# Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges
## 5 Related Existing Solutions in Cloud Levels
### Solutions at Host Level
As part of the kernel-based virtual machine manager (KVM), a smart virtualization monitoring system was integrated by sending the status of each VM running on a hypervisor. The intelligent device classifies and actively rectifies attack patterns. Via the cloud API, the appropriate action that should be taken will be communicated. In [7], the authors proposed a Security-aware Virtual Machine Placement Algorithm (SMOOP) based on multi-objective optimization to search for a pare-to-optimal method to reduce cloud's overall security risks. SMOOP assessed cloud security from four perspectives: hypervisor vulnerabilities, networking, co-residence, and VM vulnerabilities. The proposed vulnerability assessment was location-specific and spans multi dimensions. Compared to existing solutions, the experimental findings indicate the efficacy of the proposed method and the enhancement. A study in [96] evaluated the private cloud infrastructure tools called Ceilometer and Monasca to test the ability of information collected in a short time to detect the resource constraints and determine the effect of resource consumption of host systems. Both tools were analyzed, and the evaluation revealed that the Monasca achieved better performance than Ceilometer. The authors in [97] implemented signature-based network intrusion detection (NIDS) such as OSSEC as host-based IDS and SNORT for the detection of intrusions at the cloud VM instances and network level. In addition, the study discussed the flow of traffic and monitoring on various occasions. The results show that the proposed systems were able to detect attacks on host VMs and can send alerts to the organization. A recent study in [98] introduced a new hypervisor-based cloud IDS that utilizes online multivariate statistical change analysis to detect anomalous network behaviors. In the proposed system, the hypervisor benefits from a collection of instances to introduce an instance-oriented new feature mode that exploits the correlated and individual behaviors of instances to enhance the detection capability. The proposed approach was evaluated using a newly collected cloud intrusion dataset that includes a wide diversity of attack vectors. According to [99], a parameterized scheduling policy focused on minimizing the makespan, combined with an energy-efficiency policy based on the hibernation of every virtual machine whenever possible, could reduce the energy consumption of large-scale data centers without affecting the overall performance of cloud computing systems. In the same work, the authors described a model for reducing energy consumption in cloud computing environments that can reduce the energy consumption of a cloud computing system by up to 45%. The proposed model is divided into two parts: an energy-aware independent batch scheduler and a set of energy-efficiency policies for idle VM hibernation. The experimental results show the good performance of the proposed model. Furthermore, in [100], the online non-clairvoyant scheduling algorithm Highest Scaled Importance First (HSIF) method was proposed, in which HSIF chooses an active job with the highest scaled importance to minimize the sum of scaled importance-based flow time and energy consumed. The use of HSIF in data centers and battery-based devices reduces power consumption and improves computing capability. To determine which cloud scheduling solution is more important to select, this paper [101] proposed an energy-efficient task-scheduling algorithm based on best-worst (BWM) and the technique for order preference by similarity to ideal solution (TOPSIS). The experimental results demonstrate that, when compared to its counterparts, the proposed approach can effectively reduce energy consumption. Furthermore, the proposed approach can significantly improve VM utilization, making it suitable for exploring large-scale problems.