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Register a new userRouting Algorithms for Recursively-Defined Data Centre Networks
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Added by
Alejandro Erickson
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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The server-centric data centre network architecture can accommodate a wide variety of network topologies. Newly proposed topologies in this arena often require several rounds of analysis and experimentation in order that they might achieve their full potential as data centre networks. We propose a family of novel routing algorithms on two well-known data centre networks of this type, (Generalized) DCell and FiConn, using techniques that can be applied more generally to the class of networks we call completely connected recursively-defined networks. In doing so, we develop a classification of all possible routes from server-node to server-node on these networks, called general routes of order $t$, and find that for certain topologies of interest, our routing algorithms efficiently produce paths that are up to 16% shorter than the best previously known algorithms, and are comparable to shortest paths. In addition to finding shorter paths, we show evidence that our algorithms also have good load-balancing properties.
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In this paper, we consider the bandwidth-delay-hop constrained routing problem in large-scaled software defined networks. A number of demands, each of which specifies a source vertex and a sink vertex, are required to route in a given network. We are asked to select a subset of demands, and assign a routing path for each selected demand without violating the hop and delay constraints, while assuring that the bandwidth occupied in each edge is not beyond its capacity. The goal is to maximize the throughput (the total bandwidth) of the selected demands. We develop an efficient heuristic algorithm for the problem, which consists of three main steps, namely, computing feasible paths for each demand, sorting the demands with some priority rules, selecting a path for each demand. The algorithm is tested with networks of actual sizes and topologies, generated by Huawei Technologies Company. The experiments show that the proposed approach outperforms existing algorithms both in throughput and in running time. In the experiments, our algorithm achieves more than 90% of the total bandwidth of the given demands within 10 seconds. Moreover, a large part of our algorithm can run in parallel which largely speeds up the process when using multi-core processors.
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Data centres are growing in numbers and size, and their networks expanding to carry larger amounts of traffic. The traffic profile is constantly varying, particularly in cloud data centres where tenants arrive, leave, and may change their resource requirements in between, and so the network configuration must change at a commensurate rate. Software-Defined Networking - programmatic control of network configuration - has been critical to meeting the demands of modern data centre network management, and has been the subject of intense focus by the research community, working in conjunction with industry. In this survey, we review Software-Defined Networking research targeting the management and operation of data centre networks.
This paper deals with generating of an optimized route for multiple Vehicle routing Problems (mVRP). We used a methodology of clustering the given cities depending upon the number of vehicles and each cluster is allotted to a vehicle. k- Means clustering algorithm has been used for easy clustering of the cities. In this way the mVRP has been converted into VRP which is simple in computation compared to mVRP. After clustering, an optimized route is generated for each vehicle in its allotted cluster. Once the clustering had been done and after the cities were allocated to the various vehicles, each cluster/tour was taken as an individual Vehicle Routing problem and the steps of Genetic Algorithm were applied to the cluster and iterated to obtain the most optimal value of the distance after convergence takes place. After the application of the various heuristic techniques, it was found that the Genetic algorithm gave a better result and a more optimal tour for mVRPs in short computational time than other Algorithms due to the extensive search and constructive nature of the algorithm.
This paper provides an algorithmic framework for obtaining fast distributed algorithms for a highly-dynamic setting, in which *arbitrarily many* edge changes may occur in each round. Our algorithm significantly improves upon prior work in its combination of (1) having an $O(1)$ amortized time complexity, (2) using only $O(log{n})$-bit messages, (3) not posing any restrictions on the dynamic behavior of the environment, (4) being deterministic, (5) having strong guarantees for intermediate solutions, and (6) being applicable for a wide family of tasks. The tasks for which we deduce such an algorithm are maximal matching, $(degree+1)$-coloring, 2-approximation for minimum weight vertex cover, and maximal independent set (which is the most subtle case). For some of these tasks, node insertions can also be among the allowed topology changes, and for some of them also abrupt node deletions.
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