Channels have SSL encryption enabled at each level of the Cresco hierarchy, using a unique encryption signature per connection.


During any loss of connectivity, the agents re-establish communications as needed.


Cresco utilizes robust, efficient proven communications libraries to ensure reliable, low-latency communications are always available.


Server-less management of resources.


Supported communication protocols include AutoWire, MQTT, OpenWire, REST, RSS and Atom, STOMP, WSIF, Websockets, and XMPP.

Software Defined

Graph language to allowing for the quick deployment of highly complex, multi-agent applications.

  • Cresco in Healthcare

    "Constellation: A secure self-optimizing framework for genomic processing. "e-Health Networking, Applications and Services (Healthcom), 2016 IEEE 18th International Conference on. IEEE, 2016.
  • Cresco in Pervasive Flows

    "An Edge-Focused Model for Distributed Streaming Data Applications", International Workshop on Pervasive Flow of Things (PerFoT'18). IEEE, 2018.
  • Cresco in Smart Cities

    Edge Computing in Smart Cities. Poster session presented at the 2017 Smart Cities Connect Conference and Expo, Austin, TX, USA, June 2017.
  • Cresco in High-Speed Network Monitoring

    "Edge-enabled Distributed Network Measurement", Second International Workshop on Smart Edge Computing and Networking (SmartEdge'18). IEEE, 2018.
  • Cresco in Defense Management

    Edge Computing in Defense Management. Poster session presented at the 2017 Defense Innovation Challenges, Tampa, FL, USA, October 2017.
  • Cresco in Defense Management

    Edge framework for resource-aware distributed applications. Poster session presented at the 2018 Defense TechConnect Fall Summit, Tampa, FL, USA, October 2018.
  • Cresco Framework

    "Cresco: A distributed agent-based edge computing framework." Network and Service Management (CNSM), 2016 12th International Conference on. IEEE, 2016.
  • Cresco Framework

    "Edge Framework for a Resource-aware Distributed Applications". Poster session presented TNC18 (Geant), Trondheim, Norway, June 2018.


Efforts related to Internet of Things (IoT), Cyber-Physical Systems (CPS), Machine to Machine (M2M) technologies, and Industrial Internet aim to improve decision velocity and accuracy for a wide-range of data-driven applications. By the year 2020 there will be an estimated 50 billion network connected devices globally and 43 trillion gigabytes of electronic data. The coordination of devices and related data from individual sensors to city- or battlefield-wide applications will be critical. Current practices of moving data directly from end-devices to remote and potentially distant cloud computing services will not be sufficient to manage future device and data growth.

IoT Gateway Model : Data is communicated directly from devices or through gateways to remote data centers for inference model generation and processing.

The Challenge

  • There exist technical constraints (speed of light, computational, storage, throughput, power, etc.) on the movement and processing of data remotely.
  • There exist a data-value paradox, where information that is required to model data value has an indeterminate collection rate and duration.
  • This is to say that the value of data is not known until the cost of transmission and processing have been incurred.
  • There exist privacy, legal, and operational constrains in the exposing of protected data to remote systems.

Edge Computing

Edge Computing is the migration of computational functionality to sources of data generation. The importance of edge computing increases with the size and complexity of devices and resulting data. In addition, the coordination of global edge-to-edge communications, shared resources, high-level application scheduling, monitoring, measurement, and Quality of Service (QoS) enforcement will be critical to address the rapid growth of connected devices and associated data.

Cresco Edge Computing Framework

We present a new distributed agent-based framework designed to address the challenges of edge computing. This actor-model framework implementation is designed to manage large numbers of geographically distributed services, comprised from heterogeneous resources and communication protocols, in support of low-latency real-time streaming applications. As part of this framework, an application description language was developed and implemented. Using the application description language a number of high-order management modules have been implemented including solutions for resource and workload comparison, performance observation, scheduling, and provisioning.

Computational resources, edge applications, and resulting data flow are managed by the Cresco framework allowing end-to-end pipeline management.

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