A Virtualized SGE-based Computational Cluster for Heterogeneous Environments

The computing and modeling environment of IIASA was studied in the context of computation-intensive ad resource-demanding applications/models which are being developed and used by the researchers/scientists of IIASA. High Performance Computing applicatins can be classified into two broad computing fields; sequential distributed and parallel distributed applications and these applications has been developed for heterogeneous operating system architectures such as Linux, Windows and Solaris etc. Majority of IIASA applications/models belong to the latter class of computing and these applications are resource demanding when the extensive and repetitive use of these applications is required according to the need of some research study. Not every sequential application can be easily parallelized; therefore, instead of re-programming sequental applications into parallel ones, the idea of distributing such applications on computing cluster/grid is often an effective approach for accelerating the work. In the light of available computing resources and modest modeling environment of IIASA, the virtualization and Sun Grid Engine (batch job scheduler and manager for cluster/grid) was efficiently exploited and designed, built and tested. This resulted in a computational cluster supporting multiple operating systems and multiple sequential distributed and parallel distributed applications/models along with multiple job execution types such as binaries and JAVA

Building distributed sensor network applications using BIP

International audienceThe exponential increase in the demands for the deployment of large-scale sensor networks, makes the efficient development of functional applications necessary. Nevertheless, the existence of scarce resources and the derived application complexity, impose significant constraints and requires high design expertise. Consequently, the probability of discovering design errors, once the application is implemented, is considerably high. To address these issues, there is a need for the availability of early-stage validation, performance evaluation and rapid prototyping techniques at design time. In this paper we present a novel approach for the co-design of mixed software/hardware applications for distributed sensor network systems. This approach uses BIP, a formal framework facilitating modeling, analysis and implementation of real-time embedded, heterogeneous systems. Our approach is illustrated through the modeling and deployment of a Wireless Multimedia Sensor Network (WMSN) application. We emphasize on its merits, notably validation of functional and non-functional requirements through statistical model-checking and automatic code generation for sensor network platforms

GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing

Clusters, grids, and peer-to-peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. The management of resources and scheduling of applications in such large-scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a grid environment, it is hard and even impossible to perform scheduler performance evaluation in a repeatable and controllable manner as resources and users are distributed across multiple organizations with their own policies. To overcome this limitation, we have developed a Java-based discrete-event grid simulation toolkit called GridSim. The toolkit supports modeling and simulation of heterogeneous grid resources (both time- and space-shared), users and application models. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management. To demonstrate suitability of the GridSim toolkit, we have simulated a Nimrod-G like grid resource broker and evaluated the performance of deadline and budget constrained cost- and time-minimization scheduling algorithms

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

Distributed simulation and the grid: Position statements

The Grid provides a new and unrivaled technology for large scale distributed simulation as it enables collaboration and the use of distributed computing resources. This panel paper presents the views of four researchers in the area of Distributed Simulation and the Grid. Together we try to identify the main research issues involved in applying Grid technology to distributed simulation and the key future challenges that need to be solved to achieve this goal. Such challenges include not only technical challenges, but also political ones such as management methodology for the Grid and the development of standards. The benefits of the Grid to end-user simulation modelers also are discussed

HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks

A recent approach in modeling and analysis of the supply and demand in heterogeneous wireless cellular networks has been the use of two independent Poisson point processes (PPPs) for the locations of base stations (BSs) and user equipments (UEs). This popular approach has two major shortcomings. First, although the PPP model may be a fitting one for the BS locations, it is less adequate for the UE locations mainly due to the fact that the model is not adjustable (tunable) to represent the severity of the heterogeneity (non-uniformity) in the UE locations. Besides, the independence assumption between the two PPPs does not capture the often-observed correlation between the UE and BS locations. This paper presents a novel heterogeneous spatial traffic modeling which allows statistical adjustment. Simple and non-parameterized, yet sufficiently accurate, measures for capturing the traffic characteristics in space are introduced. Only two statistical parameters related to the UE distribution, namely, the coefficient of variation (the normalized second-moment), of an appropriately defined inter-UE distance measure, and correlation coefficient (the normalized cross-moment) between UE and BS locations, are adjusted to control the degree of heterogeneity and the bias towards the BS locations, respectively. This model is used in heterogeneous wireless cellular networks (HetNets) to demonstrate the impact of heterogeneous and BS-correlated traffic on the network performance. This network is called HetHetNet since it has two types of heterogeneity: heterogeneity in the infrastructure (supply), and heterogeneity in the spatial traffic distribution (demand).Comment: JSA