Abstract. The TCP congestion control protocol is mainly designed for bandwidth symmetric paths. As two-way asymmetric connections will probably become common case in the future with the widespread use of ADSL, satellites and other high-speed technologies, it is important to make sure that congestion will be properly handled in these environments. To this end, we propose in this paper a new Adaptive Class-based Queuing mechanism called ACQ for handling two-way TCP trac over links that exhibit bandwidth asymmetry. ACQ runs at the entry of the slow link and relies on two separate classes, one for ACK packets and one for Data packets. ACQ proposes to adapt the weights of both classes according to the crossing trac in order to maximize some utility function dened by the user or the network operator. We show by simulations that our mechanism is able to reach a good utilization of the available resources, managing then to maximize the satisfaction of the user of such asymmetric connections.
The cloud computing paradigm has recently attracted many industries and academic attention. It provides network access on demand and offers applications, platforms, or access to a shared pool of hardware and software resources. For traditional deployment, the user reserves the most required resources. However, this system does not guarantee an optimal use of resources and is not profitable for users. The characteristic feature of the elasticity of the cloud Computing gives the Cloud the ability to perform an automatic up / down scale resources proportional to demand. However, classical deployment only considers the use of resources based on alarm, and does not consider the quality perceived by the end user. The aim of this paper is to set up a private IAAS Cloud infrastructure and complete it by supervision tools so we could optimize the management of the cloud elasticity based on users’ point of view or QoE. We have also used a Machine learning algorithm to predict the load charge of the physical machines of the cloud so that providers could manage efficiently their data centers.
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