As the adoption of microservices architectures in cloud deployments grows, so does the challenge of ensuring rapid recovery and minimal energy consumption in the face of disasters. This paper introduces a Generalized Stochastic Petri Net (GSPN) model specifically designed to quantify recovery times and electrical consumption in such environments. The model supports the development of resilient and eco-conscious systems by allowing precise manipulation and planning based on various configuration scenarios. We identify critical architectural elements and define intervals that yield significant improvements. Our findings not only enhance the understanding of energy and recovery dynamics in microservices but also serve as a crucial tool for system designers aiming to optimize both performance and sustainability. The implications of this research facilitate a strategic approach to disaster recovery planning, contributing to the broader field of cloud computing resilience.

Home automation or domotics is a typical representative of everything as a service (XaaS). Individual houses are equipped with Internet of Things (IoT) sensors and home facilities capable of self-assessment to offer comfortable, secure, and high-quality home services to their residents. However, assessing such systems with a high level of diversity is paramount of importance and challenging to assimilate. Domotics XaaS requires a high quality of service (QoS) in service performance and operational availability. In that regard, we propose, in this paper, a modeling approach based on stochastic Petri nets (SPN) for the performability quantification of domotics architectures. SPN performability models are developed following the architecture of a home automation system consisting of several IoT sensors/devices to evaluate the trade-offs between performance and availability of home automation services. The inter-dependency between performance and availability metrics is evaluated. The metrics include, for example, the mean response time (MRT) and the number of discarded packets. Sensitivity analysis using the design of experiments (DoE) is performed to identify the system's impacting components and performability bottleneck. Simulation results highlight the useful aspects of the proposed performability models for architectural and operational optimization of home automation XaaS infrastructures.

The Internet of Robotic Things (IoRT) has emerged as a game-changing player in the fourth industrial revolution (Industry 4.0) in which the robotic automation technologies are integrated with and reinforced by advanced computing paradigms like cloud/fog/IoT in order to maximize productivity of production chains of smart robotic agents in factories. A single robotic agent can comprise hundreds of sensors and actuators, and production processes performed by multiple agents can demand high computational costs, possibly only via remote resources in cloud or fog computing layers. In this context, it is paramount of importance to assimilate the performance of such computing paradigms for production chains of robotic agents in industrial factories and to provide IoRT system designers with assessment tools to comprehend and anticipate the operational performance of their projects at different development stages. This paper proposes an M/M/c/K model for the performance evaluation of IoRT systems integrated with fog-cloud computing paradigms. Focusing on routing strategies, we demonstrated that the proposed model allows evaluating how different configurations of components in an IoRT architecture impact the performance of the system using the performance metrics including mean response time, component utilization, number of messages, and drop rate. The performance modeling and evaluation of a specific IoRT in this paper can help computing system administrators to design and adopt advanced cloud/fog/IoT computing paradigms to maximize manufacturing productivity in industrial factories.

Rising aggressive virus pandemics urge to conduct studies on dependability and security of modern computing systems to secure autonomous and continuous operations of healthcare systems. In that regard, we propose to quantify dependability and security measures of an Internet-of-Medical Things (IoMT) infrastructure relied on an integrated physical architecture of cloud/fog/edge (CFE) computing paradigms in this article. We propose a reliability/availability quantification methodology for the IoMT infrastructure using a hierarchical model of three levels: 1) fault tree (FT) of overall IoMT infrastructure consisting of CFE member systems; 2) FT of subsystems within CFE member systems; and 3) continuous-time Markov chain (CTMC) models of components/devices in the subsystems. We incorporate a number of failure modes for the underlying subsystems, including Mandel-bug related failures and non-Mandel bugs related failure, as well as failures due to cyber-security attacks on software subsystems. Five case-studies of configuration alternation and four operational scenarios of the IoMT infrastructure are considered to comprehend the dependability characteristics of the IoMT physical infrastructure. The metrics of interest include reliability over time, steady state availability (SSA), sensitivity of SSA wrt. selected mean time to failure—equivalent (MTTFeq) and mean time to recovery—equivalent (MTTReq), and sensitivity of SSA wrt. frequencies of cyber-security attacks on software subsystems. The analysis results help comprehend operational behaviors and properties of a typical IoMT infrastructure. The findings of this study can improve the design and implementation of real-world IoMT infrastructures consisting of cloud, fog, and edge computing paradigms.

The reuse of past coefficient vectors of the NLMS for reducing the steady-state MSD in a low signal-to-noise ratio (SNR) was proposed recently. Its convergence analysis has not been studied yet, so we first derive a steady-state analysis for the NLMS with reusing coefficient vectors for a special case. In addition, this approach slows down the convergence speed while decreasing the steady-state MSD in proportion to the number of reusing coefficient vectors. To address this trade-off, we propose a novel NLMS algorithm which can change the reusing order to achieve both fast convergence speed and low steady-state MSD. The reusing order is decreased or increased by comparing the squared output error with a threshold. The experimental results show that the theoretical results match well with simulation results and the proposed algorithm has fast convergence speed and small steady-state MSD compared to the conventional NLMS.

A platform that supported Sequential Consistency (SC) for all codes --- not only the well-synchronized ones --- would simplify the task of programmers. Recently, several hardware architectures that support high-performance SC by committing groups of instructions at a time have been proposed. However, for a platform to support SC, it is insufficient that the hardware does; the compiler has to support SC as well.