207 research outputs found
Economic-based Distributed Resource Management and Scheduling for Grid Computing
Computational Grids, emerging as an infrastructure for next generation
computing, enable the sharing, selection, and aggregation of geographically
distributed resources for solving large-scale problems in science, engineering,
and commerce. As the resources in the Grid are heterogeneous and geographically
distributed with varying availability and a variety of usage and cost policies
for diverse users at different times and, priorities as well as goals that vary
with time. The management of resources and application scheduling in such a
large and distributed environment is a complex task. This thesis proposes a
distributed computational economy as an effective metaphor for the management
of resources and application scheduling. It proposes an architectural framework
that supports resource trading and quality of services based scheduling. It
enables the regulation of supply and demand for resources and provides an
incentive for resource owners for participating in the Grid and motives the
users to trade-off between the deadline, budget, and the required level of
quality of service. The thesis demonstrates the capability of economic-based
systems for peer-to-peer distributed computing by developing users'
quality-of-service requirements driven scheduling strategies and algorithms. It
demonstrates their effectiveness by performing scheduling experiments on the
World-Wide Grid for solving parameter sweep applications
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
Mapping a Group of Jobs in the Error Recovery of a the Grid-Based Workflow within SLA Context
The error recovery mechanism receives an important
position in the system supporting Service Level Agree-
ments (SLAs) for the Grid-based work
ow. If one sub-job
of the work
ow is late, a group of directly aected sub-jobs
should be re-mapped in a way that does not aect the start
time of other sub-jobs in the work
ow and is as inexpen-
sive as possible. With the distinguished workload and re-
source characteristics as well as the goal of the problem,
this problem needs new method to be solved. This paper
presents a mapping algorithm, which can cope with the
problem. Performance measurements deliver good evalu-
ation results on the quality and eciency of the metho
Mapping of SLA-Based Workflows with Light Communication onto Grid Resources
Service Level Agreements (SLAs) are currently one of the
major research topics in Grid Computing. Among those system compo-
nents that support SLA-aware Grid jobs, the SLA mapping mechanism
has an important position. It is responsible for assigning sub-jobs of
the work
ow to Grid resources in a way that meets the user's dead-
line and minimizes costs. Assuming many dierent kinds of sub-jobs and
resources, the process of mapping an SLA-based work
ow with light
communication denes an unfamiliar and dicult problem. This paper
presents a solution to this problem. The quality and eciency of the
algorithm is validated through performance measurements
Designing a Resource Broker for Heterogeneous Grids
Grids provide uniform access to aggregations of heterogeneous resources and
services such as computers, networks and storage owned by multiple
organizations. However, such a dynamic environment poses many challenges for
application composition and deployment. In this paper, we present the design of
the Gridbus Grid resource broker that allows users to create applications and
specify different objectives through different interfaces without having to
deal with the complexity of Grid infrastructure. We present the unique
requirements that motivated our design and discuss how these provide
flexibility in extending the functionality of the broker to support different
low-level middlewares and user interfaces. We evaluate the broker with
different job profiles and Grid middleware and conclude with the lessons learnt
from our development experience.Comment: 26 pages, 15 figure
EVALUATING SCHEDULING METHODS FOR ENERGY COST REDUCTION IN A HETEROGENEOUS DATA CENTER ENVIRONMENT
Over the past two decades the rise of information technologies (IT) has enabled businesses to communicate, coordinate, and cooperate in unprecedented ways. However, this did not come without a price. Today, IT infrastructure accounts for a substantial fraction of the national energy consumption in most advanced countries. Subsequently, research turned to finding ways of making IT more sustainable and lessening the environmental impact of IT infrastructure. In our previous work we developed LINFIX, an innovative method for handling the scheduling problem in data centers, which substantially reduced the total energy consumption compared to commonly used practices. Due to the computational complexity of the scheduling problem, we were, however, unable to estimate the cost reduction of LINFIX compared to what is theoretically possible. In this work we employ a genetic algorithm to provide a benchmark to better assess the quality of the LINFIX solutions. While the genetic algorithm frequently finds better solutions, the additional average cost reduction when compared to LINFIX is less than 0.1 percent. Taking the computational speed into account, this confirms our hypothesis that LINFIX provides very energy efficient scheduling plans in short time
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