Performance Analysis for Pareto-Optimal Green Consolidation Based on Virtual Machines Live Migration

2017 ◽  
Vol 9 (4) ◽  
pp. 36-56
Author(s):  
Chetan Dhule ◽  
Urmila Shrawankar
Keyword(s):  
Data Centers ◽  
Virtual Machines ◽  
Energy Requirement ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Pareto Optimal ◽  
Live Migration ◽  
Main Challenge ◽  
Prime Concern ◽  
Vm Consolidation

Huge energy requirement of cloud data centers is prime concern. Dynamic Virtual Machine (VM) consolidation based on VM live migration to switched-off or put some of the under-loaded host Physical Machines (PMs) into a low power consumption mode can significantly save energy in data centers and achieve green cloud computing. Performance overheads imposed on source and destination hosts during and after VM live migration is the main focus of research. Existing VM consolidation approaches are inefficient regarding VM live migration time, application downtime, VM pre and post-migration overheads which results in Quality of Service (QoS) degradation. So, near-optimal solution which optimizes these overheads is main challenge. This paper discusses the causes of VM live migration performance overheads and comparison of different overhead optimization techniques on the basis of parameters like accuracy and migration cost. Pareto-Optimal solution is proposed to eliminate the VM performance overheads.

scholarly journals Virtual Machine Placement via Bin Packing in Cloud Data Centers

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 389 ◽  
Author(s):  
Aisha Fatima ◽  
Nadeem Javaid ◽  
Tanzeela Sultana ◽  
Waqar Hussain ◽  
Muhammad Bilal ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Vm Placement ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Vm Consolidation

With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

scholarly journals Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xialin Liu ◽  
Junsheng Wu ◽  
Gang Sha ◽  
Shuqin Liu
Keyword(s):  
Virtual Machine ◽  
Carbon Dioxide Emissions ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
High Capacity ◽  
Electrical Energy ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Vm Consolidation

Cloud data centers consume huge amount of electrical energy bringing about in high operating costs and carbon dioxide emissions. Virtual machine (VM) consolidation utilizes live migration of virtual machines (VMs) to transfer a VM among physical servers in order to improve the utilization of resources and energy efficiency in cloud data centers. Most of the current VM consolidation approaches tend to aggressive-migrate for some types of applications such as large capacity application such as speech recognition, image processing, and decision support systems. These approaches generate a high migration thrashing because VMs are consolidated to servers according to VM’s instant resource usage without considering their overall and long-term utilization. The proposed approach, dynamic consolidation with minimization of migration thrashing (DCMMT) which prioritizes VM with high capacity, significantly reduces migration thrashing and the number of migrations to ensure service-level agreement (SLA) since it keeps VMs likely to suffer from migration thrashing in the same physical servers instead of migrating. We have performed experiments using real workload traces compared to existing aggressive-migration-based solutions; through simulations, we show that our approach improves migration thrashing metric by about 28%, number of migrations metric by about 21%, and SLAV metric by about 19%.

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.

A Service Sustainable Live Migration Strategy for Multiple Virtual Machines in Cloud Data Centers

2021 ◽  
Vol 25 ◽  
pp. 100213
Author(s):  
Anurag Satpathy ◽  
Manmath Narayan Sahoo ◽  
Ashutosh Mishra ◽  
Banshidhar Majhi ◽  
Joel J.P.C. Rodrigues ◽  
...  
Keyword(s):  
Data Centers ◽  
Virtual Machines ◽  
Live Migration ◽  
Migration Strategy ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals A hybrid algorithm to reduce energy consumption management in cloud data centers

2019 ◽  
Vol 9 (1) ◽  
pp. 554
Author(s):  
Mehran Tarahomi ◽  
Mohammad Izadi
Keyword(s):  
Load Balancing ◽  
Data Centers ◽  
Virtual Machines ◽  
Thermal Characteristics ◽  
Live Migration ◽  
Cloud Data ◽  
Migration Method ◽  
Cloud Data Centers ◽  
Reduce Energy Consumption ◽  
Save Energy

<p>There are several physical data centers in cloud environment with hundreds or thousands of computers. Virtualization is the key technology to make cloud computing feasible. It separates virtual machines in a way that each of these so-called virtualized machines can be configured on a number of hosts according to the type of user application. It is also possible to dynamically alter the allocated resources of a virtual machine. Different methods of energy saving in data centers can be divided into three general categories: 1) methods based on load balancing of resources; 2) using hardware facilities for scheduling; 3) considering thermal characteristics of the environment. This paper focuses on load balancing methods as they act dynamically because of their dependence on the current behavior of system. By taking a detailed look on previous methods, we provide a hybrid method which enables us to save energy through finding a suitable configuration for virtual machines placement and considering special features of virtual environments for scheduling and balancing dynamic loads by live migration method.</p>

scholarly journals Energy Efficient and VM Consolidation Framework using Improved Spider Monkey Optimization Algorithm

2021 ◽  
Vol 10 (3) ◽  
pp. 21-26
Author(s):  
Kethavath Prem Kumar ◽  
◽  
Thirumalaisamy Ragunathan ◽  
Devara Vasumathi ◽  
◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Energy Usage ◽  
Energy Efficient Computing ◽  
Spider Monkey Optimization ◽  
Vm Consolidation

Cloud Computing is rapidly being utilized to operate informational technological services by outstanding technologies for a variety of benefits, including dynamically improved resources planning and a new service delivery method. The Cloud computing process is occurred by allowing the client devices for data access through the internet from a remote server, computers, and the databases. An internet connection is linked among the front end users such as client device, network, browser, and software application with the back end that constitutes of servers, computers, and database. For satisfying the demands of the Service Level Agreement (SLA), providers of cloud service should reduce the usage of energy. Capacity reservations oriented system is available by clouds’ providers to permit users for customizing Virtual Machines (VMs) having specified age and geographic resources, reduces the amount to be paid for cloud services. To overcome the aforementioned issue, an Improved Spider Monkey Optimization (ISMO) approach is proposed for cloud center optimization. The VM consolidation architecture based on the proposed ISMO algorithm decreases energy usage while attempting to prevent Service Level Agreement breaches. The accessibility of hosts or virtual machines (VMs) for task performance is measured by fitness. If the number of tasks to be handled increases the hosts of VMs available at right state. The proposed VM consolidation architecture decreases energy usage while also attempting to prevent Service Level Agreement breaches and also provide energy-efficient computing in data centers. The proposed approach may be utilized to provide energy-efficient computing in data centers. The energy efficiency of the proposed ISMO method is achieved 28266 whereas, the existing algorithm showed an energy efficiency of 6009 and 10001.

Live Migration for Multiple Correlated Virtual Machines in Cloud-Based Data Centers

2018 ◽  
Vol 11 (2) ◽  
pp. 279-291 ◽  
Author(s):  
Gang Sun ◽  
Dan Liao ◽  
Dongcheng Zhao ◽  
Zichuan Xu ◽  
Hongfang Yu
Keyword(s):  
Data Centers ◽  
Virtual Machines ◽  
Live Migration
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