Coordinating self-sizing and self-repair managers for multi-tier systems

International audienceComputing systems have become more and more distributed and heterogeneous, making their manual administration difficult and error-prone. The Autonomic Computing approach has been proposed to overcome this issue, by automating the administration of computing systems with the help of control loops called autonomic managers. Many research works have investigated the automation of the administration functions of computing systems and today many autonomic managers are available. However the existing autonomic manages are mostly specialized in the management of few administration concerns such as self-repair which handles server failures, and self-sizing which deals with dynamic server allocation. This makes necessary the coexistence of multiple autonomic managers for a complete system management. The coexistence of several such managers is required to handle multiple concerns, yet requires coordination mechanisms to avoid incoherent administration decisions. We investigate the use of control techniques for the design of coordination controllers, for which we exercise synchronous programming that provide formal semantics, and discrete controller synthesis to automate the construction of the controller. The paper details an application of the latter approach for the design of a coordination controller to orchestrate the execution of four self-repair and two self-sizing managers that address the availability and performance of a multi-tier replication-based system. We evaluate and demonstrate the benefits of our coordination solution by executing the RUBiS Benchmark web application

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis