An Adaptive Resource Scheduling Mechanism Based on User Behavior Feedback in Cloud Computing

2012 ◽  
Author(s):  
Ding Ding ◽  
Yidong Li ◽  
Lihua Ai ◽  
Siwei Luo
Keyword(s):  
Cloud Computing ◽  
User Behavior ◽  
Resource Scheduling ◽  
Adaptive Resource

An Enhancement of Cloud Based Sentiment Analysis and BDAAs Using SVM Based Lexicon Dictionary and Adaptive Resource Scheduling

2018 ◽  
Vol 15 (2) ◽  
pp. 437-445 ◽  
Author(s):  
S. Radha ◽  
C. Nelson Kennedy Babu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Resource Scheduling ◽  
Data Integrity ◽  
Digital Data ◽  
Large Set ◽  
Compression Technique ◽  
Adaptive Resource

At present, the cloud computing is emerging technology to run the large set of data capably, and due to fast data growth, processing of large scale data is becoming a main point of information method and customers can estimate the quality of brands of products employing the information given by new digital marketing channels in social media. Thus, every enterprise requires finding and analyzing a big amount of digital data in order to develop their reputation among the customers. Therefore, in this paper, SLA (Service Level Agreement) based BDAAs (Big Data Analytic Applications) using Adaptive Resource Scheduling and big data with cloud based sentiment analysis is proposed to provide the deep web mining, QoS and to analyze the customer behaviors about the product. In this process, the spatio-temporal compression technique can be applied to data compression for reduction of big data. The data is classified in to positive, negative or neutral by employing the SVM with lexicon dictionary based on the customers' behaviors about brand or products. In cloud computing environment, complex to the reduction of resources cost and fluctuation of resource requirements with BDAAs. As a result, it is needed to have a common Analytics as a Service (AaaS) platform that provides a BDAAs to customers in different fields as unpreserved services in a simple to utilize a way with lower cost. Therefore, SLA based BDAAs is developed to utilize the adaptive resource scheduling depending on the customer behaviors and it can provide visualization and data integrity. Our method can give privacy of cloud owner's information with help of data integrity and authentication process. Experimental results of proposed system shows that the sentiment analysis method for online product using cloud based big data is able to classify the opinions of customers accurately and effective of the algorithm in guarantee of SLA.

scholarly journals AN ADAPTIVE RESOURCE SCHEDULING FRAMEWORK WITH SCHEDULE INTERVAL FILLING FOR CLOUD SERVICES

2021 ◽  
Vol 9 (2) ◽  
pp. 968-977
Author(s):  
B.SivaRama Krishna, Et. al.
Keyword(s):  
Cloud Computing ◽  
Resource Scheduling ◽  
Cloud Services ◽  
Task Completion ◽  
Proposed Model ◽  
Schedule Interval ◽  
Significant Research ◽  
Interval Filling ◽  
Adaptive Resource ◽  
Distributed Cloud

Application level resource scheduling in distributed cloud computing is a significant research objective that grabbed the attention of many researchers in recent literature. Minimal resource scheduling failures, robust task completion and fair resource usage are the critical factors of the resource scheduling strategies. Hence, this manuscript proposed a scalable resource-scheduling model for distributed cloud computing environments that aimed to achieve the scheduling metrics. The proposed model called " Modified Resource Scheduling with Schedule Interval Filling " schedules the resource to respective task such that the optimal utilization of resource idle time achieved. The proposed model performs the scheduling in hierarchical order and they are optimal idle resource allocation, if no individual resource is found to allocate then it allocates optimal multiple idle resources with considerable schedule intervals filling. The experimental results evincing that the proposed model is scalable and robust under the adapted metrics.

Application-adaptive resource scheduling in a computational grid

2006 ◽  
Vol 7 (10) ◽  
pp. 1634-1641 ◽  
Author(s):  
Cui-ju Luan ◽  
Guang-hua Song ◽  
Yao Zheng
Keyword(s):  
Resource Scheduling ◽  
Computational Grid ◽  
Adaptive Resource
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