14296502
Benchmarking the CLI for I/O-intensive computing
Common Language Infrastructure, or CLI, is a standardized virtual machine, which increasingly becomes popular on a wide range of platforms. In this paper we developed three I/O-intensive benchmarks for the CLI using various techniques. The first benchmark is designed in accordance with an application behavioural model that rebuilds the behavior of real world I/O-intensive applications. The second benchmark is a trace driven simulator that simulates five I/O-intensive applications. The third benchmark is a micro I/O-Intensive benchmark used to emulate a simple Web server. In addition, the performances of the benchmarks are evaluated on the SSCLI. The results suggest that the CLI is a potential virtual machine for I/O-intensive computing.
18934252
No More Energy-Performance Trade-Off: A New Data Placement Strategy for RAID-Structured Storage Systems
Many real-world applications like Video-On-Demand (VOD) and Web servers require prompt responses to access requests. However, with an explosive increase of data volume and the emerging of faster disks with higher power requirements, energy consumption of disk based storage systems has become a salient issue. To achieve energy-conservation and prompt responses simultaneously, in this paper we propose a novel energy-saving data placement strategy, called Striping-based Energy-Aware (SEA), which can be applied to RAID-structured storage systems to noticeably save energy while providing quick responses. Further, we implement two SEA-powered RAID-based data placement algorithms, SEA0 and SEA5, by incorporating the SEA strategy into RAID-0 and RAID-5, respectively. Extensive experimental results demonstrate that compared with three well-known data placement algorithms Greedy, SP, and HP, SEA0 and SEA5 reduce mean response time on average at least 52.15% and 48.04% while saving energy on average no less than 10.12% and 9.35%, respectively.
14172554
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%).
6486437
Scheduling security-critical real-time applications on clusters
Security-critical real-time applications such as military aircraft flight control systems have mandatory security requirements in addition to stringent timing constraints. Conventional real-time scheduling algorithms, however, either disregard applications' security needs and thus expose the applications to security threats or run applications at inferior security levels without optimizing security performance. In recognition that many applications running on clusters demand both real-time performance and security, we investigate the problem of scheduling a set of independent real-time tasks with various security requirements. We build a security overhead model that can be used to reasonably measure security overheads incurred by the security-critical tasks. Next, we propose a security-aware real-time heuristic strategy for clusters (SAREC), which integrates security requirements into the scheduling for real-time applications on clusters. Further, to evaluate the performance of SAREC, we incorporate the earliest deadline first (EDF) scheduling policy into SAREC to implement a novel security-aware real-time scheduling algorithm (SAEDF). Experimental results from both real-world traces and a real application show that SAEDF significantly improves security over three existing scheduling algorithms (EDF, least laxity first, and first come first serve) by up to 266.7 percent while achieving high schedulability.
38411105
Dynamic relocation of hybrid tasks: Strategies and methodologies
Reconfiguration-based architectures are increasingly gaining attention of designers due to their benefits of flexibility, re-programmability and high computational performance. The combination of general purpose processors and reconfigurable fabrics (e.g., FPGAs), may provide those valuable characteristics, which are becoming essential for modern and future embedded systems. Such hybrid systems permit the existence of hardware tasks, which shall be properly managed by the operating system, thus allowing for their coexistence with software tasks. Nevertheless, in order to completely exploit this feature, the operating system must be capable of relocating a task between hardware and software execution domains. Runtime relocation of tasks (including preemption and resumption) between two devices following different computation paradigms (parallel vs. instruction based) however is a challenging job. In this work we propose a comprehensive and embracing methodology, which starts from a unified task representation, and goes to the final implementation of such hybrid tasks. For its accomplishment, a framework is proposed to help the user in designing a hybrid task, which also generates automatically the underlying infrastructure that is in charge of performing the dynamic relocation of a hybrid task. In order to prove the applicability of our concept and the efficiency of our framework, a case study is presented including its results.
9162975
PEARL: Performance, Energy, and Reliability Balanced Dynamic Data Redistribution for Next Generation Disk Arrays
Contemporary disk arrays consist purely of hard disk drives, which normally provide huge storage capacities with low-cost and high-throughput for data-intensive applications. Nevertheless, they have some inhenrent disadvantages such as long access latencies, high annual disk replacement rates, fragile physical characteristics, and energy-inefficiency due to their build-in mechanical and electronic mechanisms. Flash-memory based solid state disks, on the other hand, although currently more expensive and inadequate in write cycles, offer much faster aread accesses and are much more robust and energy efficient. To combine the complementary merits of hard disks and flash disks, in this paper we propose a hybrid disk array architecture named HIT (hybrid disk storage) for data-intensive applications. Next, a dynamic data redistribution strategy called PEARL (performance, energy, and reliability balanced), which can periodically redistribute data between flash and hard disks to adapt to the changing data access patterns, is developed on top of the HIT architecture.
1916824
A Reinforcement Learning Approach for Host-Based Intrusion Detection Using Sequences of System Calls
Intrusion detection has emerged as an important technique for network security. Due to the complex and dynamic properties of intrusion behaviors, machine learning and data mining methods have been widely employed to optimize the performance of intrusion detection systems (IDSs). However, the results of existing work still need to be improved both in accuracy and in computational efficiency. In this paper, a novel reinforcement learning approach is presented for host-based intrusion detection using sequences of system calls. A Markov reward process model is introduced for modeling the behaviors of system call sequences and the intrusion detection problem is converted to predicting the value functions of the Markov reward process. A temporal different learning algorithm using linear basis functions is used for value function prediction so that abnormal temporal behaviors of host processes can be predicted accurately and efficiently. The proposed method has advantages over previous algorithms in that the temporal property of system call data is well captured in a natural and simple way and better intrusion detection performance can be achieved. Experimental results on the MIT system call data illustrate that compared with previous work, the proposed method has better detection accuracy with low training costs.
6328787
ACGT - A Comparative Genomics Tool
SUMMARY
ACGT (a comparative genomics tool) is a genomic DNA sequence comparison viewer and analyzer. It can read a pair of DNA sequences in GenBank, Embl or Fasta formats, with or without a comparison file, and provide users with many options to view and analyze the similarities between the input sequences. It is written in Java and can be run on Unix, Linux and Windows platforms.


AVAILABILITY
The ACGT program is freely available with documentation and examples at website: http://db.systemsbiology.net/projects/local/mhc/acgt/