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Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction

Acker Alexander, Wittkopp Thorsten, Nedelkoski Sasho, Bogatinovski Jasmin, Kao Odej

Annals of Computer Science and Information Systems

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2020

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Vol. 21

7--10

EN

The rapid growth and distribution of IT systems increases their complexity and aggravates operation and maintenance. To sustain control over large sets of hosts and the connecting networks, monitoring solutions are employed and constantly enhanced. They collect diverse key performance indicators (KPIs) (e.g. CPU utilization, allocated memory, etc.) and provide detailed information about the system state. Storing such metrics over a period of time naturally raises the motivation of predicting future KPI progress based on past observations. This allows different ahead of time optimizations like anomaly detection or predictive maintenance. Predicting the future progress of KPIs can be defined as a time series forecasting problem. Although, a variety of time series forecasting methods exist, forecasting the progress of IT system KPIs is very hard. First, KPI types like CPU utilization or allocated memory are very different and hard to be modelled by the same model. Second, system components are interconnected and constantly changing due to soft- or firmware updates and hardware modernization. Thus a frequent model retraining or fine-tuning must be expected. Therefore, we propose a lightweight solution for KPI series prediction based on historic observations. It consists of a weighted heterogeneous ensemble method composed of two models - a neural network and a mean predictor. As ensemble method a weighted summation is used, whereby a heuristic is employed to set the weights. The lightweight nature allows to train models individually on each KPI series and makes model retraining feasible when system changes occur. The modelling approach is evaluated on the available FedCSIS 2020 challenge dataset and achieves an overall R^2 score of 0.10 on the preliminary 10\% test data and 0.15 on the complete test data. We publish our code on the following github repository: https://github.com/citlab/fed\_challenge.

2

Linking a performance management system and competencies: qualitative research

Korenková Veronika, Závadský Ján, Lis Marcin

Engineering Management in Production and Services

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2019

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Vol. 11, no. 1

51--67

EN

The main purpose of the paper is to identify the group of indicators that are most widely used in the manufacturing area worldwide, to identify the responsibility and authority for measuring and evaluating business performance, and to create an illustrative competency-based model for a performance management system within a business. The paper covers two areas that are important in the maintenance of sustainable business performance. The first area focuses on a performance management system and its key performance indicators as an important element in every performance management system within a business. The article also presents the theoretical background of the Z-MESOT method, which is applied to define the consistency of these indicators in practice. The second area is dedicated to defining a competency-based model and competences related to the measurement and assessment of performance, which have been extracted from other general competences. This paper presents findings from qualitative research to eliminate the bottlenecks of the Z-MESOT matrix that was transposed into a questionnaire. The questionnaire, as well as structured interviews, helped identify differences in responsibility attributes of the Z-MESOT matrix regarding the size of the researched businesses. The paper offers a list of competences related to the key performance indicators that can be used for following theoretical and practical research.

3

KPIs for optimal location of charging stations for electric vehicles: the Biella case-study

Fadda Edoardo, Manerba Daniele, Cabodi Gianpiero, Camurati Paolo, Tadei Roberto

Annals of Computer Science and Information Systems

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2019

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Vol. 18

123--126

EN

Electric vehicles are accelerating the world's transition to sustainable energy. Nevertheless, the lack of a proper charging station infrastructure in many real implementations still represents an obstacle for the spread of such a technology. In this paper, we present a real case application of optimization techniques in order to solve the location problem of electric charging stations in the district of Biella, Italy. The plan is composed by several progressive installations and decision makers pursue several objectives that might be in contrast. For this reason, we present an innovative framework based on the comparison of several ad-hoc Key Performance Indicators for evaluating many different aspects of a location solution.