SDN-cloud: a power aware resource management system for efficient energy optimization

2020 ◽  
Vol 8 (4) ◽  
pp. 321-343
Author(s):  
Swagatika Shrabanee ◽  
Amiya Kumar Rath
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Power Consumption ◽  
Service Level Agreement ◽  
Reducing Power ◽  
High Energy ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Service Rate ◽  
Content Type

PurposeIn modern cloud services, resource provisioning and allocation are significant for assigning the available resources in efficient way. Resource management in cloud becomes challenging due to high energy consumption at data center (DC), virtual machine (VM) migration, high operational cost and overhead on DC.Design/methodology/approachIn this paper, the authors proposed software-defined networking (SDN)-enabled cloud for resource management to reduce energy consumption in DC. SDN-cloud comprises four phases: (1) user authentication, (2) service-level agreement (SLA) constraints, (3) cloud interceder and (4) SDN-controller.FindingsResource management is significant for reducing power consumption in CDs that is based on scheduling, VM placement, with Quality of Service (QoS) requirements.Research limitations/implicationsThe main goal is to utilize the resources energy effectively for reducing power consumption in cloud environment. This method effectively increases the user service rate and reduces the unnecessary migration process.Originality/valueAs a result, the authors show a significant reduction in energy consumption by 20 KWh as well as over 60% power consumption in the presence of 500 VMs. In future, the authors have planned to concentrate the issues on resource failure and also SLA violation rate with respect to number of resources will be decreased.

scholarly journals An Energy and SLA-Aware Resource Management Strategy in Cloud Data Centers

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Chi Zhang ◽  
Yuxin Wang ◽  
Yuanchen Lv ◽  
Hao Wu ◽  
He Guo
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Management Strategy ◽  
Data Centers ◽  
Minimum Energy ◽  
Service Level Agreement ◽  
Cloud Data ◽  
Cpu Utilization ◽  
Cloud Data Centers ◽  
Detecting Method

Reducing energy consumption of data centers is an important way for cloud providers to improve their investment yield, but they must also ensure that the services delivered meet the various requirements of consumers. In this paper, we propose a resource management strategy to reduce both energy consumption and Service Level Agreement (SLA) violations in cloud data centers. It contains three improved methods for subproblems in dynamic virtual machine (VM) consolidation. For making hosts detection more effective and improving the VM selection results, first, the overloaded hosts detecting method sets a dynamic independent saturation threshold for each host, respectively, which takes the CPU utilization trend into consideration; second, the underutilized hosts detecting method uses multiple factors besides CPU utilization and the Naive Bayesian classifier to calculate the combined weights of hosts in prioritization step; and third, the VM selection method considers both current CPU usage and future growth space of CPU demand of VMs. To evaluate the performance of the proposed strategy, it is simulated in CloudSim and compared with five existing energy–saving strategies using real-world workload traces. The experimental results show that our strategy outperforms others with minimum energy consumption and SLA violation.

scholarly journals Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

2020 ◽  
Vol 10 (7) ◽  
pp. 2323
Author(s):  
T. Renugadevi ◽  
K. Geetha ◽  
K. Muthukumar ◽  
Zong Woo Geem
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Power Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Resource Provisioning ◽  
Total Power

Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

scholarly journals Research on Cloud Computing Resources Provisioning Based on Reinforcement Learning

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhiping Peng ◽  
Delong Cui ◽  
Jinglong Zuo ◽  
Weiwei Lin
Keyword(s):  
Cloud Computing ◽  
Reinforcement Learning ◽  
Resource Management ◽  
Queuing Theory ◽  
Service Level Agreement ◽  
Service Level ◽  
Resource Provisioning ◽  
Utilization Rate ◽  
Sequential Decision ◽  
Distributed Resources

As one of the core issues for cloud computing, resource management adopts virtualization technology to shield the underlying resource heterogeneity and complexity which makes the massive distributed resources form a unified giant resource pool. It can achieve efficient resource provisioning by using the rational implementing resource management methods and techniques. Therefore, how to manage cloud computing resources effectively becomes a challenging research topic. By analyzing the executing progress of a user job in the cloud computing environment, we proposed a novel resource provisioning scheme based on the reinforcement learning and queuing theory in this study. With the introduction of the concepts of Segmentation Service Level Agreement (SSLA) and Utilization Unit Time Cost (UUTC), we viewed the resource provisioning problem in cloud computing as a sequential decision issue, and then we designed a novel optimization object function and employed reinforcement learning to solve it. Experiment results not only demonstrated the effectiveness of the proposed scheme, but also proved to outperform the common methods of resource utilization rate in terms of SLA collision avoidance and user costs.

Evaluation of body heating protocols with graphene heated clothing in a cold environment

2017 ◽  
Vol 29 (6) ◽  
pp. 830-844 ◽  
Author(s):  
Sora Shin ◽  
Hae-Hyun Choi ◽  
Yung Bin Kim ◽  
Byung-Hee Hong ◽  
Joo-Young Lee
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Reducing Power ◽  
Continuous Heating ◽  
Body Region ◽  
Cold Environment ◽  
Experimental Conditions ◽  
Content Type ◽  
Beneficial Effects ◽  
Intermittent Heating

Purpose The purpose of this paper is to evaluate the effects of intermittent and continuous heating protocols using graphene-heated clothing and identify more effective body region for heating in a cold environment. Design/methodology/approach Eight males participated in five experimental conditions at an air temperature of 0.6°C with 40 percent relative humidity: no heating, continuous heating the chest, continuous heating the back, intermittent heating the chest, and intermittent heating the back. Findings The results showed that the electric power consumption of the intermittent heating protocol (2.49 W) was conserved by 71 percent compared to the continuous protocol (8.58 W). Rectal temperature, cardiovascular and respiratory responses showed no significant differences among the four heating conditions, while heating the back showed more beneficial effects on skin temperatures than heating the chest. Originality/value First of all, this study was the first report to evaluate cold protective clothing with graphene heaters. Second, the authors provided effective intermittent heating protocols in terms of reducing power consumption, which was able to be evaluated with the characteristics of fast-responsive graphene heaters. Third, an intermittent heating protocol on the back was recommended to keep a balance between saving electric power and minimizing thermal discomfort in cold environments.

scholarly journals An Effiecient Approach for Resource Auto-Scaling in Cloud Environments

2016 ◽  
Vol 6 (5) ◽  
pp. 2415
Author(s):  
Bahar Asgari ◽  
Mostafa Ghobaei Arani ◽  
Sam Jabbehdari
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Sla Violation ◽  
Markov Decision ◽  
Cloud Environments ◽  
Auto Scaling

<p>Cloud services have become more popular among users these days. Automatic resource provisioning for cloud services is one of the important challenges in cloud environments. In the cloud computing environment, resource providers shall offer required resources to users automatically without any limitations. It means whenever a user needs more resources, the required resources should be dedicated to the users without any problems. On the other hand, if resources are more than user’s needs extra resources should be turn off temporarily and turn back on whenever they needed. In this paper, we propose an automatic resource provisioning approach based on reinforcement learning for auto-scaling resources according to Markov Decision Process (MDP). Simulation Results show that the rate of Service Level Agreement (SLA) violation and stability that the proposed approach better performance compared to the similar approaches.</p>

Impact of hydropower consumption, foreign direct investment and manufacturing performance on CO2 emissions in the ASEAN-4 countries

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ismail Aliyu Danmaraya ◽  
Abubakar Hamid Danlami
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Value Added ◽  
The Philippines ◽  
Negative Consequences ◽  
Hydro Power ◽  
Continuous Increase ◽  
Content Type ◽  
Manufacturing Performance ◽  
Bound Test

Purpose The continuous increase in the discharges of carbon emissions (CO2) in the global atmosphere and the likely negative consequences of this practice on the atmosphere draw the attention of researchers and policymakers to argue on the causes and perpetrators of CO2 emissions. This paper aims to examine the impacts of hydropower consumption, FDI and manufacturing performance on CO2 emissions in the Association of southeast Asian nations (ASEAN)-4 countries. Design/methodology/approach The study uses the data on variables, such as hydro-power consumption, FDI, manufacturing value added and CO2 emissions spanning the period 1980–2015. Autoregressive Distributive Lag Bound test approach was used to assess the relationships among the variables. Findings The long run estimation of elasticities for all the countries indicates that the coefficient of hydro power consumption was found to be significantly related to CO2 emissions only in Malaysia. Additionally, the coefficients manufacturing performance were found to be significant in influence the amount of CO2 emission in all the ASEAN-4 countries. Furthermore, the coefficients of FDI inflows were found to be significant in explaining CO2 emissions in Malaysia and the Philippines, respectively. In the short run, the estimated results show that all the variables were found to be significant in explaining CO2 emissions in the countries under study. Research limitations/implications Singapore is excluded from the ASEAN-4 due to insufficient data on hydro energy consumption. Practical implications The study recommends that as Hydro power energy is a clean source of renewable electricity. Its consumption indicates a negative relationship with CO2 emissions. The countries should emphasize more on the use of hydro source of energy than the other sources which increase the rate of CO2 emissions in the atmosphere. Originality/value As most of the relevant previous studies did not consider the simultaneous impact of hydro energy consumption, FDI and manufacturing value added on CO2 emissions in the ASEAN-4, this study is an important contribution to the existing relevant literature.

scholarly journals DYNAMIC VIRTUAL MACHINE MIGRATION USING RATIO-BASED METHOD

2020 ◽  
pp. 1-4
Author(s):  
Haresh Damjibhai Khachariya ◽  
Jayesh N. Zalavadia
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Virtual Machines ◽  
Cloud Services ◽  
Virtual Machine Migration ◽  
Reduce Power Consumption ◽  
Efficient Resource ◽  
Cloud Datacenter ◽  
Save Energy

Cloud computing provides various services over the internet and its increasing day by day.Given the growing demands of cloud services, it requires a lot of computing resources to meet customer needs. So, the addition of energy consumption through cloud computing resources will increase day by day and become a key obstacle in the cloud environment.In cloud computing,data centers consume more energy and additionally release carbon dioxide into the atmosphere. To reduce energy consumption through the cloud datacenter, energy-efficient resource management is required. In this paper a specific technique for performing virtual machines through datacenter is given. Our goal is to reduce power consumption on the datacenter by reducing the host running in the cloud datacenter. To reduce power consumption, schedule the incoming task such a way that all the resources like ram,cpu(mips) and bandwidth utilize in equal weightage.Then after if any host is over utilized then migrate one or more vm from that host to another host as well as if any host is underutilize then migrate running vm of that host and switch off the under loaded host to save energy.

Optimal resource management and allocation for autonomous-vehicle-infrastructure cooperation under mobile edge computing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shengpei Zhou ◽  
Zhenting Chang ◽  
Haina Song ◽  
Yuejiang Su ◽  
Xiaosong Liu ◽  
...  
Keyword(s):  
Resource Management ◽  
Power Consumption ◽  
Autonomous Vehicle ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Practical Application ◽  
Automated Driving ◽  
Content Type ◽  
Optimal Resource ◽  
Optimal Resource Management

Purpose With the continuous technological development of automated driving and expansion of its application scope, the types of on-board equipment continue to be enriched and the computing capabilities of on-board equipment continue to increase and corresponding applications become more diverse. As the applications need to run on on-board equipment, the requirements for the computing capabilities of on-board equipment become higher. Mobile edge computing is one of the effective methods to solve practical application problems in automated driving. Design/methodology/approach In this study, in accordance with practical requirements, this paper proposed an optimal resource management allocation method of autonomous-vehicle-infrastructure cooperation in a mobile edge computing environment and conducted an experiment in practical application. Findings The design of the road-side unit module and its corresponding real-time operating system task coordination in edge computing are proposed in the study, as well as the method for edge computing load integration and heterogeneous computing. Then, the real-time scheduling of highly concurrent computation tasks, adaptive computation task migration method and edge server collaborative resource allocation method is proposed. Test results indicate that the method proposed in this study can greatly reduce the task computing delay, and the power consumption generally increases with the increase of task size and task complexity. Originality/value The results showed that the proposed method can achieve lower power consumption and lower computational overhead while ensuring the quality of service for users, indicating a great application prospect of the method.

scholarly journals Increasing WSN Lifetime using Clustering and Fault Tolerance Methods

2021 ◽  
Vol 17 (1) ◽  
pp. 1-6
Author(s):  
Sama Sabah ◽  
Muayad Croock
Keyword(s):  
Energy Consumption ◽  
Fault Tolerance ◽  
Power Consumption ◽  
Energy Efficient ◽  
High Energy ◽  
Tolerance Strategy ◽  
Battery Power ◽  
Wsn Lifetime ◽  
Leach Protocol ◽  
Made In

Energy consumption problems in wireless sensor networks are an essential aspect of our days where advances have been made in the sizes of sensors and batteries, which are almost very small to be placed in the patient's body for remote monitoring. These sensors have inadequate resources, such as battery power that is difficult to replace or recharge. Therefore, researchers should be concerned with the area of saving and controlling the quantities of energy consumption by these sensors efficiently to keep it as long as possible and increase its lifetime. In this paper energy-efficient and fault-tolerance strategy is proposed by adopting the fault tolerance technique by using the self-checking process and sleep scheduling mechanism for avoiding the faults that may cause an increase in power consumption as well as energy-efficient at the whole network. this is done by improving the LEACH protocol by adding these proposed strategies to it. Simulation results show that the recommended method has higher efficiency than the LEACH protocol in power consumption also can prolong the network lifetime. In addition, it can detect and recover potential errors that consume high energy.

A cross-country evaluation of energy efficiency from the sustainable development perspective

2019 ◽  
Vol 13 (4) ◽  
pp. 991-1019 ◽  
Author(s):  
Mehrdad Jalali Sepehr ◽  
Abdorrahman Haeri ◽  
Rouzbeh Ghousi
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Low Income ◽  
Clustering Algorithm ◽  
High Energy ◽  
Low Income Countries ◽  
Consumption Pattern ◽  
Data Envelopment Analysis Model ◽  
Analysis Model ◽  
Content Type

Purpose The purpose of this paper is to estimate energy efficiency of 132 countries from 2007 to 2014 according to their performance, categorizing the nations into similar groups. Design/methodology/approach Data envelopment analysis model based on Goal Programming and then K-Means clustering algorithm are used to determine the efficiency and clustering the nations based on their efficiency performances. Findings The results of the study reveal that developing low-income countries could lead to high energy-efficiency scores, and countries with different development and income levels can become efficient in the field of energy consumption. Following the nations during a seven-year period also indicates that the changes in energy-related indicators such as renewable energy consumption and energy productivity are the main drivers to move a country between clusters. Originality/value The present study aimed to investigate whether similar nations with similar energy efficiency level in a cluster are similar in their development and income level, and changing the energy consumption pattern during the seven-year period could move the countries from a cluster to another one.

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