We propose Sage, a system for uncertain network analysis. Algorithms for uncertain network analysis require large amounts of memory and computing resources as they sample a large number of network instances and run analysis on them. Sage makes uncertain network analysis simple and efficient. By extending the edge-centric programming model, Sage makes writing sampling-based analysis algorithms as simple as writing conventional graph algorithms in Pregel-like systems. Moreover, Sage proposes four optimization techniques, namely, deterministic sampling, hybrid gathering, schedule-aware caching, and copy-on-write attributes, that exploit common properties of uncertain network analysis. Extensive evaluation of Sage with eight algorithms on six real-world networks shows that the four optimizations in Sage jointly improve performance by up to 13.9X and on average 2.7X.

In cloud computing, multiple servers are consolidated into a physical machine in order to reduce the cost of deploying the servers. Guaranteeing the service level objective (SLO) of each server is one of the most important factors in a virtualization system. Particularly, isolating the I/O resources among VMs competing for a shared storage system is challenging. Recently, use of flash based Solid State Drives (SSDs) is being extended to enterprise systems. However, there are few studies for guaranteeing SLOs on such systems. In this paper, we empirically analyze the I/O behavior of a shared SSD. We show that performance SLOs of storage systems employing SSDs being shared by VMs or tasks are not satisfactory. We analyze and show that components of SSDs such as channels, DRAM buffer, and Native Command Queuing (NCQ) are the reasons behind this problem. Based on these analysis and observations, we propose two SSD-aware host level I/O schedulers that we call A+CFQ and H+BFQ, which are extensions of state-of-the-art I/O schedulers CFQ and BFQ, respectively. Through implementation and experiments on Linux, we show that the proposed I/O schedulers improve proportionality without sacrifice to performance.

The move from SLC to MLC/TLC flash memory technology is increasing SSD capacity at lower cost, but at the cost of sacrificing reliability. An approach to remedy this loss is to employ the RAID architecture with the chips that comprise SSDs. However, using the traditional RAID approach may result in negative effects as the total number of writes is increased due to the parity updates. In this paper, we describe Elastic Striping and Anywhere Parity (eSAP)-RAID, a RAID scheme that allows flexible stripe sizes and parity placement. Using performance and lifetime models that we derive of SSDs employing RAID-5 and eSAP-RAID, we show that eSAP-RAID brings about significant performance and reliability benefits by reducing parity writes compared to RAID-5. We also implement these schemes in SSDs using DiskSim with SSD Extension and validate the models using realistic workloads. We also discuss policies such as dynamic stripe sizing and selective data protection that exploits the flexible nature of eSAP. We show that through such policies particular reliability enhancement goals can be met.

SSD based cache solutions are being widely utilized to improve performance in network storage systems. With a goal of providing a cost-effective, high performing SSD cache solution, we propose a new caching solution called SRC (SSD RAID as a Cache) for an array of commodity SSDs. In designing SRC, we borrow both the well-known RAID technique and the log-structured approach and adopt them into the cache layer. In so doing, we explore a wide variety of design choices such as flush issue frequency, write units, forming stripes without parity, and garbage collection through copying rather than destaging that become possible as we make use of RAID and a log-structured approach at the cache level. Using an implementation in Linux under the Device Mapper framework, we quantitatively present and analyze results of the design space options that we considered in our design. Our experiments using realistic workload traces show that SRC performs at least 2 times better in terms of throughput than existing open source solutions. We also consider cost-effectiveness of SRC with a variety of SSD products. In particular, we compare SRC configured with MLC and TLC SATA SSDs and a single high-end NVMe SSD. We find that SRC configured as RAID-5 with low-cost MLC and TLC SATA SSDs generally outperforms that configured with a single high-end SSD in terms of both performance and lifetime per dollars spent.

It has been shown that image compression based on principal component analysis (PCA) provides good compression efficiency for hyperspectral images. However, PCA might fail to capture all the discriminant information of hyperspectral images, since features that are important for classification tasks may not be high in signal energy. To deal with this problem, we propose a hybrid compression method for hyperspectral images with pre-encoding discriminant information. A feature extraction method is first applied to the original images, producing a set of feature vectors that are used to generate feature images and then residual images by subtracting the feature-reconstructed images from the original ones. Both feature images and residual images are compressed and transmitted. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor indicate that the proposed method provides better compression efficiency with improved classification accuracy than conventional compression methods.

As the market becomes more competitive, SSD manufacturers are moving from SLC (Single-Level Cell) to MLC (Multi-Level Cell) flash memory chips that store two bits per cell as building blocks for SSDs. Recently, TLC chips, which store three bits per cell, is being considered as a viable solution due to their low cost. However, performance and lifetime of TLC chips are considerably limited and thus, pure TLC-based SSDs may not be viable as a general storage device. In this paper, we propose a hybrid SSD solution, namely HySSD, where SLC and TLC chips are used together to form an SSD solution performing in par with SLC-based products. Based on an analytical model, we propose a near optimal data distribution scheme that distributes data among the SLC and TLC chips for a given workload such that performance or lifetime may be optimized. Experiments with two types of SSDs both based on DiskSim with SSD Extension show that the analytic model approach can dynamically adjust data distribution as workloads evolve to enhance performance or lifetime.

In this paper, we propose compression algorithms for hyperspectral images based on the wavelet transform using adjacent band information. In the proposed algorithm, we divide the spectral bands into a number of groups. Then, within each group, we select a representative spectral band which has highest correlations with the remaining spectral bands within the group. The representative image is compressed using the 2-dimensional SPIHT algorithm. A characteristic of the SPIHT algorithm is that it provides information on significant coefficients of transformed images, which have large energies. We explore this characteristic and use the information to encode adjacent band images of the representative image.