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.

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.

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.

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.

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.

Security analysis and improvement on resilient storage outsourcing schemes in mobile cloud computing

The resilient storage outsourcing schemes in mobile cloud computing are analyzed. It is pointed out that the sharing-based scheme （ShS） has vulnerabilities regarding confidentiality and integrity; meanwhile, the coding-based scheme （COS） and the encryption-based scheme （EnS） have vulnerabilities on integrity. The corresponding attacks on these vulnerabilities are given. Then, the improved protocols such as the secure sharing-based protocol （SShP）, the secure coding-based protocol （SCoP） and the secure encryption- based protocol （SEnP）, are proposed to overcome these vulnerabilities. The core elements are protected through public key encryptions and digital signatures. Security analyses show that the confidentiality and the integrity of the improved protocols are guaranteed. Meanwhile, the improved protocols can keep the frame of the former schemes and have higher security. The simulation results illustrate that compared with the existing protocols, the communication overhead of the improved protocols is not significantly increased.

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.

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.

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.

Adaptive Service Selection Method in Mobile Cloud Computing

Despite the rapid advances in mobile technology, many constraints still prevent mobile devices from running resource-demanding applications in mobile environments. Cloud computing with flexibility, stability and scalability enables access to unlimited resources for mobile devices, so more studies have focused on cloud computingbased mobile services. Due to the stability of wireless networks, changes of Quality of Service （QoS） level and user＇ real-time preferences, it is becoming challenging to determine how to adaptively choose the ＂appropriate＂ service in mobile cloud computing environments. In this paper, we present an adaptive service selection method. This method first extracts user preferences from a service＇s evaluation and calculates the similarity of the service with the weighted Euclidean distance. Then, they are combined with user context data and the most suitable service is recommended to the user. In addition, we apply the fuzzy cognitive imps-based model to the adaptive policy, which improves the efficiency and performance of the algorithm. Finally, the experiment and simulation demonstrate that our approach is effective.

Mobile Cloud Computing and Applications

In 2010, cloud computing gained momentum. Cloud computing is a model for real-time, on-demand, pay-for-use network access to a shared pool of configurable computing and storage resources. It has matured from a promising business concept to a working reality in both the private and public IT sectors. The U.S. government, for example, has requested all its agencies to evaluate cloud computing alternatives as part of their budget submissions for new IT investment.

Mobile-agent-based energy-efficient scheduling with dynamic channel acquisition in mobile cloud computing

Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.

Lightweight and Compromise Resilient Storage Outsourcing with Distributed Secure Accessibility in Mobile Cloud Computing

Mobile Cloud Computing usually consists of front-end users who possess mobile devices and back-end cloud servers. This paradigm empowers users to pervasively access a large volume of storage resources with portable devices in a distributed and cooperative manner. During the period between uploading and downloading files （data）, the privacy and integrity of files need to be guaranteed. To this end, a family of schemes are proposed for different situations. All schemes are lightweight in terms of computational overhead, resilient to storage compromise on mobile devices, and do not assume that trusted cloud servers are present. Corresponding algorithms are proposed in detail for guiding off-the-shelf implementation. The evaluation of security and performance is also extensively analyzed, justifying the applicability of the proposed schemes.

Auto-Scaling Framework for Enhancing the Quality of Service in the Mobile Cloud Environments

On-demand availability and resource elasticity features of Cloud computing have attracted the focus of various research domains.Mobile cloud computing is one of these domains where complex computation tasks are offloaded to the cloud resources to augment mobile devices’cognitive capacity.However,the flexible provisioning of cloud resources is hindered by uncertain offloading workloads and significant setup time of cloud virtual machines(VMs).Furthermore,any delays at the cloud end would further aggravate the miseries of real-time tasks.To resolve these issues,this paper proposes an auto-scaling framework(ACF)that strives to maintain the quality of service(QoS)for the end users as per the service level agreement(SLA)negotiated assurance level for service availability.In addition,it also provides an innovative solution for dealing with the VM startup overheads without truncating the running tasks.Unlike the waiting cost and service cost tradeoff-based systems or threshold-rule-based systems,it does not require strict tuning in the waiting costs or in the threshold rules for enhancing the QoS.We explored the design space of the ACF system with the CloudSim simulator.The extensive sets of experiments demonstrate the effectiveness of the ACF system in terms of good reduction in energy dissipation at the mobile devices and improvement in the QoS.At the same time,the proposed ACF system also reduces the monetary costs of the service providers.

Analyzing the service availability of mobile cloud computing systems by fluid-flow approximation

Mobile cloud computing （MCC） has become a promising technique to deal with computation- or data-intensive tasks. It overcomes the limited processing power, poor storage capacity, and short battery life of mobile devices. Providing continuous and on-demand services, MCC argues that the service must be available for users at anytime and anywhere. However, at present, the service availability of MCC is usually measured by some certain metrics of a real-world system, and the results do not have broad representation since different systems have different load levels, different deployments, and many other random factors. Meanwhile, for large-scale and complex types of services in MCC systems, simulation-based methods （such as Monte- Carlo simulation） may be costly and the traditional state-based methods always suffer from the problem of state-space explosion. In this paper, to overcome these shortcomings, fluid-flow approximation, a breakthrough to avoid state-space explosion, is adopted to analyze the service availability of MCC. Four critical metrics, including response time of service, minimum sensing time of devices, minimum number of nodes chosen, and action throughput, are def＇med to estimate the availability by solving a group of ordinary differential equations even before the MCC system is fully deployed. Experimental results show that our method costs less time in analyzing the service availability of MCC than the Markov- or simulation-based methods.

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.

High-efficient energy saving processing of big data of communication under mobile cloud computing

From 21st century,it is hard for traditional storage and algorithm to provide service with high quality because of big data of communication which grows rapidly.Thus,cloud computing technology with relatively low cost of hardware facilities is created.However,to guarantee the quality of service in the situation of the rapid growth of data volume,the energy consumption cost of cloud computing begins to exceed the hardware cost.In order to solve the problems mentioned above,this study briefly introduced the virtual machine and its energy consumption model in the mobile cloud environment,introduced the basic principle of the virtual machine migration strategy based on the artificial bee colony algorithm and then simulated the performance of processing strategy to big data of communication based on artificial bee colony algorithm in mobile cloud computing environment by CloudSim3.0 software,which was compared with the performance of two algorithms,resource management(RM)and genetic algorithm(GA).The results showed that the power consumption of the migration strategy based on the artificial bee colony algorithm was lower than the other two strategies,and there were fewer failed virtual machines under the same number of requests,which meant that the service quality was higher.

Dynamic resource allocation for service in mobile cloud computing with Markov modulated arrivals

Mobile Cloud Computing (MCC) is a modern architecture that brings together cloudcomputing, mobile computing and wireless networks to assist mobile devices in storage,computing and communication. A cloud environment is developed to support a widerange of users that request the cloud resources in a dynamic environment with possible constraints. Burstiness in users service requests causes drastic and unpredictableincreases in the resource requests that have a crucial impact on policies of resourceallocation. How can the cloud system efficiently handle such burstiness when the cloudresources are limited? This problem becomes a hot issue in the MCC research area. Inthis paper, we develop a system model for the resource allocation based on the SemiMarkovian Decision Process (SMDP), able of dynamically assigning the mobile servicerequests to a set of cloud resources, to optimize the usage of cloud resources and maximize the total long-term expected system reward when the arrival process is a finitestate Markov-Modulated Poisson Process (MMPP). Numerical results show that ourproposed model performs much better than the Greedy algorithm in terms of achievinghigher system rewards and lower service requests blocking probabilities, especially whenthe burstiness degree is high, and the cloud resources are limited.

FACOR：Flexible Access Control with Outsourceable Revocation in Mobile Clouds

Access control is a key mechanism to secure outsourced data in mobile clouds. Some existing solutions are proposed to enforce flexible access control on outsourced data or reduce the computations performed by mobile devices. However, less attention has been paid to the efficiency of revocation when there are mobile devices needed to be revoked. In this paper, we put forward a new solution, referred to as flexible access control with outsourceable revocation(FACOR) for mobile clouds. The FACOR applies the attribute-based encryption to enable flexible access control on outsourced data, and allows mobile users to outsource the time-consuming encryption and decryption computations to proxies, with only requiring attributes authorization to be fully trusted. As an advantageous feature, FACOR provides an outsourceable revocation for mobile users to reduce the complicated attribute-based revocation operations. The security analysis shows that our FACOR scheme achieves data security against collusion attacks and unauthorized accesses from revoked users. Both theoretical and experimental results confirm that our proposed scheme greatly reliefs the mobile devices from heavy encryption and decryption computations, as well as the complicated revocation of access rights in mobile clouds.

Heuristic and Bent Key Exchange Secured Energy Efficient Data Transaction for Traffic Offloading in Mobile Cloud

In today’s world,smart phones offer various applications namely face detection,augmented-reality,image and video processing,video gaming and speech recognition.With the increasing demand for computing resources,these applications become more complicated.Cloud Computing(CC)environment provides access to unlimited resource pool with several features,including on demand self-service,elasticity,wide network access,resource pooling,low cost,and ease of use.Mobile Cloud Computing(MCC)aimed at overcoming drawbacks of smart phone devices.The task remains in combining CC technology to the mobile devices with improved battery life and therefore resulting in significant performance.For remote execution,recent studies suggested downloading all or part of mobile application from mobile device.On the other hand,in offloading process,mobile device energy consumption,Central Processing Unit(CPU)utilization,execution time,remaining battery life and amount of data transmission in network were related to one or more constraints by frameworks designed.To address the issues,a Heuristic and Bent Key Exchange(H-BKE)method can be considered by both ways to optimize energy consumption as well as to improve security during offloading.First,an energy efficient offloading model is designed using Reactive Heuristic Offloading algorithm where,the secondary users are allocated with the unused primary users’spectrum.Next,a novel AES algorithm is designed that uses a Bent function and Rijndael variant with the advantage of large block size is hard to interpret and hence is said to ensure security while accessing primary users’unused spectrum by the secondary user.Simulations are conducted for efficient offloading in mobile cloud and performance valuations are carried on the way to demonstrate that our projected technique is successful in terms of time consumption,energy consumption along with the security aspects covered during offloading in MCC.