Fault Aware Dynamic Resource Manager for Fault Recognition and Avoidance in Cloud

Fault tolerance(FT)schemes are intended to work on a minimized and static amount of physical resources.When a host failure occurs,the conventional FT frequently proceeds with the execution on the accessible working hosts.This methodology saves the execution state and applications to complete without disruption.However,the dynamicity of open cloud assets is not seen when taking scheduling choices.Existing optimization techniques are intended in dealing with resource scheduling.This method will be utilized for distributing the approaching tasks to the VMs.However,the dynamic scheduling for this procedure doesn’t accomplish the objective of adaptation of internal failure.The scheme prefers jobs in the activity list with the most elevated execution time on resources that can execute in a shorter timeframe,but it suffers with higher makespan;poor resource usage and unbalance load concerns.To overcome the above mentioned issue,Fault Aware Dynamic Resource Manager(FADRM)is proposed that enhances the mechanism to Multi-stage Resilience Manager at an application-level FT arrangement.Proposed FADRM method gives FT a Multi-stage Resilience Manager(MRM)in the client and application layers,and simultaneously decreases the over-head and degradations.It additionally provides safety to the application execution considering the clients,application and framework necessities.Based on experimental evaluations,Proposed Fault Aware Dynamic Resource Manager(FADRM)method 157.5 MakeSpan(MS)time,0.38 Fault Rate(FR),0.25 Failure Delay(FD)and improves 5.5 Performance Improvement Ratio(PIR)for 25,50,75 and 100 tasks and 475 MakeSpan(MS)time,0.40 Fault Rate(FR),1.30 Failure Delay(FD)and improves 6.75 improves Performance Improvement Ratio(PER)for 100,200,300 and 500 Tasks compare than existing methodologies.

Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

The modern paradigm of the Internet of Things(IoT)has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing.However,such applications cannot meet strict quality of service(QoS)requirements.The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data.Generally,cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers.Fog devices with different computing capabilities must process the data generated by the end device,so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task.In addition,when there is an inconsistent connection delay between the fog and the terminal device,the deployment of such applications becomes more complicated.In this article,we propose an algorithm that can effectively place application modules on network nodes while considering connection delay,processing power,and sensing data volume.Compared with traditional cloud computing deployment,we conducted simulations in iFogSim to confirm the effectiveness of the algorithm.The simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network consumption.Therein,latency and execution time is insensitive to the number of sensors.