Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.However,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud computing.An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs.This approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of applications.In this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing cost.We consider four cost-types for application deployment:Computation,communication,energy consumption,and violations.The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system.An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches.The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

A Broker-Based Task-Scheduling Mechanism Using Replication Approach for Cloud Systems

The reliability and availability of cloud systems have become major concerns of service providers,brokers,and end-users.Therefore,studying fault-tolerance mechanisms in cloud computing attracts intense attention in industry and academia.The task-scheduling mechanisms can improve the fault-tolerance level of cloud systems.A task-scheduling mechanism distributes tasks to a group of instances to be executed.Much work has been undertaken in this direction to improve the overall outcome of cloud computing,such as improving service qual-ity and reducing power consumption.However,little work on task scheduling has studied the problem of lost tasks from the broker’s perspective.Task loss can hap-pen due to virtual machine failures,server crashes,connection interruption,etc.The broker-based concept means that the backup task can be allocated by the bro-ker on the same cloud service provider(CSP)or a different CSP to reduce costs,for example.This paper proposes a novel fault-tolerant mechanism that employs the primary backup(PB)model of task scheduling to address this issue.The pro-posed mechanism minimizes the impact of failure events by reducing the number of lost tasks.The mechanism is further improved to shorten the makespan time of submitted tasks in cloud systems.The experiments demonstrated that the pro-posed mechanism decreased the number of lost tasks by about 13%–15%com-pared with other mechanisms in the literature.

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.