Retraction Note to: Hungarian optimization technique based efficient resource allocation using clustering unbalanced estimated cost matrix

The prosperity of cloud technology generates expanding numerous real-time applications runs online. Meantime, real time tasks scheduling is an important criteria for cloud service provider to manage its Quality of Service (QoS) because all customer's wishes gratifying resource allotment in cloud. Cloud Service Provider (CSP) constitute service level agreement with the customers before the provision of resources. Allocating the resource according to the customer need and utilizing the systems efficiently are the major concern of CSP to increase the profit in their business. For this purpose Hungarian optimization technique is used and it is a standard technique which provides load balanced allocation of resources to the task. Still it cannot be used directly for cloud Virtual Machine (VM) allocation, because load balancing will not give better makespan and the standard Hungarian method is not suitable for unbalanced cost matrix. In this paper efficient resource allocation method called Cluster Cost Matrix - Hungarian (CCM-H) algorithm is proposed to optimize the performance. Algorithm consists of two phases. In first phase algorithm calculates the weighted values of tasks and based on the value tasks are clustered to convert the unbalanced cost matrix to balanced cost matrix. In second phase, according to the balanced cost matrix VM allocation is performed using Hungarian optimization technique. The metrics used for the performance analysis are makespan and utilization factor. The proposed CCM-H algorithm is compared with various existing and standard algorithms called First Come First Serve (FCFS), Min-Min based iterative Hungarian, FCFS based iterative algorithm, Max-min based iterative algorithm Laha and Gupta (Comput Ind Eng, 2016), Group based algorithms Lu et al. (IEEE Trans Wirel Commun, 2017) and normal Hungarianalgorithm, with bench mark dataset Braun (2015) and synthetic dataset which is created with random number generation function. Output shows that how the proposed method outperforms all the existing models.