Noisy Neighbor Mitigation (NNM) is a strategy employed in cloud computing environments to regulate resource usage and prevent any single application or process from consuming an unfair share of resources, thereby negatively impacting the performance of other applications or processes sharing the same infrastructure. The 'noisy neighbor' in this context refers to an application that is excessively demanding in terms of system resources.
In a shared hosting environment, multiple tenants or users are allocated resources from a common pool. When one tenant uses more than their fair share of resources, it can lead to performance issues for others, hence the term 'noisy neighbor'. NNM strategies include resource allocation policies, bandwidth throttling, and quality of service (QoS) controls to ensure fair usage.
The term 'noisy neighbor' refers to an application or process that takes up more than its fair share of system resources, negatively impacting the performance of other applications or processes in a shared hosting environment.
NNM can be implemented through various strategies such as resource allocation policies, bandwidth throttling, and quality of service (QoS) controls.
Software such as VMware's vSphere and Microsoft's Hyper-V have built-in features to mitigate the noisy neighbor problem. These solutions provide resource allocation controls that can be used to prevent any single application from consuming too much of the system's resources.
NNM helps to ensure a level playing field in shared hosting environments, making sure that all applications have access to the resources they need to function optimally. This can lead to improved performance and reliability, and can also prevent conflicts between different tenants on the same server.
In conclusion, Noisy Neighbor Mitigation is an important strategy for maintaining performance and stability in shared hosting environments. By controlling resource usage and preventing any single application from hogging resources, it ensures that all applications can function optimally.