A minimum-cost flow model for workload optimization on cloud infrastructure

Abstract

Recent technology advancements in the areas of compute, storage and networking, along with the increased demand for organizations to cut costs while remaining responsive to increasing service demands have led to the growth in the adoption of cloud computing services. Cloud services provide the promise of improved agility, resiliency, scalability and a lowered Total Cost of Ownership (TCO). This research introduces a framework for minimizing cost and maximizing resource utilization by using an Integer Linear Programming (ILP) approach to optimize the assignment of workloads to servers on Amazon Web Services (AWS) cloud infrastructure. The model is based on the classical minimum-cost flow model, known as the assignment model.

Publication
In 2017 IEEE 10th International Conference on Cloud Computing
Mandana Saebi
Mandana Saebi
Machine Learning Engineer

My research interests include machine learning, knowledge graphs.