All-flash servers, while being widely popular for their high performance and large capacity, can incur significant energy consumption in modern storage systems. Through a motivational study, we discover that the culprit is the inefficiency in the software stack, and existing power-saving methods fail to deliver comparable performance, especially under workload bursts. Guided by the lessons learned, we propose Sandman, a scheduling framework that combines the fast resource scaling mechanism, resource monitoring, and I/O burst detection policies. Experiments show that Sandman reduces average power consumption by up to 39.38% and energy consumption by up to 33.36% while delivering performance comparable (within 5% in corner cases) to the best performance case (the busy-polling stack) in both benchmarks and field workloads.

File systems may become corrupted for many reasons despite various protection techniques. Therefore, most file systems come with a checker to recover the file system to a consistent state. However, existing checkers are commonly assumed to be able to complete the repair without interruption, which may not be true in practice. In this work, we demonstrate via fault injection experiments that checkers of widely used file systems (EXT4, XFS, BtrFS, and F2FS) may leave the file system in an uncorrectable state if the repair procedure is interrupted unexpectedly. To address the problem, we first fix the ordering issue in the undo logging of e2fsck and then build a general logging library (i.e., rfsck-lib) for strengthening checkers. To demonstrate the practicality, we integrate rfsck-lib with existing checkers and create two new checkers: rfsck-ext, a robust checker for Ext-family file systems, and rfsck-xfs, a robust checker for XFS file systems, both of which require only tens of lines of modification to the original versions. Both rfsck-ext and rfsck-xfs are resilient to faults in our experiments. Also, both checkers incur reasonable performance overhead (i.e., up to 12%) compared to the original unreliable versions. Moreover, rfsck-ext outperforms the patched e2fsck by up to nine times while achieving the same level of robustness.

The Popular Disk Concentration (PDC) technique and the Massive Array of Idle Disks (MAID) technique are two effective energy conservation schemes for parallel disk systems. The goal of PDC and MAID is to skew I/O load toward a few disks so that other disks can be transitioned to low power states to conserve energy. I/O load skewing techniques like PDC and MAID inherently affect reliability of parallel disks, because disks storing popular data tend to have high failure rates than disks storing cold data. To study reliability impacts of energy-saving techniques on parallel disk systems, we develop a mathematical modeling framework called MINT. We first model the behaviors of parallel disks coupled with power management optimization policies. We make use of data access patterns as input parameters to estimate each disk's utilization and power-state transitions. Then, we derive each disk's reliability in terms of annual failure rate from the disk's utilization, age, operating temperature, and power-state transition frequency. Next, we calculate the reliability of PDC and MAID parallel disk systems in accordance with the annual failure rate of each disk in the systems. Finally, we use real-world trace to validate out MINT model. Validation result shows that the behaviors of PDC and MAID which are modeled by MINT have a similar trend as that in the real-world.

Extracting high data bandwidth and metadata rates from parallel file systems is notoriously difficult. User workloads almost never achieve the performance of synthetic benchmarks. The reason for this is that real-world applications are not as well-aligned, well-tuned, or consistent as are synthetic benchmarks. There are at least three possible ways to address this challenge: modification of the real-world workloads, modification of the underlying parallel file systems, or reorganization of the real-world workloads using Tran formative middleware. In this paper, we demonstrate that Tran formative middleware is applicable across a large set of high performance computing workloads and is portable across the three major parallel file systems in use today. We also demonstrate that our transformative middleware layer is capable of improving the write, read, and metadata performance of I/O workloads by up to 150x, 10x, and 17x respectively, on workloads with processor counts of up to 65,536.

Long-running HPC applications guard against node failures by writing checkpoints to parallel file systems. Writing these checkpoints with petascale class machines has proven difficult and the increased concurrency demands of exascale computing will exacerbate this problem. To meet checkpointing demands and sustain application-perceived throughput at exascale, multi-tiered hierarchical storage architectures involving solid-state burst buffers are being considered. In this paper, we describe the design and implementation of cento, a multi-level, content-addressable checkpoint file system for large-scale HPC systems. cento achieves in-flight checkpoint data reduction across all compute nodes through compression and elimination of duplicate blocks over a series of checkpoints. Through a detailed analysis of checkpoint dumps, we assess the benefits of data reduction for scientific applications that are representative of production workloads. We observe upto 40% data reduction within a limited sample of representative workloads. Finally, experiments on existing systems show a decrease in checkpoint commit latencies by 5 to 20 % reducing the load on the parallel file system.

There yet exist no truly parallel file systems. Those that make the claim fall short when it comes to providing adequate concurrent write performance at large scale. This limitation causes large usability headaches in HPC. Users need two major capabilities missing from current parallel file systems. One, they need low latency interactivity. Two, they need high bandwidth for large parallel IO; this capability must be resistant to IO patterns and should not require tuning. There are no existing parallel file systems which provide these features. Frighteningly, exascale renders these features even less attainable from currently available parallel file systems. Fortunately, there is a path forward.

High-performance clusters have been widely deployed to solve challenging and rigorous scientific and engineering tasks. On one hand, high performance is certainly an important consideration in designing clusters to run parallel applications. On the other hand, the ever increasing energy cost requires us to effectively conserve energy in clusters. To achieve the goal of optimizing both performance and energy efficiency in clusters, in this paper, we propose two energy-efficient duplication-based scheduling algorithms-Energy-Aware Duplication (EAD) scheduling and Performance-Energy Balanced Duplication (PEBD) scheduling. Existing duplication-based scheduling algorithms replicate all possible tasks to shorten schedule length without reducing energy consumption caused by duplication. Our algorithms, in contrast, strive to balance schedule lengths and energy savings by judiciously replicating predecessors of a task if the duplication can aid in performance without degrading energy efficiency. To illustrate the effectiveness of EAD and PEBD, we compare them with a nonduplication algorithm, a traditional duplication-based algorithm, and the dynamic voltage scaling (DVS) algorithm. Extensive experimental results using both synthetic benchmarks and real-world applications demonstrate that our algorithms can effectively save energy with marginal performance degradation.

A critical problem with parallel I/O systems is the fact that disks consume a significant amount of energy. To design economically attractive and environmentally friendly parallel I/O systems, we propose an energy-aware prefetching strategy (PRE-BUD) for parallel I/O systems with disk buffers. We introduce a new architecture that provides significant energy savings for parallel I/O systems using buffer disks while maintaining high performance. There are two buffer disk configurations: (1) adding an extra buffer disk to accommodate prefetched data, and (2) utilizing an existing disk as the buffer disk. PRE-BUD is not only able to reduce the number of power-state transitions, but also to increase the length and number of standby periods. As such, PRE-BUD conserves energy by keeping data disks in the standby state for increased periods of time. Compared with the first prefetching configuration, the second configuration lowers the capacity of the parallel disk system. However, the second configuration is more cost-effective and energy-efficient than the first one. Finally, we quantitatively compare PRE-BUD with both disk configurations against three existing strategies. Empirical results show that PRE-BUD is able to reduce energy dissipation in parallel disk systems by up to 50 percent when compared against a non-energy aware approach. Similarly, our strategy is capable of conserving up to 30 percent energy when compared to the dynamic power management technique.

Reducing power consumption of wireless networks has become a major goal in designing modern multimedia wireless systems. In an effort to reduce power consumption, this paper addresses the issue of scheduling real-time messages in multimedia wireless networks subject to both timing and power constraints. A power-consumption model is introduced to calculate power-consumption rates in accordance with message-transmission rates. Next, a new message-scheduling scheme called Power-aware Real-time Message (PARM) is developed to generate message-transmission schedules that minimize power consumption of multimedia wireless-network interfaces and the probability of missing deadlines for real-time messages. With a power-aware scheduling policy in place, the proposed PARM scheme is very energy-efficient. Experimental results based on a wide variety of synthetic workloads and eight real-world applications show that PARM significantly reduces energy dissipation while maintaining low missed rates. PARM reduces power consumption of data transmissions by up to 99.4% (with an average of 86.7%) for synthetic network traffic and saves energy by up to 60.0% (with an average of 34.1%) in the eight real-world applications.

Energy efficient computing is becoming increasingly important as the scale of parallel computing systems is expanding. As the processing power of parallel computing systems has been incremented there has been an increased demand for large scale storage systems to store the output of these parallel computing systems. Data centers are growing at an enormous pace and it is important to investigate a means of managing the energy efficiency of large scale parallel storage systems. To address these issues we introduce EEVFS (Energy Efficient Virtual File System), which is able to manage data placement and disk states to help improve the energy efficiency of a parallel disk system. EEVFS places data on the storage disks in an energy efficient layout and attempts to predict when each disk will be idle for a large period of time, facilitating a state transition into the standby state. EEVFS should also maintain relatively high performance, so we have built a load balancing policy into the data partitioning of EEVFS. The implementation architecture and measured results are presented to demonstrate the energy efficiency and performance characteristics of EEVFS.

Cluster computing has emerged as a primary and cost-effective platform for running parallel applications, including communication-intensive applications that transfer a large amount of data among the nodes of a cluster via the interconnection network. Conventional load balancers have proven effective in increasing the utilization of CPU, memory, and disk I/O resources in a cluster. However, most of the existing load-balancing schemes ignore network resources, leaving an opportunity to improve the effective bandwidth of networks on clusters running parallel applications. For this reason, we propose a communication-aware load-balancing technique that is capable of improving the performance of communication-intensive applications by increasing the effective utilization of networks in cluster environments. To facilitate the proposed load-balancing scheme, we introduce a behavior model for parallel applications with large requirements of network, CPU, memory, and disk I/O resources. Our load-balancing scheme can make full use of this model to quickly and accurately determine the load induced by a variety of parallel applications. Simulation results generated from a diverse set of both synthetic bulk synchronous and real parallel applications on a cluster show that our scheme significantly improves the performance, in terms of slowdown and turn-around time, over existing schemes by up to 206 percent (with an average of 74 percent) and 235 percent (with an average of 82 percent), respectively.

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%.

Many energy conservation techniques have been proposed to achieve high energy efficiency in disk systems. Unfortunately, growing evidence shows that energy-saving schemes in disk drives usually have negative impacts on storage systems. Existing reliability models are inadequate to estimate reliability of parallel disk systems equipped with energy conservation techniques. To solve this problem, we propose a mathematical model - called MINT - to evaluate the reliability of a parallel disk system where energy-saving mechanisms are implemented. In this paper, we focus on modeling the reliability impacts of two well-known energy-saving techniques - the Popular Disk Concentration technique (PDC) and the Massive Array of Idle Disks (MAID). We started this research by investigating how PDC and MAID affect the utilization and power-state transition frequency of each disk in a parallel disk system. We then model the annual failure rate of each disk as a function of the disk's utilization, power state transition frequency as well as operating temperature, because these parameters are key reliability-affecting factors in addition to disk ages. Next, the reliability of a parallel disk system can be derived from the annual failure rate of each disk in the parallel disk system. Finally, we used MINT to study the reliability of a parallel disk system equipped with the PDC and MAID techniques. Experimental results show that PDC is more reliable than MAID when disk workload is low. In contrast, the reliability of MAID is higher than that of PDC under relatively high I/O load.

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.

In the past decade, parallel disk systems have been developed to address the problem of I/O performance. A critical challenge with modern parallel I/O systems is that parallel disks consume a significant amount of energy in servers and high performance computers. To conserve energy consumption in parallel I/O systems, one can immediately spin down disks when disk are idle; however, spinning down disks might not be able to produce energy savings due to penalties of spinning operations. Unlike powering up CPUs, spinning down and up disks need physical movements. Therefore, energy savings provided by spinning down operations must offset energy penalties of the disk spinning operations. To substantially reduce the penalties incurred by disk spinning operations, we developed a novel approach to conserving energy of parallel I/O systems with write buffer disks, which are used to accumulate small writes using a log file system. Data sets buffered in the log file system can be transferred to target data disks in a batch way. Thus, buffer disks aim to serve a majority of incoming write requests, attempting to reduce the large number of disk spinning operations by keeping data disks in standby for long period times. Interestingly, the write buffer disks not only can achieve high energy efficiency in parallel I/O systems, but also can shorten response times of write requests. To evaluate the performance and energy efficiency of our parallel I/O systems with buffer disks, we implemented a prototype using a cluster storage system as a testbed. Experimental results show that under light and moderate I/O load, buffer disks can be employed to significantly reduce energy dissipation in parallel I/O systems without adverse impacts on I/O performance.

Cluster storage systems are essential building blocks for many high-end computing infrastructures. Although energy conservation techniques have been intensively studied in the context of clusters and disk arrays, improving energy efficiency of cluster storage systems remains an open issue. To address this problem, we describe in this paper an approach to implementing an energy-efficient cluster storage system or ECOS for short. ECOS relies on the architecture of cluster storage systems in which each I/O node manages multiple disks - one buffer disk and several data disks. Given an I/O node, the key idea behind ECOS is to redirect disk requests from data disks to the buffer disk. To balance I/O load among I/O nodes, ECOS might redirect requests from one I/O node into the others. Redirecting requests is a driving force of energy saving, and the reason is two-fold. First, ECOS makes an effort to keep buffer disks active while placing data disks into standby in a long time period to conserve energy. Second, ECOS reduces the number of disk spin downs/ups in I/O nodes. The idea of ECOS was implemented in a Linux cluster, where each I/O node contains one buffer disk and two data disks. Experimental results show that ECOS improves the energy efficiency of traditional cluster storage systems where buffer disks are not employed. Adding one extra buffer disk into each I/O node seemingly has negative impact on energy saving. Interestingly, our results indicate that ECOS equipped with extra buffer disks is more energy efficient than the same cluster storage system without the buffer disks. The implication of the experiments is that using existing data disks in I/O nodes to perform as buffer disks can achieve even higher energy efficiency.

The Popular Disk Concentration (PDC) technique and the Massive Array of Idle Disks (MAID) technique are two effective energy saving schemes for parallel disk systems. The goal of PDC and MAID is to skew I/O load towards a few disks so that other disks can be transitioned to low power states to conserve energy. I/O load skewing techniques like PDC and MAID inherently affect reliability of parallel disks because disks storing popular data tend to have high failure rates than disks storing cold data. To achieve good tradeoffs between energy efficiency and disk reliability, we first present a reliability model to quantitatively study the reliability of energy-efficient parallel disk systems equipped with the PDC and MAID schemes. Then, we propose a novel strategy—disk swapping—to improve disk reliability by alternating disks storing hot data with disks holding cold data. We demonstrate that our disk-swapping strategies not only can increase the lifetime of cache disks in MAID-based parallel disk systems, but also can improve reliability of PDC-based parallel disk systems.

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.

Load balancing for clusters has been investigated extensively, mainly focusing on the effective usage of global CPU and memory resources. However, previous CPU- or memory-centric load balancing schemes suffer significant performance drop under I/O-intensive workloads due to the imbalance of I/O load. To solve this problem, we propose two simple yet effective I/O-aware load-balancing schemes for two types of clusters: (1) homogeneous clusters where nodes are identical and (2) heterogeneous clusters, which are comprised of a variety of nodes with different performance characteristics in computing power, memory capacity, and disk speed. In addition to assigning I/O-intensive sequential and parallel jobs to nodes with light I/O loads, the proposed schemes judiciously take into account both CPU and memory load sharing in the system. Therefore, our schemes are able to maintain high performance for a wide spectrum of workloads. We develop analytic models to study mean slowdowns, task arrival, and transfer processes in system levels. Using a set of real I/O-intensive parallel applications and synthetic parallel jobs with various I/O characteristics, we show that our proposed schemes consistently improve the performance over existing non-I/O-aware load-balancing schemes, including CPU- and Memory-aware schemes and a PBS-like batch scheduler for parallel and sequential jobs, for a diverse set of workload conditions. Importantly, this performance improvement becomes much more pronounced when the applications are I/O-intensive. For example, the proposed approaches deliver 23.6--88.0 % performance improvements for I/O-intensive applications such as LU decomposition, Sparse Cholesky, Titan, Parallel text searching, and Data Mining. When I/O load is low or well balanced, the proposed schemes are capable of maintaining the same level of performance as the existing non-I/O-aware schemes.

In the past decades, distributed systems have been widely applied to real-time applications, most of which have fault-tolerance requirements to assure high reliability. Due to the stringent space constraints of real-time systems, the issue of schedulability becomes a major concern in the design of fault-tolerant and real-time distributed systems. Most existing real-time and fault-tolerant scheduling algorithms, which are based on the primary-backup scheme for periodic real-time tasks, introduce unnecessary redundancies by aggressively using active-backup copies. To solve this problem, we propose two novel fault-tolerant techniques, which are seamlessly integrated with fixed-priority-based scheduling algorithms. These techniques leverage redundancies to enhance schedulability in fault-tolerant and real-time distributed systems. Our fault-tolerant techniques make use of the primary-backup scheme to tolerate permanent hardware failures. The first technique (referred to as Tercos) terminates the execution of active-backup copies, when corresponding primary copies are successfully completed. Tercos is designed to reduce scheduling lengths in fault-free scenarios to enhance schedulability by virtue of executing portions of active-backup copies in passive forms. The second technique (referred to as Debus) uses a deferred-active-backup scheme to further minimize schedule lengths to improve the schedulability performance. Debus schedules active-backup copies as late as possible, while terminating active-backup copies when their primary copies are completed. Experimental results show that, compared with existing algorithms in literature, Tercos can significantly improve schedulability by up to 17.0% (with an average of 9.7%). Furthermore, empirical results reveal that Debus can enhance schedulability over Tercos by up to 12% (with an average of 7.8%).

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.

Although data duplications may be able to improve the performance of data-intensive applications on data grids, a large number of data replicas inevitably increase energy dissipation in storage resources on the data grids. In order to implement a data grid with high energy efficiency, we address in this study the issue of energy-efficient scheduling for data grids supporting real-time and data-intensive applications. Taking into account both data locations and application properties, we design a novel Distributed Energy-Efficient Scheduler (or DEES for short) that aims to seamlessly integrate the process of scheduling tasks with data placement strategies to provide energy savings. DEES is distributed in the essence - it can successfully schedule tasks and save energy without knowledge of a complete grid state. DEES encompasses three main components: energy-aware ranking, performance-aware scheduling, and energy-aware dispatching. By reducing the amount of data replications and task transfers, DEES effectively saves energy. Simulation results based on a real-world trace demonstrate that with respect to energy consumption, DEES conserves over 35% more energy than previous approaches without degrading the performance.

Large-scale parallel disk systems are frequently used to meet the demands of information systems requiring high storage capacities. A critical problem with these large-scale parallel disk systems is the fact that disks consume a significant amount of energy. To design economically attractive and environmentally friendly parallel disk systems, we developed two energy-aware prefetching strategies for parallel disk systems with disk buffers. First, we introduce a new buffer disk architecture that can provide significant energy savings for parallel disk systems while achieving high performance. Second, we design a prefetching approach to utilize an extra disk to accommodate prefetched data sets that are frequently accessed. Third, we develop a second prefetching strategy that makes use of an existing disk in the parallel disk system as a buffer disk. Compared with the first prefetching scheme, the second approach lowers the capacity of the parallel disk system. However, the second approach is more cost-effective and energy-efficient than the first prefetching technique. Finally, we quantitatively compare both of our prefetching approaches against two conventional strategies including a dynamic power management technique and a non-energy-aware scheme. Using empirical results we show that our novel prefetching approaches are able to reduce energy dissipation in parallel disk systems by 44% and 50% when compared against a non-energy aware approach. Similarly, our strategies are capable of conserving 22% and 30% of the energy when compared to the dynamic power management technique.

As disk drives become increasingly sophisticated and processing power increases, one of the most critical issues of designing modern disk systems is data reliability. Although numerous energy saving techniques are available for disk systems, most of energy conservation techniques are not effective in reliability critical environments due to their limitation of ignoring the reliability issue. A wide range of factors affect the reliability of disk systems; the most important factors - disk utilization and ages — are the focus of this study. We build a model to quantify the relationship among the disk age, utilization, and failure probabilities. Observing that the reliability of a disk heavily relies on both disk utilization and age, we propose a novel concept of safe utilization zone, where energy of the disk can be conserved without degrading reliability. We investigate an approach to improving both reliability and energy efficiency of disk systems via utilization control, where disk drives are operated in safe utilization zones to minimize the probability of disk failure. In this study, we integrate an existing energy consumption technique that operates the disks at different power modes with our proposed reliability approach. Experimental results show that our approach can significantly improve reliable while achieving high energy efficiency for disk systems.

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.

Optimizing energy consumption has become a major concern in designing economical clusters. Scheduling precedence-constrained parallel tasks on clusters is challenging because of high communication overhead. Although duplication-based strategies are applied to minimize communication overhead, most of them merely consider schedule lengths, completely ignoring energy consumption of clusters. In this regard, we propose two energy-aware duplication scheduling algorithms, called EADUS and TEBUS, to schedule precedence-constrained parallel tasks. Unlike existing duplication-based scheduling algorithms that replicate all possible predecessors of each task, the proposed algorithms judiciously replicate predecessors only if the duplication can help in conserving energy. Our energy-aware scheduling strategies are conducive to balancing the scheduling length and energy consumption of precedence-constrained parallel tasks. Extensive experimental results based on real-world applications demonstrate the effectiveness and practicality of the proposed scheduling strategies