I wrote this essay while applying for the Data Engineering and Analytics master program at the Technical University of Munich. It may serve future applicants as an example of the scientific essay you need to append to your application.

Abstract

The scalability of a computing environment impacts the performance and the ability to serve business needs. Two approaches to scale a

computing environment are scale-up and scale-out. The objective of this paper is to describe the scale-up and scale-out approaches while mentioning their limitations. In addition, this paper describes why a

computing environment should be designed to scale-out from the get go to save costs.

1. Introduction

With the increase of interest in Artificial Intelligence and the growing complexity of modern computing architectures, more powerful resources are needed to adapt to business needs. If a computing environment has reached the limits of its computational power and can‘t serve the requests of its concurrent users efficiently, it has reached the limit of its scalability [3].

The limit of scalability can be extended by providing more resources. Although, increasing the resources also increases the cost of running the environment. The two primary approaches to increase the capacity of a computing environment are vertical scaling-up and horizontal scaling-out [2].

Both approaches are not exclusive and a computing environment can be designed to scale vertically, horizontally, or both [3]. Although both strategies can be used to increase the capacity of a computing environment, each strategy follows a different approach. Choosing the right scaling approach from the get go is beneficial to scale according to business needs and saving costs.

The contribution of this paper is as follows. First, the approach of each scaling strategy is introduced. Second, the limitations of both scaling approaches are described. Third, a recommended scaling strategy is introduced. Lastly, a summary of this essay is given with a logical conclusion.

#programming #scaling #microservices #architecture #university #servers