Depth Data Stream Algorithm for Large Scale Service Computing
Yun Meng, Lizhao Liu, Qi Li, Jiangshui Hong, Lili Liu, Genshun Dong
Available Online April 2015.
- https://doi.org/10.2991/icmra-15.2015.277How to use a DOI?
- fiss buzzing service data; schematization issue; single service group; heuristic arithmetic
- The large scale service computing issue is concerned with appease the demand of a assemblage of subscribers with a magnanimity of capacitated service data at minimum deplete, but a subscriber can be severed by more than one service data. Described the physical model of mingle assimilate (or mingle acquittal)’s fiss buzzing service data schematization issue for Single service cluster and single service group data without time intermediary, expound and proved its dispose’s diathesis, illustrate that when subscriber demand equivalent to the service data capability, devised depth data stream algorithm to dispose it, the arithmetic can be made the appliance to the circumstances when some subscriber’s demand is more macroscopic than the service data capability. This arithmetic was applied to illustrate its more formidable raking productiveness in parallel ratio with other majorization arithmetics for LSSC. Trial run fruits attested the effectiveness of this arithmetic.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Yun Meng AU - Lizhao Liu AU - Qi Li AU - Jiangshui Hong AU - Lili Liu AU - Genshun Dong PY - 2015/04 DA - 2015/04 TI - Depth Data Stream Algorithm for Large Scale Service Computing BT - 3rd International Conference on Mechatronics, Robotics and Automation PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/icmra-15.2015.277 DO - https://doi.org/10.2991/icmra-15.2015.277 ID - Meng2015/04 ER -