Fuzzy Firefly Based Intelligent Algorithm for Load Balancing inMobile Cloud Computing

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail.

Edge Cloud Selection in Mobile Edge Computing(MEC)-Aided Applications for Industrial Internet of Things(IIoT)Services

In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method.

Modelling Mobile-X Architecture for Offloading in Mobile Edge Computing

Mobile Edge Computing(MEC)assists clouds to handle enormous tasks from mobile devices in close proximity.The edge servers are not allocated efficiently according to the dynamic nature of the network.It leads to processing delay,and the tasks are dropped due to time limitations.The researchersfind it difficult and complex to determine the offloading decision because of uncertain load dynamic condition over the edge nodes.The challenge relies on the offload-ing decision on selection of edge nodes for offloading in a centralized manner.This study focuses on minimizing task-processing time while simultaneously increasing the success rate of service provided by edge servers.Initially,a task-offloading problem needs to be formulated based on the communication and pro-cessing.Then offloading decision problem is solved by deep analysis on taskflow in the network and feedback from the devices on edge services.The significance of the model is improved with the modelling of Deep Mobile-X architecture and bi-directional Long Short Term Memory(b-LSTM).The simulation is done in the Edgecloudsim environment,and the outcomes show the significance of the proposed idea.The processing time of the anticipated model is 6.6 s.The following perfor-mance metrics,improved server utilization,the ratio of the dropped task,and number of offloading tasks are evaluated and compared with existing learning approaches.The proposed model shows a better trade-off compared to existing approaches.

Research on Mobile Internet Mobile Agent System Dynamic Trust Model for Cloud Computing

This paper analyzes the reasons for the formation of security problems in mobile agent systems, and analyzes and compares the security mechanisms and security technologies of existing mobile agent systems from the perspective of blocking attacks. On this basis, the host protection mobile agent protection technology is selected, and a method to enhance the security protection of mobile agents (referred to as IEOP method) is proposed. The method first encrypts the mobile agent code using the encryption function, and then encapsulates the encrypted mobile agent with the improved EOP protocol IEOP, and then traces the suspicious execution result. Experiments show that using this method can block most malicious attacks on mobile agents, and can protect the integrity and confidentiality of mobile agents, but the increment of mobile agent tour time is not large.

Energy-Optimal and Delay-Bounded Computation Offloading in Mobile Edge Computing with Heterogeneous Clouds

By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.

On Cost Aware Cloudlet Placement for Mobile Edge Computing

As accessing computing resources from the remote cloud inherently incurs high end-to-end(E2E)delay for mobile users,cloudlets,which are deployed at the edge of a network,can potentially mitigate this problem.Although some research works focus on allocating workloads among cloudlets,the cloudlet placement aiming to minimize the deployment cost(i.e.,consisting of both the cloudlet cost and average E2E delay cost)has not been addressed effectively so far.The locations and number of cloudlets have a crucial impact on both the cloudlet cost in the network and average E2E delay of users.Therefore,in this paper,we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing(CAPABLE)strategy,where both the cloudlet cost and average E2E delay are considered in the cloudlet placement.To solve this problem,a Lagrangian heuristic algorithm is developed to achieve the suboptimal solution.After cloudlets are placed in the network,we also design a workload allocation scheme to minimize the E2E delay between users and their cloudlets by considering the user mobility.The performance of CAPABLE has been validated by extensive simulations.

A Survey of Mobile Cloud Computing

Mobile Cloud Computing (MCC) is emerging as one of the most important branches of cloud computing. In this paper, MCC is defined as cloud computing extended by mobility, and a new ad-hoc infrastructure based on mobile devices. It provides mobile users with data storage and processing services on a cloud computing platform. Because mobile cloud computing is still in its infancy, we aim to clarify confusion that has arisen from different views. Existing works are reviewed, and an overview of recent advances in mobile cloud computing is provided. We investigate representative infrastructures of mobile cloud computing and analyze key components. Moreover, emerging MCC models and services are discussed, and challenging issues are identified that will need to be addressed in future work.

Mobile Internet Mobile Agent System Dynamic Trust Model for Cloud Computing

In mobile cloud computing,trust is a very important parameter in mobile cloud computing security because data storage and data processing are performed remotely in the cloud.Aiming at the security and trust management of mobile agent system in mobile cloud computing environment,the Human Trust Mechanism(HTM)is used to study the subjective trust formation,trust propagation and trust evolution law,and the subjective trust dynamic management algorithm(MASTM)is proposed.Based on the interaction experience between the mobile agent and the execution host and the third-party recommendation information to collect the basic trust data,the public trust host selection algorithm is given.The isolated malicious host algorithm and the integrated trust degree calculation algorithm realize the function of selecting the trusted cluster and isolating the malicious host,so as to enhance the security interaction between the mobile agent and the host.Given algorithm simulation and verification were carried out to prove its feasibility and effectiveness.

Adaptive Application Offloading Decision and Transmission Scheduling for Mobile Cloud Computing

Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.

Survey on Three Components of Mobile Cloud Computing: Offloading, Distribution and Privacy

Mobile Cloud Computing (MCC) brings rich computational resource to mobile users, network operators, and cloud computing providers. It can be represented in many ways, and the ultimate goal of MCC is to enable execution of rich mobile application with rich user experience. Mobility is one of the main characteristics of MCC environment where user can be able to continue their work regardless of movement. This literature review paper presents the state-of-the-art survey of MCC. Also, we provide the communication architecture of MCC and taxonomy of mobile cloud in which specifically concentrates on offloading, mobile distribution computing, and privacy. Through an extensive literature review, we found that MCC is a technologically beneficial and expedient paradigm for virtual environments in terms of virtual servers in a distributed environment, multi-tenant architecture and data storing in a cloud. We further identified the drawbacks in offloading, mobile distribution computing, privacy of MCC and how this technology can be used in an effective way.

Joint Resource Allocation Using Evolutionary Algorithms in Heterogeneous Mobile Cloud Computing Networks

The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.

Building a Platform to Bridge Low End Mobile Phones and Cloud Computing Services

Two waves of technology are dramatically changing daily life: cloud computing and mobile phones. New cloud computing services such as webmail and content rich data search have emerged. However, in order to use these services, a mobile phone must be able to run new applications and handle high network bandwidth. Worldwide, about 3.45 billion mobile phones are low end phones; they have low bandwidth and cannot run new applications. Because of this technology gap, most mobile users are unable to experience cloud computing services with their thumbs. In this paper, a novel platform, Thumb-in-Cloud, is proposed to bridge this gap. Thumb-in-Cloud consists of two subsystems: Thumb-Machine and Thumb-Gateways. Thumb-Machine is a virtual machine built into a low end phone to enable it to run new applications. Thumb-Gateways can tailor cloud computing services by reformatting and compressing the service to fit the phone's profile.

＂Smart Cafe＂： A Mobile Local Computing System Based On Indoor Virtual Cloud

With network developing and virtualization rising,more and more indoor environment(POIs) such as cafe,library,office,even bus and subway can provide plenty of bandwidth and computing resources.Meanwhile many people daily spending much time in them are still suffering from the mobile device with limited resources.This situation implies a novel local cloud computing paradigm in which mobile device can leverage nearby resources to facilitate task execution.In this paper,we implement a mobile local computing system based on indoor virtual cloud.This system mainly contains three key components:1) As to application,we create a parser to generate the "method call and cost tree" and analyze it to identify resourceintensive methods.2) As to mobile device,we design a self-learning execution controller to make offloading decision at runtime.3) As to cloud,we construct a social scheduling based application-isolation virtual cloud model.The evaluation results demonstrate that our system is effective and efficient by evaluating CPUintensive calculation application.Memoryintensive image translation application and 1/O-intensive image downloading application.

Computation Partitioning in Mobile Cloud Computing: A Survey

Mobile devices are increasingly interacting with clouds,and mobile cloud computing has emerged as a new paradigm.An central topic in mobile cloud computing is computation partitioning,which involves partitioning the execution of applications between the mobile side and cloud side so that execution cost is minimized.This paper discusses computation partitioning in mobile cloud computing.We first present the background and system models of mobile cloud computation partitioning systems.We then describe and compare state-of-the-art mobile computation partitioning in terms of application modeling,profiling,optimization,and implementation.We point out the main research issues and directions and summarize our own works.

Achieving Mobile Cloud Computing through Heterogeneous Wireless Networks

A Mobile Cloud Computing environment with remote computing and vertical handover capabilities will be considered. A fuzzy logic model will be suggested for both the offloading and handover functions using energy, delay and computation complexity as criteria. Based on the application requirements, some rules will be proposed and evaluated when deciding on the offloading then the selection of the network that keeps the traffic carried to the cloud at an acceptable level. All this ought to be achieved in real time while the application is still running. The proposed engine will be implemented and run on an Android mobile device connected to Google’s App Engine servers. Results will be presented and analyzed, and conclusions will be provided.

Efficient Hierarchical Multi-Server Authentication Protocol for Mobile Cloud Computing

With the development of communication technologies,various mobile devices and different types of mobile services became available.The emergence of these services has brought great convenience to our lives.The multi-server architecture authentication protocols for mobile cloud computing were proposed to ensure the security and availability between mobile devices and mobile services.However,most of the protocols did not consider the case of hierarchical authentication.In the existing protocol,when a mobile user once registered at the registration center,he/she can successfully authenticate with all mobile service providers that are registered at the registration center,but real application scenarios are not like this.For some specific scenarios,some mobile service providers want to provide service only for particular users.For this reason,we propose a new hierarchical multi-server authentication protocol for mobile cloud computing.The proposed protocol ensures only particular types of users can successfully authenticate with certain types of mobile service providers.The proposed protocol reduces computing and communication costs by up to 42.6%and 54.2%compared to two superior protocols.The proposed protocol can also resist the attacks known so far.

Energy Aware Task Assignment with Cost Optimization in Mobile Cloud Computing

In this paper, we are investigating the power consumption of mobile device while performing offloading system. The offloading system is way in which mobile application can be divided into local and remote execution in order to alleviate the CPU energy consumption. However, existing offloading systems do not consider data transfer communication energy while performing mobile offloading system. They have just focused on mobile CPU energy consumption. In this paper, we are investigating the energy consumption mobile CPU and communication energy collaboratively while performing mobile offloading for complex application. To cope up with the above problem, we have proposed Energy Efficient Task Scheduler (EETS) algorithm, whose aim is to determine optimal tasks execution in offloading system in order to minimize mobile CPU and communication energy. Simulation results show that EETS outperforms as compared to baseline approaches.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

Editorial: When the Cloud Computing Becomes Mobile!

Editorial: When the Cloud Computing Becomes Mobile!