Third order differential equations with fixed critical points

Cataloged from PDF version of article.The singular point analysis of third order ordinary differential equations which are algebraic in y and y′ is presented. Some new third order ordinary differential equations that pass the Painlevé test as well as the known ones are found. © 2008 Elsevier Inc. All rights reserved

Non-polynomial fourth order equations which pass the Painlevé test

The singular point analysis of fourth order ordinary differential equations in the non-polynomial class are presented. Some new fourth order ordinary differential equations which pass the Painlevé test as well as the known ones are found. © 2005 Verlag der Zeitschrift für Naturforschung, Tübingen

ANAEROBIC DIGESTION OF ORGANIC WASTES FROM AGRO-FOOD ACTIVITIES IN TUNISIA

Transfert de technologie de biométhanisation-cogénération en Tunisi

Local organisation of clay particles and its relation to volume change of remoulded clays

International audienc

Influence des parois métalliques d'un implant dans le bilan de transmission d'un système de télé-alimentation, et de télésurveillance d'implants orthopédiques intracorporels

International audienc

Using a vision cognitive algorithm to schedule virtual machines

Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NP-hard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption