# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 1 Introduction  
Infrastructure as a Service (IaaS) has been very popular in the cloud computing area over the past few years. Popular IaaS cloud providers, such as Rackspace and Amazon EC2, are delivering these virtual resources to customers in different data centers over the Internet. However, due to the huge demand for cloud computing, thousands of large-scale cloud computing centers have been established, which leads to a great deal of power consumption [1]. The main reason of such high energy consumption is not the power consumption by large quantities of hardware, but the inefficient usage of these cloud resources. Therefore, trying to develop adaptive strategies to dynamically adjust each resource in cloud data centers is very important from an energy-efficiency perspective. One of the solutions for improving energy efficiency is to leverage the capabilities of virtualization technology [2], which improves the efficiency of resource utilization by sharing a physical machine(PM) among multiple virtual machines (VMs). The live migration [3] of virtual machine to dynamically scheduled resource can transfer VMs from one PM to another, while keeping the services provided bythe corresponding VM still available. This technique makes it possible to dynamically optimize the placement of VMs in the different purpose of energy efficiency [4] or load balance [5], according to the resource usage at any time. The key mechanism to improve energy efficiency in cloud data centers is called VM consolidation [6], which aims at migrating VMs into a lower number of PMs to increase the utilization of resources in cloud data centers. However, in some scenarios, packing too many VMs into one single PM may lead to poor Quality-of-service (QoS); since VMs on one PM always share the same underlying physical resources. Therefore, VM consolidation strategies, designed to dynamically adjust VMs' placement, should comprehensively ensure reliable QoS, which is often defined via Service Level Agreements (SLAs) [7]. Besides, energy consumption [8] and VM migration costs [1] during each VM consolidation step should also be taken into consideration. Threshold-based strategy [6] uses upper and lower threshold to select proper source PM, which is often considered as the first step of VM consolidation. The key point of these threshold-based strategies can be concluded as comparing each PM's current status with the defined threshold that is either static or adaptive [1]. Although researches on static threshold [9] show that set 0.6 as the upper threshold of CPU utilization could achieve a better performance in energy consumption and VM migrations, it still cannot achieve good performance in SLA violation, due to the dynamic resource usage in an IaaS environment. Different from static method, adaptive threshold strategy can leverage the real-time usage of different resource to dynamically adjust its threshold. Most of the existing studies [1,4,9,10] only consider one resource demand such as CPU utilization while ignoring other infrastructure resource requests like memory or bandwidth, etc. However, the resource requirement of different workloads, like computing-intensive, input and output (I/O)-intensive or hybrid-intensive, are very diverse [5], as computing-intensive workloads usually call for more CPU/RAM resources, while I/O-intensive workloads prefer consuming more network bandwidth. Besides, selecting a VM only based on one resource will cause saturation in terms of this resource and can lead towards no further improvement in utilization with other types of resources underutilized [11]. Based on that, we propose a novel self-adaptive VM consolidation strategy based on the dynamic multi-thresholds adjustment mechanism, which is conducted by comparing the predicted future requests of each multi-dimensional infrastructure resources with their current environment status. The dynamic multi-thresholds are used for PM selection, as the first step in VM consolidation.