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Host-side flash caching emerges as an effective solution for improving the performance of virtual machines (VMs) in cloud computing environment. However, for VMs with the commonly used Copy-on-Write (COW) virtual disks, flash caching in fact has negative impacts since it brings lots of unnecessary cache writes, hurting both the VM performance and the flash endurance. This paper presents COWCache, a COW-aware caching solution that addresses this problem by co-designing flash caching with COW-based virtual disks. First, COWCache designs a new architecture that bridges the semantic gap between flash caching and virtual disk management for realizing the VMs' real data locality. Second, it separately manages COW metadata with fine-grained caching and journaling to improve the metadata caching efficiency. Third, it provides a novel decoupled Copy-on-Write mechanism, which decouples the amplified data requests from the critical I/O path and only admits the data with real VM locality into flash cache. COWCache also introduces a new data structure, the virtual cache map, to mitigate the memory footprint overhead for indexing the cached data in flash. Evaluations show that COWCache improves the application performance by up to 122.7% and reduces the flash cache writes by up to 78.5% compared to traditional flash caching solutions. [ABSTRACT FROM AUTHOR]

Cloud computing provides elastic data storage and processing services. Although existing research has proposed preferred search on the plaintext files and encrypted search, no method has been proposed that integrates the two techniques to efficiently conduct preferred and privacy-preserving search over large datasets in the cloud.In this paper, we propose a scheme for preferred search over encrypted data (PSED) that can take users’ search preferences into the search over encrypted data. In the search process, we ensure the confidentiality of not only keywords but also quantified preferences associated with them. PSED constructs its encrypted search index using Lagrange coefficients and employs secure inner-product calculation for both search and relevance measurement. The dynamic and scalable property of cloud computing is also considered in PSED. A series of experiments have been conducted to demonstrate the efficiency of the proposed scheme when deploying it in real-world scenarios. [ABSTRACT FROM AUTHOR]

Existing data management tools have some limitations such as restrictions to specific file systems or shortage of transparence to applications. In this paper, we present a new data management tool called AIP, which is implemented via the standard data management API, and hence it supports multiple file systems and makes data management operations transparent to applications. First, AIP provides centralized policy-based data management for controlling the placement of files in different storage tiers. Second, AIP uses differentiated collections of file states to improve the execution efficiency of data management policies, with the help of the caching mechanism of file states. Third, AIP also provides a resource arbitration mechanism for controlling the rate of initiated data management operations. Our results from representative experiments demonstrate that AIP has the ability to provide high performance, to introduce low management overhead, and to have good scalability. [ABSTRACT FROM AUTHOR]

With the exponential growth of digital data, it is becoming more and more popular to store data in shared distributed storage systems inside the same organization. In such shared distributed storage systems, an ordinary user usually does not have the control permission over the whole system, and thus cannot secure data storage or data sharing of his own files. To solve this issue, this paper proposes a new system architecture to secure file storing and sharing efficiently over untrusted shared storage and network environments. Based on this architecture, this paper designs and implements a stackable secure storage system called Corslet. Corslet can run directly on deployed underlying storage systems without modification, while bringing end-to-end confidentiality and integrity as well as efficient access control for user data. For individual users, Corslet is easy to use, and does not require users to maintain or manage any keys on their client machines locally. The Bonnie++ and IOzone benchmark results show that the throughput of Corslet over NFS can achieve more than 90% of native NFS throughput in most tests, proving that Corslet can provide enhanced security for user data while maintaining acceptable performance. [ABSTRACT FROM AUTHOR]

Mathematically, the Black-Scholes model of American option pricing is a free boundary problem of partial differential equation. It is well known that this model is a nonlinear problem, and it has no closed form solution. We can only obtain an approximate solution by numerical method, but the precision and stability are hard to control, because the singularity at the exercise boundary near expiration date has a great effect on precision and stability for numerical method. We propose a new numerical method, FDA method, to solve the American option pricing problem, which combines advantages the Semi-Analytical Method and the Front-Fixed Difference Method. Using the FDA method overcomes the difficulty resulting from the singularity at the terminal of optimal exercise boundary. A large amount of calculation shows that the FDA method is more accurate and stable than other numerical methods. [ABSTRACT FROM AUTHOR]

This paper proposes two viable computing strategies for distributed parallel systems: domain division with sub-domain overlapping and asynchronous communication. We have implemented a parallel computing procedure for simulation of Ti thin film growing process of a system with 1000 × 1000 atoms by means of the Monte Carlo (MC) method. This approach greatly reduces the computation time for simulation of large-scale thin film growth under realistic deposition rates. The multi-lattice MC model of deposition comprises two basic events: deposition, and surface diffusion. Since diffusion constitutes more than 90% of the total simulation time of the whole deposition process at high temperature, we concentrated on implementing a new parallel diffusion simulation that reduces communication time during simulation. Asynchronous communication and domain overlapping techniques are used to reduce the waiting time and communication time among parallel processors. The parallel algorithms we propose can simulate the thin film growth of a system with many more particles than before under realistic deposition rates, and can provide a more efficient means for computer simulation of thin film growth. [ABSTRACT FROM AUTHOR]