An Extensive Exploration of Techniques for Resource and Cost Management in Contemporary Cloud Computing Environments

Resource and cost optimization techniques in cloud computing environments target minimizing expenditure while ensuring efficient resource utilization. This study categorizes these techniques into three primary groups: Cloud and VM-focused strategies, Workflow techniques, and Resource Utilization and Efficiency techniques. Cloud and VM-focused strategies predominantly concentrate on the allocation, scheduling, and optimization of resources within cloud environments, particularly virtual machines. These strategies aim at a balance between cost reduction and adhering to specified deadlines, while ensuring scalability and adaptability to different cloud models. However, they may introduce complexities due to their dynamic nature and continuous optimization requirements. Workflow techniques emphasize the optimal execution of tasks in distributed systems. They address inconsistencies in Quality of Service (QoS) and seek to enhance the reservation process and task scheduling. By employing models, such as Integer Linear Programming, these techniques offer precision. But they might be computationally demanding, especially for extensive problems. Techniques focusing on Resource Utilization and Efficiency attempts to maximize the use of available resources in an energy-efficient and cost-effective manner. Considering factors like current energy levels and application requirements, these models aim to optimize performance without overshooting budgets. However, a continuous monitoring mechanism might be necessary, which can introduce additional complexities

Actes de la conférence BDA 2014 : Gestion de données - principes, technologies et applications

International audienceActes de la conférence BDA 2014 Conférence soutenue par l'Université Joseph Fourier, Grenoble INP, le CNRS et le laboratoire LIG. Site de la conférence : http://bda2014.imag.fr Actes en ligne : https://hal.inria.fr/BDA201

The Grand Challenge of Managing the Petascale Facility.

This report is the result of a study of networks and how they may need to evolve to support petascale leadership computing and science. As Dr. Ray Orbach, director of the Department of Energy's Office of Science, says in the spring 2006 issue of SciDAC Review, 'One remarkable example of growth in unexpected directions has been in high-end computation'. In the same article Dr. Michael Strayer states, 'Moore's law suggests that before the end of the next cycle of SciDAC, we shall see petaflop computers'. Given the Office of Science's strong leadership and support for petascale computing and facilities, we should expect to see petaflop computers in operation in support of science before the end of the decade, and DOE/SC Advanced Scientific Computing Research programs are focused on making this a reality. This study took its lead from this strong focus on petascale computing and the networks required to support such facilities, but it grew to include almost all aspects of the DOE/SC petascale computational and experimental science facilities, all of which will face daunting challenges in managing and analyzing the voluminous amounts of data expected. In addition, trends indicate the increased coupling of unique experimental facilities with computational facilities, along with the integration of multidisciplinary datasets and high-end computing with data-intensive computing; and we can expect these trends to continue at the petascale level and beyond. Coupled with recent technology trends, they clearly indicate the need for including capability petascale storage, networks, and experiments, as well as collaboration tools and programming environments, as integral components of the Office of Science's petascale capability metafacility. The objective of this report is to recommend a new cross-cutting program to support the management of petascale science and infrastructure. The appendices of the report document current and projected DOE computation facilities, science trends, and technology trends, whose combined impact can affect the manageability and stewardship of DOE's petascale facilities. This report is not meant to be all-inclusive. Rather, the facilities, science projects, and research topics presented are to be considered examples to clarify a point

A formal architecture-centric and model driven approach for the engineering of science gateways

From n-Tier client/server applications, to more complex academic Grids, or even the most recent and promising industrial Clouds, the last decade has witnessed significant developments in distributed computing. In spite of this conceptual heterogeneity, Service-Oriented Architecture (SOA) seems to have emerged as the common and underlying abstraction paradigm, even though different standards and technologies are applied across application domains. Suitable access to data and algorithms resident in SOAs via so-called ‘Science Gateways’ has thus become a pressing need in order to realize the benefits of distributed computing infrastructures.In an attempt to inform service-oriented systems design and developments in Grid-based biomedical research infrastructures, the applicant has consolidated work from three complementary experiences in European projects, which have developed and deployed large-scale production quality infrastructures and more recently Science Gateways to support research in breast cancer, pediatric diseases and neurodegenerative pathologies respectively. In analyzing the requirements from these biomedical applications the applicant was able to elaborate on commonly faced issues in Grid development and deployment, while proposing an adapted and extensible engineering framework. Grids implement a number of protocols, applications, standards and attempt to virtualize and harmonize accesses to them. Most Grid implementations therefore are instantiated as superposed software layers, often resulting in a low quality of services and quality of applications, thus making design and development increasingly complex, and rendering classical software engineering approaches unsuitable for Grid developments.The applicant proposes the application of a formal Model-Driven Engineering (MDE) approach to service-oriented developments, making it possible to define Grid-based architectures and Science Gateways that satisfy quality of service requirements, execution platform and distribution criteria at design time. An novel investigation is thus presented on the applicability of the resulting grid MDE (gMDE) to specific examples and conclusions are drawn on the benefits of this approach and its possible application to other areas, in particular that of Distributed Computing Infrastructures (DCI) interoperability, Science Gateways and Cloud architectures developments

An Intermediate Data-driven Methodology for Scientific Workflow Management System to Support Reusability

Automatic processing of different logical sub-tasks by a set of rules is a workflow. A workflow management system (WfMS) is a system that helps us accomplish a complex scientific task through making a sequential arrangement of sub-tasks available as tools. Workflows are formed with modules from various domains in a WfMS, and many collaborators of the domains are involved in the workflow design process. Workflow Management Systems (WfMSs) have been gained popularity in recent years for managing various tools in a system and ensuring dependencies while building a sequence of executions for scientific analyses. As a result of heterogeneous tools involvement and collaboration requirement, Collaborative Scientific Workflow Management Systems (CSWfMS) have gained significant interest in the scientific analysis community. In such systems, big data explosion issues exist with massive velocity and variety characteristics for the heterogeneous large amount of data from different domains. Therefore a large amount of heterogeneous data need to be managed in a Scientific Workflow Management System (SWfMS) with a proper decision mechanism. Although a number of studies addressed the cost management of data, none of the existing studies are related to real- time decision mechanism or reusability mechanism. Besides, frequent execution of workflows in a SWfMS generates a massive amount of data and characteristics of such data are always incremental. Input data or module outcomes of a workflow in a SWfMS are usually large in size. Processing of such data-intensive workflows is usually time-consuming where modules are computationally expensive for their respective inputs. Besides, lack of data reusability, limitation of error recovery, inefficient workflow processing, inefficient storing of derived data, lacking in metadata association and lacking in validation of the effectiveness of a technique of existing systems need to be addressed in a SWfMS for efficient workflow building by maintaining the big data explosion. To address the issues, in this thesis first we propose an intermediate data management scheme for a SWfMS. In our second attempt, we explored the possibilities and introduced an automatic recommendation technique for a SWfMS from real-world workflow data (i.e Galaxy [1] workflows) where our investigations show that the proposed technique can facilitate 51% of workflow building in a SWfMS by reusing intermediate data of previous workflows and can reduce 74% execution time of workflow buildings in a SWfMS. Later we propose an adaptive version of our technique by considering the states of tools in a SWfMS, which shows around 40% reusability for workflows. Consequently, in our fourth study, We have done several experiments for analyzing the performance and exploring the effectiveness of the technique in a SWfMS for various environments. The technique is introduced to emphasize on storing cost reduction, increase data reusability, and faster workflow execution, to the best of our knowledge, which is the first of its kind. Detail architecture and evaluation of the technique are presented in this thesis. We believe our findings and developed system will contribute significantly to the research domain of SWfMSs