The problem of super-resolution time delay estimation in multipath environments is addressed in this paper. Two cases, active and passive systems, are considered. The time delay estimation is first converted into a sinusoidal parameter estimation problem. Then the sinusoidal parameters are estimated by generalizing the Multiple Signal Classification (MUSIC) algorithm for single-experiment data. The proposed method, referred to as the MUSIC-type algorithm, approximates the Cramer-Rao bound (CRB) in terms of the mean square errors (MSEs) for different signal-to-noise ratios (SNRs) and separations of multipath components. Simulation results show that the MUSIC-type algorithm performs better than the classical correlation approach and the conventional MUSIC method for the closely spaced components in multipath environments.

Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual Machines (VMs). As these applications are communication-intensive, optimizing network transfer between VMs is critical to the performance of these applications and network utilization of data centers. Previous studies have addressed this issue by scheduling network flows with coflow semantics or optimizing VM placement with traffic considerations.However, coflow scheduling and VM placement have been conducted orthogonally. In fact, these two mechanisms are mutually dependent, and optimizing these two complementary degrees of freedom independently turns out to be suboptimal. In this paper, we present VirtCO, a practical framework that jointly schedules coflows and places VMs ahead of VM launch to optimize the overall performance of data center applications. We model the joint coflow scheduling and VM placement optimization problem, and propose effective heuristics for solving it. We further implement VirtCO with OpenStack and deploy it in a testbed environment. Extensive evaluation of real-world traces shows that compared with state-of-the-art solutions, VirtCO greatly reduces the average coflow completion time by up to 36.5%. This new framework is also compatible with and readily deployable within existing data center architectures.

Summary With the rapid development of information technology, enormous volumes of data are being generated by enterprises at all times. The management and storage of these large‐scale data have always been challenging enterprises. As these data are usually shared among users in a collaborative manner, secure data access and access performance are 2 key concerns for data storage of enterprises. However, current solutions fail to meet the requirements of enterprises since they suffer from the following drawbacks: (1) they do not support fine‐grained access control and cannot meet the strict secure data access requirements of enterprises, and (2) they suffer from the unpredictable access latency. Thus in this paper, we propose Frostor, an enterprise‐oriented cloud storage system, which addresses the secure data access issue through a user account and IP‐based fine‐grained access control mechanism, and guarantees the access performance via a two‐level performance optimization mechanism. We further implement Frostor and deploy it on the testbed environment in a real data center. Extensive evaluations have shown that Frostor implements fine‐grained access control, while achieving a significant reduction (≥60%) on access latency.

Opportunistic transmission scheduling schemes improve system capacity by taking advantage of independent time varying channels in wireless networks. In the design of such scheduling schemes, the fairness criterion plays an important role in the tradeoff of total system capacity and the achievable throughput of individual users. To meet different fairness demands with a unified opportunistic scheduling scheme, in this paper, we have extended the well known opportunistic scheduling scheme PFS into αPFS, which satisfies arbitrary fairness demands, varying from proportional fairness to maxmin fairness, through adjusting the parameter α. To further improve the achievable diversity gains of αPFS, we extend the αPFS scheme into an αPFS-P scheme. Performances of αPFS and αPFS-P are studied and compared. As demonstrated in the simulation results, both αPFS and αPFS-P can achieve adjustable fairness criteria, varying from proportional fairness to max-min fairness. Compared with αPFS, αPFS-P achieves higher diversity gains with degraded short term performance, which is still better than the performance of PFS.