A Virtual Machine Placement Algorithm with Energy-Efficiency in Cloud Computing

2017 ◽  
Vol 8 (2) ◽  
pp. 20-36
Author(s):  
Yu Cai
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Virtual Machines ◽  
Live Migration ◽  
Vm Placement ◽  
Server Cluster ◽  
Simulated Environment ◽  
Resource Requirements ◽  
Placement Algorithm

Energy efficient virtual machines (VM) management and distribution on cloud platforms is an important research subject. Mapping VMs into PMs (Physical Machines) requires knowing the capacity of each PM and the resource requirements of the VMs. It should also take into accounts of VM operation overheads, the reliability of PMs, Quality of Service (QoS) in addition to energy efficiency. In this article, the authors propose an energy efficient statistical live VM placement scheme in a heterogeneous server cluster. Their scheme supports VM requests scheduling and live migration to minimize the number of active servers in order to save the overall energy in a virtualized server cluster. Specifically, the proposed VM placement scheme incorporates all VM operation overheads in the dynamic migration process. In addition, it considers other important factors in relation to energy consumption and is ready to be extended with more considerations on user demands. The authors conducted extensive evaluations based on HPC jobs in a simulated environment. The results prove the effectiveness of the proposed scheme.

scholarly journals Energy efficient virtual machine placement algorithm with balanced resource utilization based on priority of resources

2015 ◽  
Vol 4 (2) ◽  
pp. 107-118
Author(s):  
Amin Rahimi ◽  
Leili Mohammad Khanli ◽  
Saeid Pashazadeh
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Energy Efficient ◽  
Virtual Machine Placement ◽  
Environmental Damage ◽  
Vm Placement ◽  
Increase Energy Efficiency ◽  
Placement Algorithm ◽  
Best Fit

The increasing energy consumption has become a major concern in cloud computing due to its cost and environmental damage. Virtual Machine placement algorithms have been proven to be very effective in increasing energy efficiency and thus reducing the costs. In this paper we have introduced a new priority routing VM placement algorithm and have compared it with PABFD (power-aware best fit decreasing) on CoMon dataset using CloudSim for simulation. Our experiments show the superiority of our new method with regards to energy consumption and level of SLA violations measures and prove that priority routing VM placement algorithm can be effectively utilized to increase energy efficiency in the clouds.

Energy and SLA Efficient Virtual Machine Placement in Cloud Environment Using Non-Dominated Sorting Genetic Algorithm

2021 ◽  
pp. 773-789
Author(s):  
Oshin Sharma ◽  
Hemraj Saini
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Vm Placement ◽  
Energy Consumption Reduction ◽  
Consumption Reduction ◽  
The World ◽  
Placement Algorithm

To increase the availability of the resources and simultaneously to reduce the energy consumption of data centers by providing a good level of the service are one of the major challenges in the cloud environment. With the increasing data centers and their size around the world, the focus of the current research is to save the consumption of energy inside data centers. Thus, this article presents an energy-efficient VM placement algorithm for the mapping of virtual machines over physical machines. The idea of the mapping of virtual machines over physical machines is to lessen the count of physical machines used inside the data center. In the proposed algorithm, the problem of VM placement is formulated using a non-dominated sorting genetic algorithm based multi-objective optimization. The objectives are: optimization of the energy consumption, reduction of the level of SLA violation and the minimization of the migration count.

Analytical Approach to Estimating Total Migration Time of Virtual Machines With Various Applications

2020 ◽  
Vol 11 (2) ◽  
pp. 58-75
Author(s):  
Andrew Toutov ◽  
Anatoly Vorozhtsov ◽  
Natalia Toutova
Keyword(s):  
Analytical Approach ◽  
Virtual Machines ◽  
Migration Time ◽  
Characteristic Functions ◽  
Live Migration ◽  
File Storage ◽  
Laguerre Series ◽  
Cloud Applications ◽  
Total Migration

Cloud applications and services such as social networks, file sharing services, and file storage have become increasingly popular among users in recent years. This leads to the enlargement of data centers, and an increase in the number of servers and virtual machines. In such systems, live migration is used to move virtual machines from one server to another, which affects the quality of service. Therefore, the problem of finding the total migration time is relevant. This article proposes analytical approach to obtaining analytical expression of the probability density of the total migration time based on the use of the apparatus of characteristic functions. The obtained expression is used to calculate characteristics of migration, taking into account the applications contributing the most randomness to the total migration time. To simplify the calculation of migration characteristics, the use of the Laguerre series can be recommended as giving more reliable results compared to Gram-Charlier series.

scholarly journals Energy Efficiency Optimization of Cognitive UAV-Assisted Edge Communication for Semantic Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yilong Gu ◽  
Yangchao Huang ◽  
Hang Hu ◽  
Weiting Gao ◽  
Yu Pan
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Optimization Problem ◽  
Optimization Scheme ◽  
Battery Capacity ◽  
Aerial Vehicle ◽  
Efficient Alternative ◽  
The Internet Of Things

With the consolidation of the Internet of Things (IoT), the unmanned aerial vehicle- (UAV-) based IoT has attracted much attention in recent years. In the IoT, cognitive UAV can not only overcome the problem of spectrum scarcity but also improve the communication quality of the edge nodes. However, due to the generation of massive and redundant IoT data, it is difficult to realize the mutual understanding between UAV and ground nodes. At the same time, the performance of the UAV is severely limited by its battery capacity. In order to form an autonomous and energy-efficient IoT system, we investigate semantically driven cognitive UAV networks to maximize the energy efficiency (EE). The semantic device model for cognitive UAV-assisted IoT communication is constructed. And the sensing time, the flight speed of UAV, and the coverage range of UAV communication are jointly optimized to maximize the EE. Then, an efficient alternative algorithm is proposed to solve the optimization problem. Finally, we provide computer simulations to validate the proposed algorithm. The performance of the joint optimization scheme based on the proposed algorithm is compared to some benchmark schemes. And the simulation results show that the proposed scheme can obtain the optimal system parameters and can significantly improve the EE.

scholarly journals ENERGY EFFICIENT DRYING OF SEEDS OF CEREAL CROPS

2020 ◽  
pp. 64-70
Author(s):  
Vadym Paziuk
Keyword(s):  
Energy Efficiency ◽  
Low Temperatures ◽  
Energy Efficient ◽  
High Energy ◽  
Biochemical Properties ◽  
Drying Process ◽  
Seed Material ◽  
Technical Solutions ◽  
Low Temperature Drying

Many researchers at different times have been engaged in drying cereals to preserve their nutritional properties, which is associated with the biochemical properties of materials. The technologies for drying grain crops have been developed and improved with the given recommendations aimed at carrying out the drying process at high temperatures (above 100 ° C). But the increased requirements for seed grains and the associated high energy costs do not allow the drying process to be carried out efficiently, since with large grain volumes this leads to a significant increase in material costs. The study of the laws of drying of seeds of cereals in view of improving the energy efficiency of the process is relevant. Energy efficiency is one of the main parameters influencing the choice of drying mode. In traditional technologies for drying seed material, it is dried at low temperatures, which does not allow to significantly intensify the process by increasing the temperature of the coolant, as this significantly reduces the quality of the material. The state of the art makes it possible to more accurately investigate and analyze the drying processes of cereal seeds with automatic processing and plotting of drying kinetics. The results of previous researchers were conducted on existing grain dryers, in which it is difficult to evaluate and give the correct recommendations for drying the seed material. This is due to the peculiarities of the drying process and the design of the grain dryer. To adequately assess the drying regimes, studies of drying seeds of cereals at low temperatures were carried out to preserve the seed properties of the material. To increase the energy efficiency of the drying process, a step-by-step descending low-temperature drying mode is proposed, which provides the required quality of seed material. All the proposed technical solutions for the introduction of energy-efficient regimes in the process of drying grain seeds were implemented in the recommendations for industrial drying in mine direct-flow grain dryers.

scholarly journals Improving Energy Efficiency of Multimedia Content Dissemination by Adaptive Clustering and D2D Multicast

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Long Yin ◽  
Jinsong Gui ◽  
Zhiwen Zeng
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Multimedia Content ◽  
Game Model ◽  
Multicast Group ◽  
Content Dissemination ◽  
Adaptive Clustering ◽  
Group Members ◽  
Multicast Communications

While achieving desired performance, there exist still many challenges in current cellular networks to support the multimedia content dissemination services. The conventional multimedia transmission schemes tend to serve all multicast group members with the data rate supported by the receiving user with the worst channel condition. The recent work discusses how to provide satisfactory quality of service (QoS) for all receiving users with different quality of experience (QoE) requirements, but the energy efficiency improvement of multimedia content dissemination is not its focus. In this paper, we address it based on adaptive clustering and device-to-device (D2D) multicast and propose an energy-efficient multimedia content dissemination scheme under a consistent QoE constraint. Our scheme extends the recent work with the proposed K-means-based D2D clustering method and the proposed game-based incentive mechanism, which can improve energy efficiency of multimedia content dissemination on the premise of ensuring the desired QoE for most multicast group members. In the proposed scheme, we jointly consider the cellular multicast, intracluster D2D multicast, and intercluster D2D multicast for designing the energy-efficient multimedia content dissemination scheme. In particular, we formulate the energy-efficient multicast transmission problem as a Stackelberg game model, where the macro base station (MBS) is the leader and the candidate D2D cluster heads (DCHs) are the followers. Also, the MBS acts as the buyer who buys the power from the candidate DCHs for intracluster and intercluster D2D multicast communications, and the candidate DCHs act as the sellers who earn reward by helping the MBS with D2D multicast communications. Through analyzing the above game model, we derive the Stackelberg equilibrium as the optimal allocation for cellular multicast power, intracluster D2D multicast power, and intercluster D2D multicast power, which can maximize the MBS’s utility function. Finally, the proposed scheme is verified through the simulation experiments designed in this paper.

scholarly journals Power Management as a Service (Pmaas) – An Energy Saving Service by ―Auto-Fit VM Placement‖ Algorithm in Cloud Computing for Maximum Resource Utilization at Minimum Energy Consumption Without Live-Migrating the Virtual Machines by using the Concept of Virtual-Servers

2019 ◽  
Vol 9 (2) ◽  
pp. 1437-1446
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Power Management ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Memory Storage ◽  
Vm Placement ◽  
Placement Algorithm

Now a day Energy Consumption is one of the most promising fields amongst several computing services of cloud computing. A maximum amount of Power resources are absorbed by the data centre because of huge amount of data processing which is increased abnormally. So it’s the time to think about the energy consumption in cloud environment. Existing Energy Consumption systems are limited in terms of virtualization because improper virtualization leads to loads imbalance and excessive power consumption and inefficiency in terms of computational power. Billing[1,2 ] is another exciting feature that is closely related to energy consumption, because higher or lesser billing depends on energy consumption somehow-as we know that cloud providers allow cloud users to access resources as pay-per-use, so these resources need to be optimally selected to process the user request to maximize user satisfaction in the distributed virtualized environment. There may be an inequity between the actual power consumption by the users and the provided billing records by the providers, So any false accusation that may claimed by each other to get illegal compensations. To avoid such accusation, we propose a work to consolidate the VMs using the Power Management as a Service (PMaaS) model in such a way, to reduce power consumption by maximum resource utilization without live-migration of the virtual machines by using the concept of Virtual Servers. The proposed PMaaS model uses a new “Auto-fit VM placement algorithm”, which computes tasks resource demands, models a Virtual Machine that fits those demands, and places the Virtual Machines on a Virtual server made by the collective resources (CPU, Memory, Storage and Bandwidth) from the respective schedulers directly connected to the actual physical servers and that has the minimum remaining resources which is large enough to accommodate such a Virtual Machine.

scholarly journals Task Based Virtual Machine Placement in Cloud

2020 ◽  
Vol 9 (5) ◽  
pp. 1994-1998
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Test System ◽  
Vm Placement ◽  
Cloud Architecture ◽  
Use Of Resources ◽  
Placement Algorithm

Cloud computing offers many advantages by optimizing various parameters to meet the complex requirements .Some of the problems of cloud computing are utilization of resources and less energy consumption. More research and resources heterogeneity complicates the consolidation problem inside cloud architecture. VM placement refers to an ideal mapping of a task to virtual machines (VM) and virtual machines to physical machines (PM). The task-based VM placement algorithm is introduced in this research work. Here tasks are divided in accordance with their requirements, and then search for appropriate VM, again searching for appropriate PM, where selected VM could be sent. The algorithm decreases the use of resources by devaluation of the number of dynamic PMs while further decreases the rate of dismissal of make span and assignment. CloudSim test System is used to evaluate our algorithm in this research work. The outcomes of this implementation show the effectiveness of some current algorithms such as Round robin and Shortest Job First (SJF) algorithms.

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