Hyrax: Cloud Computing on Mobile Devices using MapReduce

By Eugene Marinelli. 2009

481 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/45794b4345d63f31b5fc176faeffeda552db9f7c

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Citations in the corpus

The Internet of Things vision: Key features, applications and open issues (2014, methodology): “By applying such concepts, data may be stored and retrieved from: (i) a virtual resource cloud, made up of mobile devices in proximity [218], or (ii) a local cloudlet, which is composed of several multi-core computers connected with the remote cloud servers [219].”
Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges (2013, background): “Although researchers [45], [153] en deavor to reduce the distance of mobile devices and service provide rs by leveraging nearby mobile/fixed computing devices, sever al mechanisms, particularly resource discovery and allocati n, service consumer and provider mobility management, and distributed runt”
Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, background): “Furthermore apart from the assistance to be provided by remote cloud servers [296]–[299] it can also be provided by nearby cloudlets [300]–[303], or even by nearby mobile devices [304]–[306].”
Mobile cloud computing: A survey (2013, unknown): “In Hyrax [14] disconnectivity arising from the mobile nature of devices is handled through the fault tolerance mechanisms of Hadoop.”
A Survey of Mobile Cloud Computing Application Models (2014, background): “In [24], the authors discuss two mobile cloud application models (Hyrax [25], cloudlets [2]) and emphasize on the importance of intelligent access schemes [26].”
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications (2017, background): “The authors compared two mobile cloud platforms, the Hyrax platform [12] and virtual machine (VM) based cloudlets [13], and then reviewed intelligent access schemes utilizing the user’s location and context [14].”
A Survey of 5G Network: Architecture and Emerging Technologies (2015, methodology): “In the first method, let us consider that the other mobile devices will also act as resource providers as in [95].”
A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing (2012, background): “To the best of our knowledge, it is the .rst work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the makespan of executions as in other applications.”
Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges (2016, background): “Hyrax [48], FemtoClouds [49]), IoT devices and entities (cf.”
Heterogeneity in Mobile Cloud Computing: Taxonomy and Open Challenges (2014, methodology): “Similarly, Marinelli [73] propose a MCC platform composed of Android smartphones called Hyrax to lessen the architectural impact of non-ARM (X86) devices.”
Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture (2012, methodology): “In [18] Hyrax enables smartphone applications to form a distributed computing cloud, which implements Hadoop [19] in Android phones using MapReduce function.”
Serendipity: enabling remote computing among intermittently connected mobile devices (2012, background): “More recently, the Hyrax project envisions a somewhat similar capability to opportunistically use the resources of networked cellphones [25].”