BACKGROUND: 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. RESULTS: In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context. The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor. CONCLUSIONS: Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called "FLEXc" and available online at: http://hpcr.cs.odu.edu/flexc .

Caching is widely known to be an effective method for improving I/O performance by storing frequently used data on higher speed storage components. However, most existing studies that focus on caching performance evaluate fairly small files populating a relatively small cache. Few reports are available that detail the performance of traditional cache replacement policies on extremely large caches. Do such traditional caching policies still work effectively when applied to systems with petabytes of data? In this paper, we comprehensively evaluate the performance of several cache policies, which include First-In-First-Out (FIFO), Least Recently Used (LRU) and Least Frequently Used (LFU), on the global satellite imagery distribution application maintained by the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS). Evidence is presented suggesting traditional caching policies are capable of providing performance gains when applied to large data sets as with smaller data sets. Our evaluation is based on approximately three million real-world satellite images download requests representing global user download behavior since October 2008.

This paper is motivated by a global online satellite images distribution system operated at the Earth Resources Observation and Science (EROS) center of the U.S Geological Survey. Fundamental objectives of EROS include, but are not limited to, building high-speed and cost-effective massive data processing and storage systems to support online satellite images distribution. Hybrid storage systems -- containing solid-state drives (SSD), hard disks (HDD), and tapes -- can provide an ideal data storage solution for a wide variety of data processing centers like EROS. Large-scale hybrid storage systems will become increasingly popular in the next few years for the following two reasons. First, highly accessed storage objects in a hybrid storage system can be prefetched and cached to high-speed storage components such as solid-state drives. SSD-based hybrid storage system can provide large storage capacity, high I/O performance and data reliability. Second, hybrid storage systems are cost-effective, because inexpensive tapes help in increasing storage capacities at very low cost. Transferring data back and forth among SSDs, HDDs, and tapes plays a critical role in achieving high I/O performance. Thus, we proposed data mining algorithms that can judiciously prefetch data. Our analytical model and the experimental results reveals that our data mining prefetching algorithm increase the performance of the hybrid storage systems.

Performance improvement and energy conservation are two conflicting objectives in large scale parallel storage systems. In this paper, we propose a novel solution to achieve the twin objectives of maximizing performance and minimizing energy consumption of parallel storage systems. Specifically, a buffer-disk based architecture (BUD for short) is designed to conserve energy. A heat-based dynamic data caching strategy is developed to improve performance. The BUD architecture strives to allocate as many requests as possible to buffer disks, thereby keeping a large number of idle data disks in low-power states. This can provide significant opportunities for energy conservation while making buffer disks a potential performance bottleneck. The heat-based data caching strategy aims to achieve good load balancing in buffer disks and alleviate overall performance degradation caused by unbalanced workload. Our experimental results have shown that the proposed BUD framework and dynamic data caching strategy are able to conserve energy by 84.4% for small reads and 78.8% for large reads with slightly degraded response time.

Improving security and minimizing power consumption are crucial for large-scale data storage systems. Although a handful of studies have been focused on data security and energy efficiency, most of the existing approaches have concentrated on only one of these two metrics. In this paper, we present a new approach to integrating power optimization with security services to enhance the security of energy-efficient large-scale storage systems. In our approach, we make use of the dynamic speed control for power management technique, or DRPM, to conserve energy in secure storage systems. In this study we develop two ways of integrating confidentiality services with the dynamic disk speed control technique. The first strategy - security aggressive in nature - is focused on the improvement of storage system security with less emphasis on energy conservation. The second strategy gives higher priority to energy conservation as opposed to the security optimization. Our experimental results show that the energy-aggressive approach provides better energy savings than the security-aggressive approach. However, the quality of security achieved by the security-aggressive scheme is higher than that of the energy-aggressive approach. Moreover, the empirical results show that energy savings yielded by the two approaches become more pronounced when the data size is increased. The findings illustrate that the response time of the security-aggressive approach is more sensitive to data size than that of the energy-aggressive scheme.

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 speculatively 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.

Energy conservation has become a critical problem for real-time embedded storage systems. Although a variety of approaches for reducing energy consumption have been extensively studied, energy conservation for real-time embedded storage systems is still an open problem. In this article, we propose an energy management strategy, I/O Burstiness for Energy Conservation (IBEC), exploiting the burstiness of real-time embedded storage systems applications. Our approach aims at combining the IBEC energy-management strategy with a Linux-based disk block-scheduling mechanism to conserve the energy of storage systems. Extensive experiments are conducted involving a number of synthetic disk traces as well as real-world data-intensive traces. To evaluate the energy efficiency of IBEC, we compare the performance of IBEC against three existing strategies, namely, PA-EDF, DP-EDF, and EDF. Compared with the alternative strategies, IBEC reduces the power consumption of real-time embedded disks system by up to 60%.

In the past decade parallel disk systems have been highly scalable and able to alleviate the problem of disk I/O bottleneck, thereby being widely used to support a wide range of data-intensive applications. Optimizing energy consumption in parallel disk systems has strong impacts on the cost of backup power-generation and cooling equipment, because a significant fraction of the operation cost of data centres is incurred by energy consumption and cooling. Although flash memory is very energy-efficient compared to disk drives, flash memory is too expensive to use as a major component in large-scale storage systems. In other words, it is not a cost-effective way to make use of large flash memory to build energy-efficient storage systems. To address this problem, in this paper we proposed a hybrid disk architecture or HYBUD that integrates a non-volatile flash memory with buffer disks to build cost-effective and energy-efficient parallel disk systems. While the most popular data sets are cached in flash memory, the second most popular data sets can be stored and retrieved from buffer disks. HYBUD is energy efficient because flash memory coupled with buffer disks can serve a majority of incoming disk requests, thereby keeping a large number of other data disks in the low-power state for longer period times. Furthermore, HYBUD is cost-effective by the virtue of inexpensive buffer disks assisting flash memory to cache a huge amount of popular data. Experimental results demonstratively show that compared with two existing non-hybrid architectures, HYBUD provides significant energy savings for parallel disk systems in a very cost effective way.

Improving energy efficiency of security-aware storage systems is challenging, because security and energy efficiency are often two conflicting goals. The first step toward making the best tradeoffs between high security and energy efficiency is to profile encryption algorithms to decide if storage systems would be able to produce energy savings for security mechanisms. We are focused on encryption algorithms rather than other types of security services, because encryption algorithms are usually computation-intensive. In this study, we used the XySSL libraries and profiled operations of several test problems using Conky - a lightweight system monitor that is highly configurable. Using our profiling techniques we concluded that although 3DES is much slower than AES encryption,it more likely to save energy in security-aware storage systems using 3DES than AES. The CPU is the bottleneck in 3DES, allowing us to take advantage of dynamic power management schemes to conserve energy at the disk level.After profiling several hash functions, we noticed that the CPU is not the bottleneck for any of these functions,indicating that it is difficult to leverage the dynamic power management technique to conserve energy of a single disk where hash functions are implemented for integrity checking.

Parallel disk systems consume a significant amount of energy due to the large number of disks. To design economically attractive and environmentally friendly parallel disk systems, in this paper we design and evaluate an energy-aware prefetching strategy for parallel disk systems consisting of a small number of buffer disks and large number of data disks. Using buffer disks to temporarily handle requests for data disks, we can keep data disks in the low-power mode as long as possible. Our prefetching algorithm aims to group many small idle periods in data disks to form large idle periods, which in turn allow data disks to remain in the standby state to save energy. To achieve this goal, we utilize buffer disks to aggressively fetch popular data from regular data disks into buffer disks, thereby putting data disks into the standby state for longer time intervals. A centrepiece in the prefetching mechanism is an energy-saving prediction model, based on which we implement the energy-saving calculation module that is invoked in the prefetching algorithm. We quantitatively compare our energy-aware prefetching mechanism against existing solutions, including the dynamic power management strategy. Experimental results confirm that the buffer-disk-based prefetching can significantly reduce energy consumption in parallel disk systems by up to 50 percent. In addition, we systematically investigate the energy efficiency impact that varying disk power parameters has on our prefetching algorithm.

In the past decade parallel disk systems have been highly scalable and able to alleviate the problem of disk I/O bottleneck, thereby being widely used to support a wide range of data-intensive applications. Optimizing energy consumption in parallel disk systems has strong impacts on the cost of backup power-generation and cooling equipment, because a significant fraction of the operation cost of data centres is due to energy consumption and cooling. Although a variety of parallel disk systems were developed to achieve high performance and energy efficiency, most existing parallel disk systems lack an adaptive way to conserve energy in dynamically changing workload conditions. To solve this problem, we develop an adaptive energy-conserving algorithm, or DCAPS, for parallel disk systems using the dynamic voltage scaling technique that dynamically choose the most appropriate voltage supplies for parallel disks while guaranteeing specified performance (i.e., desired response times) for disk requests. We conduct extensive experiments to quantitatively evaluate the performance of the proposed energy-conserving strategy. Experimental results consistently show that DCAPS significantly reduces energy consumption of parallel disk systems in a dynamic environment over the same disk systems without using the DCAPS strategy.

Modern real-time wireless networks require high security level to assure confidentiality of information stored in packages delivered through wireless links. However, most existing algorithms for scheduling independent packets in real-time wireless networks ignore various security requirements of the packets. Therefore, in this paper we remedy this problem by proposing a novel dynamic security-aware packet-scheduling algorithm, which is capable of achieving high quality of security for realtime packets while making the best effort to guarantee realtime requirements (e.g., deadlines) of those packets. We conduct extensive simulation experiments to evaluate the performance of our algorithm. Experimental results show that compared with two baseline algorithms, the proposed algorithm can substantially improve both quality of security and real-time packet guarantee ratio under a wide range of workload characteristics.

Real time applications such as military aircraft flight control systems and online banking are critical with respect to security and reliability. In this paper we presented a way to integrate both by considering confidentiality and integrity services for security and nonuniform checkpoint strategy for reliability. The slack exploitation interacts in subtle ways for security in regards to the placement of checkpoint. The checkpoints are placed in to the task at low frequency in the beginning because the slack available can accommodate a large amount of work at risk and the frequency is increased there after considering the slack available. The security is applied to the data in two ways. First method introduces the security for the entire data at once whereas in the second method the data is divided into n uneven sections and each section is separately secured . That is at the start of the task basic security services are considered depending on the slack available. The security is increased gradually for the rest of the task but if there exist a fault, then at that point the security is maintained at the steady rate because of the limited slack. Compared to the first method the second method can provide up to a 32.3 percent higher security. While compared to the traditional checkpoint strategy, the non-uniform check pointing makes more efficient use of slack while increasing the overall security levels by 34.4 percent for the second method.

In the past decade cluster computing platforms have been widely applied to support a variety of scientific and commercial applications, many of which are parallel in nature. However, scheduling parallel applications on large scale clusters is technically challenging due to significant communication latencies and high energy consumption. As such, shortening schedule length and conserving energy consumption are two major concerns in designing economical and environmentally friendly clusters. In this paper, we propose an energy-efficient scheduling algorithm (TDVAS) using the dynamic voltage scaling technique to provide significant energy savings for clusters. The TDVAS algorithm aims at judiciously leveraging processor idle times to lower processor voltages (i.e., the dynamic voltage scaling technique or DVS), thereby reducing energy consumption experienced by parallel applications running on clusters. Reducing processor voltages, however, can inevitably lead to increased execution times of parallel task. The salient feature of the TDVAS algorithm is to tackle this problem by exploiting tasks precedence constraints. Thus, TDVAS applies the DVS technique to parallel tasks followed by idle processor times to conserve energy consumption without increasing schedule lengths of parallel applications. Experimental results clearly show that the TDVAS algorithm is conducive to reducing energy dissipation in large-scale clusters without adversely affecting system performance.

High performance clusters have been widely used to provide amazing computing capability for both commercial and scientific applications. However, huge power consumption has prevented the further application of large-scale clusters. Designing energy-efficient scheduling algorithms for parallel applications running on clusters, especially on the high performance heterogeneous clusters, is highly desirable. In this regard, we propose a novel scheduling strategy called energy efficient task duplication schedule (EETDS for short), which can significantly conserve power by judiciously shrinking communication energy cost when allocating parallel tasks to heterogeneous computing nodes. We present the preliminary simulation results for Gaussian and FFT parallel task models to prove the efficiency of our algorithm.

Existing packets scheduling algorithms designed for energy-efficient wireless networks ignore important features of periodic packets, thereby being inadequate for periodic packets with energy constraints. To remedy this problem, we present in this paper an approach to scheduling periodic packets in wireless networks subject to both timing and energy constraints. We propose a necessary and sufficient feasibility check for a set of periodic packets to be transmitted over a wireless link. Next, we develop an algorithm to schedule periodic packets (or ESPP for short) over a wireless link. The ESPP algorithm aims at minimizing energy dissipation of periodic packets without missing deadlines of periodic packets. We show through simulation studies that ESPP can significantly reduce energy consumption of wireless networks by an average of 46.4% while guaranteeing timing constraints of periodic packets.

Parallel disk systems, which have been widely used in building networked and data intensive applications, are highly scalable and can alleviate the problem of disk I/O bottleneck. Although a number of parallel disk systems have been developed, the systems lack a means to optimize quality of security for dynamically changing networked environments. We remedy this situation by proposing an adaptive quality of security control scheme for networked parallel disk systems (or ASPAD for short) that makes it possible for networked disk systems to adapt to changing security requirements and workload conditions. ASPAD is carried out in three phases: dynamic data partitioning, response time estimation, and adaptive security quality control. Hence, ASPAD is conducive to adaptively and expeditiously determining security schemes for disk requests in a way to improve security of networked parallel disk systems while making an effort to guarantee desired response times of the requests. To prove the efficiency of the proposed approach, we simulate a networked parallel disk system into which nine cryptographic schemes are integrated. Empirical results show that ASPAD significantly improves overall performance over an existing strategy with an average of 65%.

Parallel applications with energy and low-latency constraints are emerging in various networked embedded systems like digital signal processing, vehicle tracking, and infrastructure monitoring. However, conventional energy-driven task allocation schemes for a cluster of embedded nodes only concentrate on energy-saving when making allocation decisions. Consequently, the length of the schedules could be very long, which is unfavorable or in some situations even not tolerated. In this paper, we address the issue of allocating a group of parallel tasks on a heterogeneous embedded system with an objective of energy-saving and short-latency. A novel task allocation strategy, or BEATA (balanced energy-aware task allocation), is developed to find an optimal allocation that minimizes overall energy consumption while confining the length of schedule to an ideal range. Experimental results show that BEATA significantly improves the performance of embedded systems in terms of energy-saving and schedule length over an existing allocation scheme

Since security is of critical importance for modern storage systems, it is imperative to protect stored data from being tampered 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. Experimental results show that AWARDS significantly improves security and overall performance over an existing scheme by up to 325.0% and 358.9% (with averages of 199.3% and 213.4%)

This paper addresses the problem of improving quality of security for real-time parallel applications on heterogeneous clusters. We propose a new security- and heterogeneity-driven scheduling algorithm (SHARP for short), which strives to maximize the probability that parallel applications are executed in time without any risk of being attacked. Because of high security overhead in existing clusters, an important step in scheduling is to guarantee jobs' security requirements while minimizing overall execution times. The SHARP algorithm accounts for security constraints in addition to different processing capabilities of each node in a cluster. We introduce two novel performance metrics, degree of security deficiency and risk-free probability, to quantitatively measure quality of security provided by a heterogeneous cluster. Both security and performance of SHARP are compared with two well-known scheduling algorithms. Extensive experimental studies using real-world traces confirm that the proposed SHARP algorithm significantly improves security and performance of parallel applications on heterogeneous clusters

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%).