Head Node Selection Algorithm in Cloud Computing Data Center

Cloud computing provides multiple services such as computational services, data processing, and resource sharing through multiple nodes. These nodes collaborate for all prementioned services in the data center through the head/leader node. This head node is responsible for reliability, higher performance, latency, and deadlock handling and enables the user to access cost-effective computational services. However, the optimal head nodes’ selection is a challenging problem due to consideration of resources such as memory, CPU-MIPS, and bandwidth. The existing methods are monolithic, as they select the head nodes without taking the resources of the nodes. Still, there is a need for the candidate node which can be selected as a head node in case of head node failure. Therefore, in this paper, we proposed a technique, i.e., Head Node Selection Algorithm (HNSA), for optimal head node selection from the data center, which is based on the genetic algorithm (GA). In our proposed method, there are three modules, i.e., initial population generation, head node selection, and candidate node selection. In the first module, we generate the initial population by randomly mapping the task on different servers using a scheduling algorithm. After that, we compute the overall cost and the cost of each node based on resources. In the second module, the best optimal nodes are selected as a head node by applying the genetic operations such as crossover, mutation, and fitness function by considering the available resources. In the selected optimal nodes, one node is chosen as a head node and the other is considered as a candidate node. In the third module, the candidate node becomes the head node in the case of head node failure. The proposed method HNSA is compared against the state-of-the-art algorithms such as Bees Life Algorithm (BLA) and Heterogeneous Earliest Finished Time (HEFT). The simulation analysis shows that the proposed HNSA technique performs better in terms of execution time, memory utilization, service level sgreement (SLA) violation, and energy consumption.

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Energy efficient head node selection algorithm in wireless sensor networks

2007 IEEE International Conference on Robotics and Biomimetics (ROBIO) ◽

10.1109/robio.2007.4522363 ◽

2007 ◽

Author(s):

Wanming Chen ◽

Max Q. -H. Meng ◽

Shuai Li ◽

Tao Mei ◽

Huawei Liang ◽

...

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Energy Efficient ◽

Wireless Sensor ◽

Selection Algorithm ◽

Node Selection ◽

Head Node

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Weight-Based Data Center Selection Algorithm in Cloud Computing Environment

Advances in Intelligent Systems and Computing - Artificial Intelligence and Evolutionary Computations in Engineering Systems ◽

10.1007/978-81-322-2656-7_47 ◽

2016 ◽

pp. 515-525 ◽

Cited By ~ 3

Author(s):

Sunny Nandwani ◽

Mohit Achhra ◽

Raveena Shah ◽

Aditi Tamrakar ◽

Kiran Joshi ◽

...

Keyword(s):

Cloud Computing ◽

Data Center ◽

Selection Algorithm ◽

Computing Environment ◽

Cloud Computing Environment

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Research on Cloud Computing Resources Provide Optimization Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1049-1050.1375 ◽

2014 ◽

Vol 1049-1050 ◽

pp. 1375-1379

Author(s):

Rui Peng Shi

Keyword(s):

Cloud Computing ◽

Data Center ◽

Resource Sharing ◽

Optimal Allocation ◽

Optimization Method ◽

Computing Technology ◽

Intelligent Algorithm ◽

Dynamic Configuration ◽

Efficient Resource ◽

Core Issues

Cloud computing core issues of resource management research is to achieve efficient resource sharing and dynamic configuration. In this paper, the background of cloud computing technology to study how to optimize cloud computing data center resources optimal allocation problem. This paper compares and analyzes the application field of traditional algorithms and heuristic intelligent algorithm.

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Predictive Data Center Selection Scheme for Response Time Optimization in Cloud Computing

International Journal of Cloud Applications and Computing ◽

10.4018/ijcac.2021010105 ◽

2021 ◽

Vol 11 (1) ◽

pp. 93-111

Author(s):

Deepak Kapgate

Keyword(s):

Cloud Computing ◽

Response Time ◽

Data Center ◽

Queuing Theory ◽

Performance Metrics ◽

Forecasting Model ◽

Selection Algorithm ◽

Queuing Model ◽

Selection Scheme ◽

Selection Algorithms

The quality of cloud computing services is evaluated based on various performance metrics out of which response time (RT) is most important. Nearly all cloud users demand its application's RT as minimum as possible, so to minimize overall system RT, the authors have proposed request response time prediction-based data center (DC) selection algorithm in this work. Proposed DC selection algorithm uses results of optimization function for DC selection formulated based on M/M/m queuing theory, as present cloud scenario roughly obeys M/M/m queuing model. In cloud environment, DC selection algorithms are assessed based on their performance in practice, rather than how they are supposed to be used. Hence, explained DC selection algorithm with various forecasting models is evaluated for minimum user application RT and RT prediction accuracy on various job arrival rates, real parallel workload types, and forecasting model training set length. Finally, performance of proposed DC selection algorithm with optimal forecasting model is compared with other DC selection algorithms on various cloud configurations.

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Emergency Scheduling Optimization Simulation of Cloud Computing Platform Network Public Resources

Complexity ◽

10.1155/2021/9950198 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Dingrong Liu ◽

Zhigang Yao ◽

Liukui Chen

Keyword(s):

Genetic Algorithm ◽

Cloud Computing ◽

Objective Function ◽

Hamming Distance ◽

Fitness Function ◽

Initial Population ◽

Computing Platform ◽

Public Resource ◽

Public Resources ◽

Cloud Computing Platform

Emergency scheduling of public resources on the cloud computing platform network can effectively improve the network emergency rescue capability of the cloud computing platform. To schedule the network common resources, it is necessary to generate the initial population through the Hamming distance constraint and improve the objective function as the fitness function to complete the emergency scheduling of the network common resources. The traditional method, from the perspective of public resource fairness and priority mapping, uses incremental optimization algorithm to realize emergency scheduling of public resources, neglecting the improvement process of the objective function, which leads to unsatisfactory scheduling effect. An emergency scheduling method of cloud computing platform network public resources based on genetic algorithm is proposed. With emergency public resource scheduling time cost and transportation cost minimizing target, initial population by Hamming distance constraints, emergency scheduling model, and the corresponding objective function improvement as the fitness function, the genetic algorithm to individual selection and crossover and mutation probability were optimized and complete the public emergency resources scheduling. Experimental results show that the proposed method can effectively improve the efficiency of emergency resource scheduling, and the reliability of emergency scheduling is better.

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Research on Resource Scheduling Algorithm in Cloud Computing Data Center

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.2050 ◽

2014 ◽

Vol 926-930 ◽

pp. 2050-2053 ◽

Cited By ~ 2

Author(s):

Yi Zhang ◽

Yi Min Su

Keyword(s):

Cloud Computing ◽

Energy Consumption ◽

Data Center ◽

Resource Sharing ◽

Rapid Development ◽

Resource Scheduling ◽

Research Direction ◽

Computing Resource ◽

High Resource ◽

Cloud Resource

In recent years, with the rapid development of Internet and virtualization technology, cloud computing, which providing users with on-demand services, has become a research hotspot. Under the environment of cloud computing, the datacenter, consisted by hardware and software, is a loosely coupled resource sharing architecture. The existing cloud computing's inadequacies are as following three aspects: 1. For lacking of real adequate and effective transaction of global bidirectional-way selection, the revenue of most of cloud resource provider is too low. 2. Since not fully considering the scheduling of multi-dimensional cloud resources, existing cloud computing's utilization for multi-dimensional cloud resource is too low. 3. Because existing cloud datacenter does not fully consider the energy consumption of communication between the cloud tasks, its energy consumption is too high. Resource scheduling is a major research direction of cloud computing. First, we make a in-depth investigation and analysis of the research status of cloud computing resource scheduling, and then focus on resource scheduling method to reduce the energy consumption of cloud computing data center. Finally we set an important future research direction of cloud computing resource management research in order to provide a useful reference for cloud computing research.

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