As energy consumption becomes the key issue for the operation and maintenance of cloud datacenters [14], the authors of [15] introduced the concept of skewness to measure the unevenness of servers' resource utilization.
On one hand, in order to ensure the user experience of resource requesting, the system has to achieve fast resource allocation to timely process resource requests; on the other hand, in order to ensure the efficiency of resource allocation, how to allocate multi-dimensional resource requests to servers needs to be optimized, such that server's resource utilization can be improved.
Using the live VM migration function, cloud service providers can consolidate many VMs with small workloads into a few servers to achieve high resource utilization.
In [47], the authors proposed a VM consolidation scheme that focused on balanced resource utilization of servers across different computing resources with the goal of minimizing power consumption and resource wastage.
They introduced skewness as a metric to measure unevenness for multi-dimensional resource utilization of a server.
An appropriate initialization helps to reduce number of iterations of the solutions of RMP and pricing problems through introduction of optimal patterns, which are patterns that maximize resource utilization of active servers.
Simulation results demonstrate that, compared with other server selection schemes, the proposed scheme can achieve higher resource utilization, provide better user-perceived Quality of Service (QoS), and efficiently deal with network dynamics.
In this paper, we argue that the existing dynamic resource allocation based on resource utilization has some drawbacks in virtualized servers.
Step iii) Sort the scheduling variables with common server type in each sℓ, e subset according to pattern efficiency: The patterns of each server type will be sorted in ascending order according to their resource utilization ( sum limits_{h=1}^Hsum limits_{n_h={1}_h}^{N_h}sum limits_{r=1}^R{x}_{r,{n}_h}^{j_t} {i}_r^k ).
Server virtualization is an abstraction technique in which the level of computer resource utilization and flexibility are improved though the distribution of a single computing system into several resources by separating operation system from physical hardwares such as server, storage and others, and such techniques is expected to assume an important role in emission reduction [1, 2, 4].
