Dimitris Kardaras

The use of fuzzy cognitive maps to simulate the information systems strategic planning process

Abstract In the early 1980s articles began to focus on Strategic Planning of Information Systems (SISP) and to argue the critical importance of Information Technology (IT) in today’s organisations. Since then, a large number of models were presented in order to analyse IT from a strategic point of view and suggest new IT projects. However, researchers urge for alternative approaches to SISP, as current ones fall short in taking into consideration both the business and IT perspectives as well as they fail to tackle the complexity of the domain and suggest specific IS opportunities. This article suggests Fuzzy Cognitive Maps (FCM) as an alternative modelling approach and describes how they can be developed and used to simulate the SISP process. FCMs were successfully developed and used in several ill-structured domains, such as decision making, policy making. The proposed FCM contains 165 variables and 210 relationships from both business and IT domains. The strength of this approach lies in its capability not only to comprehensively model qualitative knowledge which dominates strategic decision making, but also to simulate and evaluate several alternative ways of using IT in order to improve organisational performance. This approach introduces computational modelling, as well as it supports scenarios development and simulation in the SISP domain.

Design and evaluation of a fuzzy cloud controller

The management of cloud deployments is still largely the responsibility of system administrators. Introducing autonomy in cloud management would entail, amongst other things, the ability for automated cloud manager systems to scale up or down the number of deployed virtual machines, and/or deploy machines of different types to meet performance and other SLAs. However, the workload of cloud deployments can exhibit high variability over short time periods. This creates the necessity of introducing autonomic behaviour in the resource management function of the cloud deployment that makes decisions in real time so to optimise cost and/or performance. In this paper, we employ fuzzy logic to design a cloud controller whose scaling policies emulate the reasoning of human cloud operators, and take into account the current system load and the system load rate of change. The fuzzy controllers performance is compared and evaluated against Amazons AWS auto-scaling policies. Results indicate the ability of the proposed fuzzy controller to adapt better to changing workloads by provisioning virtual machines appropriately to match the rate of system load change. Additionally, fuzzy rules are more intuitive than auto-scaling policies and thus easier to understand and modify by human users.