672 research outputs found

    A MULTI-FUNCTIONAL PROVENANCE ARCHITECTURE: CHALLENGES AND SOLUTIONS

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    In service-oriented environments, services are put together in the form of a workflow with the aim of distributed problem solving. Capturing the execution details of the services' transformations is a significant advantage of using workflows. These execution details, referred to as provenance information, are usually traced automatically and stored in provenance stores. Provenance data contains the data recorded by a workflow engine during a workflow execution. It identifies what data is passed between services, which services are involved, and how results are eventually generated for particular sets of input values. Provenance information is of great importance and has found its way through areas in computer science such as: Bioinformatics, database, social, sensor networks, etc. Current exploitation and application of provenance data is very limited as provenance systems started being developed for specific applications. Thus, applying learning and knowledge discovery methods to provenance data can provide rich and useful information on workflows and services. Therefore, in this work, the challenges with workflows and services are studied to discover the possibilities and benefits of providing solutions by using provenance data. A multifunctional architecture is presented which addresses the workflow and service issues by exploiting provenance data. These challenges include workflow composition, abstract workflow selection, refinement, evaluation, and graph model extraction. The specific contribution of the proposed architecture is its novelty in providing a basis for taking advantage of the previous execution details of services and workflows along with artificial intelligence and knowledge management techniques to resolve the major challenges regarding workflows. The presented architecture is application-independent and could be deployed in any area. The requirements for such an architecture along with its building components are discussed. Furthermore, the responsibility of the components, related works and the implementation details of the architecture along with each component are presented

    Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

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    In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers

    A Cross-layer Monitoring Solution based on Quality Models

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    In order to implement cross-organizational workflows and to realize collaborations between small and medium enterprises (SMEs), the use ofWeb service technology, Service-Oriented Architecture and Infrastructure-as-a- Service (IaaS) has become a necessity. Based on these technologies, the need for monitoring the quality of (a) the acquired resources, (b) the services offered to the final users and (c) the workflow-based procedures used by SMEs in order to use services, has come to the fore. To tackle this need, we propose four metric Quality Models that cover quality terms for the Workflow, Service and Infrastructure layers and an additional one for expressing the equality and inter-dependency relations between the previous ones. To support these models we have implemented a cross-layer monitoring system, whose main advantages are the layer-specific metric aggregators and an event pattern discoverer for processing the monitoring log. Our evaluation is based on the performance and accuracy aspects of the proposed cross-layer monitoring system

    InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

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    Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape

    Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks

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    The number of both service providers operating in the cloud market and customers consuming cloud-based services is constantly increasing, proving that the cloud computing paradigm has successfully delivered its potential. Nevertheless, the unceasing growth of the cloud market is posing hard challenges on its participants. On the provider side, the capability of orchestrating resources in order to maximise profits without failing customers’ expectations is a matter of concern. On the customer side, the efficient resource selection from a plethora of similar services advertised by a multitude of providers is an open question. In such a multi-cloud landscape, several research initiatives advocate the employment of software frameworks (namely, cloud resource orchestration frameworks - CROFs) capable of orchestrating the heterogeneous resources offered by a multitude of cloud providers in a way that best suits the customer’s need. The objective of this paper is to provide the reader with a systematic review and comparison of the most relevant CROFs found in the literature, as well as to highlight the multi-cloud computing open issues that need to be addressed by the research community in the near future

    Data-Intensive architecture for scientific knowledge discovery

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    This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology
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