m‐JGRIM: a novel middleware for Gridifying Java applications into mobile Grid services

Mateos, Cristian; Zunino, Alejandro; Campo, Marcelo

doi:10.1002/spe.961

Cited by 4 publications

(3 citation statements)

References 58 publications

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“…Research has also focused on the optimisation of distributed architectures, improving them from various viewpoints. For example, the authors of presented a method to facilitate the migration of a monolithic Java application to a distributed architecture through the automated dependency injection of source code. In the case of , this work described a mechanism to achieve high reliability in clustered web services, which was based on its capability of offering transparent fault‐tolerance to different types of transactions.…”

Section: Related Workmentioning

confidence: 99%

TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems

et al. 2016

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Summary Nowadays, clustered environments are commonly used in high‐performance computing and enterprise‐level applications to achieve faster response time and higher throughput than single machine environments. Nevertheless, how to effectively manage the workloads in these clusters has become a new challenge. As a load balancer is typically used to distribute the workload among the cluster's nodes, multiple research efforts have concentrated on enhancing the capabilities of load balancers. Our previous work presented a novel adaptive load balancing strategy (TRINI) that improves the performance of a clustered Java system by avoiding the performance impacts of major garbage collection, which is an important cause of performance degradation in Java. The aim of this paper is to strengthen the validation of TRINI by extending its experimental evaluation in terms of generality, scalability and reliability. Our results have shown that TRINI can achieve significant performance improvements, as well as a consistent behaviour, when it is applied to a set of commonly used load balancing algorithms, demonstrating its generality. TRINI also proved to be scalable across different cluster sizes, as its performance improvements did not noticeably degrade when increasing the cluster size. Finally, TRINI exhibited reliable behaviour over extended time periods, introducing only a small overhead to the cluster in such conditions. These results offer practitioners a valuable reference regarding the benefits that a load balancing strategy, based on garbage collection, can bring to a clustered Java system. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems

et al. 2016

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“…Important results regarding parallelization methods and development of techniques to facilitate software mobility have emerged in the last decade . These advances are aimed to traditional parallel and distributed infrastructures, which rely on fixed servers connected for predictable and stable intervals of time.…”

Section: Related Workmentioning

confidence: 99%

Improving scientific application execution on android mobile devices via code refactorings

2016

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The increasing number of mobile devices with ever-growing capabilities makes them useful for running scientific applications. However, these applications have high computational demands, whereas mobile devices have limited capabilities when compared with non-mobile devices. More importantly, mobile devices rely on batteries for their power supply. We initially measure the battery consumption of different versions of known micro-benchmarks representing common programming primitives found in scientific applications. Then, we analyze the performance of such micro-benchmarks in CPU-intensive mobile applications. We apply good programming practices and code refactorings to reduce battery consumption of scientific mobile applications. Our results show the reduction in energy usage from applying these refactorings to three scientific applications, and we consequently propose guidelines for high-performance computing applications. Our focus is on Android, the dominant mobile operating system. As a long-term contribution, our results represent one more step in the progress towards hybrid distributed infrastructures comprising fixed and mobile nodes, that is, the so-called mobile grids.In general, this device is more powerful than a smartphone. Furthermore, smartphones and tablets offer interesting hardware features such as sensors, GPS, and accelerometers.Additionally, it is important to mention the accelerated evolution of mobile devices. We can observe the constant progress in smartphones by comparing the Samsung Galaxy S, which was launched on September 9, 2010, with its successor the Samsung Galaxy SII. The Samsung Galaxy S used the Samsung S5PC110 processor, which combined a 45-nm 1-GHz ARM Cortex-A8-based CPU with 512 MB of RAM. Also, it has a 2 GB of internal storage. Regarding the battery life, it has a talk time of up to 6 h and a standby time of up to 12.5 days. The Samsung Galaxy S supports Bluetooth 3.0, WiFi 802.11b/g/n, and 3G data up to 7.2 Mbit/s. In contrast, the Galaxy S II has a 1.2-GHz dual-core processor, 1 GB of RAM, and 16 GB of internal mass storage. It has a battery standby time of up to 10.5 days and talk time of up to 8 h. Subsequent models that followed have even more capacity.Battery consumption in mobile devices is one of the most important problems in mobile computing research. The example earlier shows that while computational capacity has increased rapidly, battery power has increased slowly [3,4]. This paper aims to assess the energy capabilities of mobile devices using different micro-benchmarks commonly found in scientific applications and to show the impact of several known code refactorings to save battery in mobile devices. This means that we chose refactorings that have been proposed to improve maintainability, readability, and so on and we evaluated their impact in battery consumption. Currently mobile devices are not only devices for connection/access to external computing resources, but they are becoming core nodes of computational infrastructures in scientific projects [1]. Ther...

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“…Therefore, we conceive gridification as shaping the source code of an ordinary application according to few coding conventions, and then adding Grid concerns to it. In a previous paper (Mateos et al, 2008b), we reported preliminary comparisons between JGRIM and other approaches for gridifying software in terms of source code metrics. In this article we also report JGRIM execution performance on an Internet-based Grid, measuring execution time and network usage of two resource-intensive applications.…”

Section: Introductionmentioning

confidence: 99%

Grid-Enabling Applications with JGRIM

Mateos¹,

Zunino²,

Campo³

2009

International Journal of Grid and High Performance Computing

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The development of massively distributed applications with enormous demands for computing power, memory, storage and bandwidth is now possible with the Grid. Despite these advances, building Grid applications is still very difficult. We present JGRIM, an approach to easily gridify Java applications by separating functional and Grid concerns in the application code, and report evaluations of its benefits with respect to related approaches. The results indicate that JGRIM simplifies the process of porting applications to the Grid, and the Grid code obtained from this process performs in a very competitive way compared to the code resulting from using similar tools.

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m‐JGRIM: a novel middleware for Gridifying Java applications into mobile Grid services

Cited by 4 publications

References 58 publications

TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems

TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems

Improving scientific application execution on android mobile devices via code refactorings

Grid-Enabling Applications with JGRIM

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