An Autonomic Resource Allocation Framework for Service-Based Cloud Applications: A Proactive Approach

Author(s):  
Tushar Bhardwaj ◽  
Himanshu Upadhyay ◽  
Subhash Chander Sharma
2018 ◽  
Vol 13 (5) ◽  
pp. 837-852
Author(s):  
Yonal Kirsal ◽  
Vishnu Vardhan Paranthaman ◽  
Glenford Mapp

In order to provide ubiquitous communication, seamless connectivity is now required in all environments including highly mobile networks. By using vertical handover techniques it is possible to provide uninterrupted communication as connections are dynamically switched between wireless networks as users move around. However, in a highly mobile environment, traditional reactive approaches to handover are inadequate. Therefore, proactive handover techniques, in which mobile nodes attempt to determine the best time and place to handover to local networks, are actively being investigated in the context of next-generation mobile networks. Using this approach, it is possible to enhance channel allocation and resource management by using probabilistic mechanisms; because, it is possible to explicitly detect contention for resources. This paper presents a proactive approach for resource allocation in highly mobile networks and analyzed the user contention for common resources such as radio channels in highly mobile wireless networks. The proposed approach uses an analytical modelling approach to model the contention and results are obtained showing enhanced system performance. Based on these results an operational space has been explored and are shown to be useful for emerging future networks such as 5G by allowing base stations to calculate the probability of contention based on the demand for network resources. This study indicates that the proactive model enhances handover and resource allocation for highly mobile networks. This paper analyzed the effects of and alpha and beta, in effect, how these parameters affect the proactive resource allocation requests in the contention queue has been modelled for any given scenario from the conference paper "Exploring analytical models to maintain quality-of-service for resource management using a proactive approach in highly mobile environments".


Author(s):  
Mohan Baruwal Chhetri ◽  
Abdur Rahim Mohammad Forkan ◽  
Quoc Bao Vo ◽  
Surya Nepal ◽  
Ryszard Kowalczyk

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jie Wei ◽  
Ao Zhou ◽  
Jie Yuan ◽  
Fangchun Yang

Federated-cloud has been widely deployed due to the growing popularity of real-time applications, and hence allocating resources among clouds becomes nontrivial to meet the stringent service requirements. The challenges lie in achieving minimized latency constrained by virtual machines rental overhead and resource requirement. This becomes further complicated by the issues of datacenter selection. To this end, we propose AIMING, a novel resource allocation approach which aims to minimize the latency constrained by monetary overhead in the context of federated-cloud. Specifically, the network resources are deployed and selected according to k-means clustering. Meanwhile, the total latency among datacenters is optimized based on binary quadratic programming. The evaluation is conducted with real data traces. The results show that AIMING can reduce total datacenter latency effectively compared with other approaches.


2018 ◽  
Vol 41 ◽  
Author(s):  
Neil Malhotra

AbstractAlthough Boyer & Petersen's (B&P's) cataloguing of and evolutionary explanations for folk-economic beliefs is important and valuable, the authors fail to connect their theories to existing explanations for why people do not think like economists. For instance, people often have moral intuitions akin to principles of fairness and justice that conflict with utilitarian approaches to resource allocation.


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