4 research outputs found
Li-Fi based on security cloud framework for future IT environment
This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).Peer reviewedPublisher PD
Recommended from our members
Cloud Internet of Things for the Smart Environment of a Smart City
The environmental service area for smart city construction provides sustainability, economic, stage, and safe energy savings in building a smart city. The smart environment monitoring system can manage the agricultural environment, water quality, and air quality of smart cities through the Internet of Things and cloud computing technology. Smart environmental monitoring (SEM) systems to reduce environmental problems in smart cities are important service domains that can improve citizens\u27 quality of life. The purpose of this project is to identify services through the introduction of technologies to solve environmental problems in smart cities, the use and management of the technologies introduced. The project findings are: (a) The SEM system collects environmental data through IoT devices and analyzes it through cloud computing. (b) Data from the SEM system is collected through the Internet of Things based on a wireless sensor network. The collected data is transmitted to the cloud computing platform to be analyzed and monitored. (c) For wireless Internet connections between the two technologies can connect through unique services of Message Queuing Telemetry Transport and cloud platform. (d) The SEM applications were analyzed for Smart Agricultural Monitoring, Smart Water Quality Monitoring, and Smart Air Quality Monitoring. (e) The project is analyzed the transmitted data using Amazon Web Service and Google Cloud Platform. When the analyzed environmental parameters deviate from normal values, email notification can be sent to detect abnormalities in environmental parameters. In addition, the connection between IoT devices and cloud platforms can confirm the normal connection of IoT devices to establish a security system by obtaining credibility and reliability for the connection between the two technologies. As a result, the collaboration between IoT and cloud computing technology can show how a smart environment service domain can help smart city citizens
A Game-Theoretic Approach to Strategic Resource Allocation Mechanisms in Edge and Fog Computing
With the rapid growth of Internet of Things (IoT), cloud-centric application management raises
questions related to quality of service for real-time applications. Fog and edge computing
(FEC) provide a complement to the cloud by filling the gap between cloud and IoT. Resource
management on multiple resources from distributed and administrative FEC nodes is a key
challenge to ensure the quality of end-userās experience. To improve resource utilisation and
system performance, researchers have been proposed many fair allocation mechanisms for
resource management. Dominant Resource Fairness (DRF), a resource allocation policy for
multiple resource types, meets most of the required fair allocation characteristics. However,
DRF is suitable for centralised resource allocation without considering the effects (or
feedbacks) of large-scale distributed environments like multi-controller software defined
networking (SDN). Nash bargaining from micro-economic theory or competitive equilibrium
equal incomes (CEEI) are well suited to solving dynamic optimisation problems proposing to
āproportionatelyā share resources among distributed participants. Although CEEIās
decentralised policy guarantees load balancing for performance isolation, they are not faultproof
for computation offloading.
The thesis aims to propose a hybrid and fair allocation mechanism for rejuvenation of
decentralised SDN controller deployment. We apply multi-agent reinforcement learning
(MARL) with robustness against adversarial controllers to enable efficient priority scheduling
for FEC. Motivated by software cybernetics and homeostasis, weighted DRF is generalised by
applying the principles of feedback (positive or/and negative network effects) in reverse game
theory (GT) to design hybrid scheduling schemes for joint multi-resource and multitask
offloading/forwarding in FEC environments.
In the first piece of study, monotonic scheduling for joint offloading at the federated edge is
addressed by proposing truthful mechanism (algorithmic) to neutralise harmful negative and
positive distributive bargain externalities respectively. The IP-DRF scheme is a MARL
approach applying partition form game (PFG) to guarantee second-best Pareto optimality
viii | P a g e
(SBPO) in allocation of multi-resources from deterministic policy in both population and
resource non-monotonicity settings. In the second study, we propose DFog-DRF scheme to
address truthful fog scheduling with bottleneck fairness in fault-probable wireless hierarchical
networks by applying constrained coalition formation (CCF) games to implement MARL. The
multi-objective optimisation problem for fog throughput maximisation is solved via a
constraint dimensionality reduction methodology using fairness constraints for efficient
gateway and low-level controllerās placement.
For evaluation, we develop an agent-based framework to implement fair allocation policies in
distributed data centre environments. In empirical results, the deterministic policy of IP-DRF
scheme provides SBPO and reduces the average execution and turnaround time by 19% and
11.52% as compared to the Nash bargaining or CEEI deterministic policy for 57,445 cloudlets
in population non-monotonic settings. The processing cost of tasks shows significant
improvement (6.89% and 9.03% for fixed and variable pricing) for the resource non-monotonic
setting - using 38,000 cloudlets. The DFog-DRF scheme when benchmarked against asset fair
(MIP) policy shows superior performance (less than 1% in time complexity) for up to 30 FEC
nodes. Furthermore, empirical results using 210 mobiles and 420 applications prove the
efficacy of our hybrid scheduling scheme for hierarchical clustering considering latency and
network usage for throughput maximisation.Abubakar Tafawa Balewa University, Bauchi (Tetfund, Nigeria