Architecture for Fault Tolerance in Mobile Cloud Computing using Disease Resistance Approach

The mobile cloud computing (MCC) is one of the emerging fields in the distributed computing. MCC is an integration of both mobile computing and cloud computing. The limitations of the mobile devices are storage, battery and processing proficiency.These sensitive characteristics of mobile devices can be effectively handled with the introduction of cloud computing. The increasing functionality of the cloud and complexity of the applications causes resource failures in the cloud computing and it reduces the overall performance of the MCC environment. On the other hand, the existing approaches for resource scheduling in MCC proposed several architectures and they are only concentrated on the allocation of resources. The existing architectures are lack of fault tolerance mechanism to handle the faulty resources. To overcome the issues stated above, this paper proposes architecture for fault tolerance in MCC using Disease Resistance approach (DRFT). The main aim of the DRFT approach is to effectively handle the faultyVMs in the MCC. This DRFT approach utilizes the human disease resistance mechanism which is used as materials and methods in the proposed model. The DRFT is capable of identifying the faulty virtual machines and reschedules the tasks to the identified suitable virtual machines. This procedure ultimately leads to minimization of makespan value and it improves the overall performance of the scheduling process. To validate the effectiveness of the proposed approach, a series of simulations has been carried out using CloudSim simulator. The performance of the proposed DRFT approach is compared with the Dynamic group based fault tolerance approach (DGFT-approach). The makespan value of DRFT is reduced to 7% and the performance of DRFT is increased when compare to the DGFT approach. The experimental results show the effectiveness of the proposed approach

Disaster Recovery Services in Intercloud using Genetic Algorithm Load Balancer

Paradigm need to shifts from cloud computing to intercloud for disaster recoveries, which can outbreak anytime and anywhere. Natural disaster treatment includes radically high voluminous impatient job request demanding immediate attention. Under the disequilibrium circumstance, intercloud is more practical and functional option. There are need of protocols like quality of services, service level agreement and disaster recovery pacts to be discussed and clarified during the initial setup to fast track the distress scenario. Orchestration of resources in large scale distributed system having muli-objective optimization of resources, minimum energy consumption, maximum throughput, load balancing, minimum carbon footprint altogether is quite challenging. Intercloud where resources of different clouds are in align, plays crucial role in resource mapping. The objective of this paper is to improvise and fast track the mapping procedures in cloud platform and addressing impatient job requests in balanced and efficient manner. Genetic algorithm based resource allocation is proposed using pareto optimal mapping of resources to keep high utilization rate of processors, high througput and low carbon footprint.  Decision variables include utilization of processors, throughput, locality cost and real time deadline. Simulation results of load balancer using first in first out and genetic algorithm are compared under similar circumstances

Computation Offloading and Scheduling in Edge-Fog Cloud Computing

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.

Scheduling of fog networks with optimized knapsack by symbiotic organisms search

Internet of things as a concept uses wireless sensor networks that have limitations in power, storage, and delay when processing and sending data to the cloud. Fog computing as an extension of cloud services to the edge of the network reduces latency and traffic, so it is very useful in healthcare, wearables, intelligent transportation systems and smart cities. Scheduling is the NP-hard issues in fog computing. Edge devices due to proximity to sensors and clouds are capable of processing power and are beneficial for resource management algorithms. We present a knapsack-based scheduling optimized by symbiotic organisms search that is simulated in iFogsim as a standard simulator for fog computing. The results show improvements in the energy consumption by 18%, total network usage by 1.17%, execution cost by 15%, and sensor lifetime by 5% in our scheduling method are better than the FCFS (First Come First Served) and knapsack algorithms

Resource management in the cloud: An end-to-end Approach

Philosophiae Doctor - PhDCloud Computing enables users achieve ubiquitous on-demand , and convenient access to a variety of shared computing resources, such as serves network, storage ,applications and more. As a business model, Cloud Computing has been openly welcomed by users and has become one of the research hotspots in the field of information and communication technology. This is because it provides users with on-demand customization and pay-per-use resource acquisition methods