Antonio Roberto Mury

An Analysis of Public Clouds Elasticity in the Execution of Scientific Applications: a Survey

Elasticity can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. It is an important feature provided by cloud computing, that has been widely used in web applications and is also gaining attention in the scientific community. Considering the possibilities of using elasticity in this context, a question arises: “Are the available public cloud solutions suitable to provide elasticity to scientific applications?” To answer the question, in a first moment we present a survey on the use of cloud computing in scientific scenarios, providing an overview of the subject. Next, we describe the elasticity mechanisms offered by major public cloud providers and analyzes the limitations of the solutions in providing elasticity for scientific applications. As the main contribution of the article, we also present an analysis over some initiatives that are being developed to overcome the current challenges. In our opinion, current computational clouds are developing rapidly but have not yet reached the necessary maturity level to meet all scientific applications elasticity requirements. We expect that in the coming years the efforts being taken by numerous researchers in this area identify and address these challenges and lead to better and more mature technologies that will improve cloud computing practices.

Automatic generation of platforms in cloud computing

Cloud computing has been established in recent years as an important area of research. This reality has been consolidated because, currently, tasks such as obtainment, manipulation, sharing and exploitation of large amounts of data are common and require many computational resources, so cloud computing can contribute because it can provide this resources indefinitely, including processing, memory, storage and others, all them for immediate use. Cloud computing has many challenges, among them, the specification of work platforms in a cloud environment. In this context, the use of models can help insofar as they may contain several types of information that can be handled with the intention of automating the process of creation of an environment. A template can contain information about the user, hardware and software that can be used by computer systems to automatically build the necessary infrastructure for the operation of a virtual machine on a cloud computing environment. In addition, models can be easily exported to other formats used by cloud services providers. The use of concepts of Service Level Agreements - SLA to control the utilization of computational resources of a provider is common in computing environments and can be used to ensure the quality of services that being offered. The objective of this work is to combine these paradigms, cloud, models and SLA, in order to use the resources of a computational cloud to provide automatically a platform as a service - PaaS. This project will be developed in a web environment, using a model-driven approach to create the platforms and the management of resources and quality will be made by SLA.

A Walking Dwarf on the Clouds

Scientific computing often requires high performance and distributed computational resources to perform large scale experiments in order to achieve accurate results in due course. These needs have been addressed with dedicated high performance computing (HPC) infrastructures. New models to achieve high performance are the combined multi-core architectures with accelerators (manycore). A new dimension is also added by the Cloud Computing model, which has garnered significant attention from industrial and scientific community, as a potential model to address a broad array of computing needs. Even so, it is necessary to evaluate how scientific applications behave on these models. In this work, we propose an approach based on the Dwarfs classification to evaluate architectures, considering that applications have different characteristics in terms of resource consumption. The results show how the Dwarfs approach could help in choosing the architecture and in Cloud Computing environment evaluation when looking for better performance.

The Impact of Hypervisor Layer on Database Applications

This paper presents the impact analysis of the hypervisor layer over database applications. The conclusions were reached by performing experiments with hybrid environments, composed by bare-metal servers and two types of hypervisors. The main objectives were to compare the behaviour of database applications and to determine the maximum service capacity of a virtualized database server on different environments. With the results, it was possible to assess the scalability of a database environment, according to the type of hypervisor and the number of simultaneous connections, and decide how and when to expand or reduce the virtualized infrastructure.

Are Public Clouds Elastic Enough for Scientific Computing

Elasticity can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. It is an important feature provided by cloud computing, that has been widely used in web applications and is also gaining attention in the scientific community. Considering the possibilities of using elasticity in this context, a question arises: “Are the available public cloud solutions suitable to support elastic scientific applications?” To answer the question, we present a review of some solutions proposed by public cloud providers and point the open issues and challenges in providing elasticity for scientific applications. We also present some initiatives that are being developed in order to overcome the current problems. In our opinion, current computational clouds have not yet reached the necessary maturity level to meet all scientific applications elasticity requirements.

A bi-criteria scheduling process with CoS support on grids and clouds

This paper presents a workflow scheduling model, which is based on a hybrid bi‐criteria scheduling algorithm based on a class of service approach. The proposed model is based on the analysis of several related works on resource scheduling, trying to incorporate the most relevant aspects considered in these works, thus covering some shortcomings pointed out by them. The proposed model aims to optimize the criteria chosen by the users, based on their specified priority order and a variation limit specified by them. In order to validate this model, a set of tests were performed comparing the performance of the proposed model with the Join the Shortest Queue scheduling algorithm. The analysis of the obtained results showed an improvement in the overall quality and also a performance gain made available to the users – as they meet the priority criteria. Copyright

A Concurrency Mitigation Proposal for Sharing Environments: An Affinity Approach Based on Applications Classes

The increased use of virtualized environments has led to numerous research efforts about the possibilities and restrictions of the use of these virtualized environments in cloud computing or for resource consolidation. However, most of these studies are limited to a level of performance analysis, that does not address the effects of concurrency among the various virtual environments, and how to mitigate these effects. The study presented below proposes the concept of affinity, based on the correct combination of certain applications classes, that are able to share the same environment, at the same time, causing less loss of performance. The results show that there are combinations of applications that could share the same environment with minimum loss, but there are combinations that must be avoided. This study also shows the influence of the type of parallel library used for the implementation of these applications.

A scheduling model for workflows on grids and clouds

This paper presents a set of comparisons of the performance of a bi-criteria scheduling algorithm for Workflows with Quality of Service (QoS) support. This work serves as basis to implement a bi-criteria hybrid scheduling algorithm for workflows with QoS support, aiming to optimize the criteria chosen by the users and based on the priority ordering and relaxation specified by them. Analyzing the comparisons and obtained results, indicates a performance improvement when adopting the model proposed in this paper.

Leveraging High Performance Computing for Bioinformatics: A Methodology that Enables a Reliable Decision-Making

Bioinformatics could greatly benefit from increased computational resources delivered by High Performance Computing. However, the decision-making about which is the best architecture to deliver good performance for a set of Bioinformatics applications is a hard task. The traditional way is finding the architecture with a high theoretical peak of performance, obtained with benchmark tests. But, this is not an assured way for this decision, because each application of Bioinformatics has different computational requirements, which frequently are much different from usual benchmarks. We developed a methodology that assists researchers, even when their specialty is not high performance computing, to define the best computational infrastructure focused on their set of scientific application requirements. For this purpose, the methodology enables to define representative evaluation tests, including a model to define the correct benchmark, when the tests endorsed by the methodology could not be fully used. Further, a Gain Function allows a reliable decision-making based on the performances of a set of applications and architectures. It is also possible to consider the relative importance between applications and also between cost and performance.

Automatic services instantiation based on a process specification

A relevant challenge for cloud computing is related to quality control of provided services. Normally, cloud providers deliver services, but do not clearly guarantee their quality level. In addition, each provider uses a particular process to provide service, considering only its own business goals. This paper presents an approach based in an environment model for clouds and aims to propose a solution for these cloud challenges. It intends to define a service procurement process taking into account users requirements, and also considers service quality maintenance. This process uses an environment model containing all relevant information to create a virtual workspace (from just an individual virtualized platform to platforms hosted in clouds), considering hardware and software requirements and quality parameters, all of which are specified by users, to precisely offer the expected service. From this model, it will be possible to automatically provide infrastructure and platform as a service. The agreement negotiation happens during the service acquisition process, using automated agents to create services and to monitor their quality, generating an environment less error-prone and increasing the customer satisfaction level.