The rapid growth in virtualization solutions has driven the widespread adoption of cloud computing paradigms among various industries and applications. This has led to a growing need for XaaS solutions and equipment to enable teleworking. To meet this need, cloud operators and datacenters have to overtake several challenges related to continuity, the quality of services provided, data security, and anomaly detection issues. Mainly, anomaly detection methods play a critical role in detecting virtual machines’ abnormal behaviours that can potentially violate service level agreements established with users. Unsupervised machine learning techniques are among the most commonly used technologies for implementing anomaly detection systems. This paper introduces a novel clustering approach for analyzing virtual machine behaviour while running workloads in a system based on resource usage details (such as CPU utilization and downtime events). The proposed algorithm is inspired by the intuitive mechanism of flocking birds in nature to form reasonable clusters. Each starling movement’s direction depends on self-information and information provided by other close starlings during the flight. Analogically, after associating a weight with each data sample to guide the formation of meaningful groups, each data element determines its next position in the feature space based on its current position and surroundings. Based on a realistic dataset and clustering validity indices, the experimental evaluation shows that the new weighted fuzzy c-means algorithm provides interesting results and outperforms the corresponding standard algorithm (weighted fuzzy c-means).

虚拟化解决方案的快速增长推动了云计算范式在各个行业和应用程序中的广泛采用。这导致对 XaaS 解决方案和设备的需求不断增长，以实现远程办公。为了满足这一需求，云运营商和数据中心必须克服与连续性、提供的服务质量、数据安全和异常检测问题相关的若干挑战。主要是，异常检测方法在检测可能违反与用户建立的服务级别协议的虚拟机异常行为方面发挥着关键作用。无监督机器学习技术是实现异常检测系统最常用的技术之一。本文介绍了一种新颖的集群方法，用于根据资源使用细节（例如 CPU 利用率和停机事件）在系统中运行工作负载时分析虚拟机行为。所提出的算法受到自然界中鸟类聚集形成合理集群的直观机制的启发。每个椋鸟运动的方向取决于飞行过程中其他近距离椋鸟提供的自我信息和信息。类似地，在将权重与每个数据样本相关联以指导有意义的组的形成之后，每个数据元素根据其当前位置和周围环境确定其在特征空间中的下一个位置。基于真实的数据集和聚类有效性指标，