Maximizing the Availability of Composable Systems of Next-Generation Data Centers

The next-generation data center introduces the refactoring of the traditional data center in order to create pools of disaggregated resource units, such as processors, memory, storage, network, power, and cooling sources, named composable system (CSs) with the purpose of offering flexibility, automation, optimization, and scalability. In this paper, we solve an optimization problem to allocate CSs considering next- generation data centers. The main goal is to maximize the CS availability for the application owner, having its minimum requirements (in terms of CPU, memory, network and storage), and available budget as restrictions. This problem is modeled as a bounded multidimensional knapsack problem, and we solve it using Dynamic Programming (DP), and two Soft Computing approaches: Differential Evolution (DE) and Particle Swarm optimization (PSO). We consider two different scenarios in order to analyze heterogeneity and variability aspects when allocating CSs in a data center. Moreover, we also analyze the importance of system components to give directions and priorities of actions to upgrade the system design.