Integrated network and hosts energy management for cloud data centers

Abstract

Cloud computing provides online services to users from anywhere in a pay‐as‐you‐go basis. This leads to the construction of different forms of data centers around the world, which usually consumes huge amount of energy and produce pollution (heat and carbon dioxide footprint). Many efforts have focused on managing cloud resources to decrease their energy consumption. Since servers are the highest energy consumers in data centers, most researchers have focused on optimizing their energy consumption while other resources such as network devices and cooling systems have received less attention. In this paper, we propose a network‐aware virtual machine (VM) relocation algorithm, which optimizes the distribution of the VMs running on the data center in order to conserve energy in both servers and network devices (switches). The proposed technique targets the communicated VMs and places them closer to each other. This would reduce the traffic overhead between any communicating VMs so that fewer network devices are involved in the communication process. This also increases the VMs consolidation to some servers while leaving other servers idle. Switching off the idle servers and network devices would significantly reduce the total energy consumed by the data center. The proposed algorithm is evaluated using simulation and the performance of the proposed algorithm is compared against different previously proposed algorithms in the literature. Performance metrics used are the energy consumed by the host and by the network switches, the service level agreement violations, and the number of VM migrations. The impact of different packet sizes, the efficiency of switching the network elements to sleep mode, and the impact of resource homogeneity and heterogeneity are also studied and discussed. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of all evaluation metrics.

Cooperative Game-Based Virtual Machine Resource Allocation Algorithms in Cloud Data Centers

“…Major efforts are underway in the standard bodies to define standards and relevant encapsulations [8]. e paper [9] proposes a network-aware VM relocation algorithm, which optimizes the distribution of the VMs running on the DC in order to conserve energy in both servers and network switches. It targets the communicated VMs and places them closer to each other while reducing the traffic overhead between any communicating VMs.…”

“…It targets the communicated VMs and places them closer to each other while reducing the traffic overhead between any communicating VMs. is approach also increases the VMs consolidation to some servers while leaving other servers idle [9]. In [10], Wardat et al propose a new holistic view how to ensure the increasing demands for cloud services while guaranteeing the maximum revenue.…”

“…PM resource according to (9), and the assigned R B PM for each player is distributed among the same-type individual VMs as the same manner as the R C PM , R M PM , and R S PM resource allocation algorithms.…”

“…Step 8: for the R B PM resource allocation, the WESV idea is adopted, and the limited R B PM is shared among game players according to (9). By using the same manner as the R C PM , R M PM , and R S PM resource allocation algorithms, the assigned R B PM for each player is distributed to the same-type individual VMs in the associated PM.…”

Platform Design of Psychological Teaching Classroom Evaluation Based on Mobile Edge Computing Resource Allocation

“…In terms of the development and application of new technologies, big data processing technologies are implemented into existing business systems. Converting academic research results into a real commercial value is a problem worthy of research [7]. Audio is one of the main ways to express information.…”