In recent years, mobile cloud computing has attracted researchers and industries' interest since it maximizes gains with mobile computing in different areas, such as healthcare, sports, and business. However, the inherent challenges associated with mobile computing (e.g., battery provisioning and wireless network instability) may limit mobile cloud achievements. This paper provides an availability and reliability study of mobile clouds and evaluates distinct architectures and strategies. Our evaluation is based on hierarchical heterogeneous models and focuses on steady-state availability, reliability, annual downtime, and costs of implementation and provisioning of the cloud infrastructure. The results point out that service downtime may be significantly decreased by adopting actions such as spare batteries for mobile devices and the adoption of distinct redundancy strategies. We also propose a way to support decision-making considering private and public clouds by comparing some architectural choices while considering factors such as budget expenses for hardware purchases and power consumption. After 36 months, the private cloud has a cumulative cost that is smaller than the cost of a public cloud service.

Virtualization platforms like Proxmox VE, which extend hypervisors such as KVM with orchestration tools, are increasingly adopted in High-performance computing (HPC) for improved flexibility and resource management. This study evaluates Proxmox VE against KVM and bare-metal environments to quantify virtualization overheads in CPU-bound and inter-process communication (IPC) workloads. Using HPL and NetPIPE benchmarks, competitive runs show Proxmox VE's 0.5%-1.1% compute overhead versus bare-metal (KVM: 0.9%-1.2%), rising to 1.8%-2.0% for cooperative MPI-based HPL. Proxmox VE reduces RAM usage by 15%-20% over KVM via dynamic ballooning. Intra-VM IPC achieves native performance (120 Gbps, <0.005 μs), while intra-host IPC sustains 50 Gbps (vs. KVM's 60 Gbps) with 20%-30% latency increases. Inter-host IPC under high contention suffers 30% bandwidth loss and 25%-30% latency degradation. CPU utilization remains stable (50%) across platforms. Results demonstrate Proxmox VE's viability for CPU-bound/single-host HPC, with network optimizations needed for distributed workflows.

The evolution of mobile technology from the fifth-generation (5G) radio access has amplified challenges. As new requirements emerge, there is a need for enhanced computational capacity for planning, operation, and availability posed by 5G. Multi-access Edge Computing (MEC) and Software-defined networks (SDN) are fundamental to addressing capacity planning and availability. This paper presents a hierarchical modeling approach using reliability block diagrams (RBD) and continuous-time Markov chains (CTMC) to estimate the MEC SDN-based availability and capacity-oriented availability (COA). The proposed models allow analytical evaluations for calculating dependability metrics, showing an availability increase from 97.33% to 99.58%, with only two clustered server nodes, effectively reducing annual downtime from 357.32 hours to 0.65 hours.

As organizations adopt Data Mesh to decentralize data ownership, domain teams gain autonomy to manage and evolve their data products. While this fosters architectural flexibility, it also increases the risk of table duplication across distributed environments, affecting governance and storage efficiency. This paper introduces a graph-based methodology to detect such structural redundancies by modeling data architectures as directed graphs and applying subgraph isomorphism algorithms. A user-guided validation step confirms whether identified similarities correspond to actual duplications. The method supports both synthetic and benchmark-based scenarios, including an adapted TPC-DS schema, and enables algorithmic evaluation through execution time (ET), accuracy (ACC), and success frequency (SF). As a case study, two algorithms were tested: VF2 and a hybrid approach called Node Match, which uses degree-based filtering as a pre-step before applying VF2. Node Match demonstrated superior cost-efficiency. A Python-based tool was developed to implement the methodology, enabling graph simulation, interactive validation, and automated metric reporting.

Serverless computing abstracts infrastructure management, enabling cost-efficient and scalable application deployment. However, multiple operations can be performed simultaneously, leading to system degradation of resources exhaustion during prolonged executions. This study assesses resource utilization in serverless environments using Knative, testing four OS-container engine configurations (Ubuntu/Docker, Ubuntu/Podman, Debian/Docker, Debian/Podman) under stress workload. Results show Ubuntu paired with Docker achieves optimal resource efficiency, avoids software aging, and maintains consistent performance, making it the most effective configuration for scalable serverless deployments. The absence of progressive degradation in resource metrics underscores serverless architectures’ potential to resist software aging through autoscaling. Findings provide actionable insights for optimizing resource management in open-source serverless platforms.

Over the years, data centers have evolved to meet the most diverse demands of web services and applications, such as cloud computing, e-commerce, social networking, artificial intelligence, streaming, and healthcare services. These large data centers must meet several dependability requirements to ensure quality of service with high reliability and availability, reducing interoperability time, since this is an important competitive factor for companies. Data centers have three infrastructures: IT (Information Technology), electrical, and cooling. Therefore, for data centers to achieve high availability, these infrastructures must be designed according to redundancy specifications ranging from Tier I to Tier IV. This paper presents an SPN (Stochastic Petri Nets) model for planning the infrastructure of a Tier I data center, composed of the three subsystems mentioned. The evaluation will focus on key metrics, including availability, reliability, downtime, and uptime. A sensitivity analysis of the constructed model was performed to verify which subsystem impacts the system behavior.

Internet of Things (IoT) applications equip rural producers with decision support tools and automated solutions that boost agribusiness productivity, quality, and profit. However, most poultry farmers still use conventional methods of operation in which human workers carry out all routines for monitoring and controlling their farms at the expense of greater productivity. One of these human activities is manual weighing, which can be replaced by nonintrusive methods such as computational vision applications that estimate live poultry's weight using video cameras. Since Internet of Things (IoT) devices may have low computing power limiting the ability to process the data locally, they can transfer it to a fog or cloud data center, where they are processed. This article aims to conduct a dependability study of a poultry house automated with a computer vision-based system for estimating poultry weight considering hierarchical models (e.g., Markov chain, reliability block diagram, and closed-form equation) to represent the whole system and obtain steady-state availability and annual downtime. In addition, our purpose is to consider and compare different architectural solutions, such as edge and fog computing-based solutions. The proposed solution verified that a cloud-based application with no redundancy has a downtime of 34.14% and 9.176% hours when considering a hot-standby redundancy strategy in the office node of a cloud solution.

Design Thinking (DT) has become a fundamental tool for innovation and software quality improvement since it focuses on the user experience with approaches that provide for multidisciplinarity, prototyping, and stakeholder active collaboration. This paper aims to present a set of evidence in the software development context that employs Design Thinking concepts and practices in their activities, i.e., identifying tools, models, practices, and challenges experienced through DT in development teams. Thus, as methodology, through a set of steps guided by the research protocol, this research carried out a systematic mapping using three scientific bases, IEEE, ACM, and SCOPUS, which after verification and analysis of the results, 86 primary studies were worked on. These results can help practitioners and researchers in the area to better understand the challenges and implement more effective solutions to improve their tasks in software development using design thinking approaches. They also provide a mapping of the research about design thinking in software development, identifying areas where further research is needed.

Cloud computing is a method for accessing and managing computing resources over the internet, providing flexibility, scalability, and cost-efficiency. Cloud computing relies more and more on OS-level virtualization tools such as Docker and Podman, enabling users to create and run containers, which are widely used for application management. Given its significance in cloud infrastructures, it is crucial to have a better understanding of OS-level virtualization performance, especially in tasks related to container management (ex: creation, destruction). In this paper, we conducted benchmarking tests on Docker and Podman to evaluate their performance in various container management scenarios and with different image sizes. The results revealed that Podman excels in quickly instantiating small-sized containers, while Docker demonstrates superior performance with larger-sized containers.

The demand for various multimedia services has influenced the convergence of existing communication networks, allowing the integration of multiple traffic types through a single routable protocol (IP). Thus, we can adopt different strategies to meet quality requirements, ensure uninterrupted operations, high reliability, and lower maintenance costs. The model-based assessment enables the analysis of systems dependability without interference in the existing system and makes it possible to evaluate complex scenarios whose actual implementation may be impracticable due to costs. Evaluating a system in operation may not be trivial, or it can hinder the continuity of services. In this sense, we evaluated converged network infrastructures considering dependability metrics such as availability, reliability, mean time to failure, and downtime. We use sensitivity analysis to identify the components that can be essential on availability impact. The results allow us to identify the bottlenecks of the represented architectures.

Cloud-based architectures have grown in recent years, especially the interest in container-based solutions have sharply increased by enterprises worldwide. Containers are a form of lightweight virtualization that can be used to provide cloud services. Adopting this kind of technology in a bare- metal context is becoming strong because they can offer many benefits, like performance efficiency and costs reduction. Docker is a widespread platform for the creation and management of containers. As in any computational cloud service, Docker environments must deal with the intensive workload and may have a long-term life cycle, which might trigger some problems that compromise the system dependability. The software aging phenomenon is one of these likely problems. It is a process of cumulative errors or system misbehavior that leads to application failures and performance degradation throughout its runtime. This paper aims to monitor and evaluate software aging effects on the Docker platform in a cloud computing environment. We conducted two experimental studies with automated workloads to simulate containers' life cycle and the intensive use of Docker features, while the system was monitored. The results show high resource consumption by the operating system's network utility, in addition to memory fragmentation in the sub-processes of the Docker platform. Trends of increasing resident memory consumption were also observed in one of these scenarios.

Demand for performance, availability, and reliability in computational systems has increased lately. Improving these aspects is an important research challenge due to the applications and users diversity. In this paper, we propose a hybrid auto-scaling approach that uses reactive and proactive solutions. We focus on the operation of a Web server, where we applied the characteristic of CPU-bound in the application. The approach proposed is a CPU usage monitor and auto-scaling management that is used to improve the system's throughput and reduce the CPU idle time. Our proposal is implemented using triggering thresholds and five forecasting models: Drift, Simple Exponential Smoothing, Holt, Holt-Winters and ARIMA. The main goal of this research is to achieve a better QoS related to the cloud computing environment. One of the obtained results shows that our method represents a throughput improvement of 12.11% by using our proposal, instead of only using a threshold-based technique.

Distributed computing is bringing many advantages in cost, flexibility and availability. However, it increases the demand for performance and reliability. Resources such as CPU, memory, storage and network bandwidth, are very susceptible of presenting software aging issues. Therefore, proactive actions, also known as software rejuvenation must be performed to avoid these issues. The identification of the best moment to perform software rejuvenation is not a simple task, mostly because it may affect the system's availability and reliability. To overcome this problem, we propose an automatic forecasting strategy to support the system administrators to choose the best moment to perform software rejuvenation. Our strategy uses six time series techniques: Drift, Simple Exponential Smoothing, Holt, Holt-Winters, Linear Regression, and ARIMA. In our proposal, the most suitable one is chosen automatically as the best fit for a particular scenario. Three case studies were performed to evaluate the efficiency of our automatic strategy. Our proposal aims to increase the system's availability while decreasing the QoS violation probability. In one of our experiments, we can observe a reduction of 92.3% in the system's downtime. This research supports decision making activities and opens possibilities to foster the usage of forecasting strategies when dealing with software aging phenomenon.

The Distributed Software Development has become an option for software companies to expand their perspective and work with dispersed teams, exploiting the advantages brought by this approach. However, this way of developing software enables new challenges to arise, such as the inexistence of a formal, normalized model of a project’s data and artifacts accessible to all the individuals involved, which makes it harder for them to communicate, understand each other and what is specified on the project’s artifacts. This paper proposes a knowledge base called DKDOnto, a domain-specific ontology for distributed development, aiming to help projects with a common vocabulary, allowing to assist better the distributed software development process.

The adoption of stochastic models has been one of the central topics in various architectures. One important step to adopt it is model validation, which aims at obtaining reasonable models to represent actual behavior of services components, it has been essential to validate models against actual measurements. System-wide model simulation results can be compared with recordings from the measurement. In this paper, we accomplish the model validation to Stochastic Petri Net (SPN) models created to evaluate VoD system hosted on a private cloud system, considering MP4, MPG, Ogg and FLV formats. We proposed the performance model to represent packet transfers, and to compute performance metrics, such as throughput, packet loss, and service delivery reliability. The SPN model enables a compact representation of a large number of packets generated by video streaming. We validate the models through experimental data, using a VoD streaming service in a cloud infrastructure testbed. We demonstrate that the proposed models are accurate and can be utilized for planning the quality of service (QoS) of corporate video streaming infrastructures. A case study is presented to compare the behavior of the system. Results indicate that the model validation way adopted can be a good solution for models validation. A case study is conducted to compare the behavior of the system under distinct network scenarios. The results indicate that video streaming QoS under 3G (EVDO) networks is significantly worse than other wireless technologies, such as WiFi, 3.5G (HSPA+), and 4G (LTE).

Attacks that deny access to a service provider can occur anytime, anywhere, and most usually occur with little or no warning. Many small and midsize companies are not prepared to handle a significant outage. For an enterprise to face up to an attack of this type, it must possess a bandwidth higher than that of the attack, an infrastructure with redundant components, regular backups, firewalls for monitoring the threats and other proactive and reactive mechanisms. Otherwise, the service will be interrupted, increasing the chances of financial losses. Hierarchical modeling approaches are often used to evaluate the availability of such systems, thereby leveraging the representation of multiple failure and repair events in distinct parts of the system. This paper evaluates the impact of a distributed denial-of-service attack and malicious software in computer systems. We propose hierarchical models that represent the behavior of major system components and assess the effects of a DDoS and Malware attack on the system availability. We also estimate the likelihood of an attack, attacker benefits, feasibility, the pain factor and the propensity of the offense were present. They enable a direct analytical solution for large systems. The attack tree indices show the impact of simultaneous attacks on a computer system and the several threats which will maximize the system downtime. The results obtained from the attack tree analysis allow to plan and improve system's availability, maintainability, and reliability.

Users of computer systems wish to keep their personal data safe, updated, fair and accessible by other terminals, like personal computers, smart phones, portable consoles and PDAs. To perform these activities, one technology has become popular in our daily lives: data synchronization. Companies that provide this kind of service must do it with the greatest availability possible since their clients need their data to be available whenever they want to access it, and their customers in the legal field must avoid financial losses through SLA contract breaches. This paper presents hierarchical models for evaluating the availability of a data synchronization server infrastructure. The results show an availability of 98.82% for the proposed architecture, which means an annual downtime of 103 hours, this is more than 4 days of unavailability, where users cannot perform data synchronization.

High Performance Computing (HPC) aggregates computing power in order to solve large and complex problems in different knowledge areas. Nowadays, HPC users can utilize virtualized infrastructures as a low-cost alternative to deploy their applications. However, virtualization brings some challenges for HPC, specially in regard to overhead caused by hyper visors. In this work, our main goal is to analyze the performance of two hyper visors (KVM and Virtual Box) under HPC activities, considering full virtualization, and Para virtualization approaches. We used the HPC Challenge Benchmark (HPCC) to evaluate processor, RAM, inter-process communication and network communication performance. Our results show KVM in Para virtualization mode has a similar performance of a native cluster.

The reduction of cloud computing energy consumption can be related to the application layer management with virtualization services, or a software layer. All clouds demand a power infrastructure with high availability. A data center power system is generally classified according to four redundancy levels, named tier I to IV. This paper proposes a power load distribution algorithm in depth search (PLDA-D) to optimize the power distribution of private cloud electrical infrastructures. The PLDA-D adopts the Energy Flow Model (EFM) as the basis to design power infrastructures. The EFM is a model that computes sustainability impacts and cost issues while it respects the restrictions of each component to provide energy. In addition, a case study illustrates the applicability of the proposed PLDA-D through the analysis of a private cloud running over a data center made up of tier II electrical architecture. Considerable results were achieved though the evaluation of the proposed algorithm. For instance, a reduction of 2.95% in energy consumption as well as an improvement of over 17% on the environment impact.

The use of mobile cloud computing infrastructure can be used to improve the delivery of health services. This paper proposes a high-level model that characterizes the behavior of an mHealth system. The main objective of this work is to identify the probability of a message being delivered at a time t, whereas several components involved may fail. Various scenarios were analysed considering different possibilities for communications, battery discharge rates and timeouts. The results show that, in the best case, a message may be delivered in 1.3 minutes with a 0.9999 probability, but in the worst case may take up to 8.8 minutes.

Distributed storage systems (DSS) are becoming even more important and complex due to the emergence of paradigms such as cloud computing, and the challenges for dealing with big data. DSS provide a set of services such as publishing, file sharing and high performance utility storage. In addition, there is a growing demand to support these services with high scalability, availability and low cost. Sheepdog is a distributed storage system which aims at providing storage volumes with high availability, using clusters of common machines. This type of storage system may prove useful in the VMS (Video Monitoring Service) market and as remote storage for cloud-based applications. This paper analyzes the scalability of Sheepdog clusters, by measuring the performance with different number of nodes and different replication degrees. The system analyzed here showed better performance for read operations in small cluster sizes than in larger ones. On the other hand, proper scalability was found for the write throughput, since no significant performance degradation was noticed when the number of cluster nodes increased.

The need for high reliability, availability and performance has significantly increased in modern applications, that handle rapidly growing demands while providing uninterruptible services. Cloud computing systems fundamentally provide access to large pools of data and computational resources. Eucalyptus is a software framework largely used to implement private clouds and hybrid-style Infrastructure as a Service. It implements the Amazon Web Service (AWS) API, allowing interoperability with other AWS-based services. This article investigates the software aging effects in the Eucalyptus framework, considering workloads composed of intensive requests for remote storage attachment and virtual machine instantiations. We found problems that may be harmful to system dependability and performance, specifically regarding to RAM memory and swap space exhaustion, besides highly excessive CPU utilization by the virtual machines. We also present an approach that applies time series analysis to schedule rejuvenation, so as to reduce the downtime by predicting the proper moment to perform the rejuvenation. We experimentally evaluate our approach using an Eucalyptus test bed. The results show that our approach achieves higher availability, when compared to a threshold-triggered rejuvenation method based on continuous monitoring of resources utilization.

With the increasing adoption of cloud computing environments, studies about high availability in those systems became more and more significant. Software rejuvenation is an important mechanism to improve system availability. This paper presents a comprehensive availability model to evaluate the utilization of the live migration mechanism to enable VMM rejuvenation with minimum service interruption. Live migrations are performed observing a time-based trigger. We evaluate five different scenarios, with distinct time intervals for triggering the rejuvenation. The results show that the live migration can significantly reduce the system downtime.

This paper proposes an investigative approach to indicators of software aging in applications developed for Android, a Linux-based operating system primarily designed for touch screen mobile devices such as smartphones and tablets. Software aging is a degrading factor in systems, leading to poor performance, failures, and may ultimately result in system downtime. The methodology proposed identifies memory leaking in Android applications. In order to test the approach, case studies were performed with the Monkey tool as workload generator, employing Linux utilities to monitor the environment. Communication between the computer and the smartphone was handled with the ADB tool, a toolkit included in the Android SDK package. Experimental results confirmed both the effectiveness of the procedure and the existence of software aging phenomenon in the Foursquare application running on the Android OS platform.

Software aging refers to performance degradation faced on long-running execution software. The software infrastructure supporting Cloud Computing systems may suffer from aging effects. Such a phenomenon was already reported for virtual infrastructure managers and virtual machine monitors. In this sense, software rejuvenation is a technique of proactive fault tolerance to deal with effects of software aging. This paper proposes a comprehensive availability model for two different types of rejuvenation scheduling based on live migration mechanism, one with a test before migration, and other without. We evaluate five different scenarios, with distinct time intervals for triggering the rejuvenation. The main goal is to explain the benefits gained from the use of this type of rejuvenation technique, as well as understand the differences between the two approaches analyzed. The results show that the utilization of a schedule with a checking mechanism before rejuvenation can bring a significant improvement on system availability.

Mobile cloud computing refers to abstraction of computational power to outside of the mobile device, processed in the "clouds". This paper provides an availability and energy consumption study using hierarchical heterogeneous modeling. Different scenarios were analyzed considering wireless communication technologies, such as 3G and WiFi. The results shows that the 3G protocol causes a lower availability and is the greater responsible for the discharge of battery, but when is combined with WiFi protocol providing communication redundancy, we have the best results of availability and better results of uninterrupted operation time of battery.

Cloud computing is a computational paradigm with increasing adoption because it offers resources as services in a dynamically scalable way through the Internet. The constant concern in providing cloud computing services in a reliable and uninterrupted manner inspires availability and reliability studies. A feasible method of performing such studies is through automated fault injection, enabling to observe the behavior of the cloud architecture under many conditions. This paper presents a fault injection tool, named EucaBomber, for reliability and availability studies in the Eucalyptus cloud computing platform. EucaBomber allows to define the probability distribution associated to the time between generated events. The efficiency of EucaBomber is verified through testbed scenarios where faults and repairs are injected in a private Eucalyptus cloud. The experimental results are cross-checked with results estimated from a Reliability Block Diagram, using the same input parameters of the experimental testbed. The test scenarios also illustrate how the tool may assist cloud systems administrators and planners to evaluate the system's availability and maintenance policies.

High availability in cloud computing services is essential for maintaining customer confidence and avoiding revenue losses due to SLA violation penalties. Since the software and hardware components of cloud infrastructures may have limited reliability, fault tolerance mechanisms are a means of achieving the necessary dependability requirements. This paper investigates the benefits of a warm-standy replication mechanism in a Eucalyptus cloud computing environment. A hierarchical heterogeneous modeling approach is used to represent a redundant architecture and compare its availability to that of a non-redundant architecture. Both hardware and software failures are considered in the proposed analytical models. The results show an enhanced dependability for the proposed redundant system, as well as a decrease in the annual downtime. The results also demonstrate that the simple replacement of hardware by more reliable machines would not produce improvements in system availability to the same extent as would the fault tolerant approach.

The need for reliability, availability and performance has increased in modern applications, which need to handle rapidly growing demands while providing uninterrupted service. Cloud computing systems fundamentally provide access to large pools of data and computational resources. Eucalyptus is a software framework used to implement private clouds and hybrid-style Infrastructure as a Service. It implements the API Amazon Web Service (AWS), allowing interoperability with other AWS-based services. Elastic block storage is a technology which provides flexible allocation of remote storage volumes to the virtual machines running in a cloud computing environment. This work investigates the software aging effects on the Eucalyptus framework, considering workloads composed of intensive requests for attaching remote storage volumes to virtual machines. The results evidenced problems that may be harmful to system dependability and its performance due to RAM memory exhaustion and subsequent use of swap memory, besides high CPU utilization by the virtual machines and subsequent increase in the response time of applications running on the VMs.

Demands on software reliability and availability have increased due to the nature of present day applications. Cloud computing systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similarly to existing grid and HPC resource management and programming systems. This work investigates the software aging effects on the Eucalyptus cloud computing infrastructure considering workloads composed of provisioning different types of virtual machines.