A Case for Cooperative and Incentive-Based Coupling of Distributed Clusters

Research interest in Grid computing has grown significantly over the past five years. Management of distributed resources is one of the key issues in Grid computing. Central to management of resources is the effectiveness of resource allocation as it determines the overall utility of the system. The current approaches to superscheduling in a grid environment are non-coordinated since application level schedulers or brokers make scheduling decisions independently of the others in the system. Clearly, this can exacerbate the load sharing and utilization problems of distributed resources due to suboptimal schedules that are likely to occur. To overcome these limitations, we propose a mechanism for coordinated sharing of distributed clusters based on computational economy. The resulting environment, called \emph{Grid-Federation}, allows the transparent use of resources from the federation when local resources are insufficient to meet its users' requirements. The use of computational economy methodology in coordinating resource allocation not only facilitates the QoS based scheduling, but also enhances utility delivered by resources.Comment: 22 pages, extended version of the conference paper published at IEEE Cluster'05, Boston, M

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 Computational Economy for Grid Computing and its Implementation in the Nimrod-G Resource Brok

Computational Grids, coupling geographically distributed resources such as PCs, workstations, clusters, and scientific instruments, have emerged as a next generation computing platform for solving large-scale problems in science, engineering, and commerce. However, application development, resource management, and scheduling in these environments continue to be a complex undertaking. In this article, we discuss our efforts in developing a resource management system for scheduling computations on resources distributed across the world with varying quality of service. Our service-oriented grid computing system called Nimrod-G manages all operations associated with remote execution including resource discovery, trading, scheduling based on economic principles and a user defined quality of service requirement. The Nimrod-G resource broker is implemented by leveraging existing technologies such as Globus, and provides new services that are essential for constructing industrial-strength Grids. We discuss results of preliminary experiments on scheduling some parametric computations using the Nimrod-G resource broker on a world-wide grid testbed that spans five continents

PaPaS: A Portable, Lightweight, and Generic Framework for Parallel Parameter Studies

The current landscape of scientific research is widely based on modeling and simulation, typically with complexity in the simulation's flow of execution and parameterization properties. Execution flows are not necessarily straightforward since they may need multiple processing tasks and iterations. Furthermore, parameter and performance studies are common approaches used to characterize a simulation, often requiring traversal of a large parameter space. High-performance computers offer practical resources at the expense of users handling the setup, submission, and management of jobs. This work presents the design of PaPaS, a portable, lightweight, and generic workflow framework for conducting parallel parameter and performance studies. Workflows are defined using parameter files based on keyword-value pairs syntax, thus removing from the user the overhead of creating complex scripts to manage the workflow. A parameter set consists of any combination of environment variables, files, partial file contents, and command line arguments. PaPaS is being developed in Python 3 with support for distributed parallelization using SSH, batch systems, and C++ MPI. The PaPaS framework will run as user processes, and can be used in single/multi-node and multi-tenant computing systems. An example simulation using the BehaviorSpace tool from NetLogo and a matrix multiply using OpenMP are presented as parameter and performance studies, respectively. The results demonstrate that the PaPaS framework offers a simple method for defining and managing parameter studies, while increasing resource utilization.Comment: 8 pages, 6 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

Designing a Hadoop system based on computational resources and network delay for wide area networks

This paper proposes a Hadoop system that considers both slave server’s processing capacity and network delay for wide area networks to reduce the job processing time. The task allocation scheme in the proposed Hadoop system divides each individual job into multiple tasks using suitable splitting ratios and then allocates the tasks to different slaves according to the computational capability of each server and the availability of network resources. We incorporate software-defined networking to the proposed Hadoop system to manage path computation elements and network resources. The performance of proposed Hadoop system is experimentally evaluated with fourteen machines located in the different parts of the globe using a scale-out approach. A scale-out experiment using the proposed and conventional Hadoop systems is conducted by executing both single job and multiple jobs. The practical testbed and simulation results indicate that the proposed Hadoop system is effective compared to the conventional Hadoop system in terms of processing time