Search

The traditional neutrosophic clustering method only performs cluster analysis on the data itself, and often ignores the supervision information of data. In order to solve the above problems, a label-guided weighted semi-supervised neutrosophic clustering algorithm is proposed in the paper. On the one hand, the paired constraint information is used to construct the supervision weight coefficient and the distance measurement learning is combined to re-measure the degree of membership of the data and the cluster center; On the other hand, by minimizing the sum of squares of error between membership matrix and label matrix, the purpose of clustering results guided by label information is realized. Experiments on various data sets and comparisons with other clustering algorithms show that the new clustering algorithm can make full use of supervisory information and improve the accuracy of clustering. [ABSTRACT FROM AUTHOR]

Cloud computing is growing massively in the world of information technology. As a result of which the number of servers and virtual machines is increasing day by day. It leads to huge energy consumption and carbon emission. In this regard, task scheduling has drawn the attention of cloud service providers to reduce energy consumption and increase cloud utilization. This is a crucial issue in a heterogeneous cloud environment. In this paper, energy consumption issues of cloud datacenters are addressed and an Energy-Aware Cloud Task Scheduling (EACTS) algorithm is proposed. It is taking the concept from traditional min-min, max-min, and sufferage heuristics and merge with the energy model. These heuristics are implemented on the heterogeneous cloud environment. EACTS energy model focuses on the estimation of energy consumption in cloud datacenters. The proposed algorithms estimated the makespan, cloud utilization, and energy consumption for a benchmark dataset. The experimental results obtained from EACTS algorithm provides a valid arbitration between energy efficiency and makespan. It has shown the comparison between above mentioned algorithms for various scheduling parameters. [ABSTRACT FROM AUTHOR]

Recently, the virtual machine deployment algorithm uses physical machine less or consumes higher energy in data centers, resulting in declined service quality of cloud data centers or rising operational costs, which leads to a decrease in cloud service provider's earnings finally. According to this situation, a resource clustering algorithm for cloud data centers is proposed. This algorithm systematically analyzes the cloud data center model and physical machine's use ratio, establishes the dynamic resource clustering rules through k-means clustering algorithm, and deploys the virtual machines based on clustering results, so as to promote the use ratio of physical machine and bring down energy consumption in cloud data centers. The experimental results indicate that, regarding the compute-intensive virtual machines in cloud data centers, compared to contrast algorithm, the physical machine's use ratio of this algorithm is improved by 12% on average, and its energy consumption in cloud data center is lowered by 15% on average. Regarding the general-purpose virtual machines in cloud data center, compared to contrast algorithm, the physical machine's use ratio is improved by 14% on average, and its energy consumption in cloud data centers is lowered by 12% on average. Above results demonstrate that this method shows a good effect in the resource management of cloud data centers, which may provide reference to some extent. [ABSTRACT FROM AUTHOR]

Management of IT services is rapidly adapting to the cloud computing environment due to optimized service delivery models. Geo distributed cloud data centers act as a backbone for providing fundamental infrastructure for cloud services delivery. Conversely, their high growing energy consumption rate is the major problem to be addressed. Cloud providers are in a hunger to identify different solutions to tackle energy management and carbon emission. In this work, a multi-cloud environment is modeled as geographically distributed data centers with varying solar power generation corresponding to its location, electricity price, carbon emission, and carbon tax. The energy management of the workload allocation algorithm is strongly dependent on the nature of the application considered. The task deadline and brownout information is used to bring in variation in task types. The renewable energy-aware workload allocation algorithm adaptive to task nature is proposed with migration policy to explore its impact on carbon emission, total energy cost, brown and renewable power consumption. [ABSTRACT FROM AUTHOR]

Social Networks (SN) have become popular because they make communication more easier. Most of current Social Networks are based on the client/server model, where servers, bandwidth and maintaining costs increase with the number of users increasing. P2P technology can reduce these costs. However, in P2P network, there are some malicious peers forging identity to interrupt routing, which is called Sybil attack. Current Sybil-proof P2P networks need large routing information for supporting routing to avoid Sybil attack. But a large size routing table constrains the scalability of the system, and scalability for P2P networks is a very important parameter. In this research, a routing algorithm based on P2P was constructed to fill the requirement of SN which have a good performance in term of Sybil-Proof and reasonable size of routing table. Node reputation value and the logic distance from the current node to the destination node is as routing path selection factors. During this, Uncertainty theory is introduced to obtain more accurate results. In order to enhance the probability of routing success, the routing between friends' nodes were chosen. When to route between friends nodes, introducing comparative rules can decide the logic distance from the next routing nodes to the target nodes and select appropriate routing path. After it, the routing algorithm will abandon some routing path with small opportunity to reach the destination so as to avoid the flooding occurs in the network and reduce the risk of being attacked by the witch attack. Finally the performance of the algorithm has been verified on PeerSim simulation platform, simulation experiments have proved that the social network based on P2P has better performance to meet with a reasonable size of routing table for defending witch attack. Under the same condition, the routing table size of the algorithm is lower than Whānau method, and the ratio of routing success is more than the "Sprout", "Whānau" and "Chord" method. [ABSTRACT FROM AUTHOR]