Rapid Testing of IaaS Resource Management Algorithms via Cloud Middleware Simulation
This reduces testing effort for algorithm engineers and data center operators, but it is incremental as it builds on existing simulation methods.
The paper tackled the high effort required to test IaaS resource management algorithms by proposing a simulation approach that automatically constructs models from runtime monitoring data, eliminating the need for manual reimplementation and workload definition, with validation showing that tests match actual hardware behavior.
Infrastructure as a Service (IaaS) Cloud services allow users to deploy distributed applications in a virtualized environment without having to customize their applications to a specific Platform as a Service (PaaS) stack. It is common practice to host multiple Virtual Machines (VMs) on the same server to save resources. Traditionally, IaaS data center management required manual effort for optimization, e.g. by consolidating VM placement based on changes in usage patterns. Many resource management algorithms and frameworks have been developed to automate this process. Resource management algorithms are typically tested via experimentation or using simulation. The main drawback of both approaches is the high effort required to conduct the testing. Existing Cloud or IaaS simulators require the algorithm engineer to reimplement their algorithm against the simulator's API. Furthermore, the engineer manually needs to define the workload model used for algorithm testing. We propose an approach for the simulative analysis of IaaS Cloud infrastructure that allows algorithm engineers and data center operators to eval- uate optimization algorithms without investing additional effort to reimplement them in a simulation environment. By leveraging runtime monitoring data, we automatically construct the simula- tion models used to test the algorithms. Our validation shows that algorithm tests conducted using our IaaS Cloud simulator match the measured behavior on actual hardware.