This paper formulates a new multi-objective virtual machine placement (VMP) problem, which is a challenging task in cloud datacenters (DCs). In cloud environment, there are two stakeholders, namely, users and providers. Both sides try to take more benefit whereas a trade-off between conflicting benefits is crucial. From providers’ perspective, power consumption and resource wastage are two objectives to be optimized whereas gaining high quality of service (QoS) is a critical point for users. The unpleasant issue that a user endures in the cloud environment is network delay; this is affected by common bandwidth linkage which is shared between different users’ applications; the reason for considering bandwidth usage optimization as the third objective function in users’ viewpoint. However, inefficient network bandwidth usage has drastic impact on overall performance even makes network links to get saturated and throttles communication-intensive applications. Therefore, VMs with high affinity and traffic dependency must be physically placed as close as possible so less traffic is sent on network layers. To figure out this combinatorial multi-objective problem, we extend a hybrid multi-objective genetic-based optimization solution. To evaluate this work, we conducted extensive scenarios with variable correlation coefficients between resources in requested VMs. The simulation results prove that our proposed hybrid meta-heuristic algorithm outperforms against state-of-the-art ACO-based, well-known heuristic-based FFD algorithms, and random-based approach in terms of total power consumption, resource wastage, the total data transfer rate in network, and number of active servers in use. Also, the simulations in larger search space demonstrated proposed approach has high potential of scalability.

本文提出了一个新的多目标虚拟机放置（VMP）问题，这在云数据中心（DC）中是一项艰巨的任务。在云环境中，有两个利益相关者，即用户和提供者。双方都试图获得更多利益，而相互矛盾的利益之间的权衡至关重要。从提供商的角度来看，功耗和资源浪费是需要优化的两个目标，而获得高质量的服务（QoS）是用户的关键点。用户在云环境中忍受的不愉快问题是网络延迟。这受到不同用户应用程序之间共享的通用带宽链接的影响；从用户的角度考虑将带宽使用优化视为第三个目标函数的原因。然而，低效的网络带宽使用会对整体性能产生巨大影响，甚至会使网络链接饱和并限制通信密集型应用程序。因此，具有较高亲和力和流量依赖性的VM必须在物理上放置得尽可能近，以便在网络层上发送较少的流量。为了解决这个组合的多目标问题，我们扩展了一种基于遗传的混合多目标优化解决方案。为了评估这项工作，我们使用请求的VM中的资源之间的可变相关系数进行了广泛的场景分析。仿真结果证明，我们提出的混合元启发式算法在总功耗，资源浪费，性能，性能，性能，性能和性能上均优于基于ACO的，基于启发式的著名FFD算法和基于随机的方法。网络中的总数据传输率以及正在使用的活动服务器的数量。同样，在更大的搜索空间中进行的仿真证明了所提出的方法具有很高的可扩展性潜力。