Intelligent admission control manager for fog‐integrated cloud: A hybrid machine learning approach

Abstract

Internet of Things (IoT) and other smart devices produce data that are large in volume, variety, and velocity. Cloud not only helps in data analysis, but also provides storage and computation facility to these data. It has been experienced that for many time-critical applications, by the time request traverses back and forth to the cloud for analysis/execution, the opportunity to act upon it may get over. Therefore, time sensitivity and priority for such applications greatly matter. Fog computing, an upcoming computing infrastructure, complements the cloud and overcomes this limitation by supporting time-sensitive and priority-based applications by provisioning the computation, bandwidth, and storage. However, adopting fog-integrated cloud introduces newer resource management challenges requiring a new request scheduling scheme with appropriate quality of service/experience (QoS/QoE). In this work, an intelligent admission control manager is being proposed for placing the request based on the parameters such as CPU, memory, storage besides few other categorical parameters, for example, job priority and time sensitivity. The proposed work applies machine intelligence techniques, clustering for labeling the applications' requests followed by a decision tree, using the labeled requests, to classify the incoming requests. The proposed methodology is demonstrated in terms of accuracy, execution time, precision, recall, variation in accuracy, and execution time by introducing noise in multiple size batches to avoid the generalization error and fault tolerance. A comparative study with few well-known classifiers has also been performed to ascertain the effectiveness of the proposed model. The proposed model is light enough to be placed appropriately on the fog node.

A Secure and Distributed Placement for Quality of Service-Aware IoT Requests in Fog-Cloud of Things: A Novel Joint Algorithmic Approach

“…Machine intelligence has also been used by Sham et al [13] in smart control managers to position services based on hardware and category factors. In the suggested work, the data is labelled using clustering and then the compute tier for service offloading is predicted using a decision tree classifier.…”

PMRNA: Parameter matching of realtime and non‐realtime applications for resource provisioning in fog‐integrated cloud

“…3 In a three-tier architecture, Fog computing lies between Cloud and IoT/edge devices. 4 Cloud and Fog employ distributed computing; however, comparatively, Cloud is more centralized than Fog. 5 The Cloud datacenter houses powerful servers, managed and operated by the datacenter owners.…”

“…Their success depends on resource utilization, that is, the efficiency of the resource provisioning methods. 4 Its importance increases even more in the FiC environment, which generates RT and NRT requests. 9 The parameters such as deadline, request size, number of cores, memory, and so on, provided by the user play a vital role in the accomplishment of a better resource allocation policy and in ensuring Quality of Service (QoS) in terms of response time, ET, and the resource availability.…”

“…Fog computing addresses these challenges well and fills the gap between Cloud and IoT applications by enabling computing, storage, and networking infrastructure near the IoT devices 3 . In a three‐tier architecture, Fog computing lies between Cloud and IoT/edge devices 4 . Cloud and Fog employ distributed computing; however, comparatively, Cloud is more centralized than Fog 5 …”

“…Resource management is crucial for the Cloud and Fog, being it a business model. Their success depends on resource utilization, that is, the efficiency of the resource provisioning methods 4 . Its importance increases even more in the FiC environment, which generates RT and NRT requests 9 .…”