Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Interference beten co-located VMs may lead to performance fluctuations and degradation, especially in overbooked datacenters. To limit this problem, VMs access to physical resources needs to be controlled to ensure certain degree of isolation among them. Hover, the mapping beten virtual and physical resources must be performed in a dynamic way so that it can be adapted to the changing applications requirements, as ll as to the different set of co-located VMs. To address this problem  propose a twofold approach: (1) a Quality of Service (QoS) scheme that provides different isolation levels for VMs with different QoS requirements, and (2) a self-adaptive fuzzy Q-learning capacity controller that proactively readjusts the isolation degree based on applications performance. Our evaluation based on real cloud applications and workloads demonstrates that the efficient, adaptive mapping beten VMs and physical resources reduces the interference beten VMs, enabling the possibility of co-locating more VMs, increases overall utilization, and ensures the performance of critical applications while providing more resources to the low QoS applications.
['Seyed Saeid Masoumzadeh', 'Helmut Hlavacs', 'Luis Tomas']
I am writing a research paper on a idea A Self-Adaptive Performance-Aware Capacity Controller in Overbooked Datacenters
[{"from": "human", "value": "I am writing a research paper on a idea A Self-Adaptive Performance-Aware Capacity Controller in Overbooked Datacenters"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Interference beten co-located VMs may lead to performance fluctuations and degradation, especially in overbooked datacenters. To limit this problem, VMs access to physical resources needs to be controlled to ensure certain degree of isolation among them. Hover, the mapping beten virtual and physical resources must be performed in a dynamic way so that it can be adapted to the changing applications requirements, as ll as to the different set of co-located VMs. To address this problem  propose a twofold approach: (1) a Quality of Service (QoS) scheme that provides different isolation levels for VMs with different QoS requirements, and (2) a self-adaptive fuzzy Q-learning capacity controller that proactively readjusts the isolation degree based on applications performance. Our evaluation based on real cloud applications and workloads demonstrates that the efficient, adaptive mapping beten VMs and physical resources reduces the interference beten VMs, enabling the possibility of co-locating more VMs, increases overall utilization, and ensures the performance of critical applications while providing more resources to the low QoS applications."}]