Performance analysis of ACO‐based improved virtual machine allocation in cloud for IoT‐enabled healthcare

Abstract

The Internet of Things (IoT)-enabled healthcare environment irregularly requires the resources from the Cloud to handle massive amounts of data, which impacts the response times of the Cloud. A typical healthcare application scenario could be to monitor the heart-patients who require the highest response times, immediate attention of all the stakeholders, and very quick decisions. This paper replaces the default First-Come-First-Serve (FCFS) Virtual Machine (VM) allocation scheme of the Cloud computing in CloudSim with the implementation of Ant Colony Optimization (ACO) by the efficient tuning of parameters. An IoT-enabled healthcare environment is designed, which irregularly requires the resources from the Cloud to handle massive amounts of data to assess the impact of the response times of the Cloud. Several experiments have been carried out to assess the optimal VM allocation using ACO by varying different parameters like the variation of the number of ants, the strength of pheromone, the number of VMs, the number of hosts, the strength of computing power of processing elements, the number of users, and the size of the workloads. Experimental results indicate that the ACO optimally utilizes the resources in a Cloud when compared to the default FCFS strategy. The results indicate that on an average, the ACO gives 25% to 30% improved response times than FCFS in the given healthcare and Cloud scenarios.

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

“…That is, the closer the resource proportion v i1 : v i2 : v i3 of a VM is to that p j1 : p j2 : p j3 of a PM, the lower the resource waste, where v i1 , v i2 and v i3 represent the requested number of CPU cores, memory capacity and disk size of the VM v i , respectively; and p j1 , p j2 and p j3 denote the free number of CPU cores, memory capacity and disk size of the PM p j , respectively. Therefore, we build the resource proportion matching distance model shown in formula (12), where p jk and v ik represent the free capacity of resource type k of the PM p j and the requested resource capacity of the VM v i , respectively, and R k denotes the coefficient that adjusts the imbalanced values of parameter H = p jk • v i1 /p j1 − v ik for different resource types. For instance, if the values of the parameter H for CPU and disk resources are 2 and 200, the disk will become the dominant resource.…”

“…As a Nondominated Sorting Genetic Algorithm, it has been widely applied in solving the multiobjective problem and achieves good effectiveness [39][40][41]. However, the NSGA-II algorithm has a problem that the computation time of the fitness values (i.e., the objective functions) is too long to ensure (12)…”

“…There are some cloud resource allocation methods for big data applications [7], cloudbased software services [8], scientific applications [9], cloud manufacturing [10], workflows [11] and cloud healthcare [12]. Some algorithms or mechanisms have been applied in resource allocation, such as the grasshopper optimization algorithm (GOA) [13], antcolony optimization and deep reinforcement learning [14], the data-driven probabilistic model [15], and auction mechanisms [16].…”

Cloud healthcare services: A comprehensive and systematic literature review

“…It offers three main features than conventional computing: availability for short‐time use, service provision without requiring an up‐front commitment by customers, and affective computing resources accessible on‐demand 19 . In fact, cloud computing provides a highly‐linking setting for healthcare suppliers, having simple, quick, and comprehensive access to health information 24 . Moreover, cloud computing's distribution nature enables healthcare experts to display and keep the patient's health archives and cooperate with them and other experts 25 .…”

An energy‐aware method for task allocation in the Internet of things using a hybrid optimization algorithm

“…Internet of Things (IoT) has been offered lately as a profound change in technology domains 1‐3 . The IoT sight targets at interconnecting machines with various potentials like actuators, sensors, smartphones, radio‐frequency identification (RFID) tags, and servers during the identical heterogeneous network 4,5 . The main goal is to activate the items of the network for actively cooperating and making their resources accessible to conduct some applications allotted to the network.…”