FCMS: A fuzzy controller for CPU and memory consolidation under SLA constraints

What is it about?

Cloud Providers rely on server consolidation (the allocation of several Virtual Machines (VMs) on the same physical server) to minimize their costs. Maximizing the consolidation level is thus become one of the major goals of CPs. This is a challenging task since it requires the ability of estimating, in a resource contention scenario, multidimensional resource demands for multi-tier cloud applications that must meet Service Level Agreements (SLAs) in face of non-stationary workloads. In this paper, we cope with the problem of jointly allocating CPU and memory capacity to (a) precisely estimate their capacity required by each VM to meet its SLAs, and (b) coordinate their allocation to limit the negative effects due to the interactions of dynamic allocation mechanisms, which, if ignored, can lead to SLA violations.

Featured Image

Why is it important?

In this paper, we present the Fuzzy Controller for CPU and Memory Consolidation under SLA Constraints (FCMS), a dynamic resource allocation framework that is based on the fuzzy feedback control theory. FCMS is able to dynamically adjust the CPU and memory capacity allocated to the set of VMs hosting a given multi-tier application in such a way to meet its SLAs in face of (a) bursty and non-stationary workloads, whose intensity varies over time, and (b) the presence of other VMs that compete for the same set of physical resources, as well as to avoid any negative interaction between the CPU and memory allocation mechanisms, which, if ignored, can affects application performance and thus lead to the violation of SLAs (as reported in the literature). By means of an extensive experimental evaluation, we show that FCMS is able to achieve the above goals and works better than existing state-of-the-art alternative solution in all the considered experimental scenarios.