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With increasing availability and use of Internet of Things (IoT) devices large amounts of streaming data is now being produced at high velocity. Applications which require low latency response such as video surveillance demand a swift and efficient analysis of this data. Existing approaches employ cloud infrastructure to store and perform machine learning based analytics on this data. This centralized approach has limited ability to support analysis of real-time, large-scale streaming data due to network bandwidth and latency constraints between data source and cloud. We propose RealEdgeStream (RES) an edge enhanced stream analytics system for large-scale, high performance data analytics. The proposed approach investigates the problem of video stream analytics by proposing (i) filtration and (ii) identification phases. The filtration phase reduces the amount of data by filtering low value stream objects using configurable rules. The identification phase uses deep learning inference to perform analytics on the streams of interest. The stages are mapped onto available in-transit and cloud resources using a placement algorithm to satisfy the Quality of Service (QoS) constraints identified by a user. The job completion in the proposed system takes 49\% less time and saves 99\% bandwidth compared to a centralized cloud-only based approach.

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.

Dramatic changes in the technology landscape marked by increasing scales and pervasiveness of compute and data have resulted in the proliferation of edge applications aimed at effectively processing data in a timely manner. As the levels and fidelity of instrumentation increases and the types and volumes of available data grow, new classes of applications are being explored that seamlessly combine real-time data with complex models and data analytics to monitor and manage systems of interest. However, these applications require a fluid integration of resources at the edge, the core, and along the data path to support dynamic and data-driven application workflows, that is, they need to leverage a computing continuum. In this article, we present our vision for enabling such a computing continuum and specifically focus on enabling edge-to-cloud integration to support data-driven workflows. The research is driven by an online data-driven tsunami warning use case that is supported by the deployment of large-scale national environment observation systems. This article presents our overall approach as well as current status and next steps.

In this paper, we define accessibility on an iterated function system (IFS) and show that it provides a sufficient condition for the transitivity of this system and its corresponding skew product. Then, by means of a certain tool, we obtain the topologically mixing property. We also give some results about the ergodicity and stability of accessibility and, further, illustrate accessibility by some examples.

Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability, through Software Defined Networks (SDN) capability, e.g., OpenFlow and Network Function Visualization (NFV). We demonstrate how this multi-site computational capability can be aggregated to support video analytics, with Quality of Service and cost constraints (e.g., latency-bound analysis). The use of SDN technology enables separation of the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage SDN capability to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. Using a number of scenarios, we demonstrate the benefits and limitations of this approach for video analysis, comparing this with the baseline scenario of undertaking all such analysis at a data center located at the core of the infrastructure.

As devices existing at the edge of the network improve in their processing and data storage capacity, there is increasing potential to host and enact services on such devices. A workflow that was traditionally enacted on a data centre can be fragmented across both edge and data centre hosted resources. The following aspects are investigated in this work: (i) mechanisms for dividing a workflow across edge and cloud/data centre resources; (ii) service hosting environments that can be shared across edge and data centre resources; (iii) performance metrics that can influence service placement and selection. An "edge orchestrator" is a resource manager that makes such decisions on the behalf of a user application, and which may be centralised or distributed. An industry scenarios is used to illustrate decision points that influence such choices within an edge orchestrator. The overall objective considered is the completion of the workflow within some deadline constraint by the edge orchestrator.

Internet of Things (IoT) has been ever-growing over the last few years. The IoT devices generate a massive amount of data that should be transmitted to the cloud for computing. Cloud consolidation and centralization lead to many network hops between the IoT devices and its associated cloud which makes two critical problems: (i) high latencies (ii) high bandwidth consumption in the IoT domain. Network Function Virtualization (NFV), Software Defined Network (SDN) and fog computing have been emerged to address these problems. In the Fog-to-Cloud (F2C) architecture, Fog and cloud work together to provide computing, storage, and application services in the IoT domain. To build complex services a specific set of virtual network functions can be chained together in a specific order which is known as Service Function Chaining (SFC). The joint VNF placement and traffic routing are called SFC mapping. In this paper, we propose an Integer Linear Program (ILP) model to solve SFC mapping in the fog-to-cloud Computing System in order to minimize the overall end-to-end (e2e) latency of IoT devices. We observe that our approach reduces the overall e2e latency of IoT devices significantly. Moreover, our approach helps us to analyze the effect of a number of instances in the end-to-end latency of IoT devices.

Estimating energy costs for an industrial process can be computationally intensive and time consuming, especially as it can involve data collection from different (distributed) monitoring sensors. Industrial processes have an implicit complexity involving the use of multiple appliances (devices/ sub-systems) attached to operation schedules, electrical capacity and optimisation setpoints which need to be determined for achieving operational cost objectives. Addressing the complexity associated with an industrial workflow (i.e. range and type of tasks) leads to increased requirements on the computing infrastructure. Such requirements can include achieving execution performance targets per processing unit within a particular size of infrastructure i.e. processing & data storage nodes to complete a computational analysis task within a specific deadline. The use of ensemblebased edge processing is identifed to meet these Quality of Service targets, whereby edge nodes can be used to distribute the computational load across a distributed infrastructure. Rather than relying on a single edge node, we propose the combined use of an ensemble of such nodes to overcome processing, data privacy/ security and reliability constraints. We propose an ensemble-based network processing model to facilitate distributed execution of energy simulations tasks within an industrial process. A scenario based on energy profiling within a fisheries plant is used to illustrate the use of an edge ensemble. The suggested approach is however general in scope and can be used in other similar application domains.

Network function virtualization promises a significant advantage for addressing diverse quality of service requirements via chains of Virtual Network Functions (VNFs). Service Function Chain (SFC) mapping, composed of placing VNF in the physical network and routing traffic through them, is the key problem to meet the requirements. In the recent mission-critical applications, delay guarantee and fault tolerance are the crucial requirements wherein links and servers load balancing greatly facilitate providing the requirements. In this paper, we approach the problem of minimizing the maximum load of links and nodes in SFC mapping. After formulating the problem as a mixed-integer linear program, a batched version of the model is developed to estimate the optimal solution in a reasonable time. Then, we propose a practical solution based on the water-filling algorithm. Simulation results in different scenarios show that the algorithm can yield excellent performance compared to the optimal solution and benchmarks algorithms.

Background: Toxoplasma gondii is one of the most common zoonotic parasites. The stray cats play an important role in the infecting intermediate hosts, due to easy access to raw meat and predation of infected rodents and birds and shedding oocyst on the environment. The aim of this study was to determine the prevalence of T. gondii antibody in the serum and oocyst shedding in the feces of stray cats and its relationship with some variables such as age, sex and inhabitant in Khorramabad, west of Iran Materials and Methods: A total of 125 Stray cats trapped from different parts of city and were brought to the research laboratory to take blood and faces specimens. The blood samples of the cats (71 males and 54 females) were assayed for the prevalence of T. gondii using the IgG-ELISA, and their fecal samples collected through rectal swabs, and sugar floatation concentration method was applied for detection of oocyst. Results: From 125 cats, a total of 80 cats (64%) were positive for anti-Toxoplasma antibody, 42 cats (33.6%) were negative and 3 cats (2.4%) were in borderline. There was no significant difference in the T. gondii seropositivity between males and females, and also between cats living in different parts of city, but prevalence rate between different age groups were significant statistically (P

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.

Software-defined networks (SDNs) have proven to be an efficacious tool for undertaking complex data analysis and manipulation within data intensive applications. SDN technology allows us to separate the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage software-defined networking (SDN) to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. This strategy allows us to minimize waiting times at the destination data center and to cope with spikes in demand for computational capability. We validate our approach using a smart building application in a multi-cloud infrastructure. Results show how the in-transit processing strategy increases the computational capabilities of the infrastructure and influences the percentage of job completion without significantly impacting costs and overheads.

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.

With the increasing availability of Internet of Things (IoT) devices, and potential applications that make use of data from such devices, there is a need to better identify appropriate data processing techniques that can be applied to this data. The computational complexity of these applications, and the complexity of the requirements on the data processing techniques, often derives from the capabilities of current IoT devices and the need to integrate data streams across multiple IoT devices, which result in larger data sizes and loads on the computing infrastructure. Furthermore, due to the dynamics and uncertainties of edge environments, it is essential that these techniques are capable of adapting across a range of computational and data transfer requirements (such as execution performance) and infrastructure scales (processing nodes, storage needs, network requirements) to carry out a particular analysis task, in response to changing requirements and constraints. Approximate computing offers techniques that can simplify the overall analysis workflow, trading off loss in quality and optimality of the solution with time to reach a particular outcome. These techniques have two main advantages: (i) reduced time to execute a particular data analysis; (ii) reduced requirements on the computational infrastructure (i.e., lower energy, computational resource needs, etc) to carry out such analysis. With data processing capabilities available IoT devices and associated gateway nodes, such approximate computing can be achieved at or close to the network edge. In this paper, we propose in-transit and edge-supported approximation techniques, which can undertake partial/approximate data processing at the data generation/capture or aggregation site, prior to delivery to a cloud data center. We also demonstrate how such an approach can be used in practice by applying it to support energy optimization in built environments (utilizing a combination of sensors and cloud-based data analysis). Several approximation techniques that are relevant in this context are presented, and their relevance explored and evaluated in the context of an energy simulation application scenario.

The exponential growth of digital data sources has the potential to transform all aspects of society and our lives. However, to achieve this impact, the data has to be processed promptly to extract insights that can drive decision making. Further, traditional approaches that rely on moving data to remote data centers for processing are no longer feasible. Instead, new approaches that effectively leverage distributed computational infrastructure and services are necessary. Specifically, these approaches must seamlessly combine resources and services at the edge, in the core, and along the data path as needed. This paper presents our vision for enabling an approach for computing in the continuum, i.e., realizing a fluid ecosystem where distributed resources and services are programmatically aggregated on-demand to support emerging data-driven application workflows. This vision calls for novel solutions for federating infrastructure, programming applications and services, and composing dynamic workflows, which are capable of reacting in real-time to unpredictable data sizes, availabilities, locations, and rates.

Software-defined networks (SDNs) have proven to be an efficacious tool for undertaking complex data analysis and manipulation within data intensive applications. SDN technology allows us to separate the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage software defined networking to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. This strategy allows us to minimize waiting times at the destination data center and to cope with spikes in demand for computational capability. We validate our approach using a smart building application in a multi cloud infrastructure. We show how the in-transit processing strategy increases the computational capabilities of the infrastructure and influences the percentage of job completion without significantly impacting costs and overheads.

Cloud computing has emerged as attractive platform for computing data intensive applications. However, efficient computation of this kind of workloads requires understanding how to store, process, and analyse large volumes of data in a timely manner. Many “smart cities” applications, for instance, identify how data from building sensors can be combined together to support applications such as emergency response, energy management, etc. Enabling sensor data to be transmitted to a cloud environment for processing provides a number of benefits, such as scalability and on-demand provisioning of computational resources. In this chapter, we propose the use of a multi-layer cloud infrastructure that distributes processing over sensing nodes, multiple intermediate/gateways nodes, and large data centres. Our solution aims at utilising the pervasive computational capabilities located at the edge of the infrastructure and along the data path to reduce data movement to large data centres located “deep” into the infrastructure and perform a more efficient use of computing and network resources.

In this paper, we investigate the generalized Hyers– Ulam– Rassias stability of the functional equation∑i=1mf(mxi+∑j=1, j≠imxj)+f(∑i=1mxi)=2f(∑i=1mmxi)in fuzzy Banach spaces and some applications of our results in the stability of above mapping from a normed space to a Banach space will be exhibited.

Pervasive computational ecosystems that combine data sources and computing/communication resources in self-managed environments, such as the ones powered by Internet of Things (IoT) devices, have the potential to automate and facilitate many aspects of our lives, and impact a variety of applications, from the management of extreme events to the optimization of everyday processes. However, this vision remains mostly unrealized despite the fact that the technology to achieve it exists, largely because of the gap between our ability to collect data and our ability to gain insight from it. In this paper, we discuss the challenges associated with providing a pervasive computational ecosystem. We then present our vision of how to best support data-driven computational ecosystems and propose a conceptual architecture that leverages ideas from software-defined environments in order to combine data, computing, and communication resources. In addition, we show how this proposed architecture enables the execution of data-driven workflows on top of these resources.