# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
## 2 Literature Review
### Cloud-Provider Comparison  
One of the ways architects and developers can compare cloud-computing providers would be to compare the qualitative factors. Ref. [1] compares their technology specifications, model services, data center location, services available, microservices architecture, and cloud-computing management. In terms of the machine learning services available, Amazon AWS and the Google Cloud Platform (GCP) provide more options than Alibaba Cloud and Microsoft Azure. Similarly, ref. [2] compares AWS, Azure, and the GCP in terms of the catalog of services provided by the platforms. They conclude that AWS has the broadest reliable and stable services. Furthermore, Azure provides extensive Microsoft tool integrations for users, and the GCP provides the best data and analytic tools. However, both these works fail to provide quantifiable metrics to compare these platforms, conduct no experimentation, and do not provide any guide on choosing the best platform based on business requirements. Contrarily, some works have attempted to perform a quantitative analysis between cloud providers. Ref. [3] uses a quantitative approach and provisions various types of AWS instances and compares response time, costs per inference, and the maximum request rate for these various instances. This paper provides a detailed analysis of the various possible CPU as well as GPU instances on AWS SageMaker. In addition, ref. [4] identifies various metrics across multiple categories, such as general, performance, economics, and security metrics. Some of the ideal metrics that could be used for this project are the CPU load, mean hit time, response time, waiting time, price/performance ratio, mean time to change (MTTC), and cost. However, both of these works lack a comprehensive comparison of the cloud-computing platforms and do not provide any further assistance in application-specific comparisons. To further improve the decision making, we need to consider the various possible architectures for deploying any system. For instance, ref. [5] compares three possible architectures, namely, monolithic, microservice architecture operated by the cloud customer, and microservice architecture operated by the cloud provider. It concludes that using special services specifically designed to deploy and scale microservices, such as AWS Lambda and its alternatives, reduces infrastructure costs by 70% or more. Ref. [6], on the other hand, compares multiple zero-downtime-deployment techniques, namely the Blue Green, Canary, and Rolling deployment techniques. They conclude that Blue Green is ideal for cloud infrastructures with container-based deployments, and Rolling deployment is ideal for infrastructures with data-center-based applications. On the other hand, these papers provide a rather general overview during their comparisons, lacking a more detailed holistic comparison. In addition to these comparisons, several works have also examined fog computing, which is a decentralized infrastructure where compute and storage resources are placed at the edge of the cloud. Ref. [7] deployed a deep-learning-based object-detection model on both fog and cloud environments. The authors analyzed the differences for both high-accuracy and low-latency operation modes using metrics such as the response time, jitter, network bandwidth, and power consumption. They identify that for the high-accuracy operation mode, the cloud-computing model is preferred for tasks that require heavy computation. However, for low-latency operation tasks, the fog-computing environment is ideal because of the significantly lower response time and jitter. Ref. [8], on the other hand, performed a review of performance evaluation metrics to compare cloud, fog, and edge computing. Some of the common metrics applicable to these paradigms are resource utilization, response time, energy consumption, and latency. However, the authors do not deploy any application to compare these environments based on the listed metrics.