Quantifying cloud performance and dependability:Taxonomy, metric design, and emerging challenges

In only a decade, cloud computing has emerged from a pursuit for a service-driven information and communication technology (ICT), becoming a significant fraction of the ICT market. Responding to the growth of the market, many alternative cloud services and their underlying systems are currently vying for the attention of cloud users and providers. To make informed choices between competing cloud service providers, permit the cost-benefit analysis of cloud-based systems, and enable system DevOps to evaluate and tune the performance of these complex ecosystems, appropriate performance metrics, benchmarks, tools, and methodologies are necessary. This requires re-examining old system properties and considering new system properties, possibly leading to the re-design of classic benchmarking metrics such as expressing performance as throughput and latency (response time). In this work, we address these requirements by focusing on four system properties: (i) elasticity of the cloud service, to accommodate large variations in the amount of service requested, (ii) performance isolation between the tenants of shared cloud systems and resulting performance variability, (iii) availability of cloud services and systems, and (iv) the operational risk of running a production system in a cloud environment. Focusing on key metrics for each of these properties, we review the state-of-the-art, then select or propose new metrics together with measurement approaches. We see the presented metrics as a foundation toward upcoming, future industry-standard cloud benchmarks

Addressing the Challenges in Federating Edge Resources

This book chapter considers how Edge deployments can be brought to bear in a global context by federating them across multiple geographic regions to create a global Edge-based fabric that decentralizes data center computation. This is currently impractical, not only because of technical challenges, but is also shrouded by social, legal and geopolitical issues. In this chapter, we discuss two key challenges - networking and management in federating Edge deployments. Additionally, we consider resource and modeling challenges that will need to be addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and Paradigms; Editors Buyya, Sriram

Achieving Reproducibility in Cloud Benchmarking: A Focus on FaaS Services

openThe cloud computing industry has witnessed a rapid growth in recent years, providing businesses with an opportunity to scale their operations dynamically. With the emergence of multiple cloud providers, it has become increasingly challenging to determine which provider offers the most scalable services for a particular workload. This master thesis aims to compare the scalability of three major cloud providers: Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. The study focuses on benchmarking the scalability of their compute, storage, and database services. To achieve this, a set of well-defined benchmarks will be used to evaluate the performance of each provider. The benchmarks will be designed to simulate a range of workloads, from small to large-scale, to assess how each provider's services perform when under different load conditions. The results will be analyzed and compared to identify the strengths and weaknesses of each provider's services. This study will provide valuable insights into which cloud provider offers the most scalable services, and will help businesses make informed decisions when choosing a cloud provider for their specific needs. The findings of this study will contribute to the ongoing discussion on the performance of cloud services, and will offer guidance to businesses on selecting the most appropriate cloud provider to meet their scalability requirements.The cloud computing industry has witnessed a rapid growth in recent years, providing businesses with an opportunity to scale their operations dynamically. With the emergence of multiple cloud providers, it has become increasingly challenging to determine which provider offers the most scalable services for a particular workload. This master thesis aims to compare the scalability of three major cloud providers: Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. The study focuses on benchmarking the scalability of their compute, storage, and database services. To achieve this, a set of well-defined benchmarks will be used to evaluate the performance of each provider. The benchmarks will be designed to simulate a range of workloads, from small to large-scale, to assess how each provider's services perform when under different load conditions. The results will be analyzed and compared to identify the strengths and weaknesses of each provider's services. This study will provide valuable insights into which cloud provider offers the most scalable services, and will help businesses make informed decisions when choosing a cloud provider for their specific needs. The findings of this study will contribute to the ongoing discussion on the performance of cloud services, and will offer guidance to businesses on selecting the most appropriate cloud provider to meet their scalability requirements