Resource Scheduling and Load Balancing Fusion Algorithm with Deep Learning Based on Cloud Computing

2018 ◽

Vol 13 (3) ◽

pp. 54-72 ◽

Cited By ~ 2

Author(s):

Xiaojing Hou ◽

Guozeng Zhao

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Energy Consumption ◽

Load Balancing ◽

Data Centers ◽

Learning Strategy ◽

Resource Scheduling ◽

High Energy ◽

Fusion Algorithm ◽

Low Efficiency

With the wide application of the cloud computing, the contradiction between high energy cost and low efficiency becomes increasingly prominent. In this article, to solve the problem of energy consumption, a resource scheduling and load balancing fusion algorithm with deep learning strategy is presented. Compared with the corresponding evolutionary algorithms, the proposed algorithm can enhance the diversity of the population, avoid the prematurity to some extent, and have a faster convergence speed. The experimental results show that the proposed algorithm has the most optimal ability of reducing energy consumption of data centers.

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DEEP LEARNING BASED LOAD BALANCING USING MULTIDIMENSIONAL QUEUING LOAD OPTIMIZATION ALGORITHM FOR CLOUD ENVIRONMENT.

June-2020 - International Journal of Engineering Sciences & Research Technology ◽

10.29121/ijesrt.v9.i10.2020.15 ◽

2020 ◽

Vol 9 (10) ◽

pp. 156-167

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Load Balancing ◽

Response Time ◽

Success Rate ◽

Virtual Machines ◽

Service Providers ◽

Resource Scheduling ◽

Cloud Environment ◽

Load Optimization

Cloud computing becoming one of the most advanced and promising technologies in these days for information technology era. It has also helped to reduce the cost of small and medium enterprises based on cloud provider services. Resource scheduling with load balancing is one of the primary and most important goals of the cloud computing scheduling process. Resource scheduling in cloud is a non-deterministic problem and is responsible for assigning tasks to virtual machines (VMs) by a servers or service providers in a way that increases the resource utilization and performance, reduces response time, and keeps the whole system balanced. So in this paper, we presented a model deep learning based resource scheduling and load balancing using multidimensional queuing load optimization (MQLO) algorithm with the concept of for cloud environment Multidimensional Resource Scheduling and Queuing Network (MRSQN) is used to detect the overloaded server and migrate them to VMs. Here, ANN is used as deep learning concept as a classifier that helps to identify the overloaded or under loaded servers or VMs and balanced them based on their basis parameters such as CPU, memory and bandwidth. In particular, the proposed ANN-based MQLO algorithm has improved the response time as well success rate. The simulation results show that the proposed ANN-based MQLO algorithm has improved the response time compared to the existing algorithms in terms of Average Success Rate, Resource Scheduling Efficiency, Energy Consumption and Response Time.

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One High Efficiency Production Line of Clinker and Slag Grinding Separately by Roller Mill in Cement Industry

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.58.83 ◽

2008 ◽

Vol 58 ◽

pp. 83-89

Author(s):

Ning Chang Liu ◽

Zhao Feng Li

Keyword(s):

Energy Consumption ◽

High Efficiency ◽

High Energy ◽

Cement Industry ◽

Grinding Process ◽

Roller Mill ◽

Low Efficiency ◽

Cement Grinding ◽

Grinding System ◽

High Energy Consumption

In cement industry, many grinding up systems are on operating now. The tradition process of tube mill grinding system is high energy consumption, so it’s low efficiency, especially in the final cement grinding process. The value and advantage of slag is recognized more and more, but it’s difficult to be grinded up. Furthermore, the disadvantage and shortages to grind up clinker compounded with slag to produce cement are obvious and adopted. The best process is to grind up slag, clinker separately. Then, these two kinds of powder are compounded by a mixer. Hereby, it introduces a design of the process to grind up clinker, slag by one roller mill.

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Energy-Efficiency in Cloud Data Centers

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Communication Infrastructures for Cloud Computing ◽

10.4018/978-1-4666-4522-6.ch011 ◽

2013 ◽

pp. 241-263

Author(s):

Burak Kantarci ◽

Hussein T. Mouftah

Keyword(s):

Cloud Computing ◽

Energy Consumption ◽

Data Center ◽

Energy Efficient ◽

Large Scale ◽

Data Centers ◽

High Energy ◽

It Services ◽

Data Center Network ◽

Cloud Data

Cloud computing aims to migrate IT services to distant data centers in order to reduce the dependency of the services on the limited local resources. Cloud computing provides access to distant computing resources via Web services while the end user is not aware of how the IT infrastructure is managed. Besides the novelties and advantages of cloud computing, deployment of a large number of servers and data centers introduces the challenge of high energy consumption. Additionally, transportation of IT services over the Internet backbone accumulates the energy consumption problem of the backbone infrastructure. In this chapter, the authors cover energy-efficient cloud computing studies in the data center involving various aspects such as: reduction of processing, storage, and data center network-related power consumption. They first provide a brief overview of the existing approaches on cool data centers that can be mainly grouped as studies on virtualization techniques, energy-efficient data center network design schemes, and studies that monitor the data center thermal activity by Wireless Sensor Networks (WSNs). The authors also present solutions that aim to reduce energy consumption in data centers by considering the communications aspects over the backbone of large-scale cloud systems.

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