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With the development of technologies such as the Internet of Things and artificial intelligence, mobile applications are becoming more and more intelligent. Compared to traditional applications realized by mobile cloud computing technology, these novel applications have a higher requirement for a task offloading scheme. However, the traditional task offloading schemes are hard pressed to meet latency and personalization requirements of these new applications. For intelligent application, how to realize personalized and fine-grained task offloading is still a challenging problem. Therefore, we propose a scheme called intelligent task offloading (iTaskOffloading) for a cloud-edge collaborative system, which can provide personalized task offloading. To be specific, we first propose the architecture of iTaskOffloading which includes the local device layer, edge cloud layer, remote cloud layer, and cognitive engine. Then we analyze the method of iTaskOffloading, which contains coarse-grained computing and fine-grained computing. Finally, we build a testbed to evaluate the proposed iTaskOffloading scheme using a typical intelligent application of emotion detection. The experimental results show that compared to the traditional cloud computing scheme, iTaskOffloading has less task duration. [ABSTRACT FROM AUTHOR]

With rapid development of unmanned aerial vehicle (UAV) technology, application of UAVs for task offloading has received increasing interest in academia. However, real-time interaction between one UAV and the mobile edge computing node is required for processing the tasks of mobile end users, which significantly increases the system overhead and is unable to meet the demands of large-scale artificial intelligence (AI)-based applications. To tackle this problem, in this article, we propose a new architecture for UAV clustering to enable efficient multi-modal multi-task offloading. With the proposed architecture, the computing, caching, and communication resources are collaboratively optimized using AI-based decision making. This not only increases the efficiency of UAV clusters, but also provides insight into the fusion of computation and communication. [ABSTRACT FROM AUTHOR]

With the growing popularity of mobile devices, a new type of peer-to-peer communication mode for mobile cloud computing has been introduced. By applying a variety of short-range wireless communication technologies to establish connections with nearby mobile devices, we can construct a mobile cloudlet in which each mobile device can either works as a computing service provider or a service requester. Although the paradigm of mobile cloudlet is cost-efficient in handling computation-intensive tasks, the understanding of its corresponding service mode from a theoretic perspective is still in its infancy. In this paper, we first propose a new mobile cloudlet-assisted service mode named Opportunistic task Scheduling over Co-located Clouds (OSCC), which achieves flexible cost-delay tradeoffs between conventional remote cloud service mode and mobile cloudlets service mode. Then, we perform detailed analytic studies for OSCC mode, and solve the energy minimization problem by compromising among remote cloud mode, mobile cloudlets mode and OSCC mode. We also conduct extensive simulations to verify the effectiveness of the proposed OSCC mode, and analyze its applicability. Moreover, experimental results show that when the ratio of data size after task execution over original data size associated with the task is smaller than 1 (i.e., [ABSTRACT FROM AUTHOR]

As mobile devices are equipped with more memory and computational capability, a novel peer-to-peer communication model for mobile cloud computing is proposed to interconnect nearby mobile devices through various short range radio communication technologies to form mobile cloudlets, where every mobile device works as either a computational service provider or a client of a service requester. Though this kind of computation offloading benefits compute-intensive applications, the corresponding service models and analytics tools are remaining open issues. In this paper we categorize computation offloading into three modes: remote cloud service mode, connected ad hoc cloudlet service mode, and opportunistic ad hoc cloudlet service mode. We also conduct a detailed analytic study for the proposed three modes of computation offloading at ad hoc cloudlet. [ABSTRACT FROM PUBLISHER]

With the emergence of mobile edge computing (MEC), the edge cloud with certain computing power is deployed closer to the mobile device, which can well solve the computing and delay requirements of the mobile device. In 5G ultra-dense heterogeneous networks, where the macro base station (MBS) and multiple dense small base stations (SBS) are deployed in the region, the offloading decision faces multiple choices. In order to solve the problem of computing offloading and resource allocation in 5G ultra-dense heterogeneous networks, we propose a collaborative optimization strategy based on multi-agent deep reinforcement learning (MADRL). At each time, the mobile device only needs to make the optimal offloading decision according to its own historical offloading decision, the allocated bandwidth and computing resources at the past time, as well as the service response delay and energy consumption at the past time, without knowing other user information and dynamic network environment information. Simulation results show that the proposed collaborative optimization strategy is better than the other three baseline schemes in terms of service response delay and energy consumption performance. [Display omitted] • A joint optimization strategy of task offloading and resource allocation for 5G ultra-dense networks is proposed. • The offloading decision of mobile devices is made based on MAPPO. • The heterogeneous edge clouds realize the allocation of bandwidth and computing resources based on convex optimization. [ABSTRACT FROM AUTHOR]