One of the key challenges for performance isolation is how to allocate resources among different tenants. Resources can include CPU, memory, disk, network, and power. Ideally, each tenant should receive the amount of resources that matches their demand and priority, without interfering with other tenants. However, this is not easy to achieve in practice, due to resource contention, variability, and heterogeneity. Resource contention occurs when multiple tenants compete for the same resource, leading to performance degradation or starvation. Variability refers to the dynamic and unpredictable changes in resource demand and availability over time. Heterogeneity means that different tenants may have different resource requirements and preferences, such as latency-sensitive or throughput-oriented workloads.
One possible solution for resource allocation is to use resource reservation and limit mechanisms, such as cgroups in Linux or virtual machines in hypervisors. These mechanisms allow the system administrator to specify the minimum and maximum amount of resources that each tenant can use, and enforce them at the kernel or hypervisor level. This way, each tenant can have a guaranteed baseline of resources, and also a fair share of the remaining resources. However, this solution requires accurate and timely estimation of resource demand and availability, which can be challenging in a dynamic and heterogeneous environment. Moreover, this solution may not be able to cope with sudden spikes or bursts of resource demand, which may require more flexibility and elasticity.
Another possible solution for resource allocation is to use feedback control mechanisms, such as proportional-integral-derivative (PID) controllers or reinforcement learning (RL) agents. These mechanisms allow the system to monitor the performance metrics of each tenant, such as response time, throughput, or quality of service, and adjust the resource allocation accordingly. This way, each tenant can have a performance target or goal, and the system can adapt to the changing resource demand and availability. However, this solution requires careful design and tuning of the feedback control parameters, which can be complex and domain-specific. Moreover, this solution may not be able to handle conflicting or competing performance goals, which may require more coordination and negotiation.