scholarly journals EMinRET: Heuristic for Energy-Aware VM Placement with Fixed Intervals and Non-preemption

2015 ◽  
Author(s):  
Nguyen Quang-Hung ◽  
Nam Thoai
Keyword(s):  
Vm Placement ◽  
Energy Aware

Minimization of VM Placement Change in Energy-Aware Resource Provisioning for Cloud Data Center

2013 ◽  
Vol 325-326 ◽  
pp. 1730-1733 ◽  
Author(s):  
Si Yuan Jing ◽  
Shahzad Ali ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Network Flow ◽  
Resource Provisioning ◽  
Approximate Algorithm ◽  
Vm Placement ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Placement Change

Numerous part of the energy-aware resource provision research for cloud data center just considers how to maximize the resource utilization, i.e. minimize the required servers, without considering the overhead of a virtual machine (abbreviated as a VM) placement change. In this work, we propose a new method to minimize the energy consumption and VM placement change at the same time, moreover we also design a network-flow-theory based approximate algorithm to solve it. The simulation results show that, compared to existing work, the proposed method can slightly decrease the energy consumption but greatly decrease the number of VM placement change

scholarly journals An Distributed Virtual Machine Placement Algorithm for Balanced Resource Utilization and Low Energy Consumption

2018 ◽  
Vol 173 ◽  
pp. 03092
Author(s):  
Bo Li ◽  
Yun Wang
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Vm Placement ◽  
Energy Aware ◽  
Cloud Data ◽  
Low Energy Consumption ◽  
Cloud Data Centers ◽  
Placement Algorithm ◽  
Heterogeneous Cloud

Virtual machine placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Traditional load balancing or energy-aware VM placement approaches either allocate VMs to PMs in centralized manner or ignore PM’s cost-capacity ratio to implement energy-aware VM placement. We address these two issues by introducing a distributed VM placement approach. A auction-based VM placement algorithm is devised for help VM to find the most suitable server in large heterogeneous cloud data centers. Our algorithm is evaluated by simulation. Experimental results show two major improvements over the existing approaches for VM placement. First, our algorithm efficiently balances the utilization of multiple types of resource by minimizing the amount of physical servers used. Second, it reduces system cost compared with existing approaches in heterogeneous environment.

Adaptive Energy-Aware Algorithms to Minimize Power Consumption and SLA Violation in Cloud Computing

2020 ◽  
Vol 13 ◽  
Author(s):  
Monika Singh ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Power Consumption ◽  
Large Scale ◽  
Minimum Energy ◽  
Service Level Agreement ◽  
Migration Policy ◽  
Service Level ◽  
Vm Placement ◽  
Energy Aware ◽  
Suggested Approach ◽  
Placement Decision

: With the establishment of virtualized datacenters on a large scale, cutting-edge technology requires more energy to deliver the services 24*7 hours. With this expansion and accumulation of information on a massive scale on datacenters, the consumption of excessive amount of power results in high operational costs and power consumption. Therefore, there is an urgent need to make the environment more adaptive and dynamic, where the overutilization and underutilization of hosts is well known to the system and active measures can be taken accordingly. To serve this purpose, an energy efficient method for the detection of overloaded and under-loaded hosts has been proposed in this paper. For implementing VM migration, VM placement decision has also been taken to save energy and reduce SLA (Service Level Agreement) rate over the cloud. In the paper, a novel adaptive heuristics approach has been presented that concerns with the utilization of resources for a dynamic consolidation of VMs based on the mustered data from the usage of resources by VMs, while ensuring the high level of relevancy to the SLA. After identification of under-load and overload hosts, VM placement decision has been taken in the way that takes minimum energy consumption. Minimum migration policy has been adopted in the proposed methodology to minimize execution time. The validation of effectiveness and efficiency of the suggested approach has been performed by using real-world workload traces in CloudSim simulator.

scholarly journals Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiong Fu ◽  
Qing Zhao ◽  
Junchang Wang ◽  
Lin Zhang ◽  
Lei Qiao
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
High Energy ◽  
Binary Particle Swarm Optimization ◽  
Vm Placement ◽  
Energy Aware ◽  
Low Energy Consumption ◽  
Initial Placement ◽  
And Storage ◽  
High Energy Consumption

In recent years, high energy consumption has gradually become a prominent problem in a data center. With the advent of cloud computing, computing and storage resources are bringing greater challenges to energy consumption. Virtual machine (VM) initial placement plays an important role in affecting the size of energy consumption. In this paper, we use binary particle swarm optimization (BPSO) algorithm to design a VM placement strategy for low energy consumption measured by proposed energy efficiency fitness, and this strategy needs multiple iterations and updates for VM placement. Finally, the strategy proposed in this paper is compared with other four strategies through simulation experiments. The results show that our strategy for VM placement has better performance in reducing energy consumption than the other four strategies, and it can use less active hosts than others.

Towards Energy-Efficient, Scalable, and Resilient IaaS Clouds

2012 ◽  
pp. 350-371
Author(s):  
Eugen Feller ◽  
Louis Rilling ◽  
Christine Morin
Keyword(s):  
Fault Tolerance ◽  
Virtual Machine ◽  
Large Scale ◽  
Data Centers ◽  
Fault Tolerant ◽  
Energy Awareness ◽  
Vm Placement ◽  
Energy Aware ◽  
Design Constraint ◽  
System Architectures

With increasing numbers of energy hungry data centers, energy conservation has now become a major design constraint for current and future Infrastructure-as-a-Service (IaaS) cloud providers. In order to efficiently manage such large-scale environments, three important properties have to be fulfilled by the management frameworks: (1) scalability, (2) fault-tolerance, and (3) energy-awareness. However, the scalability and fault tolerance capabilities of existing open-source IaaS cloud management frameworks are limited. Moreover, they are far from being energy-aware. This chapter first surveys existing efforts on building IaaS platforms. This includes both, system architectures and energy-aware virtual machine (VM) placement algorithms. Afterwards, it describes the architecture and implementation of a novel scalable, fault-tolerant, and energy-aware VM manager called Snooze. Finally, a nature-inspired energy-aware VM placement approach based on the Ant Colony Optimization is introduced.

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