A self-scalable distributed network simulation environment based on cloud computing

While parameter sweep simulations can help undergraduate students and researchers to understand computer networks, their usage in the academia is hindered by the significant computational load they convey. This paper proposes DNSE3, a service oriented computer network simulator that, deployed in a cloud computing infrastructure, leverages its elasticity and pay-per-use features to compute parameter sweeps. The performance and cost of using this application is evaluated in several experiments applying different scalability policies, with results that meet the demands of users in educational institutions. Additionally, the usability of the application has been measured following industry standards with real students, yielding a very satisfactory user experience.     

The design and evaluation of a virtual distributed computing environment

Current advances in high-speed networks such as ATM and fiber-optics, and software technologies such as the JAVA programming language and WWW tools, have made network-based computing a cost-effective, high-performance distributed computing environment. Metacomputing, a special subset of network-based computing, is a well-integrated execution environment derived by combining diverse and distributed resources such as MPPs, workstations, mass storage, and databases that show a heterogeneous nature in terms of hardware, software, and organization. In this paper we present the Virtual Distributed Computing Environment (VDCE), a metacomputing environment currently being developed at Syracuse University. VDCE provides an efficient web-based approach for developing, evaluating, and visualizing large-scale distributed applications that are based on predefined task libraries on diverse platforms. The VDCE task libraries relieve end-users of tedious task implementations and also support reusability. The VDCE software architecture is described in terms of three modules: (a) the Application Editor, a user-friendly application development environment that generates the Application Flow Graph (AFG) of an application; (b) the Application Scheduler, which provides an efficient task-to-resource mapping of AFG; and (c) the VDCE Runtime System, which is responsible for running and managing application execution and for monitoring the VDCE resources. We present experimental results of an application execution on the VDCE prototype for evaluating the performance of different machine and network configurations. We also show how the VDCE can be used as a problem-solving environment on which large-scale, network-centric applications can be developed by a novice programmer rather than by an expert in low-level details of parallel programming languages. This revised version was published online in July 2006 with corrections to the Cover Date.     

GRIMD: distributed computing for chemists and biologists

Motivation: Biologists and chemists are facing problems of high computational complexity that require the use of several computers organized in clusters or in specialized grids. Examples of such problems can be found in molecular dynamics (MD), in silico screening, and genome analysis. Grid Computing and Cloud Computing are becoming prevalent mainly because of their competitive performance/cost ratio. Regrettably, the diffusion of Grid Computing is strongly limited because two main limitations: it is confined to scientists with strong Computer Science background and the analyses of the large amount of data produced can be cumbersome it. We have developed a package named GRIMD to provide an easy and flexible implementation of distributed computing for the Bioinformatics community. GRIMD is very easy to install and maintain, and it does not require any specific Computer Science skill. Moreover, permits preliminary analysis on the distributed machines to reduce the amount of data to transfer. GRIMD is very flexible because it shields the typical computational biologist from the need to write specific code for tasks such as molecular dynamics or docking calculations. Furthermore, it permits an efficient use of GPU cards whenever is possible. GRIMD calculations scale almost linearly and, therefore, permits to exploit efficiently each machine in the network. Here, we provide few examples of grid computing in computational biology (MD and docking) and bioinformatics (proteome analysis).

Availability

GRIMD is available for free for noncommercial research at www.yadamp.unisa.it/grimd

Supplementary information

www.yadamp.unisa.it/grimd/howto.aspx     

A visual data flow environment for macromolecular crystallographic computing

This article describes the integration of programs from the widely used CCP4 macromolecular crystallography package into a modern data flow visualization environment (application visualization system [AVS]), which provides a simple graphical user interface, a visual programming paradigm, and a variety of 1-, 2-, and 3-D data visualization tools for the display of graphical information and the results of crystallographic calculations, such as electron density and Patterson maps. The CCP4 suite comprises a number of separate Fortran 77 programs, which communicate via common file formats. Each program is encapsulated into an AVS macro module, and may be linked to others in a data flow network, reflecting the nature of many crystallo-graphic calculations. Named pipes are used to pass input parameters from a graphical user interface to the program module, and also to intercept line printer output, which can be filtered to extract graphical information and significant numerical parameters. These may be passed to downstream modules, permitting calculations to be automated if no user interaction is required, or giving the user the opportunity to make selections in an interactive manner.     

SPSRG: a prediction approach for correlated failures in distributed computing systems

Failure instances in distributed computing systems (DCSs) have exhibited temporal and spatial correlations, where a single failure instance can trigger a set of failure instances simultaneously or successively within a short time interval. In this work, we propose a correlated failure prediction approach (CFPA) to predict correlated failures of computing elements in DCSs. The approach models correlated-failure patterns using the concept of probabilistic shared risk groups and makes a prediction for correlated failures by exploiting an association rule mining approach in a parallel way. We conduct extensive experiments to evaluate the feasibility and effectiveness of CFPA using both failure traces from Los Alamos National Lab and simulated datasets. The experimental results show that the proposed approach outperforms other approaches in both the failure prediction performance and the execution time, and can potentially provide better prediction performance in a larger system.     

A flexible fine-grained dynamic access control approach for cloud computing environment

Cluster Computing - As a pioneering surge of ICT technologies, offering computing resources on-demand, the exceptional evolution of Cloud computing has not gone unnoticed by the IT world. At the...     

The neural mechanisms of gustation: a distributed processing code

Whenever food is placed in the mouth, taste receptors are stimulated. Simultaneously, different types of sensory fibre that monitor several food attributes such as texture, temperature and odour are activated. Here, we evaluate taste and oral somatosensory peripheral transduction mechanisms as well as the multi-sensory integrative functions of the central pathways that support the complex sensations that we usually associate with gustation. On the basis of recent experimental data, we argue that these brain circuits make use of distributed ensemble codes that represent the sensory and post-ingestive properties of tastants.     

A life scientist's gateway to distributed data management and computing: the PathPort/ToolBus framework

The emergent needs of the bioinformatics community challenge current information systems. The pace of biological data generation far outstrips Moore's Law. Therefore, a gap continues to widen between the capabilities to produce biological (molecular and cell) data sets and the capability to manage and analyze these data sets. As a result, Federal investments in large data set generation produces diminishing returns in terms of the community's capabilities of understanding biology and leveraging that understanding to make scientific and technological advances that improve society. We are building an open framework to address various data management issues including data and tool interoperability, nomenclature and data communication standardization, and database integration. PathPort, short for Pathogen Portal, employs a generic, web-services based framework to deal with some of the problems identified by the bioinformatics community. The motivating research goal of a scalable system to provide data management and analysis for key pathosystems, especially relating to molecular data, has resulted in a generic framework using two major components. On the server-side, we employ web-services. On the client-side, a Java application called ToolBus acts as a client-side "bus" for contacting data and tools and viewing results through a single, consistent user interface.     

A heterogeneous computing environment to solve the 768-bit RSA challenge

In December 2009 the 768-bit, 232-digit number RSA-768 was factored using the number field sieve. Overall, the computational challenge would take more than 1700 years on a single, standard core. In the article we present the heterogeneous computing approach, involving different compute clusters and Grid computing environments, used to solve this problem.     

A novel sensitive method for the detection of user-defined compositional bias in biological sequences

MOTIVATION: Most biological sequences contain compositionally biased segments in which one or more residue types are significantly overrepresented. The function and evolution of these segments are poorly understood. Usually, all types of compositionally biased segments are masked and ignored during sequence analysis. However, it has been shown for a number of proteins that biased segments that contain amino acids with similar chemical properties are involved in a variety of molecular functions and human diseases. A detailed large-scale analysis of the functional implications and evolutionary conservation of different compositionally biased segments requires a sensitive method capable of detecting user-specified types of compositional bias. RESULTS: We present BIAS, a novel sensitive method for the detection of compositionally biased segments composed of a user-specified set of residue types. BIAS uses the discrete scan statistics that provides a highly accurate correction for multiple tests to compute analytical estimates of the significance of each compositionally biased segment. The method can take into account global compositional bias when computing analytical estimates of the significance of local clusters. BIAS is benchmarked against SEG, SAPS and CAST programs. We also use BIAS to show that groups of proteins with the same biological function are significantly associated with particular types of compositionally biased segments.     

CloudDMSS: robust Hadoop-based multimedia streaming service architecture for a cloud computing environment

The delivery of scalable, rich multimedia applications and services on the Internet requires sophisticated technologies for transcoding, distributing, and streaming content. Cloud computing provides an infrastructure for such technologies, but specific challenges still remain in the areas of task management, load balancing, and fault tolerance. To address these issues, we propose a cloud-based distributed multimedia streaming service (CloudDMSS), which is designed to run on all major cloud computing services. CloudDMSS is highly adapted to the structure and policies of Hadoop, thus it has additional capacities for transcoding, task distribution, load balancing, and content replication and distribution. To satisfy the design requirements of our service architecture, we propose four important algorithms: content replication, system recovery for Hadoop distributed multimedia streaming, management for cloud multimedia management, and streaming resource-based connection (SRC) for streaming job distribution. To evaluate the proposed system, we conducted several different performance tests on a local testbed: transcoding, streaming job distribution using SRC, streaming service deployment and robustness to data node and task failures. In addition, we performed three different tests in an actual cloud computing environment, Cloudit 2.0: transcoding, streaming job distribution using SRC, and streaming service deployment.     

A stochastic approach to estimating earliest start times of nodes for scheduling DAGs on heterogeneous distributed computing systems

Previously, DAG scheduling schemes used the mean (average) of computation or communication time in dealing with temporal heterogeneity. However, it is not optimal to consider only the means of computation and communication times in DAG scheduling on a temporally (and spatially) heterogeneous distributed computing system. In this paper, it is proposed that the second order moments of computation and communication times, such as the standard deviations, be taken into account in addition to their means, in scheduling “stochastic” DAGs. An effective scheduling approach which accurately estimates the earliest start time of each node and derives a schedule leading to a shorter average parallel execution time has been developed. Through an extensive computer simulation, it has been shown that a significant improvement (reduction) in the average parallel execution times of stochastic DAGs can be achieved by the proposed approach.     

Yabi: An online research environment for grid,high performance and cloud computing

Background  

There is a significant demand for creating pipelines or workflows in the life science discipline that chain a number of discrete compute and data intensive analysis tasks into sophisticated analysis procedures. This need has led to the development of general as well as domain-specific workflow environments that are either complex desktop applications or Internet-based applications. Complexities can arise when configuring these applications in heterogeneous compute and storage environments if the execution and data access models are not designed appropriately. These complexities manifest themselves through limited access to available HPC resources, significant overhead required to configure tools and inability for users to simply manage files across heterogenous HPC storage infrastructure.     

Water molecules migration at the oil-paper interface during heating: a large-scale distributed computing study

In view of the small molecular model established in the field of high voltage insulation, the actual operation of transformer cannot be fully reflected at micro-level. Therefore, this paper aims to improve the performance of computing environment and expand molecular scale. Firstly, two servers were connected through a high-speed communication network as the initial cluster architecture. Secondly, spatial decomposition and load balancing algorithms were used to improve the operation efficiency of cluster. Meanwhile, the oil-paper composite media model of 10⁵ atoms could be established based on this cluster, but it consumed a lot of time. Therefore, we analysed the relationship between operation efficiency and four characteristic quantities such as central processing unit performance, number of cores, simulation time and number of nodes. Then the highest point of cluster operating efficiency was found through continuous optimisation. It can be summarised that the calculating speed of cluster is nearly 10 times faster than that of the large server. Meanwhile, according to the results based on this cluster, it can be concluded that water molecules would migrate towards the oil during heating. When the initial moisture content in paper is high, the high water region would appear at the oil-paper interface.