[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290377-105":59,"doc-detail-290377-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","dynamic-resource-scaling-for-microservices-a-machine-learning-driven-predictive-approach-msc-research-project","Dynamic Resource Scaling for Microservices - A Machine Learning-driven Predictive Approach - MSc Research Project","","Research focuses on optimizing microservice-based applications by predicting workload and dynamically adjusting resources through cloud autoscaling. Machine learning models, including Multinomial Logistic Regression and Linear Regression, are trained on historical workload data from the Netflix NDbench dataset, then deployed with AWS Lambda, API Gateway, and load balancing on AWS. CPU utilization is categorized as High, Normal, or Low, and predictions drive concurrency updates for Lambda. High predictions increase concurrency to serve more simultaneous requests, while low predictions reduce it to improve allocation efficiency. 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All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \n\n| Signature: | Akshay Joshi |\n| --- | --- |\n| Date: | 30th January 2024 |\n\nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST:  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) . | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both foryour own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nDynamic Resource Scaling for Microservices: A Machine Learning-driven Predictive Approach  \nAkshay Joshi  \n22137696  \nAbstract  \nIn my research on ”Dynamic Resource Scaling for Microservices: A Machine Learning-driven Predictive Approach,” the focus is on optimizing microservicebased applications by predicting and adjusting resource needs using cloud autoscaling mechanisms. The machine learning models, including Multinomial Logistic Regression and Linear Regression, are trained on historical workload data, specifically the Netflix NDbench dataset.  \nFollowing the training, microservices are deployed on AWS Lambda with AWSAPI Gateway and a Load Balancer for efficient management. The machine learning outcomes categorize CPU usage into High, Normal, and Low, stored for subsequent actions. To operationalize these predictions, a dedicated webpage is developed, displaying machine learning results and Lambda function details through AWSSDK.  \nThe critical component of the research involves autoscaling by dynamically updating the concurrency of the Lambda function. When the machine learning model predicts high CPU utilization, the concurrency is increased, allowing the Lambda function to handle a higher number of simultaneous requests. Conversely, during low CPU usage, the concurrency is decreased to optimize resource allocation.  \nFor an in-depth exploration of the autoscaling methodology and its integration with machine learning predictions, further details are provided in subsequent sections.  \n1 Introduction  \nIn the software development, microservices play a crucial role. Rather than constructing a single large application, developers create small, specialized components known as microservices, each dedicated to specific tasks such as user authentication, email sending, or payment management. These microservices collaborate like a team, collectively contributing to the functionality of th","cbCaibp9gTWyX5nh","https://ap.wps.com/l/cbCaibp9gTWyX5nh","pdf",2055800,"English","# Abstract\n# Introduction","[{\"question\":\"What is the main objective of this research on microservices resource scaling?\",\"answer\":\"The research aims to predict busy and idle periods for microservices and proactively allocate resources to avoid unnecessary cost and reduce inefficiency.\"},{\"question\":\"Which machine learning models are trained and what data do they use?\",\"answer\":\"The study trains Multinomial Logistic Regression and Linear Regression using historical workload data, specifically the Netflix NDbench dataset.\"},{\"question\":\"How does the system use predictions to perform autoscaling on AWS?\",\"answer\":\"When the model predicts high CPU utilization, Lambda concurrency is increased to handle more simultaneous requests; when CPU usage is low, concurrency is decreased to optimize resource allocation.\"}]","Dynamic Resource Scaling for Microservices - A Machine Learning-driven Predictive Approach - MSc Research Project | PDF"]