A Review: Distributed Auction-Based Framework v/s Cluster-Based Framework for Auto Scalable IaaS Provisioning in Geo-Data Centers
DOI:
https://doi.org/10.26438/ijcse/v7i2.469476Keywords:
Cloud Computing, VCG mechanism, IaaS, Data Centers, Cluster, Auction, Distributed, Geo (Geographically)Abstract
This research paper proposes a cluster-based framework for Infrastructure-as-a-Service (IAAS) which enables customers effectively hosted intensified performance computing applications and cloud service providers (CSP’s) to use their resources beneficially. The solution incorporates the cluster-based framework which handles the geographical data centers grouped logically in clusters. This cluster-based framework overcomes the challenges of traditional centralized provisioning approaches. A. Efficient on-demand IaaS provisioning. B. Auto-scaling of increasing number of IaaS requests. C. Effectively use of Geographical Data center computing resources. D. Maintain Quality of Service parameter requirements for different IaaS requests. Incorporate Vickrey-Clarke-Groves (VCG) mechanism to solve exaggeration and collusion issues. The solution generated extended to host cloud applications based on mobile and how effectively it will work in a changeable environment. To pace the performance of the distributed IaaS framework vs (RCG-IaaS) regional IaaS provisioning model based on an efficient decomposition technique, Column generation as a large scale optimization tool, I use the additional performance metrics as follows: Basic Performance metric: Speedup (Su): Speed gain of using more processing nodes over a single node, Efficiency (E): Percentage of maximum performance (speedup or utilization) achievable (%), Elasticity (El): Dynamic interval of auto-scaling resources with workload variation & Cloud Productivity: QoS of Cloud (QoS): The satisfaction rate of a cloud service or benchmark testing (%), Service Cost (Cost): The price per cloud service (Compute, Storage etc.) provided ($/hour), Availability (A): Percentage of time the system is up to deliver useful work (%).
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