Trading Latency and Costs

In the context of PrEstoCloud, application micro-services, or fragments, can be placed either in a public cloud (e.g. Amazon Web Services), a private cloud (e.g. an on-site OpenStack instance), and on IoT devices located at the extreme edge of the network.
Due to the pay-as-you-go nature of public cloud, it is essential to carefully select (i) the cloud provider, and (ii) the location in which to deploy a fragment, so as to meet operational constraints (such as latency), but also ensure the deployment cost is acceptable to the application owner.
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Image source: https://dollarsandsense.sg/wp-content/uploads/2015/03/Divergence-Trading.jpg

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Adaptive elasticity in scalable fog applications

The PrEstoCloud project aims to provide a platform which not only allows a one-time deployment of edge and cloud components offering a business service, but also assures that an adequate number of instances of the application components is running. Not too many – so as not to waste resources – nor too few, as this would provoke a situation where the application would not function correctly.
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The PrEstoCloud resources management

As presented in the previous post of this blog, a central innovation of PrEstoCloud is to provide a state of the art management of resources. In other words, an entity capable of dynamically assigning computing tasks to resources while considering a complex inter-cloud infrastructure, heterogeneous cloud, and edge devices, and performance-driven application constraints.
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Workload prediction through Fast Data Streaming

In modern IoT use cases, data communication is getting more and more complex. IoT scenarios are based on an increasing amount of edge devices, which use a large variety of data types and communication protocols. These dynamic and data-intensive edge processing scenarios challenge communication infrastructures, which need to become more flexible and adaptive to new situations.
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Technical visions of PrEstoCloud

The PrEstoCloud solution is an innovative dynamic platform for proactive management of cloud-based resources, reaching the extreme edge of the network for efficient real-time Big Data processing frameworks. PrEstoCloud covers the self-adaptation to real-time changes in the execution environment, and cope with dynamics in velocity and variety.
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Technical challenges addressed by PrEstoCloud

Technical challenges addressed by PrEstoCloud are as follows:

  • Allow multi-cloud deployments and provide multi-cloud connectivity – For the deployment of data intensive applications to multiple clouds, public or private.
  • Extend the deployment and networking capabilities to the extreme edge of the network – For achieving efficient processing of the data produced at the edge.

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Face recognition in the mobile journalism application

Video Analytics microservice is a part of the Media Use case. It opens new opportunities for mobile journalism because it enables automatic tagging of video streams recorded by freelancers. Video Analytics microservice work is closely related to the image classification problem since we are looking for a particular person among all available video streams.
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MultiMedia Hub as a Part of LiveU Solution for PrEstoCloud Media Use Case

LiveU’s technology enables live, wireless video transmission from any location around the world with easy-to-use professional equipment. LiveU offers a complete range of devices for live, mobile video transmission anytime, anywhere. To manage video streams coming from these devices, LiveU developed software platform which includes MultiMedia Hub (MMH) as its core part. MMH is the software component that reconstructs multiple incoming LiveU streams.
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Smart surveillance application fragmentation: A PrEstoCloud oriented approach

Surveillance systems that capture video and audio in enterprise facilities and public places produce massive amounts of data while operating at a 24/7 mode. There is an increasing need to process, on the fly, such huge video and audio data streams to enable a quick summary of “interesting” events that are happening during a specified time frame in a particular location. Through PrEstoCloud Project, ADITESS aims to enable a novel and adaptive architecture that builds on top of a distributed computing paradigm and is ideal for smart surveillance systems that can utilize resources at cloud, fog and edge.
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Security in PrEstoCloud: Protecting resources in cloud and edge deployments

In this article we present the work performed under the first development period of the PrEstoCloud framework as far as security aspects are concerned. PrEstoCloud aims to deliver a framework which allows the deployment of complex applications on top of diverse computational resources which span from data center resources to edge resources. For such an operational environment with diverse resource types it is important to provide proper mechanisms that guarantee a certain level of security on multiple layers of the architecture. The security mechanisms providing this multi layered protection in PrEstoCloud are the following: a) perimeter security, b) end-to-end encryption and c) adoption of TPM based trusted computing.
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