# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 5 Related Work  
Virtual machine consolidation [6] is one of the techniques in a cloud resource management system to increase the energy-efficiency of IaaS Cloud, since the failure of existing PMs or the addition of new PMs are continuous events happening in cloud data centers. [PERSON] and [PERSON] [16] first proposed an architecture of energy management system for virtualized data centers where resource management is divided into local and global policies. [PERSON] et al. [17] then proposed a heuristic method to handle the optimization over multiple resources, as they considered the VM consolidation as a bin packing problem. [PERSON] et al. [18] have leveraged the min, max and shares parameters of Xen's VMM to represent minimum, maximum and proportion of the CPU allocated to VMs sharing the same resource. Threshold-based VM consolidation strategy use upper and lower threshold values to identify a PM as overloaded or underloaded, respectively. Secron [19] considers an upper threshold to prevent PM from reaching 100% utilization with performance degradation. To select the proper PMs for consolidation, [PERSON] et al. [10] propose a static CPU threshold to detect under-loaded and over-loaded PMs. However, although setting static thresholds is simple and appealing, it is not efficient for an environment with dynamic workloads, since different types of application may be running on  
Figure 4: The number of active hosts when adapting each consolidation strategy during 10 h. (**a**) when under low workload. (**b**) when under medium workload. (**c**) when under high workload  
one single PM. Therefore, threshold values should be adjusted to each workload type and level, which will help optimize the performance VM consolidation efficiently. To dynamically consolidate the virtual machines in the procedure of selecting source PM, [PERSON] [1] proposed adaptive upper and lower thresholds based on the statistical analysis of the historical data while [PERSON] [14] propose a regression-based prediction model to forecast resource utilization of both PMs and VMs. Recent research on adaptive threshold-based dynamic VM consolidation algorithms [20; 21] is using a similar solution, in which the threshold value adapts with the change of different resource utilization. However, the above methods only consider single-resource utilization of the infrastructure, yet do not consider other corresponding sources. To try to consider other multiple resources, [PERSON] [22] researched the load balance of each application among different virtual machines, but he did not consider the placement of each VM. The research of [PERSON] [23] is consistent with ours; they use multi-resource to optimize the placement of virtual machine but in order to realize the goals of the performance on power and temperature, they do not focus on the study of thresholds as it gives only two sets of thresholds of each parameter, but each threshold is set in static value. In general, although the above researches all consider multi-resources, they do not focus on the optimization of threshold-based VM consolidation in the step for PM selection. While in the second step of VM consolidation of VM selection, different algorithms are proposed. VMs that have the highest correlation of the resource utilization with other VMs are selected, which is called MC algorithm [4]. Another algorithm called MVM proposed in [24] shows that, it first sorts the VMs in descending order with respect to CPU demand and then selects the VM that satisfies the two author-defined criterions. HPG algorithm was also discussed by the same author, that is selecting the VM with lowest ratio of actual resource usage to its initial claimed resource demand. The VM which requires minimum time to complete the migration is selected for migration called MMT in [1], while the migration time is estimated as the amount of RAM utilized by a VM divided by the spare network bandwidth available for the PM.