Search

In the field of quantum chaos, spectral statistics is one of the most extensively investigated characteristics. Despite a large body of existing literature, the effects of many-body interactions on the spectral statistics of relativistic quantum systems remain poorly understood. Treating electron-electron interactions through the one-orbital mean-field Hubbard model, we address this fundamental issue using graphene billiards with the geometric shape of a circular sector as prototypical systems. Our approach is to consider the two characteristically different cases where the statistics are Poisson and Gaussian orthogonal ensemble (GOE) so the corresponding classical dynamics are typically integrable and chaotic, respectively, and to systematically investigate how the statistics change as the Hubbard interaction strength increases from zero. We find that, for energies near the Dirac point, the Hubbard interactions have a significant effect on the spectral statistics. Regardless of the type of spectral statistics to begin with, increasing the Hubbard interaction strength up to a critical value causes the statistics to approach GOE, rendering more applicable the random matrix theory. As the interaction strength increases beyond the critical value, the statistics evolve toward Poisson, due to the emergence of an energy gap rendering the quasiparticles massive. We also find that the energy levels above and below the Dirac point can exhibit different statistics, and the many-body interactions have little effect on the statistics for levels far from the Dirac point. These results reveal the intriguing interplay between many-body interactions and the spectral statistics, which we develop a physical picture to understand.

Hybrid object storage systems provide opportunities to achieve high performance and energy efficiency with low cost for enterprise data centers. Existing object storage systems, however, distribute data objects in the system without considering the heterogeneity of the underlying devices and the asymmetric data access patterns. Therefore, the system performance and energy efficiency may degrade as data are placed on improper storage devices. For example, energy-efficient high-density archive hard disk drives (archive HDDs) are significantly slower than normal HDDs and solid state disks (SSDs), which mean that the archive HDDs are not appropriate for storing frequently accessed objects. Besides, flash-based SSDs have limited write endurance, which makes SSDs vulnerable for storing write-intensive objects. In this paper, we analyze various real enterprise workloads and find that read and write requests are not uniformly distributed to data objects. Based on the observations, we propose a novel strategy, biased object storage strategy (BOSS), to reduce writes to SSDs and improve system performance for hybrid object storage systems. Different from conventional uniform and fixed data distribution strategies, the BOSS can distribute and migrate data objects to various types of devices dynamically, according to the data access patterns collected online. The experimental results show that the BOSS can reduce 64% of writes on SSDs and improve system performance by 29.51% on average, while maintaining a high level of load balance.