Scheduling preemptable tasks on parallel processors with limited availability
Parallel Computing - Special issue on new trends on scheduling in parallel and distributed systems
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
A new paradigm: Data-aware scheduling in grid computing
Future Generation Computer Systems
Optimal parallel machines scheduling with availability constraints
Discrete Applied Mathematics
Large-scale multimodal mining for healthcare with mapreduce
Proceedings of the 1st ACM International Health Informatics Symposium
Fog computing and its role in the internet of things
Proceedings of the first edition of the MCC workshop on Mobile cloud computing
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In the context of Internet of Things (IoT), data acquisition, management, and analysis for knowledge extraction has given rise to a new generation of services. The heterogeneity of services being offered today on the enormous expanse of data as available from sensors and smart devices, needs a distributed infrastructure for analysis and computation. We consider a scenario where the computing infrastructure of an IoT framework is distributed and is capable of harnessing the computing power of mobile devices connected to the network in a heterogeneous grid. Edge devices like smart-phones, gateways, etc. can potentially contribute to the infrastructure. However, such devices and communication channels to such devices are intermittently unavailable, which may be due to network failure, planned shut-down, high workload on the devices, etc. In addition to this, we consider a task offloading context where the computing infrastructure inside the IoT invites bids from connected and available devices from the network to offload a part of the computation on part of the input data-set. We expect the overall completion time to improve in this setting. We investigate the data partitioning problem under the scenario where unavailability of communication and computation are advertised a priori and we pose the problem as a scheduling problem where cost of execution of an analysis job is to be minimized. We present a constraint-based model, evaluate the same on various scenarios and present some of the results.