Recent research trends exhibit a growing imbalance between the demands of tenants' software applications and the provisioning of hardware resources. Misalignment of demand and supply gradually hinders workloads from being efficiently mapped to fixed-sized server nodes in traditional data centers. The incurred resource holes not only lower infrastructure utilization but also cripple the capability of a data center for hosting large-sized workloads. This deficiency motivates the development of a new rack-wide architecture referred to as the composable system. The composable system transforms traditional server racks of static capacity into a dynamic compute platform. Specifically, this novel architecture aims to link up all compute components that are traditionally distributed on traditional server boards, such as central processing unit (CPU), random access memory (RAM), storage devices, and other application-specific processors. By doing so, a logically giant compute platform is created and this platform is more resistant against the variety of workload demands by breaking the resource boundaries among traditional server boards. In this paper, we introduce the concepts of this reconfigurable architecture and design a framework of the composable system for cloud data centers. We then develop mathematical models to describe the resource usage patterns on this platform and enumerate some types of workloads that commonly appear in data centers. From the simulations, we show that the composable system sustains nearly up to 1.6 times stronger workload intensity than that of traditional systems and it is insensitive to the distribution of workload demands. This demonstrates that this composable system is indeed an effective solution to support cloud data center services.

Multiuser and cooperative diversity gains have been studied extensively in recent years to improve communication reliability and throughput of wireless networks. Multiuser diversity gains have been exploited by scheduling users with the best channel to transmit in each time slot while cooperative diversity gains have been exploited by having users relay each other's messages to the destination. Most works in the literature consider these advantages separately and in systems that rely on a central scheduler. In this work, we show that the throughput of distributed random access systems can be improved by exploiting both cooperative and multiuser diversity where the transmission probabilities of users are adjusted according to their local channel state information. The fully-loaded stability region is derived as the performance measure of our proposed transmission strategy. The optimal transmission control that maximizes the fully-loaded region is shown to be a threshold-like function. That is, each user shall transmit only when its effective channel is sufficiently reliable. The effective channel in this case is defined as each user's contribution to the weighted sum throughput of the network.