Abstract
Cloud computing has currently permeated all walks of life. It has proven extremely useful for organizations and individual users to save costs by leasing compute resources that they need. This has led to an exponential growth in cloud computing based research and development. A substantial number of frameworks, approaches and techniques are being proposed to enhance various aspects of clouds, and add new features. One of the constant concerns in this scenario is creating a testbed that successfully reflects a real-world cloud datacenter. It is vital to simulate realistic, repeatable, standardized CPU and memory workloads to compare and evaluate the impact of the different approaches in a cloud environment. This paper introduces Cloudy, which is an open-source workload generator that can be used within cloud instances, Virtual Machines (VM), containers, or local hosts. Cloudy utilizes resource usage traces of machines from Google and Alibaba clusters to simulate up to 16000 different, real-world CPU and memory load patterns. The tool also provides a variety of machine metrics for each run, that can be used to evaluate and compare the performance of the VM, container or host. Additionally, it includes a web-based visualization component that offers a number of real-time statistics, as well as overall statistics of the workload such as seasonal trends, and autocorrelation. These statistics can be used to further analyze the real-world traces, and enhance the understanding of workloads in the cloud.
Highlights
Cloud computing has become an important part of most organizations these days
Once all the dependencies and file placements are automatically handled, the workload can be started by running the workload.sh script, passing the name of the trace to use (TN), maximum memory to use in GB (MMG) and time scaling in seconds (TSS) as arguments
For the experiments in this paper, the maximum memory to use was set to 16 GB, and the scaling was at the default of 5 minutes
Summary
Cloud computing has become an important part of most organizations these days. By leasing compute resources from cloud providers, organizations can save on hardware and setup costs. Researchers have explored different aspects of cloud computing such as live migration [1] [2], vertical elasticity [3], horizontal elasticity, remote memory [4], workload prediction, container placement, virtual machine consolidation, and load balancing [5]. In order to evaluate proposed approaches similar to these, it is important to have a standard tool that can be used to benchmark them. This entails an environment that simulates the resource usage patterns seen in the real world. The tool should preferably log a variety of performance and system statistics These statistics are extremely important to observe the positive or negative effects of the approach under test.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
