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Aiming at the differences in the contribution of feature data to the cluster centers in the clustering algorithm and the sensitivity of the algorithm to the initial cluster centers, this paper proposes a Weighted Knowledge Amountbased Intuitionistic Fuzzy C-means Method（WKAIFCM）. Firstly, this method fuzzed the original dataset intuitionistically and improved the latest intuitionistic fuzzy knowledge measure to calculate the knowledge amount which utilized in the feature weighting of dataset. Secondly, this method initialized the cluster centers to improve the calculation accuracy and clustering efficiency of high-dimensional feature dataset by kernel space density and kernel distance. Finally, based on the principle of sample distance between clusters and the principle of minimum knowledge amount, this method established a clustering optimization model to get the optimal iterative algorithm. The experimental results based on the UCI artificial dataset show that the proposed method can greatly improve the accuracy and iterative efficiency of clustering. The classification accuracy and execution efficiency are increased by 10.63% and 31.75% respectively, and this method has good universality and stability. This paper introduces a new theory of knowledge measure into fuzzy clustering for the first time and gets extraordinary results, which creates a new case for the potential application of this theory in other related fields. [ABSTRACT FROM AUTHOR]

With the aim of satisfaction of demand response, this paper proposed an improved polar bear optimization based approach for cost optimization on multi-tenant colocation data centers. This paper used a one-to-many reverse auction model to reveal the relationship between tenants and operator, and encouraged tenants to voluntarily participate in the auction and submit their energy-saving plans and expected rewards. Then, this algorithm adopted the improved polar bear algorithm to solve the optimal tenant combination and minimize the cost. In detail, this method leveraged the sigmoid function to process the discretize problem. Meanwhile, to enhance the ability of jumping out of the local optimum, it integrated a mutation strategy into the algorithm. To further improve the searching ability in a global view, the method designed an adaptive field of view strategy instead of fixed field of view to dynamically adjust the scope of the local search, and reduced the probability of the algorithm trapping into the local optimal solution. Finally, the paper compared the proposed algorithm with several classical algorithms. The experimental results show that the proposed algorithm not only gains the least cost alone, but also improves the efficiency. [ABSTRACT FROM AUTHOR]

The new trend to design a data center network is to deploy switches and two-port servers on the nodes and edges in an interconnection network, the logic graph of which can be abstracted as a compound graph. Node-independent spanning tree (NIST) is an important structure that can be used to design reliable communication protocols, fault-tolerant broadcasting, and secure message distribution, IP fast rerouting, etc in data center networks. Given a compound graph G (Kn), this paper firstly showed that if the diameter of G was d, then the diameter of G( Kn) was either 2d or 2d + 1. Supposed that there were n NIST rooted at an arbitrary node in an n-regular n-node-connected interconnection network G, this paper presented a unified method to construct n NIST in G (Kn) by proposing an efficient algorithm with time complexity 0 (N), where N was the number of nodes . Ana-lysis of NIST on compound graph Qn(Kn) shows that the maximal height is only the diameter plus 3 . Also, simulation experiments on the augmented based on data center networks show the correctness from another aspect. [ABSTRACT FROM AUTHOR]

While conducting bandwidth allocation over elastic optical data center network EODCN, it is important to balance the revenue of cloud service providers and the connection performance of tenants. This paper proposed a bandwidth allocation algorithms to effectively assign optical bandwidth for multiple connection requests based on subcarriers so as to maximize revenue. Firstly, on the basis of a revenue model and a concept of virtual subcarrier, it constructed an integer linear programming formulation of the studied bandwidth allocation problem. Then, it proposed a dynamic programming-based algorithm and a heuristics-based algorithm correspondingly, which respectively focus on achieving maximum net revenue and gaining better time efficiency. Finally, it conducted simulations to validate the efficiency of the designed algorithms. The simulation results show that both of the proposed algorithms are superior to the existed methods in system revenue obtaining in bandwidth allocation over EODCN. [ABSTRACT FROM AUTHOR]

The shuffle time accounts for a large part of the total running time of MapReduce jobs. Optimizing for the shuffle phase can greatly improve the performance of MapReduce. There are always some periodic data flow in the data center network. This paper formulated the shuffle scheduling problem with the aim to minimize the makespan of MapReduce jobs with future periodic data flow available. Then it proposed an effective algorithm, which was a 3/2-approximation algorithm to the shuffle scheduling problem when all the future idle time slots had the same duration. Simulation results demonstrate the proposed algorithm is very promising, can effectively utilize network resources and reduce the makespan of shuffle. [ABSTRACT FROM AUTHOR]

The traditional ECMP algorithm cannot effectively schedule elephant flows that may cause load unbalancing and network congestion in data center networks. For this problem, this paper proposed an ADMLB algorithm. Firstly, the controller obtained the load information of each switches and links, detected and marked the elephant flows in the end host. Then, it called the improved ant colony algorithm to solve the multipath problems according to the bandwidth requirements of the elephant flows. Finally, it scheduled the elephant flow. The experimental results show that the ADMLB algorithm reduces the link delay and effectively improves the link bandwidth utilization compared with the traditional ECMP and other existing algorithms. [ABSTRACT FROM AUTHOR]

In order to minimize the average completion time of a series of geo-distributed data analytical jobs, this paper first introduced online random scheduling algorithm ranT A. ranT A actually showed the preference on the task assignment under the consideration of both computing capacity of edges and the network bandwidth. Furthermore, in order to avoid overloading those edges with low computing capacities, this paper proposed data redistribution mechanism ranT A-data based on ranT A by redistributing some data to the central data center along with the tasks. The result of ranT A -data could be proved concentrated on its optimum with high probability. Extensive simulations show that ranTA-data improves nearly 30% performance compared with traditional algorithms. [ABSTRACT FROM AUTHOR]

The traditional methods may cause network congestion and load imbalance when scheduling the elephant flows in data center networks. To deal with these issuses, this paper proposed scheme ACO-SDN, which was based on ACO (ant colony) algorithm in SDN data center networks. It built an ILP model according to the flow scheduling problem, with the object of minimize the maximum link utilization. Then, it employed ant colony algorithm to solve the ILP model through redefining its parameters and operations. It obtained the optimal route of rerouting elephant flows. The experimental results show that, in comparisons with ECMP( equal-cost multi-path routing) and GFF (global first fit) algorithms, ACO-SDN algorithm lowers down the maximum link utilization rate, and effectively improves the network bisection bandwidth of networks. [ABSTRACT FROM AUTHOR]