A Survey of Computation Offloading for Mobile Systems

Transactions on Emerging Telecommunications Technologies, 2019

Mobile edge computing (MEC) is a recent technology that intends to free mobile devices from computationally intensive workloads by offloading them to a nearby resource-rich edge architecture. It helps to reduce network traffic bottlenecks and offers new opportunities regarding data and processing privacy. Moreover, MEC-based applications can achieve lower latency level compared to cloud-based ones. However, in a multitask multidevice context, the decision of the part to offload becomes critical. Actually, it must consider the available communication resources, the resulting delays that have to be met during the offloading process, and particularly, both local and remote energy consumption. In this paper, we consider a multitask multidevice scenario where smart mobile devices retain a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and we derive an equivalent multiple-choice knapsack problem formulation. Because of the short decision time constraint and the NP-hardness of the obtained problem, the optimal solution implementation is infeasible. Hence, we propose a solution that provides, in pseudopolynomial time, the optimal or near-optimal solutions depending on the problem's settings. In order to evaluate our solution, we carried out a set of simulation experiments to evaluate and compare the performances of the different components of this solution. Finally, the obtained results in terms of execution's time as well as energy consumption are satisfactory and very encouraging.

ACM Transactions on Internet Technology, 2019

Although mobile devices today have powerful hardware and networking capabilities, they fall short when it comes to executing compute-intensive applications. Computation offloading (i.e., delegating resource-consuming tasks to servers located at the edge of the network) contributes toward moving to a mobile cloud computing paradigm. In this work, a two-level resource allocation and admission control mechanism for a cluster of edge servers offers an alternative choice to mobile users for executing their tasks. At the lower level, the behavior of edge servers is modeled by a set of linear systems, and linear controllers are designed to meet the system’s constraints and quality of service metrics, whereas at the upper level, an optimizer tackles the problems of load balancing and application placement toward the maximization of the number the offloaded requests. The evaluation illustrates the effectiveness of the proposed offloading mechanism regarding the performance indicators, such a...

Mathematical Problems in Engineering, 2015

Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis orient...

Applied Sciences, 2020

With the development of the Internet of Things (IoT), the amount of data is growing and becoming more diverse. There are several problems when transferring data to the cloud, such as limitations on network bandwidth and latency. That has generated considerable interest in the study of edge computing, which processes and analyzes data near the network terminals where data is causing. The edge computing can extract insight data from a large number of data and provide fast essential services through simple analysis. The edge computing has a real-time advantage, but also has disadvantages, such as limited edge node capacity. The edge node for edge computing causes overload and delays in completing the task. In this paper, we proposes an efficient offloading model through collaboration between edge nodes for the prevention of overload and response to potential danger quickly in emergencies. In the proposed offloading model, the functions of edge computing are divided into data-centric an...

Information Security Theory and Practice, 2018

With the rapid advancements in innovative technologies like cloud computing, internet of things, and mobile computing, the paradigm to delegate the heavy computational tasks from trusted and resourceconstrained devices to potentially untrusted and more powerful services has gained a lot of attention. Ensuring the verifiability of the outsourced computation along with the security and privacy requirements is an active research area. Several cryptographic protocols have been proposed by using pairing-based cryptographic techniques based on bilinear maps of suitable elliptic curves. However, the computational overhead of bilinear maps forms the most expensive part of those protocols. In this paper, we propose a new 1−checkable algorithm under the one-malicious version of a two-untrusted-program model. Our solution is approximately twice as efficient as the single comparably efficient 1−checkable solution in the literature, and requires only 4 elliptic curve point additions in the preimage and 6 field multiplications in the image of the bilinear map.

Sensors

Recent developments in telecommunication world have allowed customers to share the storage and processing capabilities of their devices by providing services through fast and reliable connections. This evolution, however, requires building an incentive system to encourage information exchange in future telecommunication networks. In this study, we propose a mechanism to share bandwidth and processing resources among subscribers using smart contracts and a blockchain-based incentive mechanism, which is used to encourage subscribers to share their resources. We demonstrate the applicability of the proposed method through two use cases: (i) exchanging multimedia data and (ii) CPU sharing. We propose a universal user-to-user and user-to-operator payment system, named TelCash, which provides a solution for current roaming problems and establishes trust in X2X communications. TelCash has a great potential in solving the charges of roaming and reputation management (reliance) problems in t...

Sustainable Computing: Informatics and Systems, 2021

ACM Computing Surveys, 2019

Mobile devices (MDs) face resource scarcity challenges owing to limited energy and computational resources. Mobile cloud computing (MCC) offers a resource-rich environment to MDs for offloading compute-intensive tasks encountering resource scarcity challenges. However, users are unable to exploit its full potential owing to challenges of distance, limited bandwidth, and seamless connectivity between the remote cloud (RC) and MDs in the conventional MCC model. The cloudlet-based solution is widely used to address these challenges. The response of the cloudlet-based solution is faster than the conventional mobile cloud-computing model, rendering it suitable for the Internet of Things (IoT) and Smart Cities (SC). However, with the increase in devices and workloads, the cloudlet-based solution has to deal with resource-scarcity challenges, thus, forwarding the requests to remote clouds. This study has been carried out to provide an insight into existing cloudlet-based mobile augmentatio...

Electronics

With the proliferation of the Internet of Things (IoT) and the development of wireless communication technologies such as 5G, new types of services are emerging and mobile data traffic is growing exponentially. The mobile computing model has shifted from traditional cloud computing to mobile edge computing (MEC) to ensure QoS. The main feature of MEC is to “sink” network resources to the edge of the network to meet the needs of delay-sensitive and computation-intensive services, and to provide users with better services. Computation offloading is one of the major research issues in MEC. In this paper, we summarize the state of the art in task offloading in MEC. First, we introduce the basic concepts and typical application scenarios of MEC, and then we formulate the task offloading problem. In this paper, we analyze and summarize the state of research in the industry in terms of key technologies, schemes, scenarios, and objectives. Finally, we provide an outlook on the challenges an...

Transactions on Emerging Telecommunications Technologies, 2017

Fog and mobile edge computing is a paradigm that augments resource-scarce mobile devices with resource-rich network servers to enable ubiquitous computing. Smartphone applications rely on code offloading techniques to leverage high-performance computing opportunities available on edge and cloud servers for compute-intensive applications. Mobile (ARM) and edge/cloud (x86) architectures are heterogeneous and necessitate dynamic binary translation for compiled code migration that increases the application execution time. The application execution time and energy consumption should be lesser on the edge/cloud server as compared with the local mobile execution for optimal offload. Multimedia-based applications contain a large set of single instruction multiple data (SIMD) instructions that are compute and resource intensive. However, dynamic binary translation techniques of SIMD instructions lose the parallelism and optimization because of inefficient vector-to-scalar translation. We present a framework for SIMD instruction translation and offloading for mobile devices (SIMDOM) in cloud and edge environments. The SIMDOM framework reduces the execution overhead of migrated vectorized multimedia application by using vector-to-vector instruction mappings. The framework maps and translates ARM SIMD intrinsic instructions to x86 SIMD intrinsic instructions such that an application programmed for the mobile platform can be executed on the cloud server without any modification. The offload decision is based on inputs from the device energy, network, and application profilers. Experiments show that SIMDOM framework provides 84.78%, 3.41%, and 79.93% energy, time, and performance efficiency, respectively, compared with local offload-disabled execution. Compared with compiled code offloading, the SIMDOM framework provides 55.99%, 57.50%, and 96.23% energy, time, and performance efficiency, respectively.

Journal of Mathematics

The emerging technology of mobile cloud is introduced to overcome the constraints of mobile devices. We can achieve that by offloading resource intensive applications to remote cloud-based data centers. For the remote computing solution, mobile devices (MDs) experience higher response time and delay of the network, which negatively affects the real-time mobile user applications. In this study, we proposed a model to evaluate the efficiency of the close-end network computation offloading in MEC. This model helps in choosing the adjacent edge server from the surrounding edge servers. This helps to minimize the latency and increase the response time. To do so, we use a decision rule based Heuristic Virtual Value (HVV). The HVV is a mapping function based on the features of the edge server like the workload and performance. Furthermore, we propose availability of a virtual machine resource algorithm (AVM) based on the availability of VM in edge cloud servers for efficient resource alloc...

2019 International Symposium on Electrical and Electronics Engineering (ISEE)

This paper studies the mobile edge offloading scenario consisting of one mobile device (MD) with multiple independent tasks and various remote edge devices. In order to save energy, the user's device can offload the tasks to available access points for edge computing. Data compression is exploited to reduce offloaded data size prior to wireless transmission to minimize the execution latency. The problem of jointly optimizing the task allocation decision and the data compression ratio to minimize the total tasks' execution latency and the MD's energy consumption concurrently is proposed. We show that the design problem is a non-convex optimization one but it can be recasted into a convex one through a semidefinite relaxation (SDR) based approach. Numerical simulations demonstrate the outperformance of the proposed scheme compared to the benchmark one.

ACM Transactions on Cyber-Physical Systems, 2018

The Internet-of-Things (IoT) envisions an infrastructure of ubiquitous networked smart devices offering advanced monitoring and control services. The current art in IoT architectures utilizes gateways to enable application-specific connectivity to IoT devices. In typical configurations, IoT gateways are shared among several IoT edge devices. Given the limited available bandwidth and processing capabilities of an IoT gateway, the service quality (SQ) of connected IoT edge devices must be adjusted over time not only to fulfill the needs of individual IoT device users but also to tolerate the SQ needs of the other IoT edge devices sharing the same gateway. However, having multiple gateways introduces an interdependent problem, the binding, i.e., which IoT device shall connect to which gateway. In this article, we jointly address the binding and allocation problems of IoT edge devices in a multigateway system under the constraints of available bandwidth, processing power, and battery lifetime. We propose a distributed trade-based mechanism in which after an initial setup, gateways negotiate and trade the IoT edge devices to increase the overall SQ. We evaluate the efficiency of the proposed approach with a case study and through extensive experimentation over different IoT system configurations regarding the number and type of the employed IoT edge devices. Experiments show that our solution improves the overall SQ by up to 56% compared to an unsupervised system. Our solution also achieves up to 24.6% improvement on overall SQ compared to the state-of-the-art SQ management scheme, while they both meet the battery lifetime constraints of the IoT devices.

Chinese Journal of Mechanical Engineering, 2021

The electrification of vehicle helps to improve its operation efficiency and safety. Due to fast development of network, sensors, as well as computing technology, it becomes realizable to have vehicles driving autonomously. To achieve autonomous driving, several steps, including environment perception, path-planning, and dynamic control, need to be done. However, vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions. Intelligent and connected vehicles (ICV) cloud control system (CCS) has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation. This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs, and cloud control system architecture design, as well as its core technologies development. Based on the analysis, the chall...

Sensors, 2022

Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share data with third parties, edge or cloud servers, to train their models. This architecture is often impractical due to the high bandwidth requirements, legalization, and privacy vulnerabilities. The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and legalization. FL can co-train models across distributed clients, such as mobile phones, automobiles, hospitals, and more, through a centralized server, while maintaining data localization. FL can therefore be viewed as a stimulating factor in the EC paradigm as it enables collaborative learning and model optimization. Although the existing surveys have taken into account applications of FL in EC environments, there has not been any systematic survey discussing FL implementation and challenges in the EC paradigm. This paper aims to provide a systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems. In this survey, we review the fundamentals of EC and FL, then we review the existing related works in FL in EC. Furthermore, we describe the protocols, architecture, framework, and hardware requirements for FL implementation in the EC environment. Moreover, we discuss the applications, challenges, and related existing solutions in the edge FL. Finally, we detail two relevant case studies of applying FL in EC, and we identify open issues and potential directions for future research. We believe this survey will help researchers better understand the connection between FL and EC enabling technologies and concepts.

Springer eBooks, 2017

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Journal of Cloud Computing

In recent years, with the rapid growth of edge data, the novel cloud-edge collaborative architecture has been proposed to compensate for the lack of data processing power of traditional cloud computing. On the other hand, on account of the increasing demand of the public for data privacy, federated learning has been proposed to compensate for the lack of security of traditional centralized machine learning. Deploying federated learning in cloud-edge collaborative architecture is widely considered to be a promising cyber infrastructure in the future. Although each cloud-edge collaboration and federated learning is hot research topic respectively at present, the discussion of deploying federated learning in cloud-edge collaborative architecture is still in its infancy and little research has been conducted. This article aims to fill the gap by providing a detailed description of the critical technologies, challenges, and applications of deploying federated learning in cloud-edge colla...

Entropy, 2022

As an emerging computing model, edge computing greatly expands the collaboration capabilities of the servers. It makes full use of the available resources around the users to quickly complete the task request coming from the terminal devices. Task offloading is a common solution for improving the efficiency of task execution on edge networks. However, the peculiarities of the edge networks, especially the random access of mobile devices, brings unpredictable challenges to the task offloading in a mobile edge network. In this paper, we propose a trajectory prediction model for moving targets in edge networks without users' historical paths which represents their habitual movement trajectory. We also put forward a mobility-aware parallelizable task offloading strategy based on a trajectory prediction model and parallel mechanisms of tasks. In our experiments, we compared the hit ratio of the prediction model, network bandwidth and task execution efficiency of the edge networks by using the EUA data set. Experimental results showed that our model is much better than random, non-position prediction parallel, non-parallel strategy-based position prediction. Where the task offloading hit rate is closed to the user's moving speed, when the speed is less 12.96 m/s, the hit rate can reach more than 80%. Meanwhile, we we also find that the bandwidth occupancy is significantly related to the degree of task parallelism and the number of services running on servers in the network. The parallel strategy can boost network bandwidth utilization by more than eight times when compared to a non-parallel policy as the number of parallel activities grows.

Quantitative Evaluation of Systems, 2016

Application partitioning splits the executions into local and remote parts. Through optimal partitioning, the device can obtain the most benefit from computation offloading. Due to unstable resources at the wireless network (bandwidth fluctuation, network latency, etc.) and at the service nodes (different speed of the mobile device and cloud server, memory, etc.), static partitioning solutions in previous work with fixed bandwidth and speed assumptions are unsuitable for mobile offloading systems. In this paper, we study how to effectively and dynamically partition a given application into local and remote parts, while keeping the total cost as small as possible. We propose a novel min-cost offloading partitioning (MCOP) algorithm that aims at finding the optimal partitioning plan (determine which portions of the application to run on mobile devices and which portions on cloud servers) under different cost models and mobile environments. The simulation results show that the proposed algorithm provides a stable method with low time complexity which can significantly reduce execution time and energy consumption by optimally distributing tasks between mobile devices and cloud servers, and in the meantime, it can well adapt to environmental changes, such as network perturbation.