Optimizing Resource Availability in Composable Data Center Infrastructures To meet service level agreement (SLA) requirements, the majority of enterprise IT infrastructure is typically overprovisioned, underutilized, noncompliant and lacking in required agility resulting in significant inefficiencies. As enterprises introduce and migrate to nextgeneration applications designed to be horizontally scalable, they require infrastructure that can manage the duality of legacy and next generation application requirements. To address this, composable data center infrastructure disaggregates and refactors compute, storage, network and other infrastructure resources in to shared resources pools that can be xe2x80x9ccomposedxe2x80x9d and allocated ondemand. In this paper, we model a theorical problem of resource allocation in a composable data center infrastructure as a bounded multidimensional knapsack and then apply multiobjective optimization algorithms, Nondominated Sorting Genetic Algorithm (NSGAII) and Generalized Differential Evolution (GDE3), to allocate resources efficiently. The main goal is to maximize resource availability for the application owner, while meeting minimum requirements (in terms of CPU, memory, network, and storage) within budget constraints. We consider two different scenarios to analyze heterogeneity and variability aspects when allocating resources on composable data center infrastructure.

Partial Server Pooling in Redundancy Systems Partial sharing allows providers to possibly pool a fraction of their resources when full pooling is not beneficial to them. Recent work in systems without sharing has shown that redundancy can improve performance considerably. In this paper, we combine partial sharing and redundancy by developing partial sharing models for providers operating multiserver systems with redundancy. Two MMN queues with redundant service models are considered. Copies of an arriving job are placed in the queues of servers that can serve the job. Partial sharing models for canceloncomplete and cancelonstart redundancy models are developed. For canceloncomplete, it is shown that the Pareto efficient region is the full pooling configuration. For a cancelonstart policy, we conjecture that the Pareto frontier is always nonempty and is such that at least one of the two providers is sharing all of its resources. For this system, using bargaining theory the sharing configuration that the providers may use is determined. Mean response time and probability of waiting are the performance metrics considered.