Federated cloud facilitates data sharing across multiple organizations. Existing distributed storage systems cannot support efficient data search with complicated query requirements. This paper proposes a DS-index solution to provide distributed search in federated cloud, targeting to support multi-attribute range search with secondary index. DS-index includes a three-layer architecture with a multi-attribute index overlay, a tree-based P2P network layer, and a federated cloud layer. In addition, a dynamic mapping algorithm has been presented for DS-index to map between different layers. We have also defined a Markov Chain-based cost model to facilitate node selection and mapping. The proposed DS-index solution is capable to support multi-attribute range queries in the federated cloud. The experiments have shown that, the DS-index solution with cost model can save the computation resource and reduce network bandwidth consumption by around 30% comparing to the one without cost model. It can also reduce the splitting/ merging times by around 20% comparing to the state-of-the-art solution.

Visualizing and analyzing large-scale datasets are both critical and challenging, as they require substantial resources for data processing and storage. While the speed of supercomputers continues to set higher standard, the I/O systems have not kept in pace, resulting in a significant performance bottleneck. To alleviate the I/O bottleneck for scientific visualization applications, we propose a Visualization via a Heterogeneous Distributed Storage Infrastructure (VH-DSI) solution to improve I/O speed and accelerate overall visualization performance. VH-DSI replaces the traditional parallel file system with a distributed file system to support visualization applications. A new scheduling algorithm HeterSche is proposed in VH-DSI to assign computing tasks to data nodes with the consideration of cluster heterogeneity and data locality. VH-DSI also includes a design to support POSIX-IO for distributed file system. The performance evaluation has shown that the proposed VH-DSI solution can achieve significant performance improvement for visualization applications. Compared to the traditional visualization, the VH-DSI solution reduces the response time by at least 5 times. The HeterSche scheduling algorithm is capable to speed up visualization compared to other scheduling algorithms especially for large scale datasets.

As Applications are moved from physical servers to virtual machines sharing storage resources, they experience large variation in I/O latencies. While maintaining average performance in such virtualized environments is important to conform to service level agreements (SLA), cloud users also expect their applications to have minimum variation in tail end latencies like 90th percentile latency for predictable performance. This becomes a challenging problem as the deviation in the application's 90th percentile I/O latency from average latency under storage resource sharing (VM consolidation) can be very high. We show through experiments under VM consolidation that during peak loads this latency variation from average can be as much as 5 times compared to when the application has exclusive access to the storage devices. This variation in performance exists for both Hard drives (HDD) and Solid state drives (SSD). To minimize this large latency variation, we propose a dynamic I/O redirection and caching mechanism called Virt Cache. Virt Cache can pro-actively detect storage device contention at the storage server and temporarily redirect the peaking virtual disk workload to a dynamically instantiated distributed read-write cache. We have implemented our system in Gluster FS, a commonly used distributed file system deployed as a backing store in the cloud. Our system can achieve from 50% to 83% reduction in the 90th percentile latency deviation from average compared to previous work as we move from low load conditions to peak non uniform consolidated VM workloads. With our Virt Cache system, Cloud providers can guarantee predictable performance for the cloud users as if their application has exclusive access to the storage resources.

Emerging DNA sequencing technologies open up exciting new opportunities for genome sequencing by generating read data with a massive throughput. However, produced reads are significantly shorter and more error-prone compared to the traditional Sanger shotgun sequencing method. This poses challenges for de-novo DNA fragment assembly algorithms in terms of both accuracy (to deal with short, error-prone reads) and scalability (to deal with very large input data sets). In this paper we present a scalable parallel algorithm for correcting sequencing errors in high-throughput short-read data. It is based on spectral alignment and uses the CUDA programming model. Our computational experiments on a GTX 280 GPU show runtime savings between 10 and 19 times (for different error-rates using simulated datasets as well as real Solexa/Illumina datasets).

We present a method to compute the delay constrained multicast routing tree by employing chaotic neural networks. Experimental result shows that the noisy chaotic neural network (NCNN) provides optimal solution more often compared to the transiently chaotic neural network (TCNN) and the Hopfield neural network (HNN). Furthermore, compared with the bounded shortest multicast algorithm (BSMA), the noisy chaotic neural network is able to find multicast trees with lower cost.

We propose a novel approach, i.e., a noisy chaotic neural network with variable thresholds (NCNN-VT), to solve the frequency assignment problem in satellite communications. The objective of this NP-complete optimization problem is to minimize cochannel interference between two satellite systems by rearranging frequency assignments. The NCNN-VT model consists N times M of noisy chaotic neurons for an N-carrier M-segment problem. The NCNN-VT facilitates the interference minimization by mapping the objective to variable thresholds (biases) of the neurons. The performance of the NCNN-VT is demonstrated by solving a set of benchmark problems and randomly generated test instances. The NCNN-VT achieves better solutions, i.e., smaller interference with much lower computation cost compared to existing algorithms.

In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks. The objective of the BSP is to design an optimal time-division multiple-access (TDMA) frame structure with minimal TDMA frame length and maximal channel utilization. A two-phase optimization is adopted to achieve the two objectives with two different energy functions, so that the G-NCNN not only finds the minimum TDMA frame length but also maximizes the total node transmissions. In the first phase, we propose a G-NCNN which combines the noisy chaotic neural network (NCNN) and the gradual expansion scheme to find a minimal TDMA frame length. In the second phase, the NCNN is used to find maximal node transmissions in the TDMA frame obtained in the first phase. The performance is evaluated through several benchmark examples and 600 randomly generated instances. The results show that the G-NCNN outperforms previous approaches, such as mean field annealing, a hybrid Hopfield network-genetic algorithm, the sequential vertex coloring algorithm, and the gradual neural network.

In this paper we propose a hybrid method to solve the broadcast scheduling problem in packet radio networks. In the first stage, we use a backtracking sequential coloring algorithm to obtain a minimal TDMA frame length and the corresponding transmission assignments. In the second stage, we employ the noisy chaotic neural network to find the maximum node transmission based on the results obtained in the previous stage. Simulation results show that this hybrid method outperforms previous approaches, such as mean field annealing, a hybrid of the Hopfield neural network and genetic algorithms, the sequential vertex coloring algorithm, and the gradual neural network.