A Virtual Resource Pricing Mechanism Based on Three-Side Gaming Model in Large-Scale Cloud Environments

2020 ◽  
Vol 16 (3) ◽  
pp. 17-32
Author(s):  
Peng Xiao
Keyword(s):  
Large Scale ◽  
Network Virtualization ◽  
Virtual Networks ◽  
Challenging Problem ◽  
Resource Configuration ◽  
Pricing Mechanism ◽  
User Costs ◽  
Virtualization Technology ◽  
Cloud Environments ◽  
Resource Allocation Efficiency

Recently, network virtualization technology has become a promising approach to efficiently share physical substrate networks in a cloud. However, finding an appropriate mapping between nodes and links in virtual networks is still a challenging problem. To overcome the demerits of existing price mechanisms, the author presents a game-based pricing model, in which resource configuration and provision among virtual networks is defined as a two-phrase gaming model. In this gaming model, a cooperative gaming model is applied to optimize resource benefits, while a non-cooperative gaming model is used to balance user costs and provider benefits. Extensive experiments are conducted in a real-world cloud, and the results show that this pricing mechanism can effectively improve the resource allocation efficiency as well as the resource profits of cloud providers. In addition, it also exhibits better robustness than many existing methods when a cloud system is facing intensive workloads.

Resource Configuration for the Collaborative Development of Product

2012 ◽  
Vol 215-216 ◽  
pp. 540-543
Author(s):  
Fu Hong Zeng ◽  
Lan Hua Zhou
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Collaborative Planning ◽  
Matching Model ◽  
Collaborative Development ◽  
Resource Configuration ◽  
Manufacturing Enterprises

In order to meet the reasonable matching of resource for collaborative development of products in manufacturing enterprises including involvement of suppliers on a large scale, a Generalized Design Resource Pool (GDRP) and It’s Resource Particles (RP) are defined, a multi-project collaborative planning and resource particles constraint-matching model with realization algorithm is presented. Finally, a case of developing mobile phone to an enterprise is presented to verify the effectiveness and feasibility of the presented approach.

A Resource Finding Mechanism for Network Virtualization Environment

2012 ◽  
Vol 433-440 ◽  
pp. 5078-5086
Author(s):  
Xiao Ling Li ◽  
Huai Min Wang ◽  
Chang Guo Guo ◽  
Bo Ding ◽  
Xiao Yong Li
Keyword(s):  
Network Virtualization ◽  
Combinatorial Auction ◽  
Virtual Network ◽  
Challenging Problem ◽  
Auction Mechanism ◽  
Similarity Principle ◽  
Functional Attributes ◽  
Large Numbers ◽  
Optimal Resource ◽  
Virtual Resources

There are large numbers of infrastructure resources in network virtualization environment (NVE), how to quickly and accurately find the resources that virtual network required is a challenging problem. Pointing to this problem, a resource finding mechanism for network virtualization environment (NVERFM) is proposed. NVERFM is mainly comprised of three modules, virtual resources publishing module (VRPM), virtual resources clustering framework (VRCF), and virtual resources finding module (VRFM). VRPM is responsible for publishing the infrastructure resources to VRCF; and the published information contains functional and non-functional attributes. VRCF is responsible for classifying the published information into different clustering according to the attributes from high priority to low priority. VRFM mainly completes resource finding based on resource similarity principle. Finding the resource clustering that meet the user’s requirements; and then combinatorial auction mechanism is used to help users choose the optimal infrastructure resource. Finally, experiments are used to validate NVERFM, and the results show that NVERFM can not only help users find the optimal resource, but also improve the efficiency.

Community Detection in Large-Scale Social Networks

2018 ◽  
pp. 189-206 ◽  
Author(s):  
S Rao Chintalapudi ◽  
M. H. M. Krishna Prasad
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Large Scale ◽  
The Other ◽  
Edge Density ◽  
Challenging Problem ◽  
Overlapping Community Detection ◽  
Clustering Problem ◽  
Detection Algorithms ◽  
Overlapping Community

Community Structure is one of the most important properties of social networks. Detecting such structures is a challenging problem in the area of social network analysis. Community is a collection of nodes with dense connections than with the rest of the network. It is similar to clustering problem in which intra cluster edge density is more than the inter cluster edge density. Community detection algorithms are of two categories, one is disjoint community detection, in which a node can be a member of only one community at most, and the other is overlapping community detection, in which a node can be a member of more than one community. This chapter reviews the state-of-the-art disjoint and overlapping community detection algorithms. Also, the measures needed to evaluate a disjoint and overlapping community detection algorithms are discussed in detail.

scholarly journals The Spectrum of Density Perturbations in an Expanding Universe

1974 ◽  
Vol 63 ◽  
pp. 175-193
Author(s):  
Joseph Silk
Keyword(s):  
Large Scale ◽  
Background Radiation ◽  
Large Scale Structure ◽  
Challenging Problem ◽  
Expanding Universe ◽  
Related Matter ◽  
Widespread Acceptance ◽  
Density Perturbations ◽  
Density Inhomogeneities ◽  
The Universe

Perhaps the most challenging problem confronting a cosmologist is to reconcile the observed large-scale structure of the Universe with the Friedmann-Lemaître cosmological models that have gained such widespread acceptance in recent years (cf. however the alternative viewpoint, as exemplified in this Symposium by Arp and others). In this review, I shall look anew at the spectrum of density inhomogeneities that survive decoupling of matter and radiation at z ~ 1000 and provide the primordial fluctuations that can eventually generate galaxies. A closely related matter, that of the associated fluctuations in the background radiation, is discussed elsewhere in this volume by Doroshkevich, Sunyaev and Zel'dovich.

scholarly journals Technology of computing risks visualization for distributed production infrastructures

2018 ◽  
Vol 224 ◽  
pp. 02071
Author(s):  
Dmitrii Voronin ◽  
Victoria Shevchenko ◽  
Olga Chengar
Keyword(s):  
Large Scale ◽  
Multidimensional Data ◽  
Cloud Infrastructure ◽  
Visualization Technique ◽  
Multidimensional Data Visualization ◽  
Cloud Environments ◽  
Research Gap ◽  
Radar Chart ◽  
Distributed Production ◽  
Support Decision Making

Scientific problems related to the classification, assessment, visualization and management of risks in the cloud environments have been considered. The analysis of the state-of-the-art methods, offered for these problems solving, has been carried out taking into account the specificity of the cloud infrastructure oriented on large-scale tasks processing in distributed production infrastructures. Unfortunately, not much of scientific and objective researches had been focused on the developing of effective approaches for cloud risks visualization providing the necessary information to support decision-making in distributed production infrastructures. In order to fill this research gap, this study attempts to propose a risks visualization technique that is based on radar chart implementation for multidimensional data visualization.

scholarly journals Heuristic Approaches to Attribute Reduction for Generalized Decision Preservation

2019 ◽  
Vol 9 (14) ◽  
pp. 2841 ◽  
Author(s):  
Nan Zhang ◽  
Xueyi Gao ◽  
Tianyou Yu
Keyword(s):  
Large Scale ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
High Dimensional ◽  
Data Sets ◽  
Challenging Problem ◽  
Research Fields ◽  
Heuristic Strategies ◽  
Significance Measures ◽  
Original Information

Attribute reduction is a challenging problem in rough set theory, which has been applied in many research fields, including knowledge representation, machine learning, and artificial intelligence. The main objective of attribute reduction is to obtain a minimal attribute subset that can retain the same classification or discernibility properties as the original information system. Recently, many attribute reduction algorithms, such as positive region preservation, generalized decision preservation, and distribution preservation, have been proposed. The existing attribute reduction algorithms for generalized decision preservation are mainly based on the discernibility matrix and are, thus, computationally very expensive and hard to use in large-scale and high-dimensional data sets. To overcome this problem, we introduce the similarity degree for generalized decision preservation. On this basis, the inner and outer significance measures are proposed. By using heuristic strategies, we develop two quick reduction algorithms for generalized decision preservation. Finally, theoretical and experimental results show that the proposed heuristic reduction algorithms are effective and efficient.

Towards a New Data Replication Management in Cloud Systems

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20
Author(s):  
Abdenour Lazeb ◽  
Riad Mokadem ◽  
Ghalem Belalem
Keyword(s):  
Large Scale ◽  
Data Replication ◽  
Cloud Provider ◽  
Large Scale Data ◽  
Dynamic Replication ◽  
Replication Strategy ◽  
Multiple Copies ◽  
Cloud Environments ◽  
Real Challenge ◽  
Scale Data

Applications produce huge volumes of data that are distributed on remote and heterogeneous sites. This generates problems related to access and sharing data. As a result, managing data in large-scale environments is a real challenge. In this context, large-scale data management systems often use data replication, a well-known technique that treats generated problems by storing multiple copies of data, called replicas, across multiple nodes. Most of the replication strategies in these environments are difficult to adapt to cloud environments. They aim to achieve the best performance of the system without meeting the important objectives of the cloud provider. This article proposes a new dynamic replication strategy. The proposed algorithm significantly improves provider gain without neglecting customer satisfaction.

Quality of Service on Link Aggregation Network Virtualization for Docker Containers

2019 ◽  
Vol 886 ◽  
pp. 227-232
Author(s):  
Yanapat Chuchuen ◽  
Kritwara Rattanaopas ◽  
Sarapee Chunkaew
Keyword(s):  
Traffic Control ◽  
Load Balance ◽  
High Speed ◽  
Large Scale ◽  
Network Virtualization ◽  
Network Bandwidth ◽  
Working Together ◽  
Multiple Network ◽  
Implementation Tool

Docker engine is an extremely powerful tool for PaaS platform of cloud computing. It gives benefits for large-scale of internet services. Web service is basic service for everyone who requires to access internet that web infrastructure must has scalability with load-balance web server called reverse proxy. The key answers for a large-scale web must have multiple web servers working together with high speed bandwidth. Moreover, multiple clusters can find in the same data center there are required to assign priority and quality of each cluster service. We investigate load-balance assign link aggregation with network QoS by using pipework script and traffic control tool in frontend reverse proxy server on each cluster. Our research evaluates scenario of network QoS ratios which include 50/50, 60/40, 70/30 and 80/20. We compare network bandwidth between both web reverse proxy clusters. The results present our designed and implementation tool not only can control network QoS on each web reverse proxy cluster in all load-balance link aggregation modes which include round-robin, XOR and ALB but also those of clusters can access multiple network interface. In experiment, average network bandwidths in all QoS cases are around 200 MB per second for link aggregation of 2 gigabit interface.

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