}.(jpg|jpeg)"
+            count = self.count_values_with_prefix(objects, prefix_pattern)
+            # print(f"DEBUG :: Bucket {stage_1_bucket} has {count} objects that matches the pattern")
+            if count >= 100:
+                self.test_result[TC_num_sub] = "PASS"
+        except ClientError:
+            print(f"Couldn't get objects for bucket {bucket.name}")
+            raise
+        print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}\n")
+
+    def count_values_with_prefix(self,objects, prefix_pattern):
+        count = 0
+        for o in objects:
+            if re.match(prefix_pattern, o.key):
+                # print(f"DEBUG :: Object key '{o.key}' follows the pattern '{prefix_pattern}'")
+                count += 1
+            else:
+                print(f"Object key '{o.key}' does NOT follows the pattern '{prefix_pattern}'")
+        return count
+
+    def validate_bucket_objects(self, TC_num, bucket_num):
+        bucket = self.buckets[bucket_num]
+        bucket_res = self.s3_resources.Bucket(self.buckets[bucket_num])
+
+        self.test_result[TC_num] = "FAIL"
+        try:
+            objects = list(bucket_res.objects.all())
+            print(f"{self.buckets[bucket_num]} contains {len(objects)} objects")
+            if bucket_num == 2:
+                prefix_pattern = r"test_\d{2}.txt"
+            else:
+                prefix_pattern = r"test_\d{2}.(jpg|jpeg)"
+            count = self.count_values_with_prefix(objects, prefix_pattern)
+            # print(f"DEBUG :: Bucket {stage_1_bucket} has {count} objects that matches the pattern")
+            if count >= 100:
+                self.test_result[TC_num] = "PASS"
+                self.total_points += 20
+            else:
+                missing = 100 - count
+                self.total_points = self.total_points + 20 - min(missing, 20)
+        except ClientError:
+            print(f"Couldn't get objects for bucket {bucket.name}")
+            raise
+        print(f"Test status of {TC_num} : {self.test_result[TC_num]}\n")
+
+    def validate_s3_output_objects(self, TC_num):
+        in_bucket = self.s3_resources.Bucket(self.buckets[0])
+        try:
+            in_objects = list(in_bucket.objects.all())
+            print(f"{self.buckets[0]} contains {len(in_objects)} objects")
+
+            # Check if all the folders of stage-1, stage-2, stage-3, and output bucket are non-empty
+            self.check_non_empty_folders(1,TC_num=TC_num)
+            self.check_non_empty_folders(2,TC_num=TC_num)
+            self.check_non_empty_folders(3,TC_num=TC_num)
+            self.check_non_empty_folders(4,TC_num=TC_num)
+
+        except ClientError:
+            print(f"Couldn't get objects for bucket {in_bucket.name}")
+            raise
+        else:
+            return
+
+    # You have to make sure to run the workload generator and it executes within 15 mins
+    # of polling for cloudwatch metrics.
+    def check_lambda_duration_each(self, functionName, TC_num, subcase, threshold):
+        TC_num_sub = TC_num + "_" + str(chr(96 + subcase))
+
+        response = self.cloudwatch.get_metric_data(
+            MetricDataQueries=[
+                {
+                    'Id': 'testDuration',
+                    'MetricStat': {
+                        'Metric': {
+                            'Namespace': 'AWS/Lambda',
+                            'MetricName': 'Duration',
+                            "Dimensions": [
+                                {
+                                    "Name": "FunctionName",
+                                    "Value": functionName
+                                }
+                            ]
+                        },
+                        'Period': 600,
+                        'Stat': 'Average'
+                    },
+                    'ReturnData': True,
+                },
+            ],
+            StartTime=datetime.now().utcnow() - timedelta(minutes=15),
+            EndTime=datetime.now().utcnow(),
+            ScanBy='TimestampAscending'
+        )
+        print(response['MetricDataResults'][0]['Values'])
+        values = response['MetricDataResults'][0]['Values']
+        if not values:
+            self.test_result[TC_num_sub] = "FAIL"
+            print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}")
+            return
+        if max(values) > threshold:
+            self.test_result[TC_num_sub] = "FAIL"
+        else:
+            self.test_result[TC_num_sub] = "PASS"
+        print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}")
+
+    def check_lambda_duration(self, TC_num):
+        self.check_lambda_duration_each('video-splitting', TC_num, 1, threshold=2000)
+        self.check_lambda_duration_each('face-recognition', TC_num, 2, threshold=10000)
+
+    def check_lambda_concurrency_each(self,functionName, TC_num,subcase, threshold):
+        TC_num_sub = TC_num + "_" + str(chr(96 + subcase))
+
+        response = self.cloudwatch.get_metric_data(
+            MetricDataQueries=[
+                {
+                    'Id': 'testConcurrency',
+                    'MetricStat': {
+                        'Metric': {
+                            'Namespace': 'AWS/Lambda',
+                            'MetricName': 'ConcurrentExecutions',
+                            "Dimensions": [
+                                {
+                                    "Name": "FunctionName",
+                                    "Value": functionName
+                                }
+                            ]
+                        },
+                        'Period': 600,
+                        'Stat': 'Maximum'
+                    },
+                    'ReturnData': True,
+                },
+            ],
+            StartTime=datetime.now().utcnow() - timedelta(minutes=15),
+            EndTime=datetime.now().utcnow(),
+            ScanBy='TimestampAscending'
+        )
+        print(response['MetricDataResults'][0]['Values'])
+        values = response['MetricDataResults'][0]['Values']
+        if not values:
+            self.test_result[TC_num_sub] = "FAIL"
+            print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}")
+            return
+        if max(values) < threshold:
+            self.test_result[TC_num_sub] = "FAIL"
+        else:
+            self.test_result[TC_num_sub] = "PASS"
+        print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}")
+
+    def check_lambda_concurrency(self,TC_num):
+        self.check_lambda_concurrency_each('video-splitting', TC_num,1, threshold=3)
+        self.check_lambda_concurrency_each('face-recognition', TC_num,2, threshold=3)
+
+    def check_bucket_exist(self, bucket):
+        if not bucket:
+            print(f"Bucket name is empty!")
+            return False
+        try:
+            self.s3.head_bucket(Bucket=bucket)
+            print(f"Bucket {bucket} Exists!")
+            return True
+        except botocore.exceptions.ClientError as e:
+            # If a client error is thrown, then check that it was a 404 error.
+            # If it was a 404 error, then the bucket does not exist.
+            error_code = int(e.response['Error']['Code'])
+            if error_code == 403:
+                print("Private Bucket. Forbidden Access!")
+                return True
+            elif error_code == 404:
+                print(f"Bucket {bucket} does Not Exist!")
+                return False
+    def empty_s3_bucket(self, bucket_name):
+        bucket = self.s3_resources.Bucket(bucket_name)
+        bucket.objects.all().delete()
+        print(f"{bucket_name} S3 Bucket is now EMPTY !!")
+
+    def count_bucket_objects(self, bucket_name):
+        bucket = self.s3_resources.Bucket(bucket_name)
+        count = 0
+        for index in bucket.objects.all():
+            count += 1
+        #print(f"{bucket_name} S3 Bucket has {count} objects !!")
+        return count
+
+    def validate_s3_buckets_initial_each(self, bucket_num, TC_num):
+        TC_num_sub = TC_num + "_" + str(chr(97 + bucket_num))
+        isExist = self.check_bucket_exist(self.buckets[bucket_num])
+        self.test_result[TC_num_sub] = "FAIL"
+        if isExist:
+            obj_count = self.count_bucket_objects(self.buckets[bucket_num])
+            print(f"S3 Bucket:{self.buckets[bucket_num]} has {obj_count} object(s)")
+            if obj_count == 0:
+                self.test_result[TC_num_sub] = "PASS"
+            else:
+                self.test_result[TC_num_sub] = "FAIL"
+        print(f"Test status of {TC_num_sub} : {self.test_result[TC_num_sub]}\n")
+
+    def validate_s3_buckets_initial(self, TC_num):
+        print(" - Run this BEFORE the workload generator client starts. Press Ctrl^C to exit.")
+        print(" - WARN: If there are objects in the S3 buckets; they will be deleted")
+        print(" ---------------------------------------------------------")
+
+        for i in range(len(self.buckets)):
+            self.validate_s3_buckets_initial_each(bucket_num=i, TC_num=TC_num)
+        if self.test_result[TC_num + "_" + str(chr(97 + 0))] == "FAIL" or \
+            self.test_result[TC_num + "_" + str(chr(97 + 1))] == "FAIL" or \
+                self.test_result[TC_num + "_" + str(chr(97 + 2))] == "FAIL":
+            self.total_points -= 10
+
+
+    def download_from_s3(self, bucket, folder_name):
+        objects = self.s3.list_objects_v2(Bucket=bucket, Prefix=folder_name)
+        for obj in objects.get('Contents', []):
+            # Get key (filename) of the object
+            key = obj['Key']
+            self.s3.download_file(bucket, key, f"/tmp/{key}")
+
+    def check_end_to_end(self, TC_num):
+        print(" - Make sure workload generator is started. Press Ctrl^C to exit.")
+        print(" ---------------------------------------------------------")
+        print("Proceed? (y/n)")
+        proceed = input()
+        if proceed == "y":
+            start_time = time.time()
+            self.test_result[TC_num] = "FAIL"
+
+            out_bucket = self.s3_resources.Bucket(self.buckets[2])
+            while len(list(out_bucket.objects.all())) <= 100 or (time.time()-start_time)<=400:
+                objects = out_bucket.objects.all()
+                print(f"Total objects in output bucket: {len(list(objects))}, current time elapsed: {time.time()-start_time}")
+                if len(list(objects)) == 100:
+                    self.end_to_end_latency = time.time() - start_time
+                    print(f"End-to-end latency is: {self.end_to_end_latency}")
+                    break
+            if(time.time() - start_time) < 400:
+                self.test_result[TC_num] = "PASS"
+
+            if self.end_to_end_latency <= 300:
+                self.total_points += 30
+            elif 300 < self.end_to_end_latency <=400:
+                self.total_points += 20
+            elif self.end_to_end_latency > 400:
+                objects = out_bucket.objects.all()
+                missing = 100 - len(list(objects))
+                self.total_points = self.total_points + 20 - min(missing, 20)
+            print(f"Test status of {TC_num} : {self.test_result[TC_num]}\n")
+
+
+    def check_correctness(self, TC_num):
+        if os.path.exists(self.output_folder):
+            shutil.rmtree(self.output_folder)
+        if not os.path.exists(self.output_folder):
+            os.makedirs(self.output_folder)
+        out_bucket = self.s3_resources.Bucket(self.buckets[2])
+        objects = out_bucket.objects.all()
+        print(f"Downloading {len(list(objects))} objects from the {self.buckets[2]} bucket ...")
+        for o in objects:
+            # print(self.output_folder,out_bucket,o.key)
+            self.s3.download_file(self.buckets[2], o.key, os.path.join(self.output_folder,o.key))
+        match_count = 0
+        for i,filename in enumerate(sorted(os.listdir(self.output_folder))):
+            with open(os.path.join(self.output_folder, filename),"r") as f:
+                line = f.read()
+                prefix_pattern = r"test_\d{2}.txt"
+                if not re.match(prefix_pattern, filename):
+                    self.test_result[TC_num] = "FAIL"
+                    print(f"Filename does not follow the required pattern {prefix_pattern}. Cannnot check correctness test.")
+                    return
+                filenum = int(filename.split("_")[1].split(".")[0]) % len(self.match)
+                if line.strip() == self.match[filenum]:
+                    match_count += 1
+                else:
+                    print(f"{filename} content {line.strip()} did not match with {self.match[i % len(self.match)]}")
+        missing = 100 - match_count
+        print(f"Missing correct results = {missing}")
+        if missing == 0:
+            self.test_result[TC_num] = "PASS"
+        else:
+            self.test_result[TC_num] = "FAIL"
+        print(f"Test status of {TC_num} : {self.test_result[TC_num]}\n")
+        self.total_points = self.total_points + 30 - min(missing, 30)
+
+    def display_menu(self):
+        print("\n")
+        print("=============================================================================")
+        print("======== Welcome to CSE546 Cloud Computing AWS Console ======================")
+        print("=============================================================================")
+        print(f"IAM ACCESS KEY ID: {self.access_key}")
+        print(f"IAM SECRET ACCESS KEY: {self.secret_key}")
+        print("=============================================================================")
+        print("1 - Validate all Lambda functions")
+        print("2 - Validate S3 Buckets names and initial states")
+        print("3 - End-to-end pipeline testing")
+        print("4 - Validate Stage-1 bucket objects")
+        print("5 - Validate Output bucket objects")
+        print("6 - Check the correctness of the results")
+        print("0 - Exit")
+        print("Enter a choice:")
+        choice = input()
+        return choice
+
+    def main(self):
+        while(1):
+            choice = self.display_menu()
+            if int(choice) == 1:
+                self.validate_lambda_exists('Test_1')
+            elif int(choice) == 2:
+                self.validate_s3_buckets_initial('Test_2')
+            elif int(choice) == 3:
+                self.check_end_to_end('Test_3')
+            elif int(choice) == 4:
+                self.validate_bucket_objects('Test_4',bucket_num=1)
+            elif int(choice) == 5:
+                self.validate_bucket_objects('Test_5',bucket_num=2)
+            elif int(choice) == 6:
+                self.check_correctness('Test_6')
+            elif int(choice) == 0:
+                break
+        print(f"Total points : {self.total_points}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Grading Script')
+    parser.add_argument('--access_key', type=str, help='ACCCESS KEY ID of the grading IAM user')
+    parser.add_argument('--secret_key', type=str, help='SECRET KEY of the grading IAM user')
+    parser.add_argument('--asu_id', type=str, help='10-digit ASU ID')
+
+    args = parser.parse_args()
+
+    access_key = args.access_key
+    secret_key = args.secret_key
+    asu_id = args.asu_id
+    input_bucket = asu_id+"-input"
+    output_bucket = asu_id+"-output"
+    stage_1_bucket = asu_id+"-stage-1"
+    buckets = [input_bucket, stage_1_bucket, output_bucket]
+    lambda_names = ["video-splitting", "face-recognition"]
+    region = 'us-east-1'
+
+    aws_obj = aws_grader(access_key, secret_key, buckets, lambda_names,region, asu_id)
+    aws_obj.main()
diff --git a/Tooling/Project_2/src/face-recognition-code.py b/Tooling/Project_2/src/face-recognition-code.py
index 9594dc1..9407f53 100644
--- a/Tooling/Project_2/src/face-recognition-code.py
+++ b/Tooling/Project_2/src/face-recognition-code.py
@@ -15,30 +15,33 @@ import torch
 mtcnn = MTCNN(image_size=240, margin=0, min_face_size=20) # initializing mtcnn for face detection
 resnet = InceptionResnetV1(pretrained='vggface2').eval() # initializing resnet for face img to embeding conversion
 
-def face_recognition_function(folder_name):
-    if not os.path.exists(folder_name):
-        print(f"Folder {folder_name} does not exist!")
-        return None
-    name = ""
-    pics = sorted(os.listdir(folder_name))
-    return_names = dict()
-    for pic in pics:
-        path = os.path.join(folder_name, pic)
-        img = cv2.imread(path, cv2.IMREAD_COLOR)
-        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-        face,prob = mtcnn(img, return_prob=True, save_path=None)
-        saved_data = torch.load('/tmp/data.pt')  # loading data.pt file
-        if face != None:
-            emb = resnet(face.unsqueeze(0)).detach()  # detech is to make required gradient false
-            embedding_list = saved_data[0]  # getting embedding data
-            name_list = saved_data[1]  # getting list of names
-            dist_list = []  # list of matched distances, minimum distance is used to identify the person
-            for idx, emb_db in enumerate(embedding_list):
-                dist = torch.dist(emb, emb_db).item()
-                dist_list.append(dist)
-            idx_min = dist_list.index(min(dist_list))
-            pic_extension = pic.split(".")[0]
-            return_names[pic_extension] = name_list[idx_min]
-    return return_names
+def face_recognition_function(key_path):
+    # Face extraction
+    img = cv2.imread(key_path, cv2.IMREAD_COLOR)
+    boxes, _ = mtcnn.detect(img)
+
+    # Face recognition
+    key = os.path.splitext(os.path.basename(key_path))[0].split(".")[0]
+    img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+    face, prob = mtcnn(img, return_prob=True, save_path=None)
+    saved_data = torch.load('/tmp/data.pt')  # loading data.pt file
+    if face != None:
+        emb = resnet(face.unsqueeze(0)).detach()  # detech is to make required gradient false
+        embedding_list = saved_data[0]  # getting embedding data
+        name_list = saved_data[1]  # getting list of names
+        dist_list = []  # list of matched distances, minimum distance is used to identify the person
+        for idx, emb_db in enumerate(embedding_list):
+            dist = torch.dist(emb, emb_db).item()
+            dist_list.append(dist)
+        idx_min = dist_list.index(min(dist_list))
+
+        # Save the result name in a file
+        with open("/tmp/" + key + ".txt", 'w+') as f:
+            f.write(name_list[idx_min])
+        return name_list[idx_min]
+    else:
+        print(f"No face is detected")
+    return
+
 
 
diff --git a/Tooling/Project_2/src/video-splitting-cmdline.py b/Tooling/Project_2/src/video-splitting-cmdline.py
index e11ef3a..091c5bb 100644
--- a/Tooling/Project_2/src/video-splitting-cmdline.py
+++ b/Tooling/Project_2/src/video-splitting-cmdline.py
@@ -8,15 +8,11 @@ import subprocess
 import math
 
 
-def video_splitting_cmdline(video_filename):
+def video_splitting_cmdline(self, video_filename):
     filename = os.path.basename(video_filename)
-    outdir = os.path.splitext(filename)[0]
-    outdir = os.path.join("/tmp",outdir)
-    output_dir = outdir
-    if not os.path.exists(outdir):
-        os.makedirs(outdir)
+    outfile = os.path.splitext(filename)[0] + ".jpg"
 
-    split_cmd = '/usr/bin/ffmpeg -ss 0 -r 1 -i ' +video_filename+ ' -vf fps=1/10 -start_number 0 -vframes 10 ' + outdir + "/" + 'output-%02d.jpg -y'
+    split_cmd = 'ffmpeg -i ' + video_filename + ' -vframes 1 ' + '/tmp/' + outfile
     try:
         subprocess.check_call(split_cmd, shell=True)
     except subprocess.CalledProcessError as e:
@@ -25,6 +21,5 @@ def video_splitting_cmdline(video_filename):
 
     fps_cmd = 'ffmpeg -i ' + video_filename + ' 2>&1 | sed -n "s/.*, \\(.*\\) fp.*/\\1/p"'
     fps = subprocess.check_output(fps_cmd, shell=True).decode("utf-8").rstrip("\n")
-    fps = math.ceil(float(fps))
-    return outdir
+    return outfile
 
diff --git a/Tooling/Project_2/workload_generator/README.md b/Tooling/Project_2/workload_generator/README.md
index f9c03dc..7fcad20 100644
--- a/Tooling/Project_2/workload_generator/README.md
+++ b/Tooling/Project_2/workload_generator/README.md
@@ -4,22 +4,21 @@ This repository contains code examples for you to use our workload generator.
 
 Usage:
 ```
-usage: workload_generator.py [-h] [--access_key AWS_ACCESS_KEY_ID] [--secret_key AWS_SECRET_KEY] [--input_bucket S3_INPUT_BUCKET_NAME] [--testcase_folder TESTCASE_FOLDER]
+usage: workload_generator.py [-h] [--access_key AWS_ACCESS_KEY_ID] [--secret_key AWS_SECRET_KEY] [--asu_id ASU_ID] [--testcase_folder TESTCASE_FOLDER]
 
 Upload videos to input S3
 
 options:
+
   -h, --help            show this help message and exit
   --access_key ACCESS_KEY
                         ACCCESS KEY of the grading IAM user
   --secret_key SECRET_KEY
                         SECRET KEY of the grading IAM user
-  --input_bucket INPUT_BUCKET
-                        Name of the input bucket, e.g. 1234567890-input
-  --output_bucket OUTPUT_BUCKET
-                        Name of the output bucket, e.g. 1234567890-stage-1
+  --asu_id ASU_ID       10-digit ASU ID, e.g. 1234567890
   --testcase_folder TESTCASE_FOLDER
                         the path of the folder where videos are saved, e.g. test_cases/test_case_1/
+
 ```
 
 Examples:
@@ -29,7 +28,7 @@ The following command sends videos from test_case_1 folder to 1234567890-input b
 python3 workload_generator.py \
  --access_key <ACCESS_KEY> \
  --secret_key <SECRET_KEY> \
- --input_bucket 1234567890-input \
+ --asu_id 1234567890 \
  --testcase_folder test_cases/test_case_1/ \
 ```
 
diff --git a/Tooling/Project_2/workload_generator/workload_generator_p2.py b/Tooling/Project_2/workload_generator/workload_generator_p2.py
new file mode 100644
index 0000000..95a87d2
--- /dev/null
+++ b/Tooling/Project_2/workload_generator/workload_generator_p2.py
@@ -0,0 +1,120 @@
+from boto3 import client as boto3_client
+import os
+import argparse
+import time
+from datetime import datetime
+import json
+
+
+timestamps = {}
+
+start_time = time.time()
+parser = argparse.ArgumentParser(description='Upload videos to input S3')
+# parser.add_argument('--num_request', type=int, help='one video per request')
+parser.add_argument('--access_key', type=str, help='ACCCESS KEY of the grading IAM user')
+parser.add_argument('--secret_key', type=str, help='SECRET KEY of the grading IAM user')
+parser.add_argument('--asu_id', type=str, help='10-digit ASU ID, e.g. 1234567890')
+parser.add_argument('--testcase_folder', type=str,
+					help='the path of the folder where videos are saved, e.g. test_cases/test_case_1/')
+
+args = parser.parse_args()
+
+access_key = args.access_key
+secret_key = args.secret_key
+asu_id = args.asu_id
+input_bucket = asu_id + "-input"
+stage1_bucket = asu_id + "-stage-1"
+output_bucket = asu_id + "-output"
+test_cases = args.testcase_folder
+region = 'us-east-1'
+
+s3 = boto3_client('s3', aws_access_key_id=access_key,
+				  aws_secret_access_key=secret_key, region_name=region)
+
+
+def clear_input_bucket(input_bucket):
+	global s3
+	list_obj = s3.list_objects_v2(Bucket=input_bucket)
+	print(list_obj)
+	try:
+		for item in list_obj["Contents"]:
+			key = item["Key"]
+			s3.delete_object(Bucket=input_bucket, Key=key)
+	except:
+		print("Nothing to clear in input bucket")
+
+
+def clear_output_bucket(output_bucket):
+	global s3
+	list_obj = s3.list_objects_v2(Bucket=output_bucket)
+	try:
+		for item in list_obj["Contents"]:
+			key = item["Key"]
+			s3.delete_object(Bucket=output_bucket, Key=key)
+	except:
+		print("Nothing to clear in output bucket")
+
+
+def upload_to_input_bucket_s3(input_bucket, path, name):
+	global s3
+	s3.upload_file(path + name, input_bucket, name)
+
+
+def write_to_file(outfilename, save_dict):
+	with open(outfilename, 'w') as f:
+		f.write(json.dumps(save_dict))
+
+
+def upload_files(input_bucket, test_dir):
+	for filename in os.listdir(test_dir):
+		if filename.endswith(".mp4") or filename.endswith(".MP4"):
+			print("Uploading to input bucket..  name: " + str(filename))
+			filename_raw = filename.split(".mp4")[0]
+			timestamps[filename_raw] = time.time()
+			upload_to_input_bucket_s3(input_bucket, test_dir, filename)
+
+# Stagger the requests by 3 seconds
+def upload_files_v2(input_bucket, test_dir):
+	for filename in os.listdir(test_dir):
+		if filename.endswith(".mp4") or filename.endswith(".MP4"):
+			print("Uploading to input bucket..  name: " + str(filename))
+			filename_raw = filename.split(".mp4")[0]
+			timestamps[filename_raw] = datetime.timestamp(datetime.now())
+			upload_to_input_bucket_s3(input_bucket, test_dir, filename)
+			time.sleep(1)
+
+
+print("Clearing all the buckets ...")
+clear_input_bucket(input_bucket)
+clear_input_bucket(stage1_bucket)
+clear_input_bucket(output_bucket)
+
+print("Starting the upload in 3 sec ...")
+time.sleep(3)
+# upload_files(input_bucket, test_cases)
+upload_files_v2(input_bucket, test_cases)
+
+end_time = time.time()
+print("Time to run = ", end_time - start_time, "(seconds)")
+print(f"Timestamps: start {start_time}, end {end_time}")
+
+print("Waiting for 10 sec to finish all the processing of the functions ...")
+time.sleep(10)
+if timestamps:
+	for filename in os.listdir(test_cases):
+		out_bucket = output_bucket
+		response = s3.list_objects(Bucket=out_bucket, Prefix=filename.split(".mp4")[0])
+		if "Contents" in response:
+			time_lastmodified = datetime.timestamp(response['Contents'][0]['LastModified'])
+			timestamps[filename.split(".mp4")[0]] = time_lastmodified - timestamps[filename.split(".mp4")[0]]
+
+	filtered_values = [value for value in timestamps.values() if 0 <= value <= 200]
+	if filtered_values:
+		minimum = min(filtered_values)
+		maximum = max(filtered_values)
+		average = sum(filtered_values) / len(filtered_values)
+		print("Minimum:", minimum)
+		print("Maximum:", maximum)
+		print("Average:", average)
+	else:
+		print("No values between 20 and 200 found in the dictionary.")