Mobile Cloud Offloading Research Articles

Utility Optimization for Multi-User Task Offloading in Mobile Ad Hoc Cloud: A Stochastic Game Approach

Offloading tasks to the mobile ad hoc cloud (MAHC) can improve the utility of mobile devices in scenarios without infrastructure. However, when multiple resource demanders (RDs) offload tasks to multiple resource providers (RPs) in the MAHC with task random generation, how to optimize the utility of RDs and the service quality of the MAHC is an issue worth researching. To this end, we investigate this issue from the stochastic game perspective. Firstly, we combine the obtained time, energy, and monetary revenue as the utility of each RD and construct the utility improvement of each RD as a static non-cooperative game. Secondly, according to the randomness of RD’s task generation, we transform the single-shot non-cooperative game into a stochastic game in an infinite time horizon to obtain the long-term utility. Thirdly, to reduce the computational complexity for achieving Nash equilibrium, we propose an action reduction alternate backward iteration algorithm according to the games characteristics and prove its convergence. Finally, we compare the utility obtained from the static game with the stochastic game, the numerical results indicating that the utility of each RD can be effectively improved by utilizing the stochastic game.

A survey of research on computation offloading in mobile cloud computing

A survey of research on computation offloading in mobile cloud computing

Lyapunov Optimization-Based Trade-Off Policy for Mobile Cloud Offloading in Heterogeneous Wireless Networks

In order to improve mobile users’ service experience, mobile cloud computing (MCC) is promoted. Although MCC can alleviate the burdens of Smart mobile devices (SMDs) by offloading computation-intensive applications to the cloud, it also aggravates computing and storage overheads in cloud centers and bandwidth overhead on wireless links for offloading workloads of mobile users. Therefore, we should carefully design the offloading policy to decrease these overheads while easing the burdens of SMDs. To this end, we investigate the offloading policy in heterogeneous wireless networks. In this paper, a queue model is built to formulate the mobile users’ workload offloading problem and Lyapunov optimization framework is proposed to make trade-off between system offloading utility and queue backlog. For deterministic WiFi connections, a Lagrangian optimization method is proposed to decide the optimal offloading workloads. Furthermore, considering random WiFi connection durations, a multi-stage stochastic programming method is proposed. The experimental results show effectiveness of the Lagrangian optimization offloading method for deterministic WiFi connection and the multi-stage stochastic programming method for random WiFi connection.

EFFORT: Energy efficient framework for offload communication in mobile cloud computing

SummaryThere is an abundant expansion in the race of technology, specifically in the production of data, because of the smart devices, such as mobile phones, smart cards, sensors, and Internet of Things (IoT). Smart phones and devices have undergone an enormous evolution in a way that they can be used. More and more new applications, such as face recognition, augmented reality, online interactive gaming, and natural language processing are emerging and attracting the users. Such applications are generally data intensive or compute intensive, which demands high resource and energy consumption. Mobile devices are known for the resource scarcity, having limited computational power and battery life. The tension between compute/data intensive application and resource constrained mobile devices hinders the successful adaption of emerging paradigms. In the said perspective, the objective of this article is to study the role of computation offloading in mobile cloud computing to supplement mobile platforms ability in executing complex applications. This article proposes a systematic approach (EFFORT) for offload communication in the cloud. The proposed approach provides a promising solution to partially solve energy consumption issue for communication‐intensive applications in a smartphone. The experimental study shows that our proposed approach outperforms its counterparts in terms of energy consumption and fast processing of smartphone devices. The battery consumption was reduced to 19% and the data usage was reduced to 16%.

A novel energy estimation model for constraint based task offloading in mobile cloud computing

The utility of mobile applications has been increased enormously due to the advancements in science and technology to assist the users for various purposes. The main intention of integrating cloud services and resources with the mobile application is to reduce battery usage and to improve the efficiency of mobile devices. Thus the process of shifting a task that can be run on the cloud resources for assistance is referred to as task offloading. The process of task offloading is critical in the field of Mobile Cloud Computing. The major issue that pairs with task offloading are the communication cost estimation of the devices. To overcome the above-mentioned issues and to create an effective task offloading model, A Novel Mobile Cloud Computing framework called Rule Generation based Energy Estimation Model (RG-EEM) is designed. The energy required for executing the task in the local mobile device and cloud is estimated by implementing an energy estimation algorithm. Then a novel constraints specific rule generation algorithm is used to estimate the task execution time and memory utilization of the task, from which the decision has to be taken for offloading or local execution by considering all the possible affecting characteristics. Further, a novel task clustering and scheduling algorithm are implemented to execute the task in the cloud server effectively which will help in allocating similar tasks to a particular virtual machine in the cloud. The RG-EEM algorithm will also help in partitioning the task and parallel execution. The effectiveness of the RG-EEM technique is evaluated using the parametric measures and compared with the existing techniques.

Near-optimal and learning-driven task offloading in a 5G multi-cell mobile edge cloud

With development well underway, 5G is envisioned as an enabler of lighting fast mobile services, such as virtual reality, augmented reality, live video analytics, and etc. In particular, multi-cell Mobile Edge Clouds (MEC) with 5G base stations endowed with computing capability are able to promote the Quality of Services (QoS) of mobile users by executing tasks in the edge cloud. Due to the varying 5G network conditions and limited computation capacity of each base station in the multi-cell MEC, as well as the stringent QoS requirements, a fundamental and challenging problem is how to offload user tasks to the edge cloud, such that the energy consumption of mobile devices is minimized. In this paper, we first formulate the offline and online location-aware mobile task offloading problems in a multi-cell MEC. For the offline location-aware mobile task offloading problem, we then develop an exact solution and an approximation algorithm with an approximation ratio. For the online problem, we thirdly propose a novel deep reinforcement learning-based offloading algorithm for mobile users to obtain the optimal offloading policy. We finally conduct extensive experiments by simulations to evaluate the proposed algorithms against existing benchmarks. The experimental results show that the proposed algorithms are promising and outperform the benchmark algorithms by significantly reducing energy cost of mobile devices and delays experienced by mobile users.

Novel Sustainable and Heterogeneous Offloading Management Techniques in Proactive Cloudlets

Cloudlet-based mobile cloud offloading is an emerging technology designed to augment mobile elements by migrating resource-hungry components to adjacent local resource pooling. However, the Cloudlet resources are usually limited in terms of the computing utility, storage and network bandwidth. In this scenario, the remote Cloud infrastructure can provide additional computing and storage utility during run-time; the heterogeneous offloading methods for different mobile applications and diverse offloading resources complicate the Cloudlet-based offloading and resource allocation process. As a result, a considerable amount of delay is caused by setting up the execution environment, communication overhead and waiting in the queue, which significantly downgrade the QoS and the usability of such systems. In this paper, we propose a novel hybrid offloading model to solve the heterogeneous resource-constraint offloading issues in the Cloudlet, concerning the offloading energy and execution efficiency. A queue-based offloading framework is developed to formulate and analyze the mixed migration-based and partition-based offloading behaviors in the Cloudlet. The execution and energy-aware heterogeneous offloading resource allocation problem is formalized, and a Particle Swarm Optimization heuristic solution is presented. A SARIMA-based load prediction model is designed in the Cloudlet to achieve fine-grain proactive resource allocation. Experimental results reveal that the proposed framework can effectively reduce the offloading energy cost and execution time, compared to currently existing solutions.

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, th... | Find, read and cite all the research you need on Tech Science Press

Fuzzy simplified swarm optimization for multisite computational offloading in mobile cloud computing

Mobile Cloud Computing (MCC)’s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. However, their processing speeds, access delays, computation capability, residual memory and service charges are different which retards their usage, as it becomes time-consuming and ambiguous for making decisions. To address the aforementioned issues, this paper presents a Fuzzy Simplified Swarm Optimization based cloud Computational Offloading (FSSOCO) algorithm to achieve optimum multisite offloading. Fuzzy logic and simplified swarm optimization are employed for the identification of high powerful nodes and task decomposition respectively. The overall performance of FSSOCO is validated using the Specjvm benchmark suite and compared with the state-of-the-art offloading techniques in terms of the weighted total cost, energy consumption, and processing time.

Iris: Secure reliable live-streaming with opportunistic mobile edge cloud offloading

Abstract The ever-increasing demand for higher quality live streams is driving the need for better networking infrastructures, specially when disseminating content over highly congested areas, such as stadiums, concerts and museums. Traditional approaches to handle this type of scenario relies on a combination of cellular data, through 4G distributed antenna arrays (DAS), with a high count of WiFi (802.11) access points. This obvious requires a substantial upfront cost for equipment, planning and deployment. Recently, new efforts have been introduced to securely leverage the capabilities of wireless multipath, including WiFi multicast, 4G, and device-to-device communications. In order to solve these issues, we propose an approach that lessens the requirements imposed on the wireless infrastructures while potentially expanding wireless coverage through the crowd-sourcing of mobile devices. In order to achieve this, we propose a novel pervasive approach that combines secure distributed systems, WiFi multicast, erasure coding, source coding and opportunistic offloading that makes use of hyperlocal mobile edge clouds. We empirically show that our solution is able to offer a 11 fold reduction on the infrastructural WiFi bandwidth usage without having to modify any existing software or firmware stacks while ensuring stream integrity, authorization and authentication.

Energy‐aware dynamic task offloading and collective task execution in mobile cloud computing

SummaryThere is a good opportunity for enlightening the services of the mobile devices by introducing computational offloading using cloud technology. Offloading is a process for managing the complexity of the mobile environment by migrating computational load to the cloud. The mobile devices oblige the quick response for the offloading requests; it is dependent on network connectivity. The cloud services take long set‐up time irrespective of network connectivity. In this paper, new system architecture for the dynamic task offloading in the mobile cloud environment is proposed. The architecture includes the offloading algorithm that concentrates on energy consumption of the tasks both in the local and remote environment. The proposed algorithm formulates a collective task execution model for minimizing the energy consumption. The architecture concentrates on the network model by considering the task completion time in three different network scenarios. The experimental results show the efficiency of the suggested architecture in reducing the energy consumption and completion time of the tasks.

Resource Sharing of a Computing Access Point for Multi-User Mobile Cloud Offloading with Delay Constraints

We consider a mobile cloud computing system with multiple users, a remote cloud server, and a computing access point (CAP). The CAP serves both as the network access gateway and a computation service provider to the mobile users. It can either process the received tasks from mobile users or offload them to the cloud. We jointly optimize the offloading decisions of all users, together with the allocation of computation and communication resources, to minimize the overall cost of energy consumption, computation, and maximum delay among users. The joint optimization problem is formulated as a mixed-integer program. We show that the problem can be reformulated and transformed into a non-convex quadratically constrained quadratic program, which is NP-hard in general. We then propose an efficient solution to this problem by semidefinite relaxation and a novel randomization mapping method. Furthermore, when there is a strict delay constraint for processing each user's task, we further propose a three-step algorithm to guarantee the feasibility and local optimality of the obtained solution. Our simulation results show that the proposed solutions give nearly optimal performance under a wide range of parameter settings, and the addition of a CAP can significantly reduce the cost of multi-user task offloading compared with conventional mobile cloud computing where only the remote cloud server is available.

Joint optimization of energy and delay for computation offloading in cloudlet-assisted mobile cloud computing

In the mobile cloud computing (MCC), although offloading requests to the distant central cloud or nearby cloudlet can reduce energy consumption at the mobile devices (MDs), it may also incur a large execution delay including transmission time from the MDs to the servers and waiting time at the servers. Therefore, how to balance the energy consumption and delay performance is of great research importance. In this paper, we bring a thorough study on the energy consumption and execution delay of offloading process in a cloudlet-assisted MCC. Specifically, heterogeneity of request executions are explicitly considered. When there is a small cell base station (SBS) available, the MDs can connect with cloudlet via the SBS and if only a macro cell base station is available, the MD can connect with the central cloud through it. We derive the analytic results of the energy consumption and execution delay performance with the assumption of three different queue models at the MD, cloudlet and central cloud. Based on the theoretical analysis, the multi-objective optimization problems are formulated with the joint objectives to minimize the energy consumption and delay by finding the optimal offloading probability. The simulation results demonstrate the effectiveness of the proposed scheme.

Alleviation of void and energy hole in mobile cloud offloading using hybrid behavioral intelligence

In modern internet the usability of smart mobile devices is rapidly increased. It is not only used for commercial and communication purposes, it can be extended in the field like mobile health care, mobile learning, and mobile games. More access of smart mobile devices has a restriction due to resource constraint, this issue overwhelming via cloud computing. Heavy computations from mobile devices are offloaded to cloud and the application got executed. The problem occurs when the Electrogram data is entered into a mobile network in cloud environment which holds void and energy holes occurrences with heavy traffic and unbalanced load distribution.This paper aims to develop hybrid intelligence behavioural patterns for void and energy hole in mobile cloud offloading. The research work carried out in this paper extends its novelty in stage partitioning for concentric circles and sensor node distribution. The network area distribution of partitioning are uniformly distributed among void hole. The simulation results show the proposed approach produces high energy consumption in the mobile cloud paradigm.

Analysis of energy aware job offloading in mobile cloud

Mobile cloud computing is the combination of cloud computing and mobile computing, and provides rich computational resources to a mobile computer. In mobile cloud computing, computation offloading techniques are used to overcome the limitations of resource-constrained mobile devices. Offloading techniques perform some parts of a job of mobile devices in the cloud in behalf of mobile devices. If the cost of the operation in that part of the job in a mobile device is larger than the cost associated with offloading, the part is executed in the cloud. Traditional cost analysis models for deciding which parts of a job to execute in the cloud or a mobile device were estimated by using only the cost of offloading, which is composed of data transfer and response time required for the function call. In this paper, we propose a novel offloading cost analysis model based on the data synchronisation rate and the data exchange rate for the input of the function to improve the accuracy of offloading cost estimation. We confirm through experiments that the offloading technique with the proposed model can reduce the execution time of a job and consequentially improve the energy efficiency when compared to previous techniques.

Analysis of energy aware job offloading in mobile cloud

Mobile cloud computing is the combination of cloud computing and mobile computing, and provides rich computational resources to a mobile computer. In mobile cloud computing, computation offloading techniques are used to overcome the limitations of resource-constrained mobile devices. Offloading techniques perform some parts of a job of mobile devices in the cloud in behalf of mobile devices. If the cost of the operation in that part of the job in a mobile device is larger than the cost associated with offloading, the part is executed in the cloud. Traditional cost analysis models for deciding which parts of a job to execute in the cloud or a mobile device were estimated by using only the cost of offloading, which is composed of data transfer and response time required for the function call. In this paper, we propose a novel offloading cost analysis model based on the data synchronisation rate and the data exchange rate for the input of the function to improve the accuracy of offloading cost estimation. We confirm through experiments that the offloading technique with the proposed model can reduce the execution time of a job and consequentially improve the energy efficiency when compared to previous techniques.

Cluster-PSO Based Resource Orchestration for Multi-task Applications in Vehicular Cloud

Vehicular cloud computing (VCC) provides a vehicular user attaching several resources with different types at the same time. Additionally, the vehicular applications especially for big data processing are always complicated and may be decomposed into several fine-grained tasks. When offloading the complicated multi-task application to the vehicular clouds, the task executes individually in terms of its own computation, storage and bandwidth requirement. Different from the task offloading in mobile cloud computing which aims to optimize the energy consumption, the important metric for vehicular users is the application delay. Moreover, the moving vehicles always have the similar resource properties and may form the solution clusters when finding the resource orchestration policy, which brings an opportunity of improving resource orchestration performance. In this paper, we formulate the VCC resource orchestration as an optimization problem, and propose a cluster-particle swarm optimization (PSO) algorithm to obtain the resource orchestration policy. A fast cluster algorithm is used to divide the solution space and generate sub-swarms for better exploring the orchestration solutions. The experiment results show that the cluster-PSO algorithm can achieve a higher resource orchestration accuracy in an acceptable time comparing to the other PSO algorithms. Especially, when there are more tasks in an application and the vehicle has more optional VCC resources, the performance of the cluster-PSO based resource orchestration is outstanding.

Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm

Mobile cloud computing (MCC) is becoming a popular mobile technology that aims to augment local resources of mobile devices, such as energy, computing, and storage, by using available cloud services and functionalities. The offloading process is one of the techniques used in MCC to enhance the capabilities of mobile devices by moving mobile data and computation-intensive operations to cloud platforms. Several techniques have been proposed to perform and improve the efficiency and effectiveness of the offloading process, such as multi-criteria decision analysis (MCDA). MCDA is a well-known concept that aims to select the best solution among several alternatives by evaluating multiple conflicting criteria, explicitly in decision making. However, as there are a variety of platforms and technologies in mobile cloud computing, it is still challenging for the offloading process to reach a satisfactory quality of service from the perspective of customers’ computational service requests. Thus, in this paper, we conduct a literature review that leads to a better understanding of the usability of the MCDA methods in the offloading operation that is strongly reliant on the mobile environment, network operators, and cloud services. Furthermore, we discuss the challenges and opportunities of these MCDA techniques for offloading research in mobile cloud computing. Finally, we recommend a set of future research directions in MCDA used for the mobile cloud offloading process.

A quick-response framework for multi-user computation offloading in mobile cloud computing

The execution of much sophisticated applications on the resource-constrained mobile device will lead to the fast exhaustion of the battery of mobile device. Therefore, mobile cloud computing (MCC) is regarded as an energy-effective approach by offloading tasks from mobile device to the resource-enough cloud, which cannot only save energy for mobile devices but also prolong the operation time of battery. However, it still remains a challenging issue to coordinate task offloading among mobile devices and get offloading results quickly at the same time. In this paper, we propose an agent-based MCC framework to enable the device to receive offloading results faster by making offloading decision on the agent. Moreover, to get an offloading strategy, we formulate the problem of maximizing energy savings among multiple users under the completion time and bandwidth constraints. To solve the optimization problem, we propose a Dynamic Programming After Filtering (DPAF) algorithm. In the algorithm, firstly, the original offloading problem is transformed to the classic 0–1 Knapsack problem by the filtering process on the agent. Furthermore, we adopt dynamic programming algorithm to find an optimal offloading strategy. Simulation results show that the framework can more quickly get response from agent than other schemes and the DPAF algorithm outperforms other solutions in energy saving.

Tradeoff between execution speedup and reliability for compute-intensive code offloading in mobile device cloud

With the advent of different mobile computing technologies, mobile devices have opened up a plethora of computational infrastructure to provide improved performance for compute-intensive applications to the end users. Mobile Device Cloud (MDC) technology brings the code offloading mechanism from distant cloud to neighbor mobile devices. However, the major challenges of code offloading in MDC systems include maximization of computation speedup and reliability; unfortunately, these two performance parameters often oppose each other. In this paper, an optimization framework, namely TESAR, has been devised to tradeoff between application execution speedup and reliability while maintaining device energy within a predefined range. We also provide an algorithm for developing a dependency tree among the modules of an application so as to allow higher number of parallel executions, wherever and whenever it is possible. The emulation results of the proposed algorithm outperform the relevant state-of-the-art works in terms of application completion time, communication latency and rescheduling overhead.