7 research outputs found

    Improving Quality of Experience in Future Wireless Access Networks through Fog Computing

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    A novel model of Internet access networks is proposed, based on fog computing. The model hosts applications close to users by relying on virtual machines to dynamically move cloud or Web content to nodes located at the edge of access networks. Then it can perform proactive caching and enforce traffic policies based on the interaction between access infrastructure and external applications. By analyzing experimental data collected from public Wi-Fi hotspots, the authors quantify the benefits of this approach for bandwidth usage optimization, latency reduction, and quality of experience enhancement. Experimental results show that a significant portion (from 28 to 50 percent) of download data could be managed by the fog node. On the basis of these findings, useful insights for future-generation access networks are provided

    Improving Quality of Experience in Future Wireless Access Networks through Fog Computing

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    Resource Allocation Framework in Fog Computing for the Internet of Things Environments

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    Fog computing plays a pivotal role in the Internet of Things (IoT) ecosystem because of its ability to support delay-sensitive tasks, bringing resources from cloud servers closer to the “ground” and support IoT devices that are resource-constrained. Although fog computing offers some benefits such as quick response to requests, geo-distributed data processing and data processing in the proximity of the IoT devices, the exponential increase of IoT devices and large volumes of data being generated has led to a new set of challenges. One such problem is the allocation of resources to IoT tasks to match their computational needs and quality of service (QoS) requirements, whilst meeting both task deadlines and user expectations. Most proposed solutions in existing works suggest task offloading mechanisms where IoT devices would offload their tasks randomly to the fog layer or cloud layer. This helps in minimizing the communication delay; however, most tasks would end up missing their deadlines as many delays are experienced during offloading. This study proposes and introduces a Resource Allocation Scheduler (RAS) at the IoT-Fog gateway, whose goal is to decide where and when a task is to be offloaded, either to the fog layer, or the cloud layer based on their priority needs, computational needs and QoS requirements. The aim directly places work within the communication networks domain, in the transport layer of the Open Systems Interconnection (OSI) model. As such, this study follows the four phases of the top-down approach because of its reusability characteristics. To validate and test the efficiency and effectiveness of the RAS, the fog framework was implemented and evaluated in a simulated smart home setup. The essential metrics that were used to check if round-trip time was minimized are the queuing time, offloading time and throughput for QoS. The results showed that the RAS helps to reduce the round-trip time, increases throughput and leads to improved QoS. Furthermore, the approach addressed the starvation problem, a phenomenon that tends to affect low priority tasks. Most importantly, the results provides evidence that if resource allocation and assignment are appropriately done, round-trip time can be reduced and QoS can be improved in fog computing. The significant contribution of this research is the novel framework which minimizes round-trip time, addresses the starvation problem and improves QoS. Moreover, a literature reviewed paper which was regarded by reviewers as the first, as far as QoS in fog computing is concerned was produced

    Resource Allocation Framework in Fog Computing for the Internet of Things Environments

    Get PDF
    Fog computing plays a pivotal role in the Internet of Things (IoT) ecosystem because of its ability to support delay-sensitive tasks, bringing resources from cloud servers closer to the “ground” and support IoT devices that are resource-constrained. Although fog computing offers some benefits such as quick response to requests, geo-distributed data processing and data processing in the proximity of the IoT devices, the exponential increase of IoT devices and large volumes of data being generated has led to a new set of challenges. One such problem is the allocation of resources to IoT tasks to match their computational needs and quality of service (QoS) requirements, whilst meeting both task deadlines and user expectations. Most proposed solutions in existing works suggest task offloading mechanisms where IoT devices would offload their tasks randomly to the fog layer or cloud layer. This helps in minimizing the communication delay; however, most tasks would end up missing their deadlines as many delays are experienced during offloading. This study proposes and introduces a Resource Allocation Scheduler (RAS) at the IoT-Fog gateway, whose goal is to decide where and when a task is to be offloaded, either to the fog layer, or the cloud layer based on their priority needs, computational needs and QoS requirements. The aim directly places work within the communication networks domain, in the transport layer of the Open Systems Interconnection (OSI) model. As such, this study follows the four phases of the top-down approach because of its reusability characteristics. To validate and test the efficiency and effectiveness of the RAS, the fog framework was implemented and evaluated in a simulated smart home setup. The essential metrics that were used to check if round-trip time was minimized are the queuing time, offloading time and throughput for QoS. The results showed that the RAS helps to reduce the round-trip time, increases throughput and leads to improved QoS. Furthermore, the approach addressed the starvation problem, a phenomenon that tends to affect low priority tasks. Most importantly, the results provides evidence that if resource allocation and assignment are appropriately done, round-trip time can be reduced and QoS can be improved in fog computing. The significant contribution of this research is the novel framework which minimizes round-trip time, addresses the starvation problem and improves QoS. Moreover, a literature reviewed paper which was regarded by reviewers as the first, as far as QoS in fog computing is concerned was produced
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