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With the growing number and increasing availability of shared-use instruments and observatories, observational data is becoming an essential part of application workflows and contributor to scientific discoveries in a range of disciplines. However, the corresponding growth in the number of users accessing these facilities coupled with the expansion in the scale and variety of the data, is making it challenging for these facilities to ensure their data can be accessed, integrated, and analyzed in a timely manner, and is resulting significant demands on their cyberinfrastructure (CI). In this paper, we present the design of a push-based data delivery framework that leverages emerging in-network capabilities, along with data pre-fetching techniques based on a hybrid data management model. Specifically, we analyze data access traces for two large-scale observatories, Ocean Observatories Initiative (OOI) and Geodetic Facility for the Advancement of Geoscience (GAGE), to identify typical user access patterns and to develop a model that can be used for data pre-fetching. Furthermore, we evaluate our data pre-fetching model and the proposed framework using a simulation of the Virtual Data Collaboratory (VDC) platform that provides in-network data staging and processing capabilities. The results demonstrate that the ability of the framework to significantly improve data delivery performance and reduce network traffic at the observatories’ facilities.

One of the major endeavors of modern cyberinfrastructure (CI) is to carry content produced on remote data sources, such as sensors and scientific instruments, and to deliver it to end users and workflow applications. Maintaining data quality, data resolution, and on-time data delivery and considering the increasing number of computing, storage, and network resources are challenging tasks that require a receptive system able to adapt to ever-changing demands. In this paper, we propose a mathematical model of such system by expressing the dynamic stages of different resources in the context of edge and in-transit computing. By considering resource utilization, approximation techniques, and user constraints, our proposed model generates mappings of different workflow stages on heterogeneous geographically distributed resources. Specifically, we propose an autonomic runtime management layer that adapts the data resolution being delivered to the users by implementing feedback loops over the resources involved in the delivery and processing of data streams. The implementation of our model is based on a subscription-based data streaming framework that enables the integration of large facilities and advanced CI. Moreover, the idea of stream or request aggregation is incorporated into our framework, which eliminates redundant data streams to save bandwidth. Experimental results show that dynamically adapting data resolution and stream aggregation can overcome bandwidth limitations in wide-area streaming analytics by leveraging the resources at the edge and in-transit.

International audience; Internet of Things (IoT) is bringing an increasing number of connected devices that have a direct impact on the growth of data and energy-hungry services. These services are relying on Cloud infrastructures for storage and computing capabilities, transforming their architecture into more a distributed one based on edge facilities provided by Internet Service Providers (ISP). Yet, between the IoT device, communication network and Cloud infrastructure, it is unclear which part is the largest in terms of energy consumption. In this paper, we provide end-to-end energy models for Edge Cloud-based IoT platforms. These models are applied to a concrete scenario: data stream analysis produced by cameras embedded on vehicles. The validation combines measurements on real test-beds running the targeted application and simulations on well-known sim-ulators for studying the scaling-up with an increasing number of IoT devices. Our results show that, for our scenario, the edge Cloud part embedding the computing resources consumes 3 times more than the IoT part comprising the IoT devices and the wireless access point.

The increasing demand for high performance computing resources has led to new forms of collaboration of distributed systems, such as grid computing systems. Moreover, the need for interoperability among different grid systems through the use of common protocols and standards has become increased in the last few years. In this paper we describe and evaluate scheduling techniques for multiple grid scenarios. In particular, they consist of the proposed ''bestBrokerRank'' broker selection policy and two different variants. The first one uses the resource information in aggregated forms as input, and the second one also uses the broker average bounded slowdown as a dynamic performance metric. From our evaluations performed with simulation tools, we state that, although the aggregation algorithms lose resource information accuracy, the broker selection policies using aggregated resource data do not penalize their performance significantly. Moreover, we show that the best performance results are obtained with the coordinated policy using dynamic performance information, in addition to aggregated resource information. Therefore, we conclude that delegating part of the scheduling responsibilities to the underlying scheduling layers promotes separation of concerns and is a good way to balance the performance among the different grid systems.