45 research outputs found
Metascheduling of HPC Jobs in Day-Ahead Electricity Markets
High performance grid computing is a key enabler of large scale collaborative
computational science. With the promise of exascale computing, high performance
grid systems are expected to incur electricity bills that grow super-linearly
over time. In order to achieve cost effectiveness in these systems, it is
essential for the scheduling algorithms to exploit electricity price
variations, both in space and time, that are prevalent in the dynamic
electricity price markets. In this paper, we present a metascheduling algorithm
to optimize the placement of jobs in a compute grid which consumes electricity
from the day-ahead wholesale market. We formulate the scheduling problem as a
Minimum Cost Maximum Flow problem and leverage queue waiting time and
electricity price predictions to accurately estimate the cost of job execution
at a system. Using trace based simulation with real and synthetic workload
traces, and real electricity price data sets, we demonstrate our approach on
two currently operational grids, XSEDE and NorduGrid. Our experimental setup
collectively constitute more than 433K processors spread across 58 compute
systems in 17 geographically distributed locations. Experiments show that our
approach simultaneously optimizes the total electricity cost and the average
response time of the grid, without being unfair to users of the local batch
systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System
Interoperating Grid Infrastructures with the GridWay Metascheduler
This paper describes the GridWay Metascheduler and exposes its latest and future developments, mainly related to interoperability and interoperation. GridWay enables large-scale, reliable and efficient sharing of computing resources over grid middleware. To favor interoperability, it shows a modular architecture based on drivers, which access middleware services for resource discovery and monitoring, job execution and management, and file transfer. This paper presents two new execution drivers for BES and CREAM services, and introduces a remote BES interface for GridWay. This interface allows users to access GridWay’s job metascheduling capabilities, using the BES implementation of GridSAM. Thus, GridWay now provides to end-users more possibilities of interoperability and interoperation
Grid resource discovery based on web services
The size of grid systems has increased substantially in the last decades. Resource discovery in grid systems is a fundamental task which provides searching and locating necessary resources for a given process. Various different approaches are proposed in literature for this problem. Grid resource discovery using web services is an important approach which has resulted in many tools to become de facto standards of today's grid resource management. In this paper, we propose a survey of recent grid resource discovery studies based on web services. We provide synthesis, analysis and evaluation of these studies by classification. We also give a comparative study of different classes proposed
Advances in Grid Computing
This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems
Grid Global Behavior Prediction
Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach