| APPLICATION OF IMPROVED INTELLIGENT LOAD BALANCING MODEL IN A REPLICATED DATABASE ENVIRONMENT |
| Pages: 179-185 (7) | [Full Text] PDF (361 KB) |
| Ekong A. P, Nwachukwu E.O, Onyejegbu L. N |
| Department of Computer Science University of Port Harcourt Rivers State, Nigeria |
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Abstract - When request are randomly assigned to database replicas in a replicated database environment, it causes a situation where in some databases are heavily overtasked while others sits idle. Intelligent Load balancing is highly essential in a replicated distributed database architecture in achieving a higher throughput making sure that no single database is over-utilized, and by so doing improving the overall throughput of the system. Several techniques are proposed or deployed for load balancing but most of them makes the ideal demand for equity or fairness to still be farfetched and are plagued with several constraints such as fewness of parameters in consideration, the failure to reassigned executing processes on failed servers to active and suitable ones and the non-consideration of issues relating to replicated databases all which lead to an increase in the time spent by processes in the system. In this article, An Improved Intelligent Load Balancing (ILB) Model for a replicated database Environment has been proposed. The aim of ILB is to provide fairness to all the Databases by balancing the request among the database replicas. In our proposed solution, the number of requests attended to at any instance on the individual database in the cluster and data location is also taken into consideration and their impact on total time spent by a queued request in the system is determined. We have developed an improved intelligent load balancing algorithm which balances the load or requests on different databases in a homogenous database cluster. The result obtained showed that in replicated database, there is more 70% less time spent to attend to users request using Improved Intelligent Load Balancing than using other existing algorithms hence solving the problem of load imbalance and unfairness in request distribution. |
| Index Terms - Improved Intelligent, Load Balancing Model, Replicated Database Environment (RDE). |
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C itation - Ekong A. P, Nwachukwu E.O, Onyejegbu L. N. "APPLICATION OF IMPROVED INTELLIGENT LOAD BALANCING MODEL IN A REPLICATED DATABASE ENVIRONMENT." International Journal of Computer Science and Software Engineering 8, no. 8 (2019): 179-185. |
| A Comparison of Clustering Algorithm Specifying in Topical Cluster Tweets of the Theme of Ambon Tourism |
| Pages: 186-192 (7) | [Full Text] PDF (470 KB) |
| PR Pelupessy |
| Faculty of Information Technology, Master of Information Systems, Satya Wacana Christian University, Salatiga, Indonesia |
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Abstract - Twitter as a Social Medium under The Indonesian Social Media is nowadays commonly used as a source of data for research. With a library like Twitter4J, it is possible to access a users tweets according to the users needs. The most widely used research using Twitter data is grouping the user tweets based on a certain topic using the clustering method. This study aims at comparing the clustering algorithm to classify a tweet with a theme of the tourism of Ambon. The comparable algorithms are Lingo, Suffix Tree Clustering (STC): K-Means and K-Medoids. To compare these algorithms this study developed an application to Javanese language. The comparable aspects are purity, accuracy, precision, recall, and processing speed. A test result using 1000 tweet showed that the STC is an algorithm that has the fastest average processing time, which is 1.6161 seconds. While on the purity aspect, Lingo and STC algorithms have the same purity level, which is 1 with a level of accuracy above 80%. The study concluded that the STC algorithm is the best algorithm in the clustering tweet with the theme of Ambon Tourism. |
| Index Terms - Strategic Planning, TOGAF ADM, Enterprise Architecture Score Card |
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C itation - PR Pelupessy . "A Comparison of Clustering Algorithm Specifying in Topical Cluster Tweets of the Theme of Ambon Tourism." International Journal of Computer Science and Software Engineering 8, no. 8 (2019): 186-192. |
| Limiting Response Time in Cloud Computing Based upon Optimized Fuzzy Based Artificial Neural Network |
| Pages: 193-200 (9) | [Full Text] PDF (702 KB) |
| P Mall |
| Yogesh kumar, IET Bhaddal Technical Campus Ropar (PB) INDIA |
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Abstract - Distributed computing is a developing innovation in the field of Information Technology. This exploration points towards the foundation of execution subjective examination on stack partaking in VM to VM and after that actualized in CloudSim with Java dialect. Here real pressure is given on the investigation of asset distribution calculation with heterogeneous assets of the cloud, trailed by similar study of different calculations in distributed computing concerning adaptability, homogeneity or heterogeneity and process relocation. A past report likewise shows change of MIPS will influence the reaction time and increment in MIPS versus VM diminishes the reaction time. At the point when picture size of VM is actualized against the VM data transfer capacity then no critical impact is found on reaction time and it stays steady for which these parameters are explored. However, if there should be an occurrence of Cloudlet long length versus Host transfer speed an example is seen in which reaction time increments in proportionate way. Utilizing the changed approach the decrease in the down time of the different procedures is accomplished as appeared in comes about. |
| Index Terms - Cloud, VM, Host, VM Placement Schemes |
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C itation - P Mall. "Limiting Response Time in Cloud Computing Based upon Optimized Fuzzy Based Artificial Neural Network." International Journal of Computer Science and Software Engineering 8, no. 8 (2019): 193-200. |