Currently, enormous amounts of data are continuously processed to support our daily activities, such as managing bank accounts, streaming movies, or interacting on social networks. In recent years, cloud infrastructures have proven to be a reliable solution, not only for processing this data but also for enabling users worldwide to access it remotely. However, this processing demands vast computing resources, leading to significant energy consumption. In this paper, we present a strategy to address this problem by combining multi-objective optimization techniques with Metamorphic Testing (MT) and simulation tools to optimize cloud systems, focusing on both performance and energy consumption. To achieve this, several multi-objective genetic algorithms (MOGAs) have been integrated into the MT-EA4Cloud framework, a solution that previously applied single-objective evolutionary algorithms with MT. To determine the suitability of the proposed approach, an empirical study was conducted to analyze the behavior of the different MOGAs included in the framework. In this study, various test sets and two distinct workloads – inspired by big data analytics operations – were created to represent multiple cloud scenarios. The results clearly demonstrate that MOGAs can be effectively combined with MT to optimize cloud systems while considering multiple objectives – in this case, performance and energy consumption. A careful analysis of the results indicates that increasing the mutation rate leads to the best outcomes. In general, the NSGA-II algorithm has produced the best results in the experiments conducted in this study.

Testing is essential to improve the correctness of software systems. Metamorphic testing (MT) is an approach especially suited when the system under test lacks oracles, or they are expensive to compute. However, building an MT environment for a particular domain (e.g., cloud simulation, model transformation, machine learning) requires substantial effort. Our goal is to facilitate the construction of MT environments for specific domains. We propose a model-driven engineering approach to automate the construction of MT environments. Starting from a meta-model capturing the domain concepts, and a description of the domain execution environment, our approach produces an MT environment featuring comprehensive support for the MT process. This includes the definition of domain-specific metamorphic relations, their evaluation, detailed reporting of the testing results, and the automated search-based generation of follow-up test cases. Our method is supported by an extensible platform for Eclipse, called Gotten. We demonstrate its effectiveness by creating an MT environment for simulation-based testing of data centres and comparing with existing tools; its suitability to conduct MT processes by replicating previous experiments; and its generality by building another MT environment for video streaming APIs. Gotten is the first platform targeted at reducing the development effort of domain-specific MT environments. The environments created with Gotten facilitate the specification of metamorphic relations, their evaluation, and the generation of new test cases.

Abstract Nowadays, cloud‐native software architectures have a significant relevance due to the speed and agility they provide. These properties lead relevant organizations in different industries, like video streaming (Netflix), car‐sharing (Uber, Cabify), banking (BBVA, HSBC), and governmental agencies (NASA, FBI, CERN, ESA) to heavily rely on cloud‐native software to run their business‐critical applications. Additionally, including fault injection actions in the production infrastructure allows companies to have consistent environments, to improve applications dependability against unexpected failures, to provide better user experience, and to improve the overall system quality. Thus, cloud computing technologies allow development teams to rapidly create complex systems and to continuously deploy them, at a global scale. This work describes Pystol, a novel fault injection platform—represented as a Software Product Line—to analyze the effects caused by a wide spectrum of adverse conditions. Pystol is designed to be executed on top of cloud‐native environments, either in private or public clouds. The proposed architecture shows a way for representing feature models based on Unified Model Language (in short, UML) component diagrams. Furthermore, we present a thorough empirical study carried out in real‐world environments, providing promising results.

Abstract In this paper, we investigate how to improve the profits in cloud infrastructures by using price schemes and analyzing the user interactions with the cloud provider. For this purpose, we consider two different types of client behavior, namely regular and high-priority users. Regular users do not require a continuous service, and they can wait to be attended to. In contrast, high-priority users require a continuous service, e.g., a 24/7 service, and usually need an immediate answer to any request. A complete framework has been implemented, which includes a UML profile that allows us to define specific cloud scenarios and the automatic transformations to produce the code for the cloud simulations in the Simcan2Cloud simulator. The engine of Simcan2Cloud has also been modified by adding specific SLAs and price schemes. Finally, we present a thorough experimental study to analyze the performance results obtained from the simulations, thus making it possible to draw conclusions about how to improve the cloud profit for the cloud studied by adjusting the different parameters and resource configuration.

In this paper, we model cloud systems and the user interactions with the cloud provider using the UML2Cloud profile.In general, users request virtual machines according to their needs, but they can also subscribe to the cloud provider and wait to be notified when the requested resources are not available.In this case, users indicate a maximum subscription time, so once this time elapses without being notified, users leave the system unattended.In this paper, then, we present an exhaustive research study to measure how the user subscription times affect the overall system responsiveness.In this study, three different cloud configurations are analyzed.Each cloud processes several workloads, which are generated using two distribution functions for the user arrivals, namely a normal and a cyclic normal distribution.The purpose of this study is to find out the inflection point for the waiting time of the users, from which the cloud responsiveness and its performance do not improve.The obtained information is therefore useful for the cloud provider to improve the configuration of the cloud.

Two crucial aspects for the trustworthy utilization of domain-specific languages (DSLs) are their semantic correctness, and proper testing support for their users. Testing is frequently used to verify correctness, but is often done informally -- which may yield unreliable results -- and requires substantial effort for creating suitable test cases and oracles.

Cloud providers face the challenge of managing large amounts of heterogeneous resources in real time. It is usually very costly to conduct experiments with real cloud systems. Therefore, tools to analyze and evaluate cloud scenarios and experimental studies are very useful for them. In this paper, we model cloud systems and the user interactions with the cloud provider using the UML2Cloud profile. In general, users request virtual machines according to their needs, but they can also subscribe to the cloud provider and wait to be notified when the requested resources are not available. In this case, users indicate a maximum subscription time, so once this time elapses without being notified, users leave the system unattended. Thus, we present an exhaustive experimental study to measure how the user subscription times affect the overall system responsiveness. To this end, four different cloud configurations are analyzed, and the workloads for these studies are produced by using three distribution functions for the user arrivals, namely, a uniform, a normal, and a cyclic normal distribution. Furthermore, we also analyze the cloud performance with a workload obtained from a real trace. The purpose of this study is to find out the inflection point for the waiting time of the users, from which the cloud responsiveness and its performance do not improve. The obtained information is, therefore, useful for the cloud provider to improve the configuration of the cloud.

The validation of a cloud system can be complicated by the size of the system, the number of users that can concurrently request services, and the virtualization used to give the illusion of using dedicated machines. Unfortunately, it is not feasible to use conventional testing methods with cloud systems. This article proposes a framework, called TEA-Cloud, that integrates simulation with testing methods for validating cloud system designs. Testing is applied on both functional and nonfunctional aspects of the cloud, like performance and cost. The aim of the framework is to provide a complete methodology to help users to model both software and hardware parts of cloud systems and automatically test the validity of these clouds using a cost-effective approach. Metamorphic testing is used to overcome the lack of an oracle that checks whether the behavior observed in testing is allowed. Metamorphic testing is based on metamorphic relations (MRs). We define three families of MRs, which target issues such as performance, resource provisioning, and cost. TEA-Cloud was evaluated through an empirical study that used fault seeding (mutation) and ten MRs for testing different cloud configurations. The results were promising, with TEA-Cloud finding all seeded faults.

Designing data-centers that provide an acceptable costperformance ratio is challenging.Generally, a wide spectrum of components must be previously analyzed, such as the kind of applications to be executed in the data-center, computing/storage requirements and the network topology, among others.Since each one of these components has a direct impact on the overall system performance, the design process is complex and difficult, which usually requires the intervention of an expert.We propose a model-based approach to design datacenters.For this purpose, we have created a meta-model that describes the structure of data-center models.Then, a set of expert rules can be used to detect sub-optimal configurations, and (in some cases) correct the design.Datacenter models can be simulated, to assess their performance and scalability, for which we use a code generator into the SIMCAN tool.We have implemented our approach as an Eclipse plugin, and illustrate the usefulness of some expert rules by showing the efficiency and scalability gains of the optimized model with respect to the original one.

During the last years the use of cloud computing environments for both researchers and enterprises is a major trend. On the one hand, cloud computing is a flexible and scalable paradigm that offers hardware and software services to be purchased by users. On the other hand, users have extra costs in several procedures, like calculating how many resources are needed by the applications. These tasks could be expensive and time-consuming. In this work we present iCanCloud, a simulation platform for modeling and simulating cloud environments. iCanCloud provides a complete set of modules for modeling with high level detail, a cloud computing environments and their underlying architecture.

Nowadays, satisfying the global throughput targets of each application in High Performance Computing systems is a difficult task because of the high number of architectural configurations having a considerable impact on the overall system performance, such as the number of storage servers, features of the communication links, number of CPU cores per node, etc. In this paper we have performed a thorough study of the compared performance of scaling up HPC cluster architectures using a checkpointing application model. This study is specifically focused on multi-core HPC clusters and the scaling process is oriented towards the three main resources: computing power, communications and storage. The main goal of this work is to evaluate and analyze how evolves both scalability and bottlenecks existent on different HPC multi-core architectures using different architectural configurations. In order to achieve this goal, a set of simulation experiments has been achieved using a simulation framework, called SIMCAN, specifically designed for modeling and simulating HPC architectures. The results obtained show that the computing power is well suited thanks to the multi-core processors, while the problems are found on the storage and on the communications channels, being the storage network the main bottleneck.

The energy crisis of the last years and the ever increasing conscience about the negative effects of energy waste on the climate change have brought the sustainability both into public attention, industry, and scientific scrutiny. Energy demand has been increasing in many subsystems, specially in data centers and supercomputers. This paper considers the problem of saving energy on storage systems taking advantage of SSD devices. SSDs and magnetic disk devices offer different power characteristics, being SSD devices much less power consuming than conventional magnetic disk devices. We propose a novel power saving solution based on SSD devices, namely SSD-PASS. Our storage system obtains benefits of permanent caching on SSDs in storage nodes. Nowadays we can find solutions that do not consider the viability and feasibility of the SSD-based storage systems, in terms of monetary cost. We present a cost analysis and evaluate our proposed architecture, in terms of saved energy and performance. Our cost model takes into account magnetic disk and SSD devices reliability metrics, current energy prices, and replacement costs. The experimental results demonstrate that our solution achieves a significant reduction in energy consumption and subsequent monetary savings by up to 66\%. We have evaluated the proposed approach with realistic workloads of three well-known HPC applications.

Nowadays, simulation of large distributed environments is a very important research field. Due to the large number of components to be simulated, the execution of those simulations requires a high level of resources such as CPU and memory. Thus, in order to increase the performance of those simulations, a feasible solution consists on splitting the complete model in sub-domains, where each sub-domain is executed in a single machine. In this paper we propose a strategy to automatically accomplish the parallelization of those environments, which has been implemented and tested in the SIMCAN simulation platform.

In this paper we present new techniques for simulating high performance MPI applications on large storage networks. Performance analysis of high performance application on large storage networks is a very complex and time-consuming task. However, modelling and studying the behaviour of any application on complex network architectures is crucial to obtain good performance. The goal of this work is to predict both scalability degree and performance of any high computing applications on any network architecture. A very interesting feature of this work is that our approach does not require to modify the application in order to simulate its behaviour. Also, there is no need to modify the simulator code to test different architectures. It can be done just creating a new configuration file. In order to perform those analyses we have used SIMCAN, a simulation tool to analyzing high-performance I/O architectures, developed at University Carlos III de Madrid. To validate this work we have used the BIPS3D application on several hardware-based architectures and on our simulator. The comparative results of those environments are presented to show the accuracy and efficiency of our approach.

In this paper we introduce M-PLAT, an intelligent tutoring system for helping students to learn the basics of programming languages. In fact, the M-PLAT system represents a full collection of intelligent tutoring systems, and due to its modular and hierarchical architecture it can be upgraded to deal with a new programming language that is not yet included in the system. Thus, this tutoring system is not limited to a unique programming language, making M-PLAT a very scalable system. The best important feature of our system is that M-PLAT dynamically adapts itself to the learning style of each student, optimizing the learning time to each student.