Prince Clement Addo

Siddique Abubakr Muntaka, Edward Danso Ansong, Benjamin Yankson et al.

Uninterrupted system availability is a critical requirement for enterprise operations, yet traditional high-availability clusters suffer from limitations such as single points of failure and inefficient resource allocation. This paper introduces and evaluates the Integrated High Availability Cluster (iHAC), a hybrid architecture designed to enhance system resilience and performance. The iHAC integrates the strengths of active-active and active-passive configurations to optimize workload distribution and failover capabilities. We conducted a comparative analysis, simulating iHAC against conventional (legacy) clusters using Riverbed Modeler (OPNET). The results reveal significant performance improvements: iHAC reduced the average HTTP page response time by over 40%, from five seconds in a traditional active-active setup to under three seconds. This was achieved alongside reduced network latency and increased overall throughput. This study validates the iHAC architecture as a superior design for building robust, high-performance systems, offering a practical path to greater operational continuity and resilience.

Yiou Lin, Hang Lei, Prince Clement Addo et al.

Job search through online matching engines nowadays are very prominent and beneficial to both job seekers and employers. But the solutions of traditional engines without understanding the semantic meanings of different resumes have not kept pace with the incredible changes in machine learning techniques and computing capability. These solutions are usually driven by manual rules and predefined weights of keywords which lead to an inefficient and frustrating search experience. To this end, we present a machine learned solution with rich features and deep learning methods. Our solution includes three configurable modules that can be plugged with little restrictions. Namely, unsupervised feature extraction, base classifiers training and ensemble method learning. In our solution, rather than using manual rules, machine learned methods to automatically detect the semantic similarity of positions are proposed. Then four competitive "shallow" estimators and "deep" estimators are selected. Finally, ensemble methods to bag these estimators and aggregate their individual predictions to form a final prediction are verified. Experimental results of over 47 thousand resumes show that our solution can significantly improve the predication precision current position, salary, educational background and company scale.