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Funded by NSF Office of Cyberinfrastructure and EarthCube programs, the collaborative GeoSCIFramework (GSF) project aims to improve earthquake, tsunami, and volcano early warning applications by applying big data analytics and machine learning methods to large streams of real-time data from a mix of seismic, geodetic-related sensors, and differential interferometric synthetic aperture radar (DInSAR) satellite imagery. It will provide researchers with a suite of datasets and a means to detect, monitor, and analyze geophysical activity over a region of interest. The work presented here focuses on the longer-term evolution of volcanic processes by developing DInSAR time series over Hawaii from November 2015 to April 2021. Our automated processing routine uses the Small Baseline Subset (SBAS) method and is based off of Generic Mapping Tools (GMT5SAR) and the Generic InSAR Analysis Toolbox (GIAnT) software [Kelevitz et al., 2021; Corsa et al., 2021]. We recently containerized the ISCE2 Stack Processor and MintPY on the Summit supercomputer for more efficient processing. Our final DInSAR time series is then integrated with Global Navigation Satellite System (GNSS) data using the Ordinary Kriging interpolation method to reveal 3D motions of Earth’s surface and to refine the accuracy of our results [Samsonov et al., 2006; 2008]. We present improved, assimilated DInSAR + GNSS time series and the associated uncertainty analysis. Our current work consists of modeling interferometric products in order to generate a robust, synthetic training data set for the GSF machine learning algorithm.

The European Multidisciplinary Seafloor and water-column Observatory (EMSO) European Research Infrastructure Consortium (ERIC) is a large-scale European Strategy Forum on Research Infrastructure (ESFRI) member with strategically placed sea observatories with the essential scientific objective of real-time, long-term monitoring of environmental processes related to the interaction between the geosphere, biosphere, and hydrosphere. EMSO ERIC collects, curates, and provides high-quality oceanographic measurements from surface to deep seafloor to assess long-term time series and oceanographic modeling. In addition, EMSO ERIC has developed a set of data services that harmonize its regional facilities’ data workflows, enhancing efficiency and productivity, supporting innovation, and enabling data- and knowledge-based discovery and decision-making. These services are developed in connection with the ESFRI cluster of Environmental Research Infrastructures (ENVRI) through the adoption of FAIR data principles (findability, accessibility, interoperability, and reusability) and supported by the ENVRI-FAIR H2020 project. EMSO ERIC’s efforts in adopting FAIR principles include the use of globally unique and resolvable persistent identifiers (PIDs) in alignment with the ENVRI-FAIR task forces. We present a service for the identification and long-lasting citability of dynamic data queries on harmonized data sets generated by EMSO ERIC users. The service is aligned with the Research Data Alliance (RDA) working group on data citation and has been integrated into the EMSO ERIC data portal. User-built queries on the data portal are served by the EMSO ERIC Application Programming Interface (API), which retrieves the user requested data and provides a Uniform Resource Identifier (URI) to the query, visualizations, and data sets in CSV and NetCDF formats. The data portal allows users to request a PID to the data query by providing mandatory and optional metadata information through an online form. The mandatory metadata consists of the description of the data and specific information about the creators, personal or organizational, including their identifiers and affiliations. The optional metadata consists of different types of titles and descriptions that the user finds compelling. The service provides a permalink to a web page maintained within the data portal with the PID reference, metadata information, and the URI to the data query. The web pages associated with PIDs also provide the option to request a Digital Object Identifier (DOI) if users are authorized via the EMSO ERIC Authorization and Authentication Infrastructure (AAI) system.

Large-scale scientific facilities provide a broad community of researchers and educators with open access to instrumentation and data products generated from geographically distributed instruments and sensors. This paper discusses key architectural design, deployment, and operational aspects of a production cyberinfrastructure for the acquisition, processing, and delivery of data from large scientific facilities using experiences from the National Science Foundation's Ocean Observatories Initiative. This paper also outlines new models for data delivery and opportunities for insights in a wide range of scientific and engineering domains as the volumes and variety of data from facilities grow.

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.

The Virtual Data Collaboratory is a federated data cyberinfrastructure designed to drive data-intensive, interdisciplinary, and collaborative research that will impact researchers, educators, and entrepreneurs across a broad range of disciplines and domains as well as institutional and geographic boundaries.

International audience; EMSO is a distributed Research Infrastructure currently comprising nine Regional Facilities (RFs) and three shallow water test sites, strategically located all the way from the southern entrance of the Arctic Ocean across to the North Atlantic through the Mediterranean to the Black Sea. Since the beginning of 2021 Norway has been integrated as a new EMSO ERIC member, extending the geographical coverage to the Nordic Sea and the Arctic. EMSO’s extension will benefi t from an experienced team managing moored observatories, ocean gliders and the Mohn Ridge Seafl oor and Water Column Observatory.

Large-scale multiuser scientific facilities, such as geographically distributed observatories, remote instruments, and experimental platforms, represent some of the largest national investments and can enable dramatic advances across many areas of science. Recent examples of such advances include the detection of gravitational waves and the imaging of a black hole’s event horizon. However, as the number of such facilities and their users grow, along with the complexity, diversity, and volumes of their data products, finding and accessing relevant data is becoming increasingly challenging, limiting the potential impact of facilities. These challenges are further amplified as scientists and application workflows increasingly try to integrate facilities’ data from diverse domains. In this paper, we leverage concepts underlying recommender systems, which are extremely effective in e-commerce, to address these data-discovery and data-access challenges for large-scale distributed scientific facilities. We first analyze data from facilities and identify and model user-query patterns in terms of facility location and spatial localities, domain-specific data models, and user associations. We then use this analysis to generate a knowledge graph and develop the collaborative knowledge-aware graph attention network (CKAT) recommendation model, which leverages graph neural networks (GNNs) to explicitly encode the collaborative signals through propagation and combine them with knowledge associations. Moreover, we integrate a knowledge-aware neural attention mechanism to enable the CKAT to pay more attention to key information while reducing irrelevant noise, thereby increasing the accuracy of the recommendations. We apply the proposed model on two real-world facility datasets and empirically demonstrate that the CKAT can effectively facilitate data discovery, significantly outperforming several compelling state-of-the-art baseline models.

The White paper released from ENVRI-FAIR Task Force 2 on Authentication, Authorisation and Accountingdescribes the work done so far, recommendations, plans and converge points for building an AAI Federated interoperable system accorsi ENV RIs

Data collected by large-scale instruments, observatories, and sensor networks are key enablers of scientific discoveries in many disciplines. However, ensuring that these data can be accessed, integrated, and analyzed in a democratized and timely manner remains a challenge. In this article, we explore how state-of-the-art techniques for data discovery and access can be adapted to facility data and develop a conceptual framework for intelligent data access and discovery.

Computational science depends on complex, data intensive applications operating on datasets from a variety of scientific instruments. A major challenge is the integration of data into the scientist's workflow. Recent advances in dynamic, networked cloud resources provide the building blocks to construct reconfiguration, end-to-end infrastructure that can increase scientific productivity, but applications are not taking advantage of them. In our previous work, we introduced Dy-N amo, that enabled CASA scientists to improve the efficiency of their operations and effortlessly leverage capabilities of the cloud resources available to them that previously remained underutilized. However, the provided workflow automation did not satisfy all the operational requirements of CASA. Custom scripts were still in production to manage workflow triggering, while multiple layer2 connections would have to be allocated to maintain network QoS requirements. In this work, we enhance the DyNamo system with ensemble workflow management capabilities, end-to-end infrastructure monitoring, as well as more advanced network manipulation mechanisms. To accommodate CASA's operational needs we also extended the newly integrated Pegasus Ensemble Manager with file and time based triggering functionality, that improves managing workflow ensembles. Additionally, Virtual Software Defined Exchange (vSDX) capabilities have been extended, enabling link adaptation, flow prioritization and traffic control between endpoints. We evaluate the effects of the DyNamo's vSDX policies by using two CASA workflow ensembles competing for network resources, and we show that traffic shaping of the ensembles can lead to a fairer use of the network links.

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.

As the number of people who interact on social networks increases, and coupled with the greater capability made available within our computational devices, there is the potential to establish “Social Clouds”—a resource sharing infrastructure that enable people who have trust relationships to come together to share computational/ data services within a community. Social clouds can also provide the means to enhance multi-user collaboration and greatly stimulate the exchange of resources among participants. Recent research in the establishment and use of Social Clouds has raised significant interest by proposing an environment where users are able to trade resources mediated by a social networking mechanism. In such a cloud environment the incentives for sharing can represent a solution for improving resource utilisation and for making available additional capacity to friends and collaborators. In this paper we demonstrate how revenue can be earned within a social cloud community, by executing internal (intra community) and external (inter community) tasks. A number of different scenarios are first investigated through simulation, using the PeerSim simulator, in order to validate our approach. We use two key metrics: revenue and reputation, to evaluate how the system dynamics change as new tasks are added to one or more communities for execution, along with additional behaviours, such as nodes migrating from one community to another, or selectively reporting on the outcome of task execution. Subsequently, we develop a practical deployment using a federated cloud scenario using the CometCloud system—deployed over three sites: Cardiff (UK), Rutgers and Indiana. We show how approaches that have been simulated in PeerSim can be implemented in practice.

Computational science today depends on complex, data-intensive applications operating on datasets from a variety of scientific instruments. A major challenge is the integration of data into the scientist's workflow. Recent advances in dynamic, networked cloud resources provide the building blocks to construct reconfigurable, end-to-end infrastructure that can increase scientific productivity. However, applications have not adequately taken advantage of these advanced capabilities. In this work, we have developed a novel network-centric platform that enables high-performance, adaptive data flows and coordinated access to distributed cloud resources and data repositories for atmospheric scientists. We demonstrate the effectiveness of our approach by evaluating time-critical, adaptive weather sensing workflows, which utilize advanced networked infrastructure to ingest live weather data from radars and compute data products used for timely response to weather events. The workflows are orchestrated by the Pegasus workflow management system and were chosen because of their diverse resource requirements. We show that our approach results in timely processing of Nowcast workflows under different infrastructure configurations and network conditions. We also show how workflow task clustering choices affect throughput of an ensemble of Nowcast workflows with improved turnaround times. Additionally, we find that using our network-centric platform powered by advanced layer2 networking techniques results in faster, more reliable data throughput, makes cloud resources easier to provision, and the workflows easier to configure for operational use and automation.

Data and services provided by shared facilities, such as large-scale observing facilities, have become important enablers of scientific insights and discoveries across many science and engineering disciplines. Ensuring satisfactory quality of service can be challenging for facilities, due to their remote locations and to the distributed nature of the instruments, observatories, and users, as well as the rapid growth of data volumes and rates. This research explores how knowledge of the facilities usage patterns, coupled with emerging cyberinfrastructures can be leveraged to improve their performance, usability, and scientific impact. We propose a framework with a smart, internet-scale cache augmented with prefetching and data placement strategies to improve data delivery performance for scientific facilities. Our evaluations, which are based on the NSF Ocean Observatories Initiative, demonstrate that our framework is able to predict user requests and reduce data movements by more than 56% across networks.

The performance of modern Big Data frameworks, e.g. Spark, depends greatly on high-speed storage and shuffling, which impose a significant memory burden on production data centers. In many production situations, the persistence and shuffling intensive applications can suffer a major performance loss due to lack of memory. Thus, the common practice is usually to over-allocate the memory assigned to the data workers for production applications, which in turn reduces overall resource utilization. One efficient way to address the dilemma between the performance and cost efficiency of Big Data applications is through data center computing resource disaggregation. This paper proposes and implements a system that incorporates the Spark Big Data framework with a novel in-memory distributed file system to achieve memory disaggregation for data persistence and shuffling. We address the challenge of optimizing performance at affordable cost by co-designing the proposed in-memory distributed file system with large-volume DIMM-based persistent memory (PMEM) and RDMA technology. The disaggregation design allows each part of the system to be scaled independently, which is particularly suitable for cloud deployments. The proposed system is evaluated in a production-level cluster using real enterprise-level Spark production applications. The results of an empirical evaluation show that the system can achieve up to a 3.5- fold performance improvement for shuffle-intensive applications with the same amount of memory, compared to the default Spark setup. Moreover, by leveraging PMEM, we demonstrate that our system can effectively increase the memory capacity of the computing cluster with affordable cost, with a reasonable execution time overhead with respect to using local DRAM only.

Modern Cyberinfrastructures (CIs) operate to bring content produced from remote data sources such as sensors and scientific instruments and deliver it to end users and workflow applications. Maintaining data quality/resolution and on-time data delivery while considering an increasing number of computing, storage and network resources requires a reactive system, able to adapt to changing demands. In this paper, we propose a modelization of such system by expressing the dynamic stage of resources in the context of edge and in-transit computing. By considering resource utilization, approximation techniques and users' constraints, our proposed engine is generating mappings of workflow stages on heterogeneous geo-distributed resources. We specifically propose a 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 the data streams. We implement our model into a subscription-based data streaming framework which enables integration of large facilities and advanced CIs. Experimental results show that dynamically adapting data resolution can overcome bandwidth limitation in wide area streaming analytics.

Computational demand has brought major changes to Advanced Cyber-Infrastructure (ACI) architectures. It is now possible to run scientific simulations faster and obtain more accurate results. However, power and energy have become critical concerns. Also, the current roadmap toward the new generation of ACI includes power budget as one of the main constraints. Current research efforts have studied power and performance tradeoffs and how to balance these (e.g., using Dynamic Voltage and Frequency Scaling (DVFS) and power capping for meeting power constraints, which can impact performance). However, applications may not tolerate degradation in performance, and other tradeoffs need to be explored to meet power budgets (e.g., involving the application in making energy-performance-quality tradeoff decisions). This paper proposes using the properties of AMR-based algorithms (e.g., dynamically adjusting the resolution of a simulation in combination with power capping techniques) to schedule or re-distribute the power budget. It specifically explores the opportunities to realize such an approach using checkpointing as a proof-of-concept use case and provides a characterization of a representative set of applications that use Adaptive Mesh Refinement (AMR) methods, including a Low-Mach-Number Combustion (LMC) application. It also explores the potential of utilizing power capping to understand power-quality tradeoffs via simulation.

As data analytics applications become increasingly important in a wide range of domains, the ability to develop large-scale and sustainable platforms and software infrastructure to support these applications has significant potential to drive research and innovation in both science and business domains. This paper characterizes performance and power-related behavior trends and tradeoffs of the two predominant frameworks for Big Data analytics (i.e., Apache Hadoop and Spark) for a range of representative applications. It also evaluates system design knobs, such as storage and network technologies and power capping techniques. Experimental results from empirical executions provide meaningful data points for exploring the potential of software-defined infrastructure for Big Data processing systems through simulation. The results provide better understanding of the design space to build multi-criteria application-centric models as well as show significant advantages of software-defined infrastructure in terms of execution time, energy and cost. It motivates further research focused on in-memory processing formulations regarding systems with deeper memory hierarchies and software-defined infrastructure.

A Distributed Denial of Service (DDoS) attack is an attempt to make an online service, a network, or even an entire organization, unavailable by saturating it with traffic from multiple sources. DDoS attacks are among the most common and most devastating threats that network defenders have to watch out for. DDoS attacks are becoming bigger, more frequent, and more sophisticated. Volumetric attacks are the most common types of DDoS attacks. A DDoS attack is considered volumetric, or high-rate, when within a short period of time it generates a large amount of packets or a high volume of traffic. High-rate attacks are well-known and have received much attention in the past decade; however, despite several detection and mitigation strategies have been designed and implemented, high-rate attacks are still halting the normal operation of information technology infrastructures across the Internet when the protection mechanisms are not able to cope with the aggregated capacity that the perpetrators have put together. With this in mind, the present paper aims to propose and test a distributed and collaborative architecture for online high-rate DDoS attack detection and mitigation based on an in-memory distributed graph data structure and unsupervised machine learning algorithms that leverage real-time streaming data and analytics. We have successfully tested our proposed mechanism using a real-world DDoS attack dataset at its original rate in pursuance of reproducing the conditions of an actual large scale attack.

Large scale observatories are shared-use resources that provide open access to data from geographically distributed sensors and instruments. This data has the potential to accelerate scientific discovery. However, seamlessly integrating the data into scientific workflows remains a challenge. In this paper, we summarize our ongoing work in supporting data-driven and data-intensive workflows and outline our vision for how these observatories can improve large-scale science. Specifically, we present programming abstractions and runtime management services to enable the automatic integration of data in scientific workflows. Further, we show how approximation techniques can be used to address network and processing variations by studying constraint limitations and their associated latencies. We use the Ocean Observatories Initiative (OOI) as a driving use case for this work.

Data staging has been shown to be very effective for supporting data intensive in-situ workflows and coupling of applications. Experimental sciences are increasingly becoming collaborative among geographically distributed teams, and include experimental instruments and HPC facilities. This new way of doing science poses new challenges due to data sizes, complexity of computation, and the use of wide area networks between couplings. In this paper, we explore how the staging abstraction can be extended to support such workflows. Specifically, we develop a NUMA-like abstraction that orchestrates multiple distributed local-area staging abstractions, and provides asynchronous data put/get semantics to enable data sharing across them. To mask data movement overhead and provide in-time data access, we propose the use of predictive prefetching approaches that leverage the iterative nature of the coupling. We evaluate our prototype implementation using a fusion workflow and show that our design can effectively and transparently support widearea coupled workflows. Additionally, results show that the use of prefetching techniques leads to significant gains in data access times of data that needs to be moved over the wide area network.

International audience; The emergence of Internet of Things (IoT) is participating to the increase of data- and energy-hungry applications. As connected devices do not yet offer enough capabilities for sustaining these applications, users perform computation offloading to the cloud. To avoid network bottlenecks and reduce the costs associated to data movement, edge cloud solutions have started being deployed, thus improving the Quality of Service. In this paper, we advocate for leveraging on-site renewable energy production in the different edge cloud nodes to green IoT systems while offering improved QoS compared to core cloud solutions. We propose an analytic model to decide whether to offload computation from the objects to the edge or to the core Cloud, depending on the renewable energy availability and the desired application QoS. This model is validated on our application use-case that deals with video stream analysis from vehicle cameras.

Service-based access models coupled with emerging application deployment technologies are enabling opportunities for realizing highly customized software-defined environments, which can support dynamic and data-driven applications. However, this requires rethinking traditional resource federation models to support dynamic resource compositions, which can adapt to evolving application needs and the dynamic state of underlying resources. In this paper, we present a programmable approach that leverages software-defined techniques to create a dynamic space-time infrastructure service composition. We propose the use of Constraint Programming as a formal language to allow users, applications, and service providers to define the desired state of the execution environment. The resulting distributed software-defined environment continually adapts to meet objectives/constraints set by the users, applications, and/or resource providers. We present the design and prototype implementation of such distributed software-defined environment. We use a cancer informatics workflow to demonstrate the operation of our framework using resources from five different cloud providers, which are aggregated on-demand based on dynamic user and resource provider constraints.

Emerging scientific simulations on leadership class systems are generating huge amounts of data and processing this data in an efficient and timely manner is critical for generating insights from the simulations. However, the increasing gap between computation and disk I/O speeds makes traditional data analytics pipelines based on post-processing cost prohibitive and often infeasible. In this paper, we investigate an alternate approach that aims to bring the analytics closer to the data using in-situ execution of data analysis operations. Specifically, we present the design, implementation and evaluation of a framework that can support in-situ feature-based objects tracking on distributed scientific datasets. Central to this framework is a scalable decentralized and online clustering, a cluster tracking algorithm, which executes in-situ (on different cores) in parallel with the simulation processes, and retrieves data from the simulations directly via on-chip shared memory. The results from our experimental evaluation demonstrate that the in-situ approach significantly reduces the cost of data movement, that the presented framework can support scalable feature-based objects tracking, and that it can be effectively used for in-situ analytics in large scale simulations.

The complexity of many problems in science and engineering requires computational capacity exceeding what the average user can expect from a single computational center. While many of these problems can be viewed as a set of independent tasks, their collective complexity easily requires millions of core-hours on any high-power computing (HPC) resource, and throughput that can't be sustained by a single, multiuser queuing system. An exploration of the use of aggregated HPC resources to solve large-scale engineering problems shows that it's possible to build a computational federation that's easy for end users to implement, and is elastic, resilient, and scalable. Here, the authors argue that the fusion of federated computing and real-life engineering problems can be brought to the average user if relevant middleware is provided. They report on the use of federation of 10 distributed heterogeneous HPC resources to perform a large-scale interrogation of the parameter space in the microscale fluid flow problem.

Clouds are rapidly joining high-performance computing (HPC) systems, clusters, and grids as viable platforms for scientific exploration and discovery. As a result, understanding application formulations and usage modes that are meaningful in such a hybrid infrastructure, and how application workflows can effectively utilize it, is critical. Here, three hybrid HPC/grid and cloud cyber infrastructure usage modes are explored: HPC in the Cloud, HPC plus Cloud, and HPC as a Service, presenting illustrative scenarios in each case and outlining benefits, limitations, and research challenges.

The goal of Grid computing is to integrate the usage of computer resources from cooperating partners in the form of Virtual Organizations (VO). One of its key functions is to match jobs to execution resources efficiently. For interoperability between VOs, this matching operation occurs in resource brokering middleware, commonly referred to as the meta-scheduler or meta-broker. In this paper, we present an approach to a meta-scheduler architecture, combining hierarchical and peer-to-peer models for flexibility and extensibility. Interoperability is further promoted through the introduction of a set of protocols, allowing meta-schedulers to maintain sessions and exchange job and resource state using Web Services. Our architecture also incorporates a resource model that enables an efficient resource matching across multiple Virtual Organizations, especially where the compute resources and state are dynamic. Experiments demonstrate these new functional features across three distributed organizations (BSC, FIU, and IBM), that internally use different job scheduling technologies, computing infrastructure and security mechanisms. Performance evaluations through actual system measurements and simulations provide the insights on the architecture's effectiveness and scalability.

Social Clouds provide the capability to share resources among participants within a social network-leveraging on the trust relationships already existing between such participants. In such a system, users are able to trade resources between each other rather than make use of capability offered at a centralized data center. Although such an environment has significant potential for improving resource utilization and making available additional capacity that remains dormant, incentives for sharing remain an important hurdle limiting its effective. In this paper, we utilize the socioeconomic model proposed by Silvio Gesell to demonstrate how a 'virtual currency' can be used to incentivise sharing of resources within a 'community'. We subsequently demonstrate, through simulations, the benefit provided to participants within such a community using a variety of economic such as overall credits gained and technical number of successfully completed transactions metrics. Further, we describe our implementation of such a Social Cloud using CometCloud. CometCloud is an autonomic computing engine for cloud and grid environments. It supports highly heterogeneous and dynamic federated cloud/Grid infrastructures, integration of public/private clouds and autonomic cloudbursts. We demonstrate the implementation of two designs on the basis of the master/worker approach: i one tuple space per cluster and ii one coordination tuple space and multiple transient spaces-one per each cluster. Finally, we discuss an extended version of our Social Cloud model where intermediary relay nodes take on more active roles as traders in a transaction. Copyright © 2013 John Wiley & Sons, Ltd.