A Recursive-Adjustment Co-allocation Scheme in Data Grid Environments

2014 International Conference on Smart Computing

Liu

Wang

et al. 2014

Self Cite

The improvement of sanitary conditions and changes of life style making people's health awareness began to rise. The progress of medical and the promotion of medicine and health, the concept of preventive healthcare is widespread. People begin to concern about the quality of the environment where they stayed. The environmental comfort index include temperature, related humidity, illumination, noise and carbon dioxide. Among these, we monitor and record the values of temperature, related humidity and carbon dioxide. In this paper, we built an environment quality monitoring system, which can adjust the air quality in door and monitor the concentration of harmful gases like formaldehyde, volatile organic compounds and carbon monoxide. If the environment comfort value is not up to standard, the related equipment will be turned on. The system will alert if the content of harmful gases exceed the standard. We hope that based on these realtime data, the proposed system can help people make right and timely decisions, and act on time to maintain a beneficial environment in the monitored area.

Section: Nosql Db Architecturementioning

confidence: 99%

Implementation of an environmental quality and harmful gases monitoring system

2014 International Conference on Smart Computing

Liu

Wang

et al. 2014

Self Cite

“…initialPT ≤ ClientMaxBandwidth /Number of Replica Source (1) In our previous work [23,25,26,27,28], we proposed a replica selection cost model in which we defined a formula for calculating the weighting. First, we get a score based on the states of the various server devices:…”

Section: The Dynamic Adjustment Strategymentioning

confidence: 99%

“…This method selects the servers most likely to provide optimum transfer rates because bandwidth quality can vary unpredictably due to the sharing nature of the Internet. Another way is to use co-allocation technology [17,21,23,24,25,26,27,28,30] to download data. Co-allocation of data transfers enables the clients to download data from multiple locations by establishing multiple connections in parallel.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Adjustment Strategy for File Transformation in Data Grids

Lecture Notes in Computer Science

Wang

2007

Self Cite

Abstract. In this paper, we propose a dynamic file transfer scheme with coallocation architecture, called Dynamic Adjustment Strategy, a dynamic file transfer scheme with co-allocation architecture that reduce the file transfer times and improves the performance in Data Grid environments. Our approach reduces the idle time faster servers spend waiting for the slowest server, and decreases file transfer completion time. We also present a new toolkit, called Cyber-Transformer, with a friendly graphical user interface interface running on the client side that makes it easy for inexperienced users to manage replicas and download the files in Data Grid environments. We also provide an effective scheme for reducing the cost of reassembling data blocks.

“…In previous works [2,3,7,14,16,20,23,24,25,26], several co-allocation strategies were addressed. However there are still drawbacks in these approaches, such as: faster servers wait for the slowest one to deliver its final block.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we propose the Anticipative Recursively-Adjusting Mechanism, a dynamic coallocation scheme based on the co-allocation architecture for Grid environment file transfers [23,24,25]. The algorithm adjusts continuously the workload on each selected replica server according to the finish rates for previously assigned transfers, and anticipates the bandwidth status for next section.…”

Section: Introductionmentioning

confidence: 99%

An Anticipative Recursively-Adjusting Mechanism for redundant parallel file transfer in data grids

Chi

2008 13th Asia-Pacific Computer Systems Architecture Conference

et al. 2008

In data grids, co-allocation architectures were developed to enable parallel downloads of datasets from selected replica servers. As Internet is usually the underlying network ofa grid, network bandwidth plays as the key factor affectingfile transfers between clients and servers. In this paradigm, there are still some challenges which need to be solved, such as to reduce differences in finish times between selected replica servers; to avoid traffic congestion resulting from transferring the same blocks in different links among servers and clients; and to manage network performance variations among parallel transfers. In this paper, we propose the Anticipative Recursively-Adjusting Mechanism (ARAM) scheme to adjust the workloads on selected replica servers and handle unpredictable variations in network performance by those servers. Our algorithm is based on using the finish rates of previously assigned transfers to anticipate the bandwidth status for next section in order to adjust workloads, and to reduce file transfer times in grid environments. Our approach is useful in grid environments with unstable network link. It not only reduces idle time wasted waiting for the slowest server, but also decreases file transfer completion times.