Jasmina Bogojeska

Classifying server behavior and predicting impact of modernization actions

Today the decision of when to modernize which elements of the server HW/SW stack is often done manually based on simple business rules. In this paper we alleviate this problem by supporting the decision process with an automated approach based on incident tickets and server attributes data. As a first step we identify and rank servers with problematic behavior as candidates for modernization using a random forest classifier. Second, this predictive model is used to evaluate the impact of different modernization actions and suggest the most effective ones. We show that our chosen model yields high quality predictions and outperforms traditional linear regression models on a large set of real data.

Predicting Disk Replacement towards Reliable Data Centers

Disks are among the most frequently failing components in todays IT environments. Despite a set of defense mechanisms such as RAID, the availability and reliability of the system are still often impacted severely. In this paper, we present a highly accurate SMART-based analysis pipeline that can correctly predict the necessity of a disk replacement even 10-15 days in advance. Our method has been built and evaluated on more than 30000 disks from two major manufacturers, monitored over 17 months. Our approach employs statistical techniques to automatically detect which SMART parameters correlate with disk replacement and uses them to predict the replacement of a disk with even 98% accuracy.

Multi-View Incident Ticket Clustering for Optimal Ticket Dispatching

We present a novel technique that optimizes the dispatching of incident tickets to the agents in an IT Service Support Environment. Unlike the common skill-based dispatching, our approach also takes empirical evidence on the agents speed from historical data into account. Our solution consists of two parts. First, a novel technique clusters historic tickets into incident categories that are discriminative in terms of agents performance. Second, a dispatching policy selects, for an incoming ticket, the fastest available agent according to the target cluster. We show that, for ticket data collected from several Service Delivery Units, our new dispatching technique can reduce service time between

Impact of HW and OS type and currency on server availability derived from problem ticket analysis

35%

Analysis of Labor Efforts and their Impact Factors to Solve Server Incidents in Datacenters

and

Hierarchical Incident Ticket Classification with Minimal Supervision

44%

Solving Linear SVMs with Multiple 1D Projections

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On the adoption and impact of predictive analytics for server incident reduction

Technology refresh is an important component in data center management. The goal of this paper is to assess the impact of HW and OS currency on server availability based on a large set of incident tickets and server attributes data collected from several different IT environments. In order to achieve this we first identify the server failure incidents using a machine learning method for automatic ticket classification. Then we conduct the data analysis to inspect the impact of HW and OS type along with their currency on the rates of server failures. This can further be used to derive guidelines to support the technology refresh decisions in the data centers.

Transfer learning for server behavior classification in small IT environments

A companys IT infrastructure delivers the basic hardware, networking, operating system, and middleware support to the business applications. IT service providers perform incident and problem resolution, as well as user administration and change implementation required to maintain the availability and service provided for the business. As a result, they become increasingly challenged with delivering better, faster, and cheaper services to their customers. With the variety of incident tickets reported on a daily basis, understanding where and how much effort is spent to resolve them is critical. Moreover, analyzing the effort data identifies opportunities for self-service and automation, as well as what modernization strategies businesses should implement to reduce incident volumes and, by association, labor effort. In this paper, we conduct a large scale study on the incident and server factors that affect technician effort and quantify their impact. We show that the nature of the incidents and their complexity, the assigned support groups, as well as the underlying OS type play a major role in how much labor effort is spent towards resolving such tickets.

Assisting database management

In this paper, we introduce a novel approach for incident ticket classification that aims at minimizing the manual labelling effort while achieving good-quality predictions. To accomplish this, we devise a two-stage technique that employs hierarchical clustering using a combination of graph clustering (community finding) and topic modelling as first stage, followed by either another round of hierarchical clustering or an active learning approach as second stage. We evaluate the performance of our method in terms of manual labelling effort, prediction quality and efficiency on three real-world datasets and demonstrate that classical approaches to text classification are not well suited for incident ticket texts.