Introduction to the Special Issue "Applications in Self-Aware Computing Systems and their Evaluation"

The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Umeå, Sweden on 20 June 2019. The goal of this one-day workshop was to bring together researchers and practitioners from academic environments and from the industry to share their solutions, ideas, visions, and doubts in self-aware computing systems in general and in the evaluation and measurements of such systems in particular. The workshop aimed to enable discussions, partnerships, and collaborations among the participants. This special issue follows the theme of the workshop. It contains extended versions of workshop presentations as well as additional contributions

Self-Protection Mechanisms for Web Applications - A Case Study

Self-protection mechanisms aim to improve security of software systems at runtime. They are able to automatically prevent and/or react to security threats by observing the state of a system and its execution environment, by reasoning on the observed state, and by applying enhanced security strategies appropriate for the current threat. Self-protection mechanisms complement traditional security solutions which are mostly static and focus on the boundaries of a system, missing in this way the overall picture of a system's security. This paper presents several self-protection mechanisms which have been developed in the context of a case study concerning a home banking system. Essentially, the mechanisms described in this paper aim to improve the security of the system in the following two scenarios: users' login and bank operations. Furthermore, the proposed self-protection mechanisms are presented through the taxonomy proposed in (Yuan, 2014)

model driven reverse engineering approaches a systematic literature review

This paper explores and describes the state of the art for what concerns the model-driven approaches proposed in the literature to support reverse engineering. We conducted a systematic literature review on this topic with the aim to answer three research questions. We focus on various solutions developed for model-driven reverse engineering, outlining in particular the models they use and the transformations applied to the models. We also consider the tools used for model definition, extraction, and transformation and the level of automation reached by the available tools. The model-driven reverse engineering approaches are also analyzed based on various features such as genericity, extensibility, automation of the reverse engineering process, and coverage of the full or partial source artifacts. We describe in detail and compare fifteen approaches applying model-driven reverse engineering. Based on this analysis, we identify and indicate some hints on choosing a model-driven reverse engineering approach from the available ones, and we outline open issues concerning the model-driven reverse engineering approaches

Self-Configuration and Self-Optimization Process in Heterogeneous Wireless Networks

Self-organization in Wireless Mesh Networks (WMN) is an emergent research area, which is becoming important due to the increasing number of nodes in a network. Consequently, the manual configuration of nodes is either impossible or highly costly. So it is desirable for the nodes to be able to configure themselves. In this paper, we propose an alternative architecture for self-organization of WMN based on Optimized Link State Routing Protocol (OLSR) and the ad hoc on demand distance vector (AODV) routing protocols as well as using the technology of software agents. We argue that the proposed self-optimization and self-configuration modules increase the throughput of network, reduces delay transmission and network load, decreases the traffic of HELLO messages according to network’s scalability. By simulation analysis, we conclude that the self-optimization and self-configuration mechanisms can significantly improve the performance of OLSR and AODV protocols in comparison to the baseline protocols analyzed

Combination of self-organization mechanisms to enhance service discovery in open systems

Decentralized systems have emerged as an alternative to centralized approaches for dealing with dynamic requirements in new business models. These systems should provide mechanisms that contribute to flexibility and facilitate adaptation to changes in the environment. In this paper, we present two self-organization mechanisms for a decentralized service discovery system in order to improve its performance. These mechanisms are based on local actions of agents that only consider local information about queries they forward during the discovery process. The self-organization actions are chosen by each agent individually when the agent considers them to be appropriate. The actions are: remaining in the system, leaving the system, cloning, and changing structural relations with other agents. We have evaluated each self-organization mechanism separately but also the combination of the two as the environmental conditions in the service demand change. The results show that the proposed self-organization mechanisms considerably improve the performance of the service discovery systemDel Val Noguera, E.; Rebollo Pedruelo, M.; Botti Navarro, VJ. (2014). Combination of self-organization mechanisms to enhance service discovery in open systems. Information Sciences. 279:138-162. doi:10.1016/j.ins.2014.03.109S13816227

reference architecture and framework

M. Adorni, F. Arcelli, S. Bandini, L. Baresi, C. Batini, A. Bianchi, D. Bianchini, M. Brioschi, A. Caforio, A. Cali, P. Cappellari, C. Cappiello, T. Catarci, A. Corallo, V. De Antonellis, C. Franza, G. Giunta, A. Limonta, G. Lorenzo, P. Losi, A. Maurino, M. Melideo, D. Micucci, S. Modafferi, E. Mussi, L. Negri, C. Pandolfo, B. Pernici, P. Plebani, D. Ragazzi, C. Raibulet, M. Riva, N. Simeoni, C. Simone, G. Solazzo, F. Tisato, R. Torlone, G. Vizzari, and A. Zill