Feedback-Based Resource Utilization for Smart Home Automation in Fog Assistance IoT-Based Cloud

2022 ◽  
pp. 803-824
Author(s):  
Basetty Mallikarjuna
Keyword(s):  
Resource Management ◽  
Response Time ◽  
Home Health Care ◽  
Traffic Management ◽  
Smart Home ◽  
Service Level Agreement ◽  
Management Systems ◽  
Management Approach ◽  
Management Technique ◽  
Cloud Data

In this article, the proposed feedback-based resource management approach provides data processing, huge computation, large storage, and networking services between Internet of Things (IoT)-based Cloud data centers and the end-users. The real-time applications of IoT, such as smart city, smart home, health care management systems, traffic management systems, and transportation management systems, require less response time and latency to process the huge amount of data. The proposed feedback-based resource management plan provides a novel resource management technique, consisting of an integrated architecture and maintains the service-level agreement (SLA). It can optimize energy consumption, response time, network bandwidth, security, and reduce latency. The experimental results are tested with the IFogSim tool kit and have proved that the proposed approach is effective and suitable for smart communication in IoT-based cloud.

Feedback-Based Resource Utilization for Smart Home Automation in Fog Assistance IoT-Based Cloud

2020 ◽  
Vol 3 (1) ◽  
pp. 41-63 ◽  
Author(s):  
Basetty Mallikarjuna
Keyword(s):  
Resource Management ◽  
Response Time ◽  
Home Health Care ◽  
Smart Home ◽  
Service Level Agreement ◽  
Management Systems ◽  
Management Approach ◽  
Management Technique ◽  
Cloud Data ◽  
Network Bandwidth

In this article, the proposed feedback-based resource management approach provides data processing, huge computation, large storage, and networking services between Internet of Things (IoT)-based Cloud data centers and the end-users. The real-time applications of IoT, such as smart city, smart home, health care management systems, traffic management systems, and transportation management systems, require less response time and latency to process the huge amount of data. The proposed feedback-based resource management plan provides a novel resource management technique, consisting of an integrated architecture and maintains the service-level agreement (SLA). It can optimize energy consumption, response time, network bandwidth, security, and reduce latency. The experimental results are tested with the IFogSim tool kit and have proved that the proposed approach is effective and suitable for smart communication in IoT-based cloud.

Energy Efficient, Resource-Aware, Prediction Based VM Provisioning Approach for Cloud Environment

2020 ◽  
Vol 11 (3) ◽  
pp. 22-41
Author(s):  
Akkrabani Bharani Pradeep Kumar ◽  
P. Venkata Nageswara Rao
Keyword(s):  
Energy Consumption ◽  
Electrical Power ◽  
Service Level Agreement ◽  
Service Level ◽  
Management Approach ◽  
Cloud Data ◽  
Proposed Model ◽  
Key Aspects ◽  
Cloud Infrastructures ◽  
Resource Aware

Over the past few decades, computing environments have progressed from a single-user milieu to highly parallel supercomputing environments, network of workstations (NoWs) and distributed systems, to more recently popular systems like grids and clouds. Due to its great advantage of providing large computational capacity at low costs, cloud infrastructures can be employed as a very effective tool, but due to its dynamic nature and heterogeneity, cloud resources consuming enormous amount of electrical power and energy consumption control becomes a major issue in cloud datacenters. This article proposes a comprehensive prediction-based virtual machine management approach that aims to reduce energy consumption by reducing active physical servers in cloud data centers. The proposed model focuses on three key aspects of resource management namely, prediction-based delay provisioning; prediction-based migration, and resource-aware live migration. The comprehensive model minimizes energy consumption without violating the service level agreement and provides the required quality of service. The experiments to validate the efficacy of the proposed model are carried out on a simulated environment, with varying server and user applications and parameter sizes.

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 State of current data storage market and development of tools for increasing data storage systems reliability

2019 ◽  
Vol 135 ◽  
pp. 04076 ◽  
Author(s):  
Marina Bolsunovskaya ◽  
Svetlana Shirokova ◽  
Aleksandra Loginova
Keyword(s):  
Data Storage ◽  
Traffic Management ◽  
Smart Home ◽  
Storage Systems ◽  
Storage System ◽  
Large Data ◽  
Current Data ◽  
Management Systems ◽  
Software Complex ◽  
Market State

This paper is devoted to the problem of developing and application of data storage systems (DSS) and tools for managing such systems to predict failures and provide fault tolerance specifications. Nowadays DSS are widely used for collecting data in Smart Home and Smart Cites management systems. For example, large data warehouses are utilized in traffic management systems. The results of the current data storage market state analysis are shown, and the project the purpose of which is to develop a hardware and software complex to predict failures in the storage system is presented.

Dynamic Resource Allocation Method for Load Balance Scheduling Over Cloud Data Center Networks

2021 ◽  
Author(s):  
Sakshi Chhabra ◽  
Ashutosh Kumar Singh
Keyword(s):  
Resource Allocation ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Service Level Agreement ◽  
Service Level ◽  
Dynamic Resource Allocation ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Dynamic Resource

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.

Service level agreement based energy-efficient resource management in cloud data centers

2014 ◽  
Vol 40 (5) ◽  
pp. 1621-1633 ◽  
Author(s):  
Yongqiang Gao ◽  
Haibing Guan ◽  
Zhengwei Qi ◽  
Tao Song ◽  
Fei Huan ◽  
...  
Keyword(s):  
Resource Management ◽  
Energy Efficient ◽  
Data Centers ◽  
Service Level Agreement ◽  
Service Level ◽  
Cloud Data ◽  
Efficient Resource ◽  
Cloud Data Centers

scholarly journals CPU based prediction with Self Organizing Map in Dynamic Cloud Data Centers

2020 ◽  
Vol 10 ◽  
Author(s):  
Nabila Djennane ◽  
Meziane Yacoub ◽  
Rachida Aoudjit ◽  
Samia Bouzefrane
Keyword(s):  
Resource Management ◽  
Cost Effective ◽  
Resource Provisioning ◽  
Management Systems ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Cloud Data ◽  
Demand Prediction ◽  
The Future ◽  
Self Organizing

Backgroud: The major objective of resource management systems in the cloud environments is to assist providers in making consistent and cost-effective decisions related to the dynamic resource allocation. However, because of the demand changes of the applications and the exponential evolution of the cloud, the resource management systems are constantly called into question with regard to their ability to guarantee an effective resource provisioning. Objective: To tackle these challenges, the future demand prediction is a practical solution that has been adopted in the literature. The prediction has widely relied on the CPU utilization since it is considered as a leading cause of the Quality of Service dropping. Method: The successful application of artificial intelligence techniques in forecasting problems motivated us to use the Kohonen Self Organizing Maps that tries to capture the gathered empirical CPU load time series in regular behaviors to perform an accurate forecast. The proposed solution is a two-step approach that first classifies the collected data and then predicts the future CPU load. Results and conclusion: The experimental results show that our proposed system outperforms other models reported in the literature. In addition, we proved that Self Organizing Maps known for its strength in classification is also effective for prediction.

Sign in / Sign up

Export Citation Format

Share Document