4,065 research outputs found
Can intelligent optimisation techniques improve computing job scheduling in a Grid environment? review, problem and proposal
In the existing Grid scheduling literature, the reported methods and strategies are mostly related to high-level schedulers such as global schedulers, external schedulers, data schedulers, and cluster schedulers. Although a number of these have previously considered job scheduling, thus far only relatively simple queue-based policies such as First In First Out (FIFO) have been considered for local job scheduling within Grid contexts. Our initial research shows that it is worth investigating the potential impact on the performance of the Grid when intelligent optimisation techniques are applied to local scheduling policies. The research problem is defined, and a basic research methodology with a detailed roadmap is presented. This paper forms a proposal with the intention of exchanging ideas and seeking potential collaborators
A Delay-Optimal Packet Scheduler for M2M Uplink
In this paper, we present a delay-optimal packet scheduler for processing the
M2M uplink traffic at the M2M application server (AS). Due to the
delay-heterogeneity in uplink traffic, we classify it broadly into
delay-tolerant and delay-sensitive traffic. We then map the diverse delay
requirements of each class to sigmoidal functions of packet delay and formulate
a utility-maximization problem that results in a proportionally fair
delay-optimal scheduler. We note that solving this optimization problem is
equivalent to solving for the optimal fraction of time each class is served
with (preemptive) priority such that it maximizes the system utility. Using
Monte-Carlo simulations for the queuing process at AS, we verify the
correctness of the analytical result for optimal scheduler and show that it
outperforms other state-of-the-art packet schedulers such as weighted round
robin, max-weight scheduler, fair scheduler and priority scheduling. We also
note that at higher traffic arrival rate, the proposed scheduler results in a
near-minimal delay variance for the delay-sensitive traffic which is highly
desirable. This comes at the expense of somewhat higher delay variance for
delay-tolerant traffic which is usually acceptable due to its delay-tolerant
nature.Comment: Accepted for publication in IEEE MILCOM 2016 (6 pages, 7 figures
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
Bulk Scheduling with the DIANA Scheduler
Results from the research and development of a Data Intensive and Network
Aware (DIANA) scheduling engine, to be used primarily for data intensive
sciences such as physics analysis, are described. In Grid analyses, tasks can
involve thousands of computing, data handling, and network resources. The
central problem in the scheduling of these resources is the coordinated
management of computation and data at multiple locations and not just data
replication or movement. However, this can prove to be a rather costly
operation and efficient sing can be a challenge if compute and data resources
are mapped without considering network costs. We have implemented an adaptive
algorithm within the so-called DIANA Scheduler which takes into account data
location and size, network performance and computation capability in order to
enable efficient global scheduling. DIANA is a performance-aware and
economy-guided Meta Scheduler. It iteratively allocates each job to the site
that is most likely to produce the best performance as well as optimizing the
global queue for any remaining jobs. Therefore it is equally suitable whether a
single job is being submitted or bulk scheduling is being performed. Results
indicate that considerable performance improvements can be gained by adopting
the DIANA scheduling approach.Comment: 12 pages, 11 figures. To be published in the IEEE Transactions in
Nuclear Science, IEEE Press. 200
Hardware as a service - enabling dynamic, user-level bare metal provisioning of pools of data center resources.
We describe a “Hardware as a Service (HaaS)” tool for isolating pools of compute, storage and networking resources. The goal of HaaS is to enable dynamic and flexible, user-level provisioning of pools of resources at the so-called “bare-metal” layer. It allows experimental or untrusted services to co-exist alongside trusted services. By functioning only as a resource isolation system, users are free to choose between different system scheduling and provisioning systems and to manage isolated resources as they see fit. We describe key HaaS use cases and features. We show how HaaS can provide a valuable, and somehwat overlooked, layer in the software architecture of modern data center management. Documentation and source code for HaaS software are available at: https://github.com/CCI-MOC/haasPartial support for this work was provided by the MassTech Collaborative Research Matching Grant Program, National Science Foundation award #1347525 and several commercial partners of the Mass Open Cloud who may be found at http://www.massopencloud.org.http://www.ieee-hpec.org/2014/CD/index_htm_files/FinalPapers/116.pd
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