A distributed system architecture for a distributed application environment

Bauer, Michael; Coburn, Neil; Erickson, Doreen L.; Finnigan, P. J.; Hong, James Won‐Ki; Larson, Per-Åke; Pachl, Jan; Slonim, Jacob; Taylor, David J.; Teorey, Toby J.

doi:10.1147/sj.333.0399

Cited by 23 publications

(9 citation statements)

References 11 publications

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“…Technical assistance, when available from the vendor, is generic and does not alwnys solve specific implementation problems. Since our initial proof of concept did not require all the fonturos included with commercial middleware, it was determined that constructing our own middle lnyer would clarify) design issues, ensure local control, allow for better maintenance, and still give us the option of converting to a commercial product down the road [9].…”

Section: Picking An Implementation Strategymentioning

confidence: 99%

Developing the cooperative mission development environment

Rouff

Robbert

1997

Proceedings of the International ACM SIGGROUP Conference on Supporting Group Work : The Integration Challenge the Integration C

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The Cooperative Mission Development Environment (CMDE) was developed to unite independent applications and databases into a cooperating tool set capable of sharing data. As missions developed stand-alone tools to meet immediate needs and to automate paper functions, a proliferation of development and automation tools came into existence. It soon became apparent that data in one tool could be used by another tool and a significant improvement in productivity could be obtained by combining the tools and sharing the data between them. This paper examines: how the independent systems arose, why the need developed to integrate the independent systems, the integration approaches considered, the process used for integration, how coordination between tool developers was handled, challenges faced during integration, plus current challenges, and future plans.

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Section: Picking An Implementation Strategymentioning

confidence: 99%

Developing the cooperative mission development environment

Rouff

Robbert

1997

Proceedings of the International ACM SIGGROUP Conference on Supporting Group Work : The Integration Challenge the Integration C

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“…Distributed systems (Bauer et al , 1994; Georgiadis et al , 2002), in general, are those systems that have the following properties:Cooperation – devices communicate via message passing to solve a problem collectively. A shared memory or a synchronizing clock is not always necessary for this cooperation.Coupling – Whether devices are on a LAN, WAN, or workstations on the single network acting like clusters or even geographically distant.Heterogeneity – Devices may have different hardware and software configurations.…”

Section: Introductionmentioning

confidence: 99%

Exploring compression and parallelization techniques for distribution of deep neural networks over Edge–Fog continuum – a review

Nazir

Mir

Qureshi

2020

IJICC

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PurposeThe trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.Design/methodology/approachThis review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.FindingsDL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.Originality/valueTo the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.

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“…The process of integrating heterogeneous databases and applications behind GUI front ends, to seamlessly access and update data has been examined theoretically [1,2] and promised commercially [3,4]. Companies tout the simplicity of achieving integration through use of their products, but a single product does not always meet required time, money, cross platform and reliability constraints.…”

Section: Introductionmentioning

confidence: 99%

A cooperative mission development environment for crossplatform integration

Robert

Rouff

Burkhardt

1997

Proceedings of the 1997 ACM Symposium on Applied Computing - SAC '97

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B r i d g e w a t e r State C o l l e g e B r i d g e w a t e r , M A 0 2 3 2 5 U S A m r o b b e r t @ bently.edu C h r i s t o p h e r R o u f f G o d d a r d S p a c e Flight C e n t e r G r e e n b e l t , M D 20771 U S A chris.rouff@ g s f c . n a s a . g o v ABSTRACTThe Cooperative Mission Development Environment (CMDE) is a middleware approach to represent heterogeneous applications and databases as homogeneous objects for transparent data access. In order to maintain data and application independence as well as maximize flexibility, a three-tiered client/server architecture was designed. This paper examines the architecture of a three-tiered, tool independent model and its implementation with current technology. The model exemplifies designing for future growth without waiting for the current software to mature. KeywordsMiddleware, heterogeneous databases, data sharing INTRODUCTIONA scaleable, client/server system, which remains functional with current technology and personnel, has been implemented to test, track and manage NASA's mission requirements. CMDE evolved in response to a situation common in a large enterprise. Many processes were manually completed causing delays and duplication of effort. As processes were computerized, focus was on the solution of immediate problems. User interfaces were designed to resemble traditional paper forms. Off-the-shelf products were installed to meet specific needs. No overall architecture for integration and growth existed.

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A distributed system architecture for a distributed application environment

Cited by 23 publications

References 11 publications

Developing the cooperative mission development environment

Developing the cooperative mission development environment

Exploring compression and parallelization techniques for distribution of deep neural networks over Edge–Fog continuum – a review

A cooperative mission development environment for crossplatform integration

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