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.

Service providers may build elastic systems on public clouds. The public cloud may offer economies of scale, but there are some considerations to take into account. Infrastructure-as-a-Service (IaaS) providers charge their customers by the use of virtual machines (VMs), and wrong deployment decisions may lead to financial losses. This paper proposes an approach for estimating systems' performance, use of VM instances and its related costs on a public cloud. This work proposes a Stochastic Petri Net (SPN)-based formal modeling strategy to represent elastic systems deployed on a public cloud and a cost model to predict the use of VM instances. The approach enables designers to plan and tune elastic architectures based on Mean Response Time (MRT) estimation. Using our strategy it is possible to estimate the impact of each deployment configuration on the evaluated metrics. Our modeling strategy considers reactive scaling policies. The model represents a remote infrastructure for supporting external users requests. By combining different instance types, simultaneous jobs per VM instance, stepsizes and scaling thresholds, it is possible to offer different response times for each deployment configuration. One case study was performed to evaluate the approach. Our approach has proven to be feasible and it highlights the most effective scenarios to minimize MRT and reduce costs.

The blockchain shared ledger emerged as an alternative to the bureaucratic banking system that may take days to confirm a payment or a transfer between clients. The blockchain concept evolved and became viable for various applications beyond the domain of financial transactions. Blockchains become a way to reach better relationships through contract validation, documents transfer, and personal and business data security. Recently, the blockchain-as-a-service has debuted on Microsoft Data Centers, and now many share an infrastructure that can change and improve their security routines. This paper evaluates the feasibility of a blockchain-as-a-service infrastructure and helps those who plan to deploy or sell blockchains. A modeling methodology based on Dynamical Reliability Block Diagrams (DRBD) is adopted to evaluate two dependability attributes: system's reliability and availability. The proposed infrastructure contains the minimum requirements to deploy the Hyperledger Cello, a platform to create and manage blockchains. The availability results pointed out a system downtime of 121 hours per year and reliability issues that must be addressed when building blockchain-as-a-service infrastructures.

Mobile Cloud Computing (MCC) helps increasing performance of intensive mobile applications by offloading heavy tasks to cloud computing infrastructures. The first step in this procedure is partitioning the application into small tasks and identifying those that are better suited for offloading. The method call partitioning strategy splits the code into a set of method calls that are offloaded to remote servers. Quite often, many applications need to make use of multiple servers for parallel processing of intensive computational operations. Predicting the behavior of such parallelizable applications is not an easy task. Deciding the number of remote servers determines the performance of the applications and the costs of the cloud usage. On one hand, users are interested in improving the performance of their applications, so they would like to use as many servers as possible, but on the other hand, they would also like to reduce their costs by using fewer cloud resources. In this paper, we propose a Stochastic Petri Net (SPN) modeling strategy to represent method call executions of mobile cloud systems. This approach enables a designer to plan and optimize MCC environments in which SPNs represent the system behavior and estimate the execution time of parallelizable applications.

The evaluation of dependability or performance of general systems usually relies on the assistance of stochastic modeling and simulation tools. Those software packages enables the creation of models and computation of metrics quickly and accurately. This paper introduces the Mercury tool, which is an integrated software that enables creating and evaluating Reliability Block Diagrams, Stochastic Petri Nets, Continuous Time Markov Chains, and Energy Flow Models. Mercury provides a graphical user interface, a script language for command-line interface, and also an API (Application Programming Interface) that enables interaction through external applications. The evaluation of models is not restricted to the assumption of Exponential distributions, which is a common constraint in other similar tools. Mercury implements a simulation framework that allows more than 25 probability distributions, as well as a moment matching method that enables expolynomial -phase-type -distributions for models solved through numerical analysis. This paper presents the main features and methods available in Mercury to aid the dependability and performance evaluation of various systems, for both academy and industry. The accuracy and applicability of the tool is illustrated by a case study of packet loss and throughput for a Video on Demand (VoD) system.

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.

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.

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.