Emanuel Ferreira Coutinho

Elasticity in cloud computing: a survey

Cloud computing is now a well-consolidated paradigm for on-demand services provisioning on a pay-as-you-go model. Elasticity, one of the major benefits required for this computing model, is the ability to add and remove resources “on the fly” to handle the load variation. Although many works in literature have surveyed cloud computing and its features, there is a lack of a detailed analysis about elasticity for the cloud. As an attempt to fill this gap, we propose this survey on cloud computing elasticity based on an adaptation of a classic systematic review. We address different aspects of elasticity, such as definitions, metrics and tools for measuring, evaluation of the elasticity, and existing solutions. Finally, we present some open issues and future directions. To the best of our knowledge, this is the first study on cloud computing elasticity using a systematic review approach.

FairCPU: Architecture for Allocation of Virtual Machines Using Processing Features

This paper proposes an architecture to handle the allocation of virtual machines based on the processing power for heterogeneous Clouds, where there is a wide variety of CPU types. Our major contribution is a novel representation of the processing capacity in terms of the Processing Unit (PU) and the CPU usage limitation in order to isolate the processing capability from the Physical Machine (PM) where the Virtual Machine (VM) is allocated. The efficiency of the proposed architecture is validated by extensive replications of five experiments using a real private cloud. The results show that it is possible to use the proposed idea to define a PU, supported by the CPU usage limitation, to enable the VMs processing power remain at the same level regardless of the PM.

Performing computation offloading on multiple platforms

An offloading framework designed for supporting multiple platforms is proposed.The solution supports Android and Windows Phone mobile applications.Developers can use static or dynamic offloading decision.The offloading technique improves the performance of mobile applications.The type of serialization used impacts the offloading performance. Mobile devices such as smart phones and tablets are increasingly important tools in daily routine. These devices generally interact with more powerful machines usually hosted on public clouds. In this context, this paper presents MpOS (Multiplatform Offloading System), a framework that supports a method-based offloading technique for applications of different mobile platforms (Android and Windows Phone). In addition, details of MpOS main components and services as well as code examples are presented. To evaluate the proposed solution and to analyse the impact of different serialization types on the offloading performance, we developed two applications and performed several experiments on both Android and Windows Phone platforms using WiFi and 4G/LTE connections to access the remote execution environments. Our results state that offloading to a cloudlet has provided the best performance for both Android and Windows Phone platforms, beyond showing that the type of serialization used by the framework directly impacts on the offloading performance.

State of the art and challenges of security SLA for cloud computing

There are users and organizations that resist adopting cloud computing solutions, due to concerns about the security and privacy of their data. A Service Level Agreement (SLA) can be used to address these concerns, increasing trust in the purchased services through the clear description of the guarantees offered by the provider to the subscribers. For this purpose, the authors performed a literature systematic mapping to enumerate existing solutions and open issues in security SLAs in cloud computing. This review is presented in this paper as well as an analysis of the state of art. This paper also presents a classification of the selected papers and a discussion about management of security SLAs in clouds.

An Autonomic Computing-based architecture for cloud computing elasticity

Elasticity is an important feature of cloud computing, and can be understood as how a computational cloud fits to variations in their workload by provisioning and deprovisioning resources. Autonomic Computing brings many concepts quite useful in the construction of elastic cloud computing solutions, such as control loops and thresholds-based rules. This paper proposes an elastic architecture for cloud computing based on concepts of Autonomic Computing. For its validation, we designed two experiments using microbenchmarks, applied in both private and hybrid clouds. Results shown cloud computing and Autonomic Computing may work well together in the elasticity provisioning.

An analysis of elasticity in cloud computing environments based on allocation time and resources

Elasticity is a key feature of cloud computing. Its application has become an increasing need due to the dynamic nature of different applications and different workloads imposed by them. This article aims to analyze the behavior of the elasticity in a cloud computing environment, based on some metrics related to response time of allocation operations and used resources in a cloud computing environment. A proposal of seven metrics for evaluation of the elasticity is presented. An experiment to validate the proposal and analysis of elasticity in a cloud computing environment is described with microbenchmarks and a scientific application. At the end, a discussion of the results and future work.

Decision Tree-Based Approaches for Handling Offloading Decisions and Performing Adaptive Monitoring in MCC Systems

Mobile cloud computing (MCC) has emerged as a solution to overcome the resource constraints of mobile devices by using computation offloading to execute mobile application tasks on remote servers, thus enhancing performance and reducing the energy consumption of mobile devices. Nevertheless, the effectiveness of an offloading solution is determined by its ability to infer when offloading will improve performance. In this context, several solutions have been proposed to handle computational offloading operations and the decisions of when and where to offload. The problem is that such decisions depend on periodic monitoring of several metrics and usually involve compute intensive task that, when executed on mobile devices, can contribute to overhead the system. Thus, this paper proposes a novel approach for handling offloading decisions using decision trees and an adaptive monitoring scheme that allows MCC systems to monitor only the metrics that are relevant to the offloading decision. The results show that computation offloading can be beneficial for improving the performance of mobile applications and the energy consumption of mobile devices can be reduced by using the proposed adaptive monitoring scheme.

Applying design thinking in disciplines of systems development

Apply theory and practice is usually a difficult task to lead in undergraduate courses. Generally disciplines involving development projects are used as an attempt to minimize this difficulty. One way of conducting this kind of discipline is defining a development methodology to be applied. This paper aims to present an approach based on design thinking to develop products and services that involve software, aligned with the best practices of Software Engineering. An assessment of the approach was carried out with students of the discipline, concluding that its use contributed to improve communication, documentation and projects monitoring.

An architecture for providing elasticity based on autonomic computing concepts

Elasticity is a feature quite important for cloud computing and it is related to how a system autonomously adapts its capacity over time to fit the workload variation. In this context, this paper proposes an elastic architecture for cloud computing based on autonomic computing concepts, such as control loops and thresholds-based rules. In order to validate the proposed solution, we designed two experiments that use microbenchmarks on private and hybrid cloud environments. The results show cloud computing and autonomic computing may be leveraged together for elasticity provisioning.

Performance analysis on scientific computing and cloud computing environments

The search for better execution times was one of the motivations for the emergence of High Performance Computing (HPC) whose importance has obtained significative preponderance in the domain of scientific computing, either in applications for an academic or industrial environment. Cloud Computing proposes the integration of various technological models for the provision of a hardware infrastructure, development platforms and applications as services based on-demand. However, with the emergence of cloud computing, a new challenge arose which was to allow the execution of HPC applications in this new environment. This paper aims to demonstrate the feasibility of using a local cluster built on a private cloud for HPC experiments, and compare with applications running in a real environment for HPC.