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About 6,038 papers for “Computation offloading”

Mobility-Assisted Opportunistic Computation Offloading

Chuanmeizhi Wang, Yong Li, Depeng Jin · IEEE Communications Letters · 2014

With the explosion in personal mobile devices, computation offloading through opportunistic networks of nearby devices to support sophisticated mobile applications with limited resources of processing and energy is gaining increased attention. In this letter, we investigate the problem of mobility-assisted opportunistic computation offloading by exploiting the contact patterns regulated by these devices' mobility. We first formulate the optimal opportunistic offloading problem with the aid of statistic property of contact rates and then utilize the method of convex optimization to determine the amounts of computation to be offloaded to other devices. Extensive simulations under realistic human mobility demonstrate the efficiency of our scheme, in terms of obtaining superior higher computation success rate over the compared benchmarks under a wide variety of settings.

Cited by 51

Computation Offloading from Mobile Devices

Arani Bhattcharya, Pradipta De · 2016

Mobile devices like smartphones can augment their low-power processors by offloading portions of mobile applications to cloud servers. However, offloading to cloud data centers has a high network latency. To mitigate the problem of network latency, recently offloading to computing resources lying within the user's premises, such as network routers, tablets or laptop has been proposed. In this paper, we determine the devices whose processors have sufficient power to act as servers for computation offloading. We perform trace-driven simulation of SPECjvm2008 benchmarks to study the performance using different hardware. Our simulation shows that offloading to current state-of-the-art processors of user devices can improve performance of mobile applications. We find that offloading to user's own laptop reduces finish time of benchmark applications by 10%, compared to offloading to a commercial cloud server.

Cited by 13

Computation Offloading Toward Edge Computing

Li Lin, Xiaofei Liao, Hai Jin et al. · Proceedings of the IEEE · 2019

We are living in a world where massive end devices perform computing everywhere and everyday. However, these devices are constrained by the battery and computational resources. With the increasing number of intelligent applications (e.g., augmented reality and face recognition) that require much more computational power, they shift to perform computation offloading to the cloud, known as mobile cloud computing (MCC). Unfortunately, the cloud is usually far away from end devices, leading to a high latency as well as the bad quality of experience (QoE) for latency-sensitive applications. In this context, the emergence of edge computing is no coincidence. Edge computing extends the cloud to the edge of the network, close to end users, bringing ultra-low latency and high bandwidth. Consequently, there is a trend of computation offloading toward edge computing. In this paper, we provide a comprehensive perspective on this trend. First, we give an insight into the architecture refactoring in edge computing. Based on that insight, this paper reviews the state-of-the-art research on computation offloading in terms of application partitioning, task allocation, resource management, and distributed execution, with highlighting features for edge computing. Then, we illustrate some disruptive application scenarios that we envision as critical drivers for the flourish of edge computing, such as real-time video analytics, smart “things” (e.g., smart city and smart home), vehicle applications, and cloud gaming. Finally, we discuss the opportunities and future research directions.

Cited by 438

Computation Offloading in C-RAN: A Sequential Computation Model

Kun Guo, Min Sheng, Lijun He et al. · 2019

In cloud radio access network (C-RAN), computation-intensive tasks can be offloaded from mobile devices (MDs) to the powerful computing node in C-RAN, i.e., baseband unit (BBU) pool, through cooperation radio at remote radio heads (RRHs), for effective task processing and improved user experience. In the existing works, computational resources in the BBU pool are always allocated to MDs exclusively, resulting in poor resource utilization and deteriorative task processing delay. Alternatively, we adopt a sequential computation model to enhance computing performance, which is proved through theoretical analyses in this paper. In this model, a task scheduling issue should be addressed in the BBU pool to determine the optimal processing order for tasks. Then, one task's completion time is jointly determined by its scheduling order and arrival time in the BBU pool. Hence, to minimize the maximum task completion time, we jointly optimize cooperative radio at RRHs and task scheduling in the BBU pool. By leveraging the specific property of formulated problem, we propose an effective computation offloading algorithm to achieve a local optimal solution in block coordinate descent manner. Finally, simulation results present the convergence and advantage of our proposed algorithm.

Cited by 4

Computation Offloading for Real-Time Applications

Tahirović, Emina · DiVA (Mälardalen University College) · 2023

With the vast and ever-growing range of applications which have started to seek real-time data processing and timing-predictable services comes an extensive list of problems when trying to establish these applications in the real-time domain. Depending on the purpose of the real-time application, the requests that they impose on resources are vastly different. Some real-time applications require large computational power, large storage capacities, and large energy storage. However, not all devices can be equipped with processors, batteries, or power banks adequate for such resource requirements. While these issues can be mitigated by offloading computations using cloud computing, this is not a universal solution for all applications. Real-time applications need to be predictable and reliable, whereas the cloud can cause high and unpredictable latencies. One possible improvement in the predictability and reliability aspect comes from offloading to the edge, which is closer than the cloud and can reduce latencies. However, even the edge comes with certain limitations, and it is not exactly clear how, where and when applications should be offloaded. The problem then presents itself as: how should real-time applications in the-edge cloud architecture be modeled? Moreover, how should they be modeled to be agnostic from certain technologies and provide support for timing analysis? Another thing to consider is the question of 'when' to offload to the edge-cloud architecture. For example, critical computations can be offloaded to the edge, while less critical computations can be offloaded to the cloud, but before one can determine 'where' to offload, one must determine 'when'. Thus, this thesis focuses on designing a new technology-agnostic mathematical model to allow holistic modeling of real-time applications on the edge-cloud continuum and provide support for timing analysis.

Cited by 0Open access

Android offloading computation over the cloud

Vijay Prakash, Deepak Dudeja, Gunjan Chhabra et al. · 2025

This paper analyzes whether Android apps may outsource computational activities to cloud servers. Due to the complexity of mobile apps, shifting computing operations to the cloud has become popular to improve performance and reduce energy consumption. This paper explains how to deploy an Android app on Cloudflare and tests it under various settings. The testing showed that cloud computing can boost Android app efficiency. The findings show that shifting computing workloads to the cloud reduces mobile device CPU utilization. This frees up device resources, allowing it to perform other tasks. However, cloud processing has limitations. Network latency between the mobile device and the cloud server may affect application performance. In addition, the cost of offloading computation to the cloud can be a concern for some users, especially in high data consumption scenarios. Our findings can be helpful for developers who want to optimize the performance of their Android applications and for researchers who wish to investigate this topic further. Future research could explore ways to minimize the impact of network latency on offloading computation and investigate the cost-effectiveness of this technique for different types of applications.

Cited by 2

Parametric analysis for adaptive computation offloading

Cheng Wang, Zhiyuan Li · 2004

Many programs can be invoked under different execution options, input parameters and data files. Such different execution contexts may lead to strikingly different execution instances. The optimal code generation may be sensitive to the execution instances. In this paper, we show how to use parametric program analysis to deal with this issue for the optimization problem of computation offloading.Computation offloading has been shown to be an effective way to improve performance and energy saving on mobile devices. Optimal program partitioning for computation offloading depends on the tradeoff between the computation workload and the communication cost. The computation workload and communication requirement may change with different execution instances. Optimal decisions on program partitioning must be made at run time when sufficient information about workload and communication requirement becomes available.Our cost analysis obtains program computation workload and communication cost expressed as functions of run-time parameters, and our parametric partitioning algorithm finds the optimal program partitioning corresponding to different ranges of run-time parameters. At run time, the transformed program self-schedules its tasks on either the mobile device or the server, based on the optimal program partitioning that corresponds to the current values of run-time parameters. Experimental results on an HP IPAQ handheld device show that different run-time parameters can lead to quite different program partitioning decisions.

Cited by 96

Efficient computation offloading for ethereum DApps

Seongjoon Park, Jaeseung Lee, Hwangnam Kim · Journal of Industrial Information Integration · 2022

This paper covers a novel solution to reduce the gas consumed by Decentralized Applications (DApps) running on Ethereum Virtual Machine (EVM). Smart contracts extend the capabilities of the Blockchain, but the strict gas policy of the EVM limits the implementation of various DApps. On the other hand, Oracle provides a means to use an external execution environment for smart contracts, but its functional limitations and additional costs limit the growth of the DApp market to DApp users. To address the gas consumption crisis of DApps, we designed an event-based resource offloading solution that allows DApp developers to run their own compute servers. By adopting our solution, DApp users only pay the constant and minimized amount of gas fee, while the external server pays highly reduced amount of gas fee instead. We describe the solution in detail with a sample DApp model and mathematically demonstrate the effectiveness of the solution compared to an Oracle-based implementation. Furthermore, we conducted empirical experiment of actual DApp using EVM and verified the reduction of the gas consumption.

Cited by 10Open access

Computation offloading for smart internet devices

Muhammad Nabeel, Abdullah Embong, Mushtaq Ali · 2015

With the incessant advancement of smart internet devices and their ubiquitous usage, users prospect the same performance as if they used to run application on resources rich desktop devices. Smart internet devices are poor in resources such as storage, capacity, processing performance and battery life. Such mobile devices deliver lower performance as they are constrained by weight, size and mobility despite all the developments. These limitations can be ameliorated by utilizing the technique known as computation offloading. Computation offloading is the process of transferring compute-intensive data to resources rich servers called surrogates to run the entire or parts of application on behalf of mobile devices. In this paper, we present a survey on steps, criterion, types, flow and necessity of computation offloading with the reviewed computation offloading schemes. The Paper also proposes suggestions and opinions for future work in related field.

Cited by 1

Exploring computation offloading in IoT systems

Sina Shahhosseini, Arman Anzanpour, Iman Azimi et al. · Information Systems · 2021

Internet of Things (IoT) paradigm raises challenges for devising efficient strategies that offload applications to the fog or the cloud layer while ensuring the optimal response time for a service. Traditional computation offloading policies assume the response time is only dominated by the execution time. However, the response time is a function of many factors including contextual parameters and application characteristics that can change over time. For the computation offloading problem, the majority of existing literature presents efficient solutions considering a limited number of parameters (e.g., computation capacity and network bandwidth) neglecting the effect of the application characteristics and dataflow configuration. In this paper, we explore the impact of the computation offloading on total application response time in three-layer IoT systems considering more realistic parameters, e.g., application characteristics, system complexity, communication cost, and dataflow configuration. This paper also highlights the impact of a new application characteristic parameter defined as Output–Input Data Generation (OIDG) ratio and dataflow configuration on the system behavior. In addition, we present a proof-of-concept end-to-end dynamic computation offloading technique, implemented in a real hardware setup, that observes the aforementioned parameters to perform real-time decision-making.

Cited by 23Open access

Dynamic Offloading Algorithm for Drone Computation

Bongjae Kim, Hong Min, Junyoung Heo et al. · 2016

In this paper, we propose a mobility-aware offloading decision method for tracking moving object on drone. It aims to shorten response time because the time constraint is a crucial criteria for offloading decision making for tasks involved in moving object recognition and tracking. Through the dwell time model of target moving object, we investigate the impact of target mobility and derive a dynamic offloading decision method considering the expected dwell time. Our proposed model is also designed to give feedback on the network failure rate so that it can estimate accurately the response time.

Cited by 3

RESOURCE MANAGEMENT FOR MOBILE COMPUTATION OFFLOADING

Chen, Hong · MacSphere (McMaster University) · 2023

Mobile computation offloading (MCO) is a way of improving mobile device (MD) performance by offloading certain task executions to a more resourceful edge server (ES), rather than running them locally on the MD. This thesis first considers the problem of assigning the wireless communication bandwidth and the ES capacity needed for this remote task execution, so that task completion time constraints are satisfied. The objective is to minimize the average power consumption of the MDs, subject to a cost budget constraint on communication and computation resources. The thesis includes contributions for both soft and hard task completion deadline constraints. The soft deadline case aims to create assignments so that the probability of task completion time deadline violation does not exceed a given violation threshold. In the hard deadline case, it creates resource assignments where task completion time deadlines are always satisfied. The problems are first formulated as mixed integer nonlinear programs. Approximate solutions are then obtained by decomposing the problems into a collection of convex subproblems that can be efficiently solved. Results are presented that demonstrate the quality of the proposed solutions, which can achieve near optimum performance over a wide range of system parameters. The thesis then introduces algorithms for static task class partitioning in MCO. The objective is to partition a given set of task classes into two sets that are either executed locally or those classes that are permitted to contend for remote ES execution. The goal is to find the task class partition that gives the minimum mean MD power consumption subject to task completion deadlines. The thesis generates these partitions for both soft and hard task completion deadlines. Two variations of the problem are considered. The first assumes that the wireless and computational capacities are given and the second generates both capacity assignments subject to an additional resource cost budget constraint. Two class ordering methods are introduced, one based on a task latency criterion, and another that first sorts and groups classes based on a mean power consumption criterion and then orders the task classes within each group based on a task completion time criterion. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions. The thesis then considers the use of digital twins (DTs) to offload physical system (PS) activity. Each DT periodically communicates with its PS, and uses these updates to implement features that reflect the real behaviour of the device. A given feature can be implemented using different models that create the feature with differing levels of system accuracy. The objective is to maximize the minimum feature accuracy for the requested features by making appropriate model selections subject to wireless channel and ES resource availability. The model selection problem is first formulated as an NP-complete integer program. It is then decomposed into multiple subproblems, each consisting of a modified Knapsack problem. A polynomial-time approximation algorithm is proposed using dynamic programming to solve it efficiently, by violating its constraints by at most a given factor. A generalization of the model selection problem is then given and the thesis proposes an approximation algorithm using dependent rounding to solve it efficiently with guaranteed constraint violations. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions.

Cited by 0Open access

Computation Offloading: Overview, Frameworks and Challenges

Jaya Ashok Suradkar, R. D. Bharati · 2016

In spite of major advances in a mobile cloud computing, applications are still very limited due to poor computation processing power, memory and limited battery life of a mobile device. Recently, offloading computation intensive task to the cloud has become a great solution to improve both battery life and performance of mobile devices. Offloading makes use of quality of services concept to remove mobile application limitations. This paper covers overview, frameworks and challenges of computation offloading. General Terms Overview, process, frameworks and challenges.

Cited by 0

Computation Offloading: Overview, Frameworks and Challenges

J. Alangara Ashok, Rajesh Kumar Dhanaraj · International Journal of Computer Applications · 2016

In spite of major advances in a mobile cloud computing, applications are still very limited due to poor computation processing power, memory and limited battery life of a mobile device. Recently, offloading computation intensive task to the cloud has become a great solution to improve both battery life and performance of mobile devices. Offloading makes use of quality of services concept to remove mobile application limitations. This paper covers overview, frameworks and challenges of computation offloading.

Cited by 7Open access