Spread of wave packets in disordered hierarchical lattices

We consider the spreading of the wave packet in the generalized Rosenzweig-Porter random matrix ensemble in the region of non-ergodic extended states $1<\gamma<2$. We show that despite non-trivial fractal dimensions $0 < D_{q}=2-\gamma<1$ characterize wave function statistics in this region, the wave packet spreading $\langle r^{2} \rangle \propto t^{\beta}$ is governed by the "diffusion" exponent $\beta=1$ outside the ballistic regime $t>\tau\sim 1$ and $\langle r^{2}\rangle \propto t^{2}$ in the ballistic regime for $t<\tau\sim 1$. This demonstrates that the multifractality exhibits itself only in {\it local} quantities like the wave packet survival probability but not in the large-distance spreading of the wave packet.Comment: Accepted in EP

Anderson Transition in Disordered Bilayer Graphene

Employing the Kernel Polynomial method (KPM), we study the electronic properties of the graphene bilayers in the presence of diagonal disorder, within the tight-binding approximation. The KPM method enables us to calculate local density of states (LDOS) without need to exactly diagonalize the Hamiltonian. We use the geometrical averaging of the LDOS's at different lattice sites as a criterion to distinguish the localized states from extended ones. We find that bilayer graphene undergoes Anderson metal-insulator transition at a critical value of disorder strength

Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services

Video streams usually have to be transcoded to match the characteristics of viewers' devices. Streaming providers have to store numerous transcoded versions of a given video to serve various display devices. Given the fact that viewers' access pattern to video streams follows a long tail distribution, for the video streams with low access rate, we propose to transcode them in an on-demand manner using cloud computing services. The challenge in utilizing cloud services for on-demand video transcoding is to maintain a robust QoS for viewers and cost-efficiency for streaming service providers. To address this challenge, we present the Cloud-based Video Streaming Services (CVS2) architecture. It includes a QoS-aware scheduling that maps transcoding tasks to the VMs by considering the affinity of the transcoding tasks with the allocated heterogeneous VMs. To maintain robustness in the presence of varying streaming requests, the architecture includes a cost-efficient VM Provisioner. This component provides a self- configurable cluster of heterogeneous VMs. The cluster is reconfigured dynamically to maintain the maximum affinity with the arriving workload. Results obtained under diverse workload conditions demonstrate that CVS2 architecture can maintain a robust QoS for viewers while reducing the incurred cost of the streaming service provider up to 85%Comment: IEEE Transactions on Parallel and Distributed System