From interactive applications to distributed laboratories

Plale, Beth; Eisenhauer, Greg; Schwan, Karsten; Heiner, Jeremy M.; Martin, V.; Vetter, Jeffrey S.

doi:10.1109/4434.678828

Cited by 25 publications

(21 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It will also utilize overlay networks to combine the lightweight data filtering and downsampling methods used in this paper with heavier-weight methods for data transformation and personalization executed by additional machines interposed into the path between data providers and consumers [4,32]. Such work will dynamically deploy lightweight IQ-Services to utilize alternative network and machine paths from data providers to consumers.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, as with the real-time collaborations outlined above, the amounts of data transmitted by filters can then be adjusted to match available network bandwidth. Figure 4 shows a prototypical real-time collaboration in which application-level data is distributed to remote collaborators and is manipulated by computational components like data clustering or data analysis [32]. The role of IQ-Services in this scenario is illustrated by the additional data filters and selectors associated as 'handlers' with the publish/subscribe event channels used for data distribution.…”

Section: Large-data Transfers Via Iq-gridftpmentioning

confidence: 99%

“…IQ-Services may be associated with clients, servers, or intermediate nodes, thereby forming an overlay network. Typical configurations of overlays used in interactive applications are described in [32,5]. We are not concerned with how to best map overlays to communication/processor networks, but note that the dynamic behavior of end users in wide area collaborations implies that it must be possible to create overlays dynamically, when desired by applications or when indicated by substantial changes in network or machine resources.…”

Section: Large-data Transfers Via Iq-gridftpmentioning

confidence: 99%

See 2 more Smart Citations

IQ‐Services: network‐aware middleware for interactive large‐data applications

Cai¹,

Eisenhauer²,

He³

et al. 2005

Concurrency and Computation

View full text Add to dashboard Cite

IQ-Services are application-specific, resource-aware code modules executed by data transport middleware. They constitute a 'thin' layer between application components and the underlying computational and communication resources that implements the data manipulations necessary to permit wide-area collaborations to proceed smoothly, despite dynamic resource variations. IQ-Services interact with the application and resource layers via dynamic performance attributes, and end-to-end implementations of such attributes also permit clients to interact with data providers. Joint middleware/resource and provider/consumer interactions implement a cooperative approach to data management for the large-data applications targeted by our research. Experimental results in this paper demonstrate substantial performance improvements attained by coordinating network-level with service-level adaptations of the data being transported and by permitting end users to dynamically deploy and use application-specific services for manipulating data in ways suitable for their current needs.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Large-data Transfers Via Iq-gridftpmentioning

confidence: 99%

Section: Large-data Transfers Via Iq-gridftpmentioning

confidence: 99%

See 1 more Smart Citation

IQ‐Services: network‐aware middleware for interactive large‐data applications

Cai¹,

Eisenhauer²,

He³

et al. 2005

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…For large-data applications like scientific visualization and collaboration, IQ-Services are application-specific services that are associated with data transport middleware. Figure 2 shows a typical scenario in which application-level data is distributed to remote collaborators and is manipulated by computational components like data clustering or data analysis [16]. The role of IQ-Services in this scenario is illustrated by the additional data filters and selectors associated as handlers with the event channels used for data distribution and transport.…”

Section: Wide-area Scientific Collaborationmentioning

confidence: 99%

IQ-services: resource-aware middleware for heterogeneous applications

Cai¹,

Eisenhauer

Poellabauer

et al.

18th International Parallel and Distributed Processing Symposium, 2004. Proceedings.

Self Cite

View full text Add to dashboard Cite

Heterogeneous computing platforms constitute a challenging execution environment for distributed applications. This paper presents a 'systems' view of effective platform usage, by demonstrating the need for application software to be continuously 'aware' of the resources currently available on their underlying heterogeneous computing platforms. Our approach to the implementation of resource awareness is one that (1) provides a 'thin' middleware layer of resource aware services that permit applications to react to changes in resource availability and resources to be managed in accordance with application needs, and that (2) develops compiler-and application-level techniques for dynamic 'service morphing', the goal being to make it easy for application-level services to adjust to runtime changes in application needs or in platform resources. The specific results presented in this paper are focused on large-data applications, for which the IQ-Services 'morphing' layer implements the data manipulations necessary to permit wide-area interactive or multimedia applications to proceed smoothly despite variations in underlying computing and network resources. IQ-Services interact with the systems layer via dynamic performance attributes, and end-to-end implementations of such attributes also enable clients to interact with servers. This results in a cooperative approach to data management for the large-data applications targeted by our research. We present and experiment with sample IQ-Services implemented for a wide-area collaboration in which multiple end users utilize different graphical views of the data being produced by a scientific simulation. The testbed used in experiments is comprised of high end machines connected via a local area backbone, remote machines connected via the Internet, and 'home' machines connected via a DSL link. Experimental results demonstrate substantial performance improvements attained by coordinating network-level with service-level adaptations of the data being transported and by permitting end users to dynamically deploy and use application-specific services for manipulating data in ways suitable for their current needs.

show abstract

“…The specific example of their use presented in this paper is the detection of hazards in continuous safety-critical applications. Our ongoing work is in applying these techniques to large-scale high performance applications running on multiprocessors and clusters of workstations, in the context of the Distributed Laboratories project described elsewhere [9]. Our approach to run-time detection imposes a relational database model upon the data arriving from the application.…”

Section: Approachmentioning

confidence: 99%