Topic
Jungle computing
About: Jungle computing is a research topic. Over the lifetime, 14 publications have been published within this topic receiving 202 citations.
Papers
4 Aug 2014
TL;DR: This paper provides a definition for Cloud, Jungle and Fog computing, and the key characteristics of them are determined; their architectures are illustrated and several main use cases are introduced.
Abstract: The distributed computing attempts to improve performance in large-scale computing problems by resource sharing. Moreover, rising low-cost computing power coupled with advances in communications/networking and the advent of big data, now enables new distributed computing paradigms such as Cloud, Jungle and Fog computing. Cloud computing brings a number of advantages to consumers in terms of accessibility and elasticity. It is based on centralization of resources that possess huge processing power and storage capacities. Fog computing, in contrast, is pushing the frontier of computing away from centralized nodes to the edge of a network, to enable computing at the source of the data. On the other hand, Jungle computing includes a simultaneous combination of clusters, grids, clouds, and so on, in order to gain maximum potential computing power. To understand these new buzzwords, reviewing these paradigms together can be useful. Therefore, this paper describes the advent of new forms of distributed computing. It provides a definition for Cloud, Jungle and Fog computing, and the key characteristics of them are determined. In addition, their architectures are illustrated and, finally, several main use cases are introduced.
109 citations
8 Jun 2011
TL;DR: Ibis/Constellation is introduced, a software platform specifically designed for distributed, heterogeneous and hierarchical computing environments and it is shown that an existing supernova detection application can be ported to Ibis/ Constellation with little effort.
Abstract: The scientific computing landscape is becoming more and more complex. Besides traditional supercomputers and clusters, scientists can also apply grid and cloud infrastructures. Moreover, the current integration of many-core technologies such as GPUs with such infrastructures adds to the complexity. To make matters worse, data distribution, hardware availability, software heterogeneity, and increasing data sizes, commonly force scientists to use multiple computing platforms simultaneously: a true computing jungle.In this paper we introduce Ibis/Constellation, a software platform specifically designed for distributed, heterogeneous and hierarchical computing environments. In Ibis/Constellation we assume that applications consist of several distinct (but somehow related) activities. These activities can be implemented independently using existing, well understood tools (e.g. MPI, CUDA, etc.). Ibis/Constellation is then used to construct the overall application by coupling the distinct activities. Using application defined labels in combination with context-aware work stealing, Ibis/Constellation provides a simple and efficient mechanism for automatically mapping the activities to the appropriate resources, taking data locality and heterogeneity into account.We show that an existing supernova detection application can be ported to Ibis/Constellation with little effort. By making small changes to the application defined labels, this example application can run efficiently in three very different HPC computing environments: a distributed set of clusters, a large 48-core machine, and a GPU cluster.
20 citations
TL;DR: It is shown that the combination of any resource that is available can result in a significant decrease in the execution time and expenses, and the possibilities of combining any available computational resource in order to reduce costs and energy consumption.
18 citations
21 May 2012
TL;DR: This paper creates a prototype Jungle-aware version of AMUSE, an astrophysical simulation framework, and shows preliminary experiments with the resulting system, using clusters, grids, stand-alone machines, and GPUs.
Abstract: High-performance scientific applications require more and more compute power. The concurrent use of multiple distributed compute resources is vital for making scientific progress. The resulting distributed system, a so-called Jungle Computing System, is both highly heterogeneous and hierarchical, potentially consisting of grids, clouds, stand-alone machines, clusters, desktop grids, mobile devices, and supercomputers, possibly with accelerators such as GPUs. One striking example of applications that can benefit greatly of Jungle Computing Systems are Multi-Model / Multi-Kernel simulations. In these simulations, multiple models, possibly implemented using different techniques and programming models, are coupled into a single simulation of a physical system. Examples include the domain of computational astrophysics and climate modeling. In this paper we investigate the use of Jungle Computing Systems for such Multi-Model / Multi-Kernel simulations. We make use of the software developed in the Ibis project, which addresses many of the problems faced when running applications on Jungle Computing Systems. We create a prototype Jungle-aware version of AMUSE, an astrophysical simulation framework. We show preliminary experiments with the resulting system, using clusters, grids, stand-alone machines, and GPUs.
18 citations
TL;DR: In this article, a hybrid adaptive resource discovery (HARD) approach is proposed to find the proper resources in a precise way in order to run a particular job on the system efficiently.
15 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 2 |
| 2020 | 1 |
| 2019 | 1 |
| 2018 | 2 |
| 2017 | 2 |
| 2014 | 2 |