23,026 research outputs found
Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid
The concept of Grid computing is becoming the most important research area in the high performance computing. Under this concept, the jobs scheduling in Grid computing has more complicated problems to discover a diversity of available resources, select the appropriate applications and map to suitable resources. However, the major problem is the optimal job scheduling, which Grid nodes need to allocate the appropriate resources for each job. In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. Our model presents the method of the jobs classifications based mainly on Fuzzy C-Mean algorithm and mapping the jobs to the appropriate resources based mainly on Genetic algorithm. In the experiments, we used the workload historical information and put it into our simulator. We get the better result when compared to the traditional algorithms for scheduling policies. Finally, the paper also discusses approach of the jobs classifications and the optimization engine in Grid scheduling
Workload characterization of the shared/buy-in computing cluster at Boston University
Computing clusters provide a complete environment
for computational research, including bio-informatics, machine
learning, and image processing. The Shared Computing Cluster
(SCC) at Boston University is based on a shared/buy-in architecture
that combines shared computers, which are free to be
used by all users, and buy-in computers, which are computers
purchased by users for semi-exclusive use. Although there exists
significant work on characterizing the performance of computing
clusters, little is known about shared/buy-in architectures. Using
data traces, we statistically analyze the performance of the SCC.
Our results show that the average waiting time of a buy-in job
is 16.1% shorter than that of a shared job. Furthermore, we
identify parameters that have a major impact on the performance
experienced by shared and buy-in jobs. These parameters include
the type of parallel environment and the run time limit (i.e., the
maximum time during which a job can use a resource). Finally,
we show that the semi-exclusive paradigm, which allows any SCC
user to use idle buy-in resources for a limited time, increases
the utilization of buy-in resources by 17.4%, thus significantly
improving the performance of the system as a whole.http://people.bu.edu/staro/MIT_Conference_Yoni.pdfAccepted manuscrip
A Study of Grid Applications: Scheduling Perspective
As the Grid evolves from a high performance cluster middleware to a multipurpose utility computing framework, a good understanding of Grid applications, their statistics and utilisation patterns is required. This study looks at job execution times and resource utilisations in a Grid environment, and their significance in cluster and network dimensioning, local level scheduling and resource management
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
Dependable Distributed Computing for the International Telecommunication Union Regional Radio Conference RRC06
The International Telecommunication Union (ITU) Regional Radio Conference
(RRC06) established in 2006 a new frequency plan for the introduction of
digital broadcasting in European, African, Arab, CIS countries and Iran. The
preparation of the plan involved complex calculations under short deadline and
required dependable and efficient computing capability. The ITU designed and
deployed in-situ a dedicated PC farm, in parallel to the European Organization
for Nuclear Research (CERN) which provided and supported a system based on the
EGEE Grid. The planning cycle at the RRC06 required a periodic execution in the
order of 200,000 short jobs, using several hundreds of CPU hours, in a period
of less than 12 hours. The nature of the problem required dynamic
workload-balancing and low-latency access to the computing resources. We
present the strategy and key technical choices that delivered a reliable
service to the RRC06
Libra: An Economy driven Job Scheduling System for Clusters
Clusters of computers have emerged as mainstream parallel and distributed
platforms for high-performance, high-throughput and high-availability
computing. To enable effective resource management on clusters, numerous
cluster managements systems and schedulers have been designed. However, their
focus has essentially been on maximizing CPU performance, but not on improving
the value of utility delivered to the user and quality of services. This paper
presents a new computational economy driven scheduling system called Libra,
which has been designed to support allocation of resources based on the users?
quality of service (QoS) requirements. It is intended to work as an add-on to
the existing queuing and resource management system. The first version has been
implemented as a plugin scheduler to the PBS (Portable Batch System) system.
The scheduler offers market-based economy driven service for managing batch
jobs on clusters by scheduling CPU time according to user utility as determined
by their budget and deadline rather than system performance considerations. The
Libra scheduler ensures that both these constraints are met within an O(n)
run-time. The Libra scheduler has been simulated using the GridSim toolkit to
carry out a detailed performance analysis. Results show that the deadline and
budget based proportional resource allocation strategy improves the utility of
the system and user satisfaction as compared to system-centric scheduling
strategies.Comment: 13 page
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