23,440 research outputs found
Efficient Resource Matching in Heterogeneous Grid Using Resource Vector
In this paper, a method for efficient scheduling to obtain optimum job
throughput in a distributed campus grid environment is presented; Traditional
job schedulers determine job scheduling using user and job resource attributes.
User attributes are related to current usage, historical usage, user priority
and project access. Job resource attributes mainly comprise of soft
requirements (compilers, libraries) and hard requirements like memory, storage
and interconnect. A job scheduler dispatches jobs to a resource if a job's hard
and soft requirements are met by a resource. In current scenario during
execution of a job, if a resource becomes unavailable, schedulers are presented
with limited options, namely re-queuing job or migrating job to a different
resource. Both options are expensive in terms of data and compute time. These
situations can be avoided, if the often ignored factor, availability time of a
resource in a grid environment is considered. We propose resource rank
approach, in which jobs are dispatched to a resource which has the highest rank
among all resources that match the job's requirement. The results show that our
approach can increase throughput of many serial / monolithic jobs.Comment: 10 page
A schema-based P2P network to enable publish-subscribe for multimedia content in open hypermedia systems
Open Hypermedia Systems (OHS) aim to provide efficient dissemination, adaptation and integration of hyperlinked multimedia resources. Content available in Peer-to-Peer (P2P) networks could add significant value to OHS provided that challenges for efficient discovery and prompt delivery of rich and up-to-date content are successfully addressed. This paper proposes an architecture that enables the operation of OHS over a P2P overlay network of OHS servers based on semantic annotation of (a) peer OHS servers and of (b) multimedia resources that can be obtained through the link services of the OHS. The architecture provides efficient resource discovery. Semantic query-based subscriptions over this P2P network can enable access to up-to-date content, while caching at certain peers enables prompt delivery of multimedia content. Advanced query resolution techniques are employed to match different parts of subscription queries (subqueries). These subscriptions can be shared among different interested peers, thus increasing the efficiency of multimedia content dissemination
Supporting text mining for e-Science: the challenges for Grid-enabled natural language processing
Over the last few years, language technology has moved rapidly from 'applied research' to 'engineering', and from small-scale to large-scale engineering. Applications such as advanced text mining systems are feasible, but very resource-intensive, while research seeking to address the underlying language processing questions faces very real practical and methodological limitations. The e-Science vision, and the creation of the e-Science Grid, promises the level of integrated large-scale technological support required to sustain this important and successful new technology area. In this paper, we discuss the foundations for the deployment of text mining and other language technology on the Grid - the protocols and tools required to build distributed large-scale language technology systems, meeting the needs of users, application builders and researchers
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services
Cloud computing providers have setup several data centers at different
geographical locations over the Internet in order to optimally serve needs of
their customers around the world. However, existing systems do not support
mechanisms and policies for dynamically coordinating load distribution among
different Cloud-based data centers in order to determine optimal location for
hosting application services to achieve reasonable QoS levels. Further, the
Cloud computing providers are unable to predict geographic distribution of
users consuming their services, hence the load coordination must happen
automatically, and distribution of services must change in response to changes
in the load. To counter this problem, we advocate creation of federated Cloud
computing environment (InterCloud) that facilitates just-in-time,
opportunistic, and scalable provisioning of application services, consistently
achieving QoS targets under variable workload, resource and network conditions.
The overall goal is to create a computing environment that supports dynamic
expansion or contraction of capabilities (VMs, services, storage, and database)
for handling sudden variations in service demands.
This paper presents vision, challenges, and architectural elements of
InterCloud for utility-oriented federation of Cloud computing environments. The
proposed InterCloud environment supports scaling of applications across
multiple vendor clouds. We have validated our approach by conducting a set of
rigorous performance evaluation study using the CloudSim toolkit. The results
demonstrate that federated Cloud computing model has immense potential as it
offers significant performance gains as regards to response time and cost
saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape
Rational bidding using reinforcement learning: an application in automated resource allocation
The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.
The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized
Advanced service monitoring configurations with SLA decomposition and selection
Service Level Agreements (SLAs) for Software Services aim to clearly identify the service level commitments established between service requesters and providers. The commitments that are agreed however can be expressed in complex notations through a combination of expressions that need to evaluated and monitored efficiently. The dynamic allocation of the responsibility for monitoring SLAs (and often different parts within them) to different monitoring components is necessary as both SLAs and the components available for monitoring them may change dynamically during the operation of a service based system. In this paper we discuss an approach to supporting this dynamic configuration, and in particular, how SLAs expressed in higher-level notations can be efficiently decomposed and appropriate monitoring components dynamically allocated for each part of the agreements. The approach is illustrated with mechanical support in the form of a configuration service which can be incorporated into SLA-based service monitoring infrastructures
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