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.

Hyperledger Fabric stands as a leading framework for permissioned block-chain systems, ensuring data security and audit-ability for enterprise applications. As applications on this platform grow, understanding its complex configuration concerning various block-chain parameters becomes vital. These configurations significantly affect the system’s performance and cost. In this research, we introduce a Stochastic Petri Net (SPN) model to analyze Hyper-ledger Fabric’s performance, considering variations in block-chain parameters, computational resources, and transaction rates. We provide case studies to validate the utility of our model, aiding block-chain administrators in determining optimal configurations for their applications. A key observation from our model highlights the block size’s role in system response time. We noted an increased mean response time, between 1 to 25 seconds, due to variations in transaction arrival rates.

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.

Software Defined Network (SDN) is an emerging paradigm for flexible network design and implementation. Availability metric of SDNs is critically demanding further studies. This paper aims to propose hierarchical models to assess the availability of SDNs. We incorporate various failure modes and recovery behaviors in the SDN including (i) link failures at network level, and (ii) software and hardware failures at network device level. We use hierarchical models in which a Reliability Graph (RG) is used to represent the reachability of hosts (and switches) in the SDN at the upper level and Stochastic Reward Net (SRN)s are used to represent the detailed failure and recovery of network devices at the lower level, respectively. We incorporate the programmable capability of the SDN at the upper level (i.e., the RG). We perform numerical analysis to assess the availability of the SDN in terms of steady state availability and downtime in minutes per year, and we also show the sensitivity analysis.