The goal of this study is to optimize energy efficiency of database clusters through prefetching and caching strategies. We design a workload-skewness scheme to collectively manage a set of hot and cold nodes in a database cluster system. The prefetching mechanism fetches popular data tables to the hot nodes while keeping unpopular data in cold nodes. We leverage a power management module to aggressively turn cold nodes in the low-power mode to conserve energy consumption. We construct a prefetching model and an energy-saving model to govern the power management module in database lusters. The energy-efficient prefetching and caching mechanism is conducive to cutting back the number of power-state transitions, thereby offering high energy efficiency. We systematically evaluate energy conservation technique in the process of managing, fetching, and storing data on clusters supporting database applications. Our experimental results show that our prefetching/caching solution significantly improves energy efficiency of the existing PostgreSQL system.

Energy saving is rapidly becoming one of the hottest topics in technology field within recent decades. With the development of technology, it brings a sheer increasing trend of data and the growth scale of clusters and data centers. Meanwhile, it also raises another essential issue into the path: energy cost. In this paper, we are diving into this key issue and evaluating energy- efficiency based on TPC-W benchmark: a notable web transaction e-commerce benchmark. We simulate the web transaction with different database sizes and collect the energy data by KILL-A-WATT. Also, we deploy this setup on four different cluster systems: PC nodes and wimpy nodes, and two different heterogeneous systems: using PC as front server and wimpy as Database server, and using wimpy as Web server and PC as Database server. Energy result demonstrates different characteristics among them, which can give lightening advice for future works in data center.

In this paper, we present a thermal-aware file assignment technique called TIGER for reducing the cooling cost of storage clusters in data centers. We show that peak inlet temperatures of storage nodes depend on not only CPU utilization but also I/O activities, which rely on file assignments in a cluster. The TIGER scheme aims to lower peak inlet temperatures of storage clusters by dynamic thermal management through file placements. TIGER makes use of cross-interference coefficients to estimate the re-circulation of hot air from the outlets to the inlets of data nodes. TIGER first calculates the thresholds of disks in each data node based on its contribution to heat re-circulation in a data center. TIGER undertakes two steps to achieve high I/O performance while reducing cooling cost. First, TIGER assigns groups of files with similar service times to shorten I/O response times. Second, TIGER ensures that load imbalance does not exceed a specified threshold. We evaluate performance of TIGER in terms of both cooling energy conservation and response time of a storage cluster. Our results confirm that TIGER reduces cooling-power requirements for clusters by offering about 10 to 15 percent cooling-energy savings without significantly degrading I/O performance.

This article presents a framework, Frog, for Context-Based File Systems (CBFSs) that aim at simplifying the development of context-based file systems and applications. Unlike existing informed-based context-aware systems, Frog is a unifying informed-based framework that abstracts context-specific solutions as views, allowing applications to make view selections according to application behaviors. The framework can not only eliminate overheads induced by traditional context analysis, but also simplify the interactions between the context-based file systems and applications. Rather than propagating data through solution-specific interfaces, views in Frog can be selected by inserting their names in file path strings. With Frog in place, programmers can migrate an application from one solution to another by switching among views rather than changing programming interfaces. Since the data consistency issues are automatically enforced by the framework, file-system developers can focus their attention on context-specific solutions. We implement two prototypes to demonstrate the strengths and overheads of our design. Inspired by an observation that there are more than 50% of small files (<4KB) in a file system, we create a Bi-context Archiving Virtual File System (BAVFS) that utilizes conservative and aggressive prefetching for the contexts of random and sequential reads. To improve the performance of random read-and-write operations, the Bi-context Hybrid Virtual File System (BHVFS) combines the update-in-place and update-out-of-place solutions for read-intensive and write-intensive contexts. Our experimental results show that the benefits of Frog-based CBFSs outweigh the overheads introduced by integrating multiple context-specific solutions.

In this paper, we present thermal-aware file assignment technique called TIGER for reducing cooling cost of storage clusters in data centers. TIGER first calculates the thresholds of disks in each node based on its contribution to heat recirculation in a data center. Next, TIGER assigns files to data nodes according to calculated thresholds. We evaluated performance of TIGER in terms of both cooling energy conservation and response time of a storage cluster. Our results confirm that TIGER reduces cooling-power requirements for clusters by offering about 10 to 15 percent cooling energy savings without significantly degrading I/O performance.

This paper presents a novel Predictive Energy-Aware Management (PEAM) system that is able to reduce the energy costs of storage systems by appropriately selecting data transmission methods. In particular, we evaluate the energy costs of three methods (1. transfer data without archiving and compression, 2. archive and transfer data, 3. compress and transfer data) in preliminary experiments. According to the results, we observe that the energy consumption of data transmission greatly varies case by case. We cannot simply apply one method in all cases. Therefore, we design an energy prediction model that can estimate the total energy cost of data transmission by using particular transmission methods. Based on the model, our predictive energy-aware management system can automatically select the most energy efficient method for data transmission. Our experimental results show that our system performs better than simply selecting any one among the three methods for data transmission in terms of energy efficiency.

We present a multicore-enabled smart storage for clusters in general and MapReduce clusters in particular. The goal of this research is to improve performance of data-intensive parallel applications on clusters by offloading data processing to multicore processors in storage nodes. Compared with traditional storage devices, next-generation disks will have computing capability to reduce computational load of host processors or CPUs. With the advance of processor and memory technologies, smart storage systems are promising devices to perform complex on-disk operations. The proposed smart storage system can avoid moving a huge amount of data back and forth between storage nodes and computing nodes in a cluster. To enhance the performance of data-intensive applications, we have designed a smart storage system called Multicore-enabled Smart Storage (McSD), in which a multicore processor is integrated in storage nodes. We have implemented a programming framework for data-intensive applications running on a computing system coupled with McSD. The programming framework aims at balancing load between computing nodes and multicore-enabled smart storage nodes. To fully utilize multicore processors in smart storage nodes, we have implemented the MapReduce model for McSDs to handle parallel computing on a cluster. A prototype of McSD has been implemented in a cluster connected by Gigabit Ethernet. Experimental results show that McSD can significantly reduce the execution times of three real-world applications - word count, string matching, and matrix multiplication. We demonstrate that the integration of multicore-enabled smart storage with MapReduce clusters is a promising approach to improving overall performance of data-intensive applications on clusters.

Recognizing that power and cooling cost for data centers are increasing, we address in this study the thermal impact of storage systems. In the first phase of this work, we generate the thermal profile of a storage server containing three hard disks. The profiling results show that disks have comparable thermal impacts as processing and networking elements to overall storage node temperature. We develop a thermal model to estimate the outlet temperature of a storage server based on processor and disk utilizations. The thermal model is validated against data acquired by an infrared thermometer as well as build-in temperature sensors on disks. Next, we apply the thermal model to investigate the thermal impact of workload management on storage systems. Our study suggests that disk-aware thermal management techniques have significant impacts on reducing cooling cost of storage systems. We further show that this work can be extended to analysis the cooling cost of data centers with massive storage capacity.

Most researches of Solid State Drives (SSDs) architectures rely on Flash Translation Layer (FTL) algorithms and wear-leveling; however, internal parallelism in Solid State Drives has not been well explored. In this research, we proposed a new strategy to improve SSD write performance by enhancing internal parallelism inside SSDs. A SDRAM buffer is added in the design for buffering and scheduling write requests. Because the same logical block numbers may be translated to different physical numbers at different times in FTL, the on-board SDRAM buffer is used to buffer requests at the lower level of FTL. When the buffer is full, same amount of data will be assigned to each storage package in SSDs to enhance internal parallelism. To accurately evaluate performance, we use both synthetic workloads and real-world applications in experiments. We compare the enhanced internal parallelism scheme with the traditional LRU strategy since it is unfair to compare an SSD having buffer with an SSD without a buffer. The simulation results demonstrate that the writing performance of our design is significantly improved compared with the LRU-cache strategy with the same amount of buffer sizes.

This paper presents an offloading framework - ORCA - to map I/O-intensive code to a cluster that consists of computing and storage nodes. To reduce data transmission among computing and storage nodes. our offloading framework partitions and schedules CPU-bound and I/O-bound modules to computing nodes and active storage nodes, respectively. From developer's perspective, ORCA helps them to deal with execution-path control, offloading executable code, and data sharing over a network. Powered by the offloading APIs, developers without any I/O offloading or network programming experience are allowed to write new I/O-intensive code running efficiently on clusters. We implement the ORCA framework on a cluster to quantitatively evaluate performance improvements offered by our approach. We run five real-world applications on both homogeneous and heterogeneous computing environments. Experimental results show ORCA speeds up the performance of all the five tested applications by a factor of up to 90.1% with an average of 75.5%. Moreover, the results confirm that ORCA reduces network burden imposed by I/O-intensive applications by a factor of anywhere between 35 to 68.

In this paper, we present an informed prefetching technique called IPODS that makes use of application-disclosed access patterns to prefetch hinted blocks in distributed multi-level storage systems. We develop a prefetching pipeline in IPODS, where an informed prefetching process is divided into a set of independent prefetching steps among multiple storage levels in a distributed system. In the IPODS system, while data blocks are prefetched from hard disks to memory buffers in remote storage servers, data blocks buffered in the servers are prefetched through networks to clients' local cache. We show that these two prefetching steps can be handled in a pipelining manner to improve I/O performance of distributed storage systems. Our IPODS technique differs itself from existing prefetching schemes in two ways. First, IPODS reduces applications' I/O stalls by keeping hinted data in clients' local caches and storage servers' fast buffers (e.g., solid state disks). Second, in a prefetching pipeline, multiple informed prefetching mechanisms semi-dependently coordinate to fetch blocks (1) from low-level (slow) to high-level (fast) storage devices in servers and (2) from high-level devices in servers to clients' local cache. The prefetching pipeline in IPODS judiciously hides network latencies in distributed storage systems, thereby reducing the overall I/O access time in distributed systems. Using a wide range of real-world I/O traces, our experiments show that IPODS can improve noticeably I/O performance of distributed storage systems.

An increasing number of commodity clusters are connected to each other by public networks, which have become a potential threat to security sensitive parallel applications running on the clusters. To address this security issue, we developed a Message Passing Interface (MPI) implementation to preserve confidentiality of messages communicated among nodes of clusters in an unsecured network. We focus on M PI rather than other protocols, because M PI is one of the most popular communication protocols for parallel computing on clusters. Our MPI implementation-called ES-MPICH2-was built based on MPICH2 developed by the Argonne National Laboratory. Like MPICH2, ES-MPICH2 aims at supporting a large variety of computation and communication platforms like commodity clusters and high-speed networks. We integrated encryption and decryption algorithms into the MPICH2 library with the standard MPI interface and; thus, data confidentiality of MPI applications can be readily preserved without a need to change the source codes of the MPI applications. MPI-application programmers can fully configure any confidentiality services in MPICHI2, because a secured configuration file in ES-MPICH2 offers the programmers flexibility in choosing any cryptographic schemes and keys seamlessly incorporated in ES-MPICH2. We used the Sandia Micro Benchmark and Intel MPI Benchmark suites to evaluate and compare the performance of ES-MPICH2 with the original MPICH2 version. Our experiments show that overhead incurred by the confidentiality services in ES-MPICH2 is marginal for small messages. The security overhead in ES-MPICH2 becomes more pronounced with larger messages. Our results also show that security overhead can be significantly reduced in ES-MPICH2 by high-performance clusters. The executable binaries and source code of the ES-MPICH2 implementation are freely available at http:// www.eng.auburn.edu/~xqin/software/es-mpich2/.

We develop a mathematical model - MREED - to quantitatively evaluate the failure rate of energy-efficient parallel storage systems. The Power-Aware Redundant Array of Inexpensive Disk (PARAID) aims to reduce energy use of commodity server-class disks without specialized hardware. The goal of PARAID is to skewed striping pattern to adapt to the system load by changing the number of powered disks. By spinning down disks during light workloads, PARAID can reduce power consumption, while still meeting performance demands. We show that MREED can be used to estimate a five-disk PARAID-0 system. We validate the accuracy of MREED using the DiskSim simulator. Our approach shows that MREED can rely on file access pattern to estimate system utilization correctly. Furthermore, even thought PARAID may achieve reasonable reliability, our model shows that PARAID's reliability is affected by data locality.

There is a growing demand for large-scale distributed storage systems to support resource sharing and fault tolerance. Although heterogeneity issues of distributed systems have been widely investigated, little attention has yet been paid to security solutions designed for distributed storage systems with heterogeneous vulnerabilities. This fact motivates us to investigate a fragment allocation scheme called S-FAS to improve security of a distributed system where storage sites have a wide variety of vulnerabilities. In the S-FAS approach, we integrate file fragmentation with the secret sharing technique in a distributed storage system with heterogeneous vulnerabilities. Storage sites in a distributed systems are classified into a variety of different server types based on vulnerability characteristics. Given a file and a distributed system, S-FAS allocates fragments of the file to as many different types of nodes as possible in the system. Data confidentiality is preserved because fragments of a file are allocated to multiple storage nodes. We develop storage assurance and dynamic assurance models to evaluate the quality of security offered by S-FAS. Analysis results show that fragment allocations made by S-FAS lead to enhanced security because of the consideration of heterogeneous vulnerabilities in distributed storage systems.

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.

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.

Steady improvements in storage capacities and CPU clock speeds intensify the performance bottleneck at the I/O subsystem of modern computers. Caching data can efficiently short circuit costly delays associated with disk accesses. Recent studies have shown that disk I/O performance gains provided by a cache buffer do not scale with cache size. Therefore, new algorithms have to be investigated to better utilize cache buffer space. Predictive prefetching and caching solutions have been shown to improve I/O performance in an efficient and scalable manner in simulation experiments. However, most predictive prefetching algorithms have not yet been implemented in real-world storage systems due to two main limitations: first, the existing prefetching solutions are unable to self regulate based on changing I/O workload; second, excessive number of unneeded blocks are prefetched. Combined, these drawbacks make predictive prefetching and caching a less attractive solution than the simple LRU management. To address these problems, in this paper we propose an automatic prefetching and caching system (or APACS for short), which mitigates all of these shortcomings through three unique techniques, namely: (1) dynamic cache partitioning, (2) prefetch pipelining, and (3) prefetch buffer management. APACS dynamically partitions the buffer cache memory, used for prefetched and cached blocks, by automatically changing buffer/cache sizes in accordance to global I/O performance. The adaptive partitioning scheme implemented in APACS optimizes cache hit ratios, which subsequently accelerates application execution speeds. Experimental results obtained from trace-driven simulations show that APACS outperforms the LRU cache management and existing prefetching algorithms by an average of over 50%.

In largely distributed clusters, computing nodes are geographically deployed in various computing sites. Information processed in a distributed cluster is shared among a group of distributed processes or users by virtue of messages passing protocols (e.g. message passing interface - MPI) running on the Internet. Because of the open accessible nature of the Internet, data encryption for these large-scale distributed clusters becomes a non-trivial and challenging problem. To address this issue, we enhanced the security of the MPI (Message Passing Interface) protocol by encrypting and decrypting messages sent and received among computing nodes. In this study we focused on MPI rather than other protocols because MPI is one of the most popular communication protocols for cluster computing environments. From among a variety of MPI implementations, we picked MPICH2 developed by the Argonne National Laboratory. The design goal of MPICH2 - a widely used MPI implementation - is to combine portability with high performance. We integrated encryption algorithms into the MPICH2 library so that data confidentiality of MPI applications could be readily preserved without a need to change the source codes of the MPI applications. since we provide a security enhanced MPI-library with the standard MPI interfact, data communications of a conventional MPI program can be secured without converting the program into the corresponding secure version. We used Sandia Micro Benchmark and Intel MPI Benchmarks to evaluate and compared the performance of original MPICH2 and Enhanced Security MPICH2. According to the performance evaluation, ES-MPICH2 provides secured Message Passing Interface by sacrificing reasonable system performance.

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, I present in this paper an approach to scheduling periodic packets in wireless networks subject to both timing and energy constraints. I propose a necessary and sufficient feasibility check for a set of periodic packets to be transmitted over a wireless link. Next, I 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. I 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.

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.

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.

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%)