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.

云技术的繁荣催生了众多在线运行的实时应用程序。同时，实时任务调度是云服务提供商管理其服务质量（QoS）的重要标准，因为所有客户的愿望都满足了云中的资源分配。云服务提供商（CSP）在提供资源之前与客户构成服务级别协议。根据客户需求分配资源并有效利用系统是CSP增加业务利润的主要关注点。为此，使用了匈牙利优化技术，它是一种标准技术，可为任务提供负载均衡的资源分配。仍然不能直接用于云虚拟机（VM）分配，因为负载平衡不会提供更好的生产期，并且标准的匈牙利方法不适用于不平衡成本矩阵。本文提出了一种有效的资源分配方法，称为集群成本矩阵–匈牙利（CCM-H）算法，以优化性能。算法包括两个阶段。在第一阶段，算法计算任务的加权值，并基于该值对任务进行聚类，以将不平衡成本矩阵转换为平衡成本矩阵。在第二阶段，根据平衡成本矩阵，使用匈牙利优化技术执行VM分配。用于性能分析的指标是制造时间和利用率。将拟议的CCM-H算法与各种现有的和标准算法（称为先来先服务（FCFS），基于Min-Min的迭代匈牙利语）进行比较，基于FCFS的迭代算法，基于Max-min的迭代算法Laha和Gupta（计算机工业，2016年），基于组的算法Lu等。（IEEE Trans Wirel Commun，2017）和正常的匈牙利算法，以及基准数据集Braun（2015）和由随机数生成函数创建的综合数据集。输出结果表明，所提出的方法优于所有现有模型。