BibTex:

    @Article{OJIOT_2018v4i1n06_Fuerst,
        title     = {Towards Adaptive Actors for Scalable IoT Applications at the Edge},
        author    = {Jonathan F{\"u}rst and
                     Mauricio Fadel Argerich and
                     Kaifei Chen and
                     Ern{\"o} Kovacs},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2018},
        volume    = {4},
        number    = {1},
        pages     = {70--86},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2018) in conjunction with the VLDB 2018 Conference in Rio de Janeiro, Brazil.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2018080519303887853107},
        urn       = {urn:nbn:de:101:1-2018080519303887853107},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Traditional device-cloud architectures are not scalable to the size of future IoT deployments. While edge and fog-computing principles seem like a tangible solution, they increase the programming effort of IoT systems, do not provide the same elasticity guarantees as the cloud and are of much greater hardware heterogeneity. Future IoT applications will be highly distributed and place their computational tasks on any combination of end-devices (sensor nodes, smartphones, drones), edge and cloud resources in order to achieve their application goals. These complex distributed systems require a programming model that allows developers to implement their applications in a simple way (i.e., focus on the application logic) and an execution framework that runs these applications resiliently with a high resource efficiency, while maximizing application utility. Towards such distributed execution runtime, we propose Nandu, an actor based system that adapts and migrates tasks dynamically using developer provided hints as seed information. Nandu allows developers to focus on sequential application logic and transforms their application into distributed, adaptive actors. The resulting actors support fine-grained entry points for the execution environment. These entry points allow local schedulers to adapt actors seamlessly to the current context, while optimizing the overall application utility according to developer provided requirements.}
    }