Energy-Aware Dynamic Migration of Virtual Machines in a Server Cluster

2017 ◽  
pp. 161-172 ◽  
Author(s):  
Dilawaer Duolikun ◽  
Ryo Watanabe ◽  
Tomoya Enokido ◽  
Makoto Takizawa
Keyword(s):  
Virtual Machines ◽  
Energy Aware ◽  
Server Cluster

An energy-aware algorithm to migrate virtual machines in a server cluster

2017 ◽  
Vol 7 (1) ◽  
pp. 32 ◽  
Author(s):  
Dilawaer Duolikun ◽  
Tomoya Enokido ◽  
Makoto Takizawa
Keyword(s):  
Virtual Machines ◽  
Energy Aware ◽  
Server Cluster

Simple estimation and energy-aware migration models of virtual machines in a server cluster

2018 ◽  
Vol 30 (21) ◽  
pp. e4771 ◽  
Author(s):  
Ryo Watanabe ◽  
Dilawaer Duolikun ◽  
Makoto Takizawa
Keyword(s):  
Virtual Machines ◽  
Energy Aware ◽  
Server Cluster ◽  
Simple Estimation

scholarly journals Task Classification Based Energy-Aware Consolidation in Clouds

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
HeeSeok Choi ◽  
JongBeom Lim ◽  
Heonchang Yu ◽  
EunYoung Lee
Keyword(s):  
Resource Utilization ◽  
Data Center ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Balance Task ◽  
Task Classification ◽  
Vm Consolidation

We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center, we propose a task consolidation algorithm based on task classification (i.e., computation-intensive and data-intensive) and resource utilization (e.g., CPU and RAM). Furthermore, we design a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA). Unlike the existing research on VM consolidation or scheduling that applies none or single threshold schemes, we focus on a double threshold (upper and lower) scheme, which is used for VM consolidation. More specifically, when a host operates with resource utilization below the lower threshold, all the VMs on the host will be scheduled to be migrated to other hosts and then the host will be powered down, while when a host operates with resource utilization above the upper threshold, a VM will be migrated to avoid using 100% of resource utilization. Based on experimental performance evaluations with real-world traces, we prove that our task classification based energy-aware consolidation algorithm (TCEA) achieves a significant energy reduction without incurring predefined SLA violations.

Energy-aware Migration of Virtual Machines in a Cluster

2016 ◽  
pp. 21-32 ◽  
Author(s):  
Dilawaer Duolikun ◽  
Shigenari Nakamura ◽  
Ryo Watanabe ◽  
Tomoya Enokido ◽  
Makoto Takizawa
Keyword(s):  
Virtual Machines ◽  
Energy Aware

scholarly journals Online Supervisory Control and Resource Management for Energy Harvesting BS Sites Empowered with Computation Capabilities

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Thembelihle Dlamini ◽  
Ángel Fernández Gambín ◽  
Daniele Munaretto ◽  
Michele Rossi
Keyword(s):  
Energy Harvesting ◽  
Energy Savings ◽  
Virtual Machines ◽  
Mobile Network ◽  
Base Stations ◽  
Traffic Load ◽  
Short Term ◽  
Energy Aware ◽  
Multi Access

The convergence of communication and computing has led to the emergence of multi-access edge computing (MEC), where computing resources (supported by virtual machines (VMs)) are distributed at the edge of the mobile network (MN), i.e., in base stations (BSs), with the aim of ensuring reliable and ultra-low latency services. Moreover, BSs equipped with energy harvesting (EH) systems can decrease the amount of energy drained from the power grid resulting into energetically self-sufficient MNs. The combination of these paradigms is considered here. Specifically, we propose an online optimization algorithm, called Energy Aware and Adaptive Management (ENAAM), based on foresighted control policies exploiting (short-term) traffic load and harvested energy forecasts, where BSs and VMs are dynamically switched on/off towards energy savings and Quality of Service (QoS) provisioning. Our numerical results reveal that ENAAM achieves energy savings with respect to the case where no energy management is applied, ranging from 57% to 69%. Moreover, the extension of ENAAM within a cluster of BSs provides a further gain ranging from 9% to 16% in energy savings with respect to the optimization performed in isolation for each BS.

scholarly journals Energy-Aware Autonomic Resource Scheduling Framework for Cloud

2019 ◽  
Vol 4 (1) ◽  
pp. 41-55 ◽  
Author(s):  
Bhupesh Kumar Dewangan ◽  
Amit Agarwal ◽  
Venkatadri M. ◽  
Ashutosh Pasricha
Keyword(s):  
Energy Consumption ◽  
Resource Utilization ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Optimal Solution ◽  
Cloud Service ◽  
Cloud Services ◽  
Energy Aware ◽  
Increasing Demand

Cloud computing is a platform where services are provided through the internet either free of cost or rent basis. Many cloud service providers (CSP) offer cloud services on the rental basis. Due to increasing demand for cloud services, the existing infrastructure needs to be scale. However, the scaling comes at the cost of heavy energy consumption due to the inclusion of a number of data centers, and servers. The extraneous power consumption affects the operating costs, which in turn, affects its users. In addition, CO2 emissions affect the environment as well. Moreover, inadequate allocation of resources like servers, data centers, and virtual machines increases operational costs. This may ultimately lead to customer distraction from the cloud service. In all, an optimal usage of the resources is required. This paper proposes to calculate different multi-objective functions to find the optimal solution for resource utilization and their allocation through an improved Antlion (ALO) algorithm. The proposed method simulated in cloudsim environments, and compute energy consumption for different workloads quantity and it increases the performance of different multi-objectives functions to maximize the resource utilization. It compared with existing frameworks and experiment results shows that the proposed framework performs utmost.

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