The increasing parameter size of Deep Neural Networks (DNNs) has significantly enhanced model performance. As large-scale DNN models typically require partitioning into multiple blocks for distributed training, existing research has predominantly focused on offline scheduling for individual or batched training tasks. However, the stochastic arrival of such tasks in edge networks poses a critical challenge for efficiently scheduling them in resource-constrained edge clusters. In this paper, aiming to minimize the average training completion time across all tasks, we first extract DNN operator graphs and partition them into coarse-grained subgraphs using a max-flow mincut algorithm. Then, we formulate the online scheduling problem for multiple distributed DNN training tasks as a Markov Decision Process (MDP) and propose a reinforcement learning-based (RL-based) solution. Extensive experiments comparing our method with three conventional baselines (FIFO, SJF, and Greedy) under diverse configurations show that our approach reduces the average training completion time by 12.95%, demonstrating its effectiveness in resource-constrained edge environments with dynamic workloads.

In the resource-constrained Internet of Things (IoT)-edge computing environment, split federated (SplitFed) learning is implemented to enhance training efficiency. This method involves each terminal device dividing its full deep neural network (DNN) model at a designated layer into a device-side model and a server-side model, then offloading the latter to the edge server. However, existing research overlooks four critical issues as follows: 1) the heterogeneity of end devices’ resource capacities and the sizes of their local data samples impact training efficiency; 2) the influence of the edge server’s computation and network resource allocation on training efficiency; 3) the data leakage risk associated with the offloaded server-side submodel; and 4) the privacy drawbacks of current centralized algorithms. Consequently, proactively identifying the optimal cut layer and server resource requirements for each end device to minimize training latency while adhering to data leakage risk rate constraint remains a challenging issue. To address these problems, this article first formulates the latency and data leakage risk of training DNN models using SplitFed learning. Next, we frame the SplitFed learning problem as a mixed-integer nonlinear programming challenge. To tackle this, we propose a decentralized proactive model offloading and resource allocation (DP-MORA) scheme, empowering each end device to determine its cut layer and resource requirements based on its local multidimensional training configuration, without knowledge of other devices’ configurations. Extensive experiments on two real-world datasets demonstrate that the DP-MORA scheme effectively reduces DNN model training latency, enhances training efficiency, and complies with data leakage risk constraints compared to several baseline algorithms across various experimental settings.

In this study, we explore an emerging research area of Continual Learning for Temporal Sensitive Question Answering (CLTSQA). Previous research has primarily focused on Temporal Sensitive Question Answering (TSQA), often overlooking the unpredictable nature of future events. In real-world applications, it’s crucial for models to continually acquire knowledge over time, rather than relying on a static, complete dataset. Our paper investigates strategies that enable models to adapt to the ever-evolving information landscape, thereby addressing the challenges inherent in CLTSQA. To support our research, we first create a novel dataset, divided into five subsets, designed specifically for various stages of continual learning. We then propose a training framework for CLTSQA that integrates temporal memory replay and temporal contrastive learning. Our experimental results highlight two significant insights: First, the CLTSQA task introduces unique challenges for existing models. Second, our proposed framework effectively navigates these challenges, resulting in improved performance.

The development of cloud computing delivery models inspires the emergence of cloud-native computing. Cloud-native computing, as the most influential development principle for web applications, has already attracted increasingly more attention in both industry and academia. Despite the momentum in the cloud-native industrial community, a clear research roadmap on this topic is still missing. As a contribution to this knowledge, this article surveys key issues during the life cycle of cloud-native applications, from the perspective of services. Specifically, we elaborate on the research domains by decoupling the life cycle of cloud-native applications into four states: building, orchestration, operation, and maintenance. We also discuss the fundamental necessities and summarize the key performance metrics that play critical roles during the development and management of cloud-native applications. We highlight the key implications and limitations of existing works in each state. The challenges, future directions, and research opportunities are also discussed.

In edge environment, many smart application instances are triggered randomly by resource-constrained Internet of Things (IoT) devices. These application instances usually consist of dependent computation components, which can be modeled as workflows in different shapes and sizes. Due to the limited computing power of IoT devices, a common approach is to schedule partial computation components of multiple workflow instances to the resource-rich edge servers to execute. However, how to schedule the stochastically arrived multiple workflow instances in edge environment with the minimum average completion time is still a challenging issue. To address such an issue, in this paper, we adopt the graph convolution neural network to transform multiple workflow instances with different shapes and sizes into embeddings, and formulate the online multiple workflow scheduling problem as a finite Markov decision process. Furthermore, we propose a policy gradient learning-based online multiple workflow scheduling scheme (PG-OMWS) to optimize the average completion time of all workflow instances. Extensive experiments are conducted on the synthetic workflows with various shapes and sizes. The experimental results demonstrate that the PG-OMWS scheme can effectively schedule the stochastically arrived multiple workflow instances, and achieve the lowest average completion time compared with four baseline algorithms in edge environments with different scales.

This paper proposes a novel architecture of Recommendation-Enabled Edge Caching and Device-to-Device (D2D) Offloading via Incentive-driven Deep Reinforcement Learning (DRL), which can not only solve the problem of inaccurate recommendation caused by sparse rating matrix, but also encourage users to participate in D2D offloading through an effective incentive mechanism. Specifically, we define Pseudo Markov Decision Process (PMDP) for the first time, which enables the conversion of the non-sequential process (e.g. rating prediction) into a sequential one, making it suitable for DRL. Then, combining Supervised Learning (SL) and DRL, a Supervised DRL for Collaborative Filtering (CF) algorithm, named SDRLCF, is proposed to predict missing ratings. After that, from the perspective of Content Service Center (CSC), the incentive-driven recommendation-enabled edge caching and D2D offloading can be formulated as a Non-Linear Integer Programming (NLIP) problem, which belongs to NP-hard, and is difficult to obtain the optimal solution in polynomial time. To address this issue, a DRL based Edge Caching and Recommendation algorithm, named DRLECR, is proposed to minimize the cost of CSC. Finally, combining with economic theory, a Reverse Auction based Payment Determination algorithm under Vickrey-Clarke-Groves (VCG) scheme, named RAPD, is proposed, which can stimulate users to participate in edge caching and D2D offloading while guaranteeing the individual rationality and truthfulness of participants. Extensive experiment results on both realistic and synthetic datasets demonstrate that the proposed algorithms outperform other baseline methods under different scenarios.

Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints.

Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices. Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of the limited resource and cause waste of resource, resulting in low system performance (the weighted sum of the number of processed tasks, the number of punished tasks, and the number of dropped tasks). Therefore, it is a challenging problem to effectively allocate the computing and communication resources to multiple mobile users. To cope with this problem, we propose a performance-aware resource allocation (PARA) scheme, the goal of which is to maximize the long-term system performance. More specifically, we first build the multiuser resource allocation architecture for computing workloads and transmitting result data to mobile devices. Then, we formulate the multiuser resource allocation problem as a Markova Decision Process (MDP). To achieve this problem, a performance-aware resource allocation (PARA) scheme based on a deep deterministic policy gradient (DDPG) is adopted to derive optimal resource allocation policy. Finally, extensive simulation experiments demonstrate the effectiveness of the PARA scheme.

With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.