Resource-intensive Mobile Applications Research Articles

Unlocking the Potential of Mobile-Edge Cloud: A Comprehensive Review and Future Directions

With the rapid expansion of the Internet of Things (IoT), the demand for computational resources continues to soar, necessitating innovative solutions to address the needs of resource-constrained IoT users. Mobile edge computing (MEC) emerges as a promising remedy, mitigating the strain imposed by resource-intensive mobile applications. Concurrently, leveraging unmanned aerial vehicles (UAVs) as aerial platforms presents an enticing opportunity to enhance connectivity in wireless networks, owing to their on-demand deployment capabilities, high cruising altitudes, and maneuverability in three-dimensional space. This paper presents a comprehensive examination of UAV-enabled aerial MEC, elucidating its advantages, challenges, and recent advancements across various domains. Topics explored include joint optimization of UAV trajectory, computation offloading, and resource allocation, UAV deployment strategies, task scheduling, load balancing, interplay with other technologies, and machine learning-driven optimizations. Additionally, the paper outlines key avenues for future research endeavors.

Unlocking the Potential of Mobile-Edge Cloud: A Comprehensive Review and Future Directions

With the rapid expansion of the Internet of Things (IoT), the demand for computational resources continues to soar, necessitating innovative solutions to address the needs of resource-constrained IoT users. Mobile edge computing (MEC) emerges as a promising remedy, mitigating the strain imposed by resource-intensive mobile applications. Concurrently, leveraging unmanned aerial vehicles (UAVs) as aerial platforms presents an enticing opportunity to enhance connectivity in wireless networks, owing to their on-demand deployment capabilities, high cruising altitudes, and maneuverability in three-dimensional space. This paper presents a comprehensive examination of UAV-enabled aerial MEC, elucidating its advantages, challenges, and recent advancements across various domains. Topics explored include joint optimization of UAV trajectory, computation offloading, and resource allocation, UAV deployment strategies, task scheduling, load balancing, interplay with other technologies, and machine learning-driven optimizations. Additionally, the paper outlines key avenues for future research endeavors.

Propose technology and management model of mobile cloud computing for the Vietnam national digital transformation program

Cloud computing is considered a digital platform to develop mobile applications for the National Digital Transformation Program 2025 with a vision to 2030. There are many solutions to build a cloud computing platform, but those solutions consume hardware resources, high communication latency and lack of mobility, thus increasing the cost of investment in building a cloud computing platform. To address the disadvantages of the above solutions, this study proposes a mobile cloud computing model aimed at providing services that only implement on mobile devices, enhancing the calculation capacity on mobile devices, minimising resource shortages and performing efficiently for resource-intensive mobile applications. To do that, the solution proposed in this study is to enable the use of the nearest resource-rich mobile devices that can provide computing services to mobile users in the vicinity. Utilising leisure resources to serve resource-intensive mobile applications helps the government to reduce hardware investment for building cloud computing platforms. In addition, this study also proposes a management model for mobile applications through the App Store that is owned and managed by the Vietnamese government. From there, the government will monitor mobile applications and not let them develop spontaneously, causing difficulties and loss of trust for users.

Multipath Transmission Control Protocol for Live Virtual Machine Migration in the Cloud Environment

For mobile cloud computing (MCC), a local virtual machine- (VM-) based cloudlet is proposed to improve the performance of real-time resource-intensive mobile applications. When a mobile device (MD) discovers a cloudlet nearby, it takes some time to build up a virtual machine (VM) inside the cloudlet before data offloading from the MD to the VM can begin. Live virtual machine migration refers to the process of transferring a running Virtual Machine (VM) from one host to another without interrupting its state. Theoretically, live migration process must not render the instance being migrated unavailable during its execution. However, in practice, there is always a service downtime associated with the process. This paper focuses on addressing the need to reduce the service downtime in case of live VM migration in cloud and providing a solution by implementing and optimizing the multipath transmission control protocol (MPTCP) ability within an Infrastructure as a service (IaaS) cloud to increase the efficiency of live migration. We have also introduced an algorithm, the α-best fit algorithm, to optimize the usage of bandwidth and to effectively streamline the MPTCP performance.

Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications

Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.

A Neuro-Fuzzy Hybrid Framework for Augmenting Resources of Mobile Device

Due to the drastic exploitation of mobile devices and mobile apps in the day-to-day activities of people, the enhancement in hardware and software tools for mobile devices is also rising rapidly to cater to the requirements of mobile users. However, the progress of resource-intensive mobile applications is still inhibited by the limited battery power, restricted memory, and scarce resources of mobile devices. By employing mobile cloud computing, mobile edge computing, and fog computing, many researchers are providing their frameworks and offloading algorithms to augment the resources of mobile devices. In the existing solutions, offloading resource-intensive tasks is adopted only for specific scenarios and also not supporting the flexible exploitation of IoT-based smart mobile applications. So, a novel neuro-fuzzy modeling framework is proposed to augment the inadequate resources of a mobile device by offloading the resource-intensive tasks to external entities, and also a Bat optimization algorithm is exploited to schedule as many tasks as possible to the augmentation entities thereby improving the total execution time of all tasks and minimizing the resource exploitation of the mobile device. In this research work, external augmentation entities like distant cloud, edge cloud, and microcontroller devices are providing Resource augmentation as a Service (RaaS) to mobile devices. An IoT-based smart transport mobile app is implemented based on the proposed framework which depicts a significant reduction in execution time, energy consumption, bandwidth utilization, and average delay. Performance analysis depicts that the neuro-fuzzy hybrid model with Bat optimization provides a significant improvement compared with proximate computing and web service frameworks on the Quality of Service (QoS) parameters namely energy consumption, execution time, bandwidth utilization, and latency. Thus, the proposed framework exhibits a feasible solution of RaaS to resource-constrained mobile devices by exploiting edge computing.

Adaptive Proximate Computing Framework for Mobile Resource Augmentation

Beyond voice and message communication, mobile devices are exploiting to access Internet resources like desktop. Nowadays, MobileApp development and its usage in various domains are also significantly rising. However, resource constraints of mobile devices like limited processing power, low storage, restricted memory and faster dissipation of energy have restricted resource intensive mobile application development and its accessibility. Cloud based mobile resource augmentation needs longer latency time due to larger number of intermediate hubs thereby prolonged execution time and deterioration of energy from mobile devices; hence, we are exploiting proximate computing entities for augmenting resources of the mobile devices by employing soft computing methodologies. The proposed proximate computing framework is intended to augment the resource scarcity of mobile devices by outsourcing their data and processing to an external proximate computing entity like an edge cloud, Raspberry PI controller, Arduino, WiFi Gateway and MNO cloud. An intelligent inventory checker mobile application which is based on the proposed framework, depicts significant mitigates in execution time and energy consumption of mobile devices. Proximate computing entities namely Arduino and Edge Cloud service have provided computation as a service to check the reorder level of every stock thereby providing seamless user experience to the mobile users. This research work provides a feasible solution for the development of resource intensive mobile application and its accessibility by mobile user regardless of the resource scarcity of mobile device.

Transient fault aware application partitioning computational offloading algorithm in microservices based mobile cloudlet networks

Mobile Cloudlet Computing paradigm (MCC) allows execution of resource-intensive mobile applications using computation cloud resources by exploiting computational offloading method for resource-constrained mobile devices. Whereas, computational offloading needs the mobile application to be partitioned during the execution in the MCC so that total execution cost is minimized. In the MCC, at the run-time network contexts (i.e., network bandwidth, signal strength, latency, etc.) are intermittently changed, and transient failures (due to temporary network connection failure, services busy, database disk out of storage) often occur for a short period of time. Therefore, transient failure aware partitioning of the mobile application at run-time is a challenging task. Since, existing MCC offers computational monolithic services by exploiting heavyweight virtual machines, which incurs with long VM startup time and high overhead, and these cannot meet the requirements of fine-grained microservices applications (e.g., E-healthcare, E-business, 3D-Game, and Augmented Reality). To cope up with prior issues, we propose microservices based mobile cloud platform by exploiting containerization which replaces heavyweight virtual machines, and we propose the application partitioning task assignment (APTA) algorithm which determines application partitioning at run-time and adopts the fault aware (FA) policy to execute microservices applications robustly without interruption in the MCC. Simulation results validate that the proposed microservices mobile cloud platform not only shrinks the setup time of run-time platform but also reduce the energy consumption of nodes and improve the application response time by exploiting APTA and FA to the existing VM based MCC and application partitioning strategies.

Media Cloud Service Scheduling Optimization for Resource-Intensive Mobile Application

How to reduce energy consumption, improve resource utilization and put forward efficient resource management model so as to improve the media cloud performance and mobile users’ quality of service (QoS) is the problem needed to be addressed. Our proposed media cloud distributed scheduling model aims to maximize the utility of media cloud. The media cloud distributed scheduling policy for resource-intensive mobile application includes media service provisioning and cloud resource scheduling among media cloud datacenter. The media cloud service scheduling optimization algorithms include two sub-algorithms. The practical example of video streaming service for mobile users is also given. The experiments study the performance of media cloud distributed scheduling algorithm and related algorithms. The experiment results show that proposed algorithm has better performance than related algorithms.

Computation Offloading and Activation of Mobile Edge Computing Servers: A Minority Game

With the ever-increasing popularity of resource-intensive mobile applications, mobile edge computing (MEC), e.g., offloading computationally expensive tasks to the cellular edge, has become a prominent technology for next generation wireless networks. Despite its great performance in terms of delay and energy, MEC suffers from restricted power allowance and computational capability of the edge nodes. Therefore, it is imperative to develop distributed mechanisms for computation offloading, so that not only the computational servers are utilized at their best capacity but also the users' latency constraints are fulfilled. In this letter, by using the theory of Minority Games, we develop a novel distributed server activation mechanism for computation offloading. Our scheme guarantees energy-efficient activation of servers as well as satisfaction of users' quality-of-experience requirements in terms of latency.

Optimal media service selection scheme for mobile users in mobile cloud

Media cloud environments can provide a large number of multimedia services to mobile clients due to its flexibility and agility. However, a number of challenges need to be addressed so that these services are efficiently provided in terms of resources’ usage and energy consumption whilst improving the quality of service (QoS) and the user’s service satisfaction. This paper proposes a new media cloud distributed scheduling scheme that addresses these challenges, suitable for resource-intensive mobile application such as mobile video streaming. The proposed scheduling policy includes media service provisioning and cloud resource scheduling within the cloud datacenter, being able to jointly improve the mobile user’s satisfaction and the media cloud supplier’s revenue. Its aims are to minimize the service time, power consumption and costs for the service provider, through a convenient tradeoff of multiple QoS parameters and, consequently, increase the user’s satisfaction by reducing waiting times, service failure rate and power consumption of the mobile device. The validity of the proposed scheme is demonstrated by running experiments based on a practical use case of video streaming for mobile clients. The experiments were defined to allow to study the effects of request rate, video length, number of video streams and job size on the performance of the proposed media cloud distributed scheduling algorithm and compare it with related algorithms. The results show that proposed algorithm has better performance in terms of request failure rate, amount of energy consumed and response time.

Distributed QoS-aware scheduling optimization for resource-intensive mobile application in hybrid cloud

In the paper, the distributed scheduling optimization model for resource-intensive mobile application is proposed. Lagrangian method is applied to achieve distributed scheduling optimization in hybrid cloud. By decomposing the Kuhn–Tucker conditions into different roles of mobile user, public cloud supplier and local cloud supplier, the scheduling optimization problem in hybrid cloud is converted into a distributed problem. The system design and example of distributed scheduling optimization for resource intensive mobile application is also given. The local or public cloud provider uses service-level agreement (SLA) in determining the share of resources to be allocated to the mobile user. The distributed scheduling optimization algorithm for resource intensive mobile application is proposed, which includes three parts: local cloud agent scheduling optimization, public cloud service scheduling and mobile application QoS optimization. The experiments study how data size, request arrival rate, number of mobile users and mobility have effect on the proposed algorithm and other related works.

Crowd-Funding: A New Resource Cooperation Mode for Mobile Cloud Computing.

Mobile cloud computing, which integrates the cloud computing techniques into the mobile environment, is regarded as one of the enabler technologies for 5G mobile wireless networks. There are many sporadic spare resources distributed within various devices in the networks, which can be used to support mobile cloud applications. However, these devices, with only a few spare resources, cannot support some resource-intensive mobile applications alone. If some of them cooperate with each other and share their resources, then they can support many applications. In this paper, we propose a resource cooperative provision mode referred to as "Crowd-funding", which is designed to aggregate the distributed devices together as the resource provider of mobile applications. Moreover, to facilitate high-efficiency resource management via dynamic resource allocation, different resource providers should be selected to form a stable resource coalition for different requirements. Thus, considering different requirements, we propose two different resource aggregation models for coalition formation. Finally, we may allocate the revenues based on their attributions according to the concept of the "Shapley value" to enable a more impartial revenue share among the cooperators. It is shown that a dynamic and flexible resource-management method can be developed based on the proposed Crowd-funding model, relying on the spare resources in the network.

Nearby live virtual machine migration using cloudlets and multipath TCP

A nearby virtual machine (VM) based cloudlet is proposed for mobile cloud computing (MCC) to enhance the performance of real-time resource-intensive mobile applications. Generally, when a mobile device (MD) discovers a cloudlet in the vicinity, it takes time to set up a VM inside the cloudlet before data offloading from the MD to the VM starts. The time between the discovery of the cloudlet and actual offloading of data is considered as the service initiation time. When multiple cloudlets are present in a nearby geographical location, initiating a service with each cloudlet may be frustrating for cloudlet users that moving from one location to another. In order to eliminate the delay caused by the service initiation time after moving away from the source cloudlet, this paper proposes a seamless live VM migration between neighbouring cloudlets. A seamless live VM migration is achieved with the prior knowledge of the migrating VM IP address in the destination cloudlet and more importantly with multipath TCP (MPTCP). We have performed a number of experiments to validate the proposed approach using Linux KVM hypervisor. The experimental results demonstrate the feasibility of the proposed approach and also show performance improvement. Specifically, there is almost zero downtime at the destination cloudlet after the migration is completed.

Directory-based incentive management services for ad-hoc mobile clouds

Mobile cloud computing is envisioned as a promising approach to augment the computational capabilities of mobile devices for emerging resource-intensive mobile applications. This augmentation is generally achieved through the capabilities of stationary resources in cloud data centers. However, these resources are mostly not free and sometimes not available. Mobile devices are becoming powerful day by day and can form a self-organizing mobile ad-hoc network of nearby devices and offer their resources as on-demand services to available nodes in the network. In the ad-hoc mobile cloud, devices can move after consuming or providing services to one another. During this process, the problem of incentives arises for a node to provide service to another device (or other devices) in the network, which ultimately decreases the motivation of the mobile device to form an ad-hoc mobile cloud. To solve this problem, we propose a directory-based architecture that keeps track of the retribution and reward valuations (in terms of energy saved and consumed) for devices even after they move from one ad-hoc environment to another. From simulation results, we infer that this framework increases the motivation for mobile devices to form a self-organizing proximate mobile cloud network and to share their resources in the network.

An experimental analysis on cloud-based mobile augmentation in mobile cloud computing

Recent consumer electronic technologies have created momentous ground for smartphones in various domains, particularly healthcare and education. However, smartphones' miniature nature imposes intrinsic limitations on computational capabilities and battery lifetime that encumber performing Resource-intensive Mobile Applications (RMAs). Cloud-based Mobile Augmentation (CMA) is the-state-of-the-art augmentation model that leverages proximate and distant clouds to increase, enhance, and optimize computing capabilities of mobile devices aiming at execution of RMAs, which breeds Mobile Cloud Computing (MCC). This study analyzes impacts of mobile-cloud distance and number of intermediate hops - as influential factors - on CMA performance which are not yet investigated. The results indicate the correlation between distance and intermediate hops on overall execution costs (time and energy) of RMAs. The mathematical modeling and benchmarking unveil that distance has negligible impact on latency, whereas intermediate hops increment and communication overhead significantly degrade application performance and complicate energy and time estimation in CMA system.

Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges

Recently, Cloud-based Mobile Augmentation (CMA) approaches have gained remarkable ground from academia and industry. CMA is the state-of-the-art mobile augmentation model that employs resource-rich clouds to increase, enhance, and optimize computing capabilities of mobile devices aiming at execution of resource-intensive mobile applications. Augmented mobile devices envision to perform extensive computations and to store big data beyond their intrinsic capabilities with least footprint and vulnerability. Researchers utilize varied cloud-based computing resources (e.g., distant clouds and nearby mobile nodes) to meet various computing requirements of mobile users. However, employing cloud-based computing resources is not a straightforward panacea. Comprehending critical factors (e.g., current state of mobile client and remote resources) that impact on augmentation process and optimum selection of cloud-based resource types are some challenges that hinder CMA adaptability. This paper comprehensively surveys the mobile augmentation domain and presents taxonomy of CMA approaches. The objectives of this study is to highlight the effects of remote resources on the quality and reliability of augmentation processes and discuss the challenges and opportunities of employing varied cloud-based resources in augmenting mobile devices. We present augmentation definition, motivation, and taxonomy of augmentation types, including traditional and cloud-based. We critically analyze the state-of-the-art CMA approaches and classify them into four groups of distant fixed, proximate fixed, proximate mobile, and hybrid to present a taxonomy. Vital decision making and performance limitation factors that influence on the adoption of CMA approaches are introduced and an exemplary decision making flowchart for future CMA approaches are presented. Impacts of CMA approaches on mobile computing is discussed and open challenges are presented as the future research directions.

Composite quality of service and decision making perspectives in wireless networks

Supporting quality of service (QoS) in wireless networks has been a very rich and interesting area of research. Many significant advances have been made in supporting QoS in single wireless networks. However, the support for the QoS across multiple heterogeneous wireless networks will be required in the future wireless networks. In connections spanning multiple wireless networks, the end-to-end QoS will depend on several factors such as mobility and connection patterns of users, and the QoS policies in each of the wireless networks. The end-to-end QoS is also affected by multiple decisions that must be made by several different network entities for resource allocation. The paper has two objectives: one is to demonstrate the decision making process for resource allocation in multiple heterogeneous wireless networks and the second is to present a novel concept of composite QoS in such wireless environment. More specifically, we present an architecture for multiple heterogeneous wireless networks, decision making process for resource request and allocation, a simulation model to study composite QoS, and several interesting results. We also present potential implications of composite QoS on users and network service providers. We also show how the QoS ideas presented in this paper can be used by wireless carriers for improved QoS support and management. The paper can form the basis for a significant further research in DSS for emerging 3G/4G wireless networks supporting QoS for a range of sophisticated and resource intensive mobile applications.