APPLIED MACHINE LEARNING IN LOAD BALANCING

A common way to maintain the quality of service on systems that are growing rapidly is by increasing server specifications or by adding servers. The utility of servers can be balanced with the presence of a load balancer to manage server loads. In this paper, we propose a machine learning algorithm that utilizes server resources CPU and memory to forecast the future of resources server loads. We identify the timespan of forecasting should be long enough to avoid dispatcher's lack of information server distribution at runtime. Additionally, server profile pulling, forecasting server resources, and dispatching should be asynchronous with the request listener of the load balancer to minimize response delay. For production use, we recommend that the load balancer should have friendly user interface to make it easier to be configured, such as adding resources of servers as parameter criteria. We also recommended from beginning to start to save the log data server resources because the more data to process, the more accurate prediction of server load will be

Analisis Keterkaitan antara Chidamber dan Kemerer (CK) Metriks dan Modularitas pada Perangkat Lunak Berbasis Objek

Modularitas adalah pengukuran kualitas yang penting dalam perangkat lunak, sedangkan CK metrics merupakan model pengukuran metriks yang populer digunakan saat ini. Kedua model penilaian memiliki keterkaitan dengan beberapa model penilaian kualitas lainnya, misalnya fleksibilitas dan kompleksitas. Akan tetapi, tidak ada bukti konkret bahwa CK metrics berkorelasi dengan modularitas. Oleh karena itu, dalam penelitian ini, kami mendemonstrasikan hubungan modularitas berdasarkan ISO 25010 dan CK metrics melalui serangkaian percobaan menggunakan metode korelasi Pearson dan metode korelasi Spearman. Tidak hanya menunjukkan hubungan antar metriks, penerapan metode korelasi Spearman dan Pearson juga dapat menunjukkan tingkat hubungan antar metriks secara kuantitatif. Dalam penelitian ini, kami menghitung nilai aspek modularitas berbasis ISO 25010 dan setiap metriks pada CK metrics menggunakan alat yang mengimplementasikan pendekatan text processing. Metriks ini diukur untuk tingkat keterlibatan untuk mengetahui faktor mana yang saling mempengaruhi. Hasil penelitian menunjukkan bahwa DIT berkorelasi positif dengan aspek CCC, sedangkan CBO, DIT, NOC, dan LCOM berkorelasi positif dengan aspek CC. Sedangkan CBO, WMC dan RFC merupakan metriks yang berkorelasi negatif dengan aspek CCC. Dan aspek yang berkorelasi negatif dengan aspek CC adalah metriks WMC dan CCC. Hasil penerapan metode korelasi Spearman dan Pearson menunjukkan bahwa setiap metriks pada CK metriks memiliki korelasi pada aspek modularitas ISO 25010

Applied Machine Learning in Load Balancing

A common way to maintain the quality of service on systems that are growing rapidly is by increasing server specifications or by adding servers. The utility of servers can be balanced with the presence of a load balancer to manage server loads. In this paper, we propose a machine learning algorithm that utilizes server resources CPU and memory to forecast the future of resources server loads. We identify the timespan of forecasting should be long enough to avoid dispatcher's lack of information server distribution at runtime. Additionally, server profile pulling, forecasting server resources, and dispatching should be asynchronous with the request listener of the load balancer to minimize response delay. For production use, we recommend that the load balancer should have friendly user interface to make it easier to be configured, such as adding resources of servers as parameter criteria. We also recommended from beginning to start to save the log data server resources because the more data to process, the more accurate prediction of server load will be