Master Thesis Proposal - Predictive Cloud-based Scaling Algorithms
Start your career adventure and meet experts in your future field of work as a thesis student at Syntronic!
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In today's fast-paced business landscape, adaptability is the key to success. Are you tired of struggling to keep up with fluctuating workloads, leading to system crashes, downtime, and potentially frustrated customers? It's time to pave the way for the future of efficiency and help us develop the scalable systems of tomorrow!
We would like to eliminate the headaches of managing server capacity and ensure optimal performance, even during peak demand. We want to develop a system that can automatically scale resources up or down based on real-time load and predict the stress of the system based on earlier data, ensuring applications run smoothly regardless of traffic spikes.
The benefit of this approach is to eliminate system crashes, downtime and overspending on infrastructure. With a scalable system you only need to pay for the resources you use, reducing operational costs while enhancing performance.
For this master thesis we would like to implement and evaluate different scaling algorithms that are relevant based on current studies and prior master theses. The goal of the algorithms is to predict the oncoming load of a system based on collected data and scale the system accordingly. Additionally, we foresee that different load patterns and algorithms can be tested and compared, based on, for example, machine learning, analysis in the frequency domain, etc. Finally, the implemented algorithms are evaluated and compared to industry-standard scaling algorithms in a cloud environment.
Don't let fluctuations in workload hold your back. Embrace the power of automatic scalability and ensure seamless performance, no matter the demands. Together, we'll drive efficiency and success to new heights, join us now!
Proposed research question:
- What is the efficiency of predictive scaling algorithms in a cloud environment?
Features:
- Scalable applications: Kubernetes clusters, containers
- Cloud based applications.
- Predictivity models
- Simulations
Background:
- We foresee that a student with a background in software engineering will excel in the proposed assignment.
- We will only accept one student per assignment.
Application:
We look forward to receiving your resume, and preferably, a personal letter in which you explain why you want to write your thesis with Syntronic.
We screen and evaluate applications on an ongoing basis.
- Department
- Studenter
- Locations
- Stockholm
Stockholm
Benefits
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Work life balance
Flexible working hours.
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Wellness
Wellness allowance and Benefit (a benefits portal that gives instant access to rewards and discounts).
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Health and insurance
Beneficial pension agreement with personal provisions and insurance. Private health insurance options.
Workplace and culture
Syntronic offers an innovative, collaborative, and inclusive working environment. We believe in the notion “choose a job you love, and you will never have to work a day in your life.” Our team of creative out-of-the-box thinkers consists of motivated engineers from all walks of life with extensive experience.
Ideas, creativity, and new perspectives flow freely in our professional environment. We are convinced that the best results are achieved in an environment where people lift each other up and help each other grow. At Syntronic, we believe that excellence can be achieved when great minds work together.
About Syntronic
Syntronic is a global design house on the frontline of new technology. Our areas of expertise are advanced product and system development, production, and aftermarket services in the telecom, automotive, industrial, and medtech sectors.
Master Thesis Proposal - Predictive Cloud-based Scaling Algorithms
Start your career adventure and meet experts in your future field of work as a thesis student at Syntronic!
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