FLEXc: Protein Flexibility Prediction Using Context-Based Statistics, Predicted Structural Features, and Sequence Information

The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions

Multi-Layer Prefetching for Hybrid Storage Systems: Algorithms, Models, and Evaluations

Parallel storage systems have been highly scalable and widely used in support of data-intensive applications. In future systems with the nature of massive data processing and storing, hybrid storage systems opt for a solution to fulfill a variety of demands such as large storage capacity, high I/O performance and low cost. Hybrid storage systems (HSS) contain both high-end storage components (e.g. solid-state disks and hard disk drives) to guarantee performance, and low-end storage components (e.g. tapes) to reduce cost. In HSS, transferring data back and forth among solid-state disks (SSDs), hard disk drives (HDDs), and tapes plays a critical role in achieving high I/O performance. Prefetching is a promising solution to reduce the latency of data transferring in HSS. However, prefetching in the context of HSS is technically challenging due to an interesting dilemma: aggressive prefetching is required to efficiently reduce I/O latency, whereas overaggressive prefetching may waste I/O bandwidth by transferring useless data from HDDs to SSDs or from tapes to HDDs. To address this problem, we propose a multi-layer prefetching algorithm that can judiciously prefetch data from tapes to HDDs and from HDDs to SSDs. To evaluate our algorithm, we develop an analytical model and the experimental results reveal that our prefetching algorithm improves the performance in hybrid storage systems

Modeling and improving security of a local disk system for write-intensive workloads

Since security is of critical importance for modern storage systems, it is imperative to protect stored data from being tampered with or disclosed. Although an increasing number of secure storage systems have been developed, there is no way to dynamically choose security services to meet disk requests ’ flexible security requirements. Furthermore, existing security techniques for disk systems are not suitable to guarantee desired response times of disk requests. We remedy this situation by proposing an adaptive strategy (referred to as AWARDS) that can judiciously select the most appropriate security service for each write request, while endeavoring to guarantee the desired response times of all disk requests. To prove the efficiency of the proposed approach, we build an analytical model to measure the probability that a disk request is completed before its desired response time. The model also can be used to derive the expected value of disk requests ’ security levels. Empirical results based on synthetic workloads as well as real I/O-intensive applications show that AWARDS significantly improves overall performance over an existing scheme by up to 358.9 % (with an average of 213.4%). Categories and Subject Descriptors: D.4.8 [Operating Systems]: Performance—Simulation, queueing theory; D.4.6 [Operating Systems]: Security a Protection—Cryptographic controls informatio

Performance Analysis of an Admission Controller for CPU- and I/O- Intensive Applications in Self-Managing Computer Systems

With rapid advances in processing power, network bandwidth, and storage capacity, computer systems are increasingly becoming extremely complex. Consequently, it becomes expensive and difficult for human beings to manually manage complex computer systems. This problem can be effectively tackled by self-managing computer systems, which are intended to meet high performance requirements in a dynamic computing environment. In this paper, we develop a performance model for self-manage computer systems under dynamic workload conditions, where both CPU- and I/O-intensive applications are running in the systems. In particular, we design in this paper a 2-dimenssional Markov chain model with two different arrival and service rate of CPU- and I/O-intensive jobs. Importantly, two serving probabilities with respect to CPU-and I/O intensive jobs are derived. To validate the analytical model, we developed an adaptive admission controller in which the model is incorporated. Experimental results demonstratively show that the controller is capable of achieving high performance for computer systems under workloads exhibiting high variability. 1

StReD: A Quality of Security Framework for Storage Resources in Data Grids

Securing storage resources in Grid environments has increasingly become a major concern to make Grids attractive for a wide range of data-intensive applications, because security requirements are often imposed on storage resources to support security-critical applications. However, existing storage systems are unable to dynamically adjust quality of security to meet the flexible security needs of complex data-intensive applications. To remedy this deficiency, we propose in this paper a quality of security framework or StReD for storage resources in Data Grids. In this framework, we integrate quality of security adaptor with an array of security services. Applications running in the framework are enabled to specify flexible security requirements and desired response times of disk requests. The framework leverages the adaptor to dynamically control quality of security for disk requests, thereby achieving good tradeoffs between security and storage system throughput. Experimental results based on a simulated Grid with storage resources show that the proposed framework is capable of significantly improving quality of security and guaranteeing desired response times of disk requests