A Game Theoretic Method for Resource Allocation in Scientific Cloud

2016 ◽  
Vol 6 (1) ◽  
pp. 15-41 ◽  
Author(s):  
Amin Nezarat ◽  
Gh Dastghaibifard
Keyword(s):  
Resource Allocation ◽  
Cooperative Game Theory ◽  
Cloud Services ◽  
Nash Equilibrium Point ◽  
New Methods ◽  
Best Response ◽  
Game Theoretic ◽  
Cloud Environments ◽  
Np Complete ◽  
Maximum Utilization

Due to the widespread use of cloud services, the need for proper and dynamic distribution will redouble the resources. One of the most complex problems in cloud environments is resource allocation such that on one hand the resource provider should obtain maximum utilization and on the other hand users want to lease best resources based on his time and budget constraints. Many studies which presented new methods for solving this NP-complete problem have used heuristic algorithm. Based on economic aspects of cloud environments, using market oriented model for solving allocation problem can decrease the complexity and converge it to the best solution in minimum time. In this paper a method has been proposed based on auction theory that it has used a non-cooperative game theory mechanism in an incomplete information environment. This game try to select best bidder for selling resource to it. At the end of the paper, the proposed algorithm was experienced in cloudsim and the simulated results showed that the authors' suggested model converge to the best response at Nash equilibrium point.

A Game-Theoretic Formulation of Multi-Agent Resource Allocation

2005 ◽  
Author(s):  
Jonathan Bredin ◽  
Rajiv T. Maheswaran ◽  
Cagri Imer ◽  
Tamer Basar ◽  
David Kotz ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Multi Agent ◽  
Game Theoretic

Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis

2020 ◽  
Vol 13 (5) ◽  
pp. 1008-1019
Author(s):  
N. Vijayaraj ◽  
T. Senthil Murugan
Keyword(s):  
Resource Allocation ◽  
Behavior Analysis ◽  
User Behavior ◽  
Low Cost ◽  
Cloud Services ◽  
Online Auction ◽  
Analysis Model ◽  
Efficient Resource ◽  
User Centric ◽  
Cloud Resources

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

Collective influence maximization for multiple competing products with an awareness-to-influence model

2021 ◽  
Vol 14 (7) ◽  
pp. 1124-1136
Author(s):  
Dimitris Tsaras ◽  
George Trimponias ◽  
Lefteris Ntaflos ◽  
Dimitris Papadias
Keyword(s):  
Diffusion Processes ◽  
Influence Maximization ◽  
Second Phase ◽  
Social Graph ◽  
Practical Applications ◽  
Influence Model ◽  
Best Response ◽  
The Social ◽  
Game Theoretic ◽  
Collective Influence

Influence maximization (IM) is a fundamental task in social network analysis. Typically, IM aims at selecting a set of seeds for the network that influences the maximum number of individuals. Motivated by practical applications, in this paper we focus on an IM variant, where the owner of multiple competing products wishes to select seeds for each product so that the collective influence across all products is maximized. To capture the competing diffusion processes, we introduce an Awareness-to-Influence (AtI) model. In the first phase, awareness about each product propagates in the social graph unhindered by other competing products. In the second phase, a user adopts the most preferred product among those encountered in the awareness phase. To compute the seed sets, we propose GCW, a game-theoretic framework that views the various products as agents, which compete for influence in the social graph and selfishly select their individual strategy. We show that AtI exhibits monotonicity and submodularity; importantly, GCW is a monotone utility game. This allows us to develop an efficient best-response algorithm, with quality guarantees on the collective utility. Our experimental results suggest that our methods are effective, efficient, and scale well to large social networks.

Efficient Fault Tolerance on Cloud Environments

2018 ◽  
Vol 8 (3) ◽  
pp. 20-31 ◽  
Author(s):  
Sam Goundar ◽  
Akashdeep Bhardwaj
Keyword(s):  
Fault Tolerance ◽  
Web Applications ◽  
Fault Tolerant ◽  
Cloud Services ◽  
Level Of Service ◽  
Cloud Environments ◽  
Computing Environments ◽  
Service Assurance ◽  
Mission Critical ◽  
The Impact

With mission critical web applications and resources being hosted on cloud environments, and cloud services growing fast, the need for having greater level of service assurance regarding fault tolerance for availability and reliability has increased. The high priority now is ensuring a fault tolerant environment that can keep the systems up and running. To minimize the impact of downtime or accessibility failure due to systems, network devices or hardware, the expectations are that such failures need to be anticipated and handled proactively in fast, intelligent way. This article discusses the fault tolerance system for cloud computing environments, analyzes whether this is effective for Cloud environments.

Stable femtocells cluster formation and resource allocation based on cooperative game theory

2019 ◽  
Vol 134 ◽  
pp. 30-41 ◽  
Author(s):  
Katty Rohoden ◽  
Rebeca Estrada ◽  
Hadi Otrok ◽  
Zbigniew Dziong
Keyword(s):  
Game Theory ◽  
Resource Allocation ◽  
Cooperative Game ◽  
Cooperative Game Theory ◽  
Cluster Formation

scholarly journals Text File Privacy on the Cloud Based on Diagonal Fragmentation and Encryption

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tawfiq Barhoom ◽  
Mahmoud Abu Shawish
Keyword(s):  
Data Privacy ◽  
Cloud Services ◽  
Text File ◽  
Layer Model ◽  
Encrypted Data ◽  
Cloud Server ◽  
Cloud Environments ◽  
Encryption Algorithms ◽  
Well Data ◽  
Encryption Method

Despite the growing reliance on cloud services and software, privacy is somewhat difficult. We store our data on remote servers in cloud environments that are untrusted. If we do not handle the stored data well, data privacy can be violated with no awareness on our part. Although it requires expensive computation, encrypting the data before sending it appears to be a solution to this problem. So far, all known solutions to protect textual files using encryption algorithms fell short of privacy expectations. Thus is because encrypting cannot stand by itself. The encrypted data on the cloud server becomes full file in the hand causing the privacy of this data to be intrusion-prone, thus allowing intruders to access the file data once they can decrypt it. This study aimed to develop an effective cloud confidentiality model based on combining fragmentation and encryption of text files to compensate for reported deficiency in encryption methods. The fragmentation method used the strategy of dividing text files into two triangles through the axis. Whereas the encryption method used the Blowfish algorithm. The research concluded that high confidentiality is achieved by building a multi-layer model: encryption, chunk, and fragmentation of every chunk to prevent intruders from reaching the data even if they were able to decrypt the file. Using the privacy accuracy equation (developed for the purpose in this research), the model achieved accuracy levels of 96% and 90% when using 100 and 200 words in each chunk on small, medium, and large files respectively.

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