Thesis: Optimizing Cold Start Latency and Resource Scaling in Serverless Platforms for High-Load Applications

High Level Description
When utilizing distributed systems that consists of many different servers or containers, startup of these can oftentimes be very slow and reduce the speed of a whole system. This is especially important in high-load applications where response times are important. As always, the performance must be balanced out with the cost of the system, where a middle ground must be reached between the response times and the cost of the applications.

This thesis will investigate optimization techniques to minimize cold start latency and enhance resource scaling efficiency in serverless function platforms, especially under high-load scenarios. The work will focus on dynamic, workload-aware adaptive solutions that improve user experience and operational cost-effectiveness without requiring changes to application code. This research seeks to understand what factors affect the response times and how the system can scale using predictive loads.

Project Description
This thesis project focuses on investigating and improving the performance of serverless computing platforms in the context of high-load applications. In particular, the work will address two of the most pressing challenges in serverless environments: cold start latency and efficient resource scaling. Cold starts can introduce unpredictable delays in request handling, while scaling inefficiencies can lead to either poor performance during traffic spikes or unnecessary costs during low-traffic periods.

The project will include both a theoretical and practical component. On the theoretical side, existing research and industry approaches to mitigating cold starts and scaling issues will be studied. On the practical side, experiments will be conducted to systematically measure and profile cold start latency, identify workload-specific bottlenecks, and evaluate strategies such as pre-warming, workload prediction, caching, and adaptive instance scaling policies. Simulated high-load scenarios will be created to compare different optimization techniques in terms of response times, reliability, and cost-effectiveness.

The outcome of this project will be a set of insights, benchmarks, and recommendations for optimizing serverless platforms under high-load conditions. Depending on the scope, this could also include a prototype implementation of a workload-aware adaptive scaling solution that integrates with an existing serverless platform. Through this work, the thesis will contribute to bridging the gap between theoretical performance models and practical techniques that can be applied in real-world serverless applications.

Who are we looking for?
We are looking for a student with a strong interest in cloud computing, distributed systems, and serverless architectures. This opportunity is well-suited for students pursuing a bachelor's or master's degree in Computer Engineering, Computer Science, Data Science, or a related field. A solid foundation in distributed systems and cloud-native principles is important, along with curiosity for practical performance optimization. Previous experience with cloud platforms as well as familiarity with serverless would be highly beneficial.

Purpose
The aim of this project is to investigate how cold start latency be systematically broken down and profiled to identify workload-specific bottlenecks. This thesis aims to investigate practical implementation to advance serverless computing performance and scalability for complex, high-load cloud applications.

An Exciting Journey with Knightec Group
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Practical Information
This is a thesis position, located at our office in Solna United, Rättarvägen 3. Start date is

Please submit your application as soon as possible, but no later than If you have any questions, you are welcome to contact Julius Jensen. Note that due to GDPR, we only accept applications through our careers page.