Table 1 Comparison between the multi-factor related work

CPU, memory and network BW

Resource scheduling

Dynamic and Integrated Resource Scheduling (DAIRS)

-It provides a dynamic and integrated resource scheduling which treats CPU, memory and network BW integrated for both physical machines and virtual machines -It provides integrated measurement of the total imbalance level of a Cloud data center as well as the average imbalance level of each server for performance evaluation

It did not consider energy-efficiency for Cloud data centers

CPU, Communication network in a data center

Virtual Machine Placement in server consolidation

Hybrid GA (HGA)

-It considers the energy consumption in both physical machines and the communication network in a data center -Compared with existing heuristic algorithms, the HGA can generate much better solutions as the HGA is basically a global search algorithm while heuristic algorithms are local search algorithms, which may be trapped at a local optimum during their search process

Its computation time is higher than other existing heuristic algorithms

CPU and RAM

Virtual machine Placement

CPU and RAM Energy aWare (CREW)

-It improves VM placement by employing a power model that considers the energy consumed by both CPU and RAM -CREW offers the best trade-off between energy saving and SLA violation

It considers CPU and RAM only and it did not consider the communication network in a data center

CPU and disk

Host overload/ underload double threshold detection and VM placement

Multi-resource double threshold method and Modified Particle Swarm Optimization (MPSO) VM placement

- It designed a method of double threshold with multi resource utilization to trigger the migration of VMs. The Modified Particle Swarm Optimization method is introduced into the consolidation of VMs to avoid falling into local optima which is a common defect in traditional heuristic algorithms

It did not consider the memory and communication network in a data center

CPU and memory utilizations

A virtual machine consolidation

A Utilization Prediction-aware VM Consolidation (UP-VMC) approach

- UP-VMC goes beyond the existing works which only consider CPU utilization by also considering memory. Combining both memory and CPU utilization, UP-VMC can better identify causes of SLA violations and consequently prevent them from happening. - In contrast to the existing VM consolidation methods which mostly rely on the current resource utilization of PMs, UP-VMC considers both current and future resource utilization

It should take into account network resource utilization and traffic to optimize VM placement.

CPU and RAM and network BW

Host overload detection

Multiple Regression Host Overload Detection (MRHOD)

-It significantly reduces energy consumption while ensuring a high level of adherence to Service Level Agreements (SLA)

It uses a workload that is insignificant for BW