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Flexible Allocation of Heterogeneous Resources to Services on an IoT Device
TL;DR: In this paper, the problem of assigning services' resource demands to a device's heterogeneous interfaces and a Mixed Integer Linear Program (MILP) formulation for it is presented.
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Abstract: In the Internet of Things (IoT), devices and gateways may be equipped with multiple, heterogeneous network interfaces which should be utilized by a large number of services. In this work, we model the problem of assigning services' resource demands to a device's heterogeneous interfaces and give a Mixed Integer Linear Program (MILP) formulation for it. For meaningful instance sizes the MILP model gives optimal solutions to the presented computationally-hard problem. We provide insightful results discussing the properties of the derived solutions with respect to the splitting of services to different interfaces.
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Citations
Resource allocation mechanisms and approaches on the Internet of Things
TL;DR: A Systematic Literature Review (SLR) is provided and the resources allocation methods in the IoT and its platforms are investigated and the open issues about resource allocation in IoT to achieve a better utilization of this technology are focused.
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Allocation of Heterogeneous Resources of an IoT Device to Flexible Services
TL;DR: In this article, a mathematical formulation of assigning services to interfaces with heterogeneous resources in one or more rounds is presented, and two algorithms to approximate the optimal solution for big instance sizes are developed.
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Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT
Zhiguo Qu,Zhiguo Qu,Yilin Wang,Yilin Wang,Le Sun,Le Sun,Dandan Peng,Dandan Peng,Zheng Li,Zheng Li +9 more
TL;DR: A comprehensive survey on QoS optimization and energy saving in cloud computing, fog computing, edge computing, and IoT environments, which summarizes the main challenges and analyze corresponding solutions proposed by existing works.
Fairness-Efficiency Allocation of CPU-GPU Heterogeneous Resources
TL;DR: An iterative, dynamic-adaptive heuristic solving algorithm Fairness-Efficiency Allocation (FEA) is designed and optimize the implementation on a virtualized platform, which collects runtime data, allocates resources and reports differences, indicating that there is a considerable fairness improvement after the resource allocation.
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Resource Management Techniques for Cloud-Based IoT Environment
TL;DR: The focus of this paper is to investigate proposed IoT based resource allocation techniques and finds which parameters must be considered for improvement in resource allocation for IoT networks, and uncovered challenges and issues of Cloud-based resource allocation.
References
RERUM: Building a reliable IoT upon privacy- and security- enabled smart objects
Henrich C. Pöhls,Vangelis Angelakis,Santiago Reinhard Suppan,Kai Fischer,George Oikonomou,Elias Z. Tragos,Rodrigo Diaz Rodriguez,Theodoros Mouroutis +7 more
- 06 Apr 2014
TL;DR: The RERUM framework will comprise an architecture, built upon novel network protocols and interfaces as well as the design of smart objects hardware that will allow IoT applications to consider security and privacy mechanisms early in their design phase, ensuring a configurable balance between reliability and privacy.
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