Department of Computer Science

Permanent URI for this collectionhttps://irepos.unijos.edu.ng/handle/123456789/11435

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    IDENTIFYING RECOVERY PATTERNS FROM RESOURCE USAGE DATA OF CLUSTER SYSTEMS
    (Science World Journal, 2018) Nentawe Gurumdimma 1 , Gideon Dadik Bibu2 , Desmond Bala Bisandu3 , Mammuan Titus Alams
    Failure of Cluster Systems has proven to be of adverse effect and it can be costly. System administrators have employed divide and conquer approach to diagnosing the root-cause of such failure in order to take corrective or preventive measures. Most times, event logs are the source of the information about the failures. Events that characterized failures are then noted and categorized as causes of failure. However, not all the ’causative’ events lead to eventual failure, as some faults sequence experience recovery. Such sequences or patterns constitute challenge to system administrators and failure prediction tools as they add to false positives. Their presence are always predicted as “failure causing“, while in reality, they will not. In order to detect such recovery patterns of events from failure patterns, we proposed a novel approach that utilizes resource usage data of cluster systems to identify recovery and failure sequences. We further propose an online detection approach to the same problem. We experiment our approach on data from Ranger Supercomputer System and the results are positive.
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    Cancelable and hybrid biometric cryptosystems: current directions and open research issues
    (2017-09-21) Abayomi Jegede; Nur Izura Udzir; Azizol Abdullah; Ramlan Mahmod
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    UNDERSTANDING ERROR LOG EVENT SEQUENCE FOR FAILURE ANALYSIS
    (Science World Journal, 2018) Nentawe Gurumdimma1 , Desmond Bala Bisandu
    Due to the evolvement of large-scale parallel systems, they are mostly employed for mission critical applications. The anticipation and accommodation of failure occurrences is crucial to the design. A commonplace feature of these large-scale systems is failure, and they cannot be treated as exception. The system state is mostly captured through the logs. The need for proper understanding of these error logs for failure analysis is extremely important. This is because the logs contain the “health” information of the system. In this paper we design an approach that seeks to find similarities in patterns of these logs events that leads to failures. Our experiment shows that several root causes of soft lockup failures could be traced through the logs. We capture the behavior of failure inducing patterns and realized that the logs pattern of failure and non-failure patterns are dissimilar.