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Distributed controllers have been proposed for Software Defined Networking to address the issues of scalability and reliability that a centralized controller suffers from. One key limitation of the distributed controllers is that the mapping between a switch and a controller is <i>statically configured</i>, which may result in uneven load distribution among(More)
—Modern data center networks are commonly organized in multi-rooted tree topologies. They typically rely on equal-cost multipath to split flows across multiple paths, which can lead to significant load imbalance. Splitting individual flows can provide better load balance, but is not preferred because of potential packet reordering that conventional wisdom(More)
In this paper, we observe that bandwidth sharing via TCP in commodity data center networks organized in multi-rooted tree topologies can lead to severe unfair-ness, which we term as the TCP Outcast problem, under many common traffic patterns. When many flows and a few flows arrive at two ports of a switch destined to one common output port, the small set of(More)
Software Defined Networking (SDN) has become a popular paradigm for centralized control in many modern networking scenarios such as data centers and cloud. For large data centers hosting many hundreds of thousands of servers, there are few thousands of switches that need to be managed in a centralized fashion, which cannot be done using a single controller(More)
Multi-rooted tree topologies are commonly used to construct high-bandwidth data center network fabrics. In these networks, switches typically rely on equal-cost multipath (ECMP) routing techniques to split traffic across multiple paths, such that packets within a flow traverse the same end-to-end path. Unfortunately, since ECMP splits traffic based on(More)
Images are often corrupted with noise during acquisition, transmission and retrieval from storage media. So the need for efficient image de-noising methods has grown with the massive and easy production of digital images and movies. Furthermore, de-noising is often necessary as a pre-processing step in image compression, segmentation, recognition etc.(More)
Noise removal and image enhancement are the important tasks addressed by many Image Processing algorithms, especially, when the images are corrupted by high noise level e.g. in the case of remote imaging, thermal imaging, night vision etc. The noise makes the image recognition more difficult as it gives a grainy, snowy or textured appearance to the image.(More)
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