Towards the Advanced Data Processing for Medical Applications Using Task Offloading Strategy

Despite their progress and improvements, mobile devices are still regarded as restricted computer devices. Users are growing more discerning, expecting to be able to run computationally intensive applications on their smartphones or tablets. As a result, Mobile Cloud Computing (MCC) integrates mobile computing and Cloud Computing in order to use offloading techniques to increase the capabilities of mobile devices. Although offloading programmes to the cloud might improve mobile device performance, it can also increase processing delay. Inevitably, the quality of user service (QoS) suffers as a result, particularly for specific applications (especially workflow applications). To address the problem of network latency, a new paradigm known as mobile edge computing (MEC) has been developed, which may be thought of as a subset of MCC. We also see deployment of Cloudlets, which are a form of edge server, to minimize latency and energy usage by offloading applications to the cloud, resulting in an efficient and cost-effective architecture as well as various decision-making techniques for offloading. And so, a literature survey regarding this will be presented which focuses on various methods of offloading and its decision-making techniques.

With a significant increase in the compute-intensive mobile applications in recent times, the seamless integration of the cloud platform with mobile devices becomes essential. As a result, the mobile cloud computing (MCC) and mobile edge computing (MEC) paradigm become prominent in today’s era. At the same time, scheduling user applications in these domains have also gained research attention. But a majority of scheduling policies for the MEC environment consider offloading and execution of tasks only between two computing platforms: (i) mobile and edge or (ii) edge and cloud. In this paper, we consider the scheduling of a set of user applications with partial offloading and execution of tasks in all the three layers of compute stack: the mobile device, edge, and the cloud, to minimize the energy consumption of the edge nodes. Our extensive simulation reveals that the proposed scheduling strategy achieves an energy reduction up to 30% with an execution speedup up to 6% while meeting the delay constraints of the applications.KeywordsRoadside cloudMobile edge computingEnergy efficient schedulingPartial offloadingHost energy consumption

Mobile edge computing (MEC) can effectively overcome the shortcomings of high-latency in mobile cloud computing (MCC) by deploying the cloud resources, e.g., storage and computational capability, to the edge. However, the limited computation capability of the MEC restricts the scalability of offloading. Therefore, the basic requirements of the MEC system are to explore effective offloading decisions and resource allocation methods. To address it, we develop a collaborative computing system composed of local computing (mobile device), MEC (edge cloud) and MCC (central cloud). Based on the proposed collaborative computing system, we design a novel Q-learning based computation offloading (QLCOF) policy to achieve the optimal resource allocation and offloading scheme by prescheduling the computation side for each task from a global perspective. Specifically, we first model the offloading decision process as a Markov decision process (MDP) and design a state loss function (STLF) to measure the quality of experience (QoE). After that, we define the cumulation of STLFs as the system loss function (SYLF) and formulate an SYLF minimization problem. Due to the difficulty to directly solve the formulated problem, we decompose it into multiple subproblems and preferentially optimize the transmission power and computation frequency of the edge cloud by the quasi-convex bisection and polynomial analysis method, respectively. Based on the precalculated offline transmission power and edge cloud computation frequency, we develop a Q-learning based offloading (QLOF) scheme to minimize the SYLF by optimizing offloading decisions. Finally, the numeral results show that the proposed QLOF scheme effectively reduces the SYLF under different parameters.

Mobile applications are advancing toward higher network and compute requirements, which are similar to the requirements of server applications. Users prefer to perform their tasks on the go from mobile devices instead of stationary desktop and server systems. Modern mobile applications limit mobile battery times as charging is lost quickly due to high processing and data demands. Resources other than energy are also limited in mobile devices (memory, CPU millions of instructions per second, etc.).Mobile edge computing (MEC) technologies facilitate resource-scarcemobile devices to enhance their capabilities and execute data/computeintensive applications while collaborating with resource-rich network servers to enable ubiquitous computing. On the other hand, mobile cloud computing provides higher resources for applications that have low response requirements (noninteractive applications). Bothmobile edge computing andmobile cloud computing enable technologies for Internet of Things (IoT), smart grids, and e-health applications.

Secure and quality-of-service-supported service-oriented architecture for mobile cloud handoff process

Contemporary vehicular applications pose stringent latency and computation requirements for the autonomous vehicles (AVs). These requirements are hard to be met by the vehicles due to limited computation capabilities. One of the significant solutions is computation offloading in which delay-sensitive and complex applications are handed over to the network. However, computation offloading at the core network incurs excessive architecture-induced delay which is inefficient for applications with tight latency, data rate and computation requirements. Mobile Edge Computing (MEC) is one of the key enablers for 5G that offers computation resources at the edge of the network resulting in ultra-low latency, powerful computation, larger coverage area and context-awareness. European Telecommunication Standards Institute (ETSI) foresees vehicular communication as a use case for MEC. Therefore, we propose application-aware hierarchical offloading scheme (HOS) for MEC-enabled distributed AV architecture. The proposed architecture divides the network into three layers according to application requirements resulting in quick-response and efficient network that meets the application requirements. To decide the computation layer, each application is treated independently in accordance with the complexity, data rate and computation requirements. Thus, every application is handled at appropriate layer so as to meet its latency and computation requirements. Further, we also analyze the impact of task size on computation offloading decision. Finally, we compare our proposed architecture with local computation and distant placed mobile cloud computing (MCC) architecture.

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs‐oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi‐MU offloading scheme. Then we survey edge server‐ (ES‐) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments

Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers’ locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers’ and users’ spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay.

The vision of augmenting computing capabilities of mobile devices, especially smartphones with least cost is likely transforming to reality leveraging cloud computing. Cloud exploitation by mobile devices breeds a new research domain called Mobile Cloud Computing (MCC). However, issues like portability and interoperability should be addressed for mobile augmentation which is a non-trivial task using component-based approaches. Service Oriented Architecture (SOA) is a promising design philosophy embraced by mobile computing and cloud computing communities to stimulate portable, complex application using prefabricated building blocks called Services. Utilizing distant cloud resources to host and run Services is hampered by long WAN latency. Exploiting mobile devices in vicinity alleviates long WAN latency, while creates new set of issues like Service publishing and discovery as well as client-server security, reliability, and Service availability. In this paper, we propose a market-oriented architecture based on SOA to stimulate publishing, discovering, and hosting Services on nearby mobiles, which reduces long WAN latency and creates a business opportunity that encourages mobile owners to embrace Service hosting. Group of mobile phones simulate a nearby cloud computing platform. We create new role of \textit{Service host} by enabling unskilled mobile owners/users to host Services developed by skilled developers. Evidently, Service availability, reliability, and Service-oriented mobile application portability will increase towards green ubiquitous computing in our mobile cloud infrastructure.

Mobile edge computing (MEC) uses multiple mobiles to compute several complex tasks that are unable to compute on a single device. Taking advantage of all abundant mobile resources and making a mobile cloud from them will be useful. This study aims to propose and implement a novel framework to cover challenges raised by application execution on resource-constrained devices. The purpose is to overcome the waste of resources in MEC and provide time efficiency to the data packets that are sent and received between offloaded and offloading devices. The main task of MEC is to offload tasks by first selecting resources and then allocating tasks to selected resources. A multiple linear regression algorithm is used for the selection of compatible devices. Particle swarm optimization techniques are used as a benchmark technique to design an algorithm for optimized resource allocation. Multiple mobile devices acted as a major component for making edge clouds. The study finds that response time of processing tasks is reduced, ineffectual resources become beneficial, increase in demand for mobile devices, and usage of mobile resources as a replacement for mobile edge cloud servers. Abundant resource usage of two or more edge clouds, that is, interedge resource usage is the originality of this research, while others are using intraedge resources only.

To realize the vision of the Internet of Things (IoT), mobile-edge computing (MEC) has recently emerged as a promising paradigm to meet the computation demand from mobile users (MUs). In this article, we study the network performance in large-scale stochastic MEC wireless networks, where the tasks can be computed locally by the local computation capabilities (LCCs) or be offloaded to MEC servers for edge computing. To this end, a MEC network is modeled featuring random node distribution, dynamic task requests, orthogonal frequency-division multiple access, task retransmission, and parallel computing in MEC servers. Given the model, a 2-D discrete-time Markov chain is first adopted to characterize the task execution process, including local computing and task offloading. Based on the coupling between communication and computing, the average outage probability of the task transmission and the average MEC computation load are derived by integrating the stochastic geometry and queuing theory. Furthermore, by jointly analyzing the local computation latency, transmission latency, and edge computation latency, we derive the average end-to-end latency of the task execution. Our results show that the LCCs in MUs can improve the network performance, including communication and computation performance, in stochastic MEC networks. In addition, useful guidelines for MEC network provisioning and planning are provided to avoid either the local computing or the task offloading being the latency performance bottleneck.

As a new paradigm of distributed computing, mobile edge computing (MEC) has gained increasing attention due to its ability to expand the capabilities of centralized cloud computing. In MEC environments, a software application typically consists of multiple micro‐services, which can be composed together in a flexible manner to achieve various user requests. However, the composition of micro‐services in MEC is still a challenging research issue arising from three aspects. Firstly, composite micro‐services constructed by ignoring the processing capabilities of different micro‐services may cause waste of edge resources. Secondly, edge servers' limitations in terms of computational power can easily cause service occupancy between composite micro‐services, severely affecting the user experience. Thirdly, in dynamic and unstable mobile environments, different edge users have different sensitivities to request latency, which increases the complexity of micro‐service composition. In order to improve edge resource utilization and user experience on micro‐service invocations, in this paper, we comprehensively consider the above three factors, and we first model the micro‐services composition problem in MEC as a constrained multi‐objective optimization problem. Then, a micro‐service composition optimization method M3C combining graph search and branch‐and‐bound strategy is proposed to find a composition solution set with low energy consumption and high success rate for multiple edge users. Finally, we perform a series of experiments on two widely used datasets. Experimental results show that our proposed approach significantly outperforms the four competing baseline approaches, and that it is sufficiently efficient for practical deployment.

In Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs) have shown great promise—especially in emergency communication scenarios—thanks to their flexible deployment and on-demand service capabilities. Meanwhile, Reconfigurable Intelligent Surfaces (RIS), as an emerging technology, can effectively extend MEC network coverage and optimize the wireless propagation environment. This paper proposes a novel RIS-assisted multi-UAV aerial MEC (AMEC) system, aiming jointly to maximize system fairness and minimize total energy consumption by optimizing UAV flight trajectories, RIS phase-shift configurations, task-offloading strategies, and resource allocation. To tackle this mixed nonconvex optimization problem, we develop the BPMADDPG algorithm: in the first phase, block coordinate descent (BCD) is used to optimize RIS phase shifts; in the second phase, a modified multi-agent deep deterministic policy gradient (MADDPG) framework enhanced with prioritized experience replay (PER) achieves coordinated multi-agent scheduling. Simulation results demonstrate that, compared with several benchmark schemes, the proposed method achieves significant reductions in energy consumption and notable improvements in fairness.