ABSTRACT Introduction Existing serverless workflow orchestration systems are predominantly designed for a single‐cloud FaaS system, leading to vendor lock‐in. This dependency restricts performance optimization, cost reduction, and the overall availability of applications. However, orchestrating serverless workflows on Jointcloud FaaS systems faces two main challenges: (1) additional overhead caused by centralized cross‐cloud orchestration; and (2) a lack of reliable failover and fault‐tolerant mechanisms for cross‐cloud serverless workflows. Methods To address these challenges, we propose Joint λ , a distributed runtime system designed to orchestrate serverless workflows on multiple FaaS systems without relying on a centralized orchestrator. Joint λ introduces a compatibility layer, Backend‐Shim, which leverages inter‐cloud heterogeneity to optimize makespan and reduce costs with on‐demand billing. By using function‐side orchestration instead of centralized nodes, it enables independent function invocations and data transfers, thereby minimizing cross‐cloud communication overhead. For high availability, it ensures exactly‐once execution via datastores and failover mechanisms for serverless workflows on Jointcloud FaaS systems. Results We validate Joint λ on two heterogeneous FaaS systems, AWS and Aliyun, using four representative workflows. Compared to the most advanced commercial orchestration services for single‐cloud serverless workflows, Joint λ reduces makespan by up to 3.3× while saving up to 65% in cost. Furthermore, Joint λ is up to 4.0× faster than state‐of‐the‐art orchestrators for cross‐cloud serverless workflows, while achieving competitive cost performance in representative scenarios. Conclusions The evaluation demonstrates that Joint λ effectively eliminates vendor lock‐in and mitigates cross‐cloud communication overhead without sacrificing economic efficiency. By incorporating decentralized function‐side orchestration and robust failover mechanisms, it provides strong execution guarantees and high availability for complex serverless workflows across heterogeneous Jointcloud FaaS environments.

Large language models (LLMs) enable versatile applications, with fine-tuning methods like LoRA optimizing them for specific domains. However, ensuring QoS for dynamic LoRA inference on heterogeneous devices is challenging due to memory contention from the shared LLM base model and KV cache. Existing solutions lack coordination between the KV cache and LoRA scheduling. We propose JointSerLoRA, a two-level inference system featuring: (1) a disaggregated architecture for KV cache consistency, (2) a global scheduler balancing adaptercache reuse and GPU load, and (3) a local scheduler with adaptive batching for prefix and adapter-aware grouping. Evaluations show that JointSerLoRA reduces end-to-end latency by 29 % and TTFT by$3.8 \times$compared to state-of-the-art systems.

ABSTRACT Production data, a critical component of industrial datasets derived from production processes, is widely used to train data‐driven models for forecasting and managing industrial processes. However, shifts in data distribution, caused by changes in production environments, operating conditions, and equipment states, disrupt the consistency between the training and deployment, and lead to catastrophic forgetting and a significant deterioration in both model prediction accuracy and stability. Although existing incremental learning methods have improved adaptability and mitigated forgetting, challenges remain in balancing knowledge retention with dynamic sample selection and ensemble optimization, particularly in complex industrial settings. To address these challenges, this paper proposes an incremental learning framework that includes two key strategies: sample‐importance‐aware buffer update and elastic weight consolidation (EWC) based learner construction for knowledge retention, and performance‐driven iterative strong learner construction with multi‐objective weight optimization. The buffer update dynamically adjusts capacity according to training loss fluctuations, selects high‐information samples guided by loss rates and uncertainty estimation, and maintains diversity through K‐means clustering. EWC consolidates previously acquired knowledge to mitigate forgetting during weak learner training. The ensemble construction evaluates individual learner performance comprehensively and iteratively adjusts model weights using a multi‐objective optimization method, balancing prediction accuracy, stability, and uncertainty. Experimental results on multiple publicly available industrial datasets, complemented by an external validation on a financial dataset, demonstrate that the proposed method outperforms several representative approaches in both accuracy and stability of prediction.

Blockchain technology has been extensively uti-lized in decentralized data-sharing applications, with the immutability of blockchain providing a witness for the circulation of data. However, current blockchain data-sharing solutions still fail to address the simultaneous screening needs of both the sender and receiver with multi-keywords. Without the capability to support bilateral simultaneous filtering, the disclosure of reasons for matching failures could inadvertently expose sensitive user data. Therefore, the challenge lies in enabling ciphertexts with multiple keywords and receivers with multiple interests to achieve mutual and simultaneous matching. Based on the technical foundations of SE (Searchable Encryption), MABE (Multi-Attribute Based Encryption), and polynomial fitting, this paper proposes a scheme called DMSA (Decentralized and Multi-keyword selective Sharing and selective Acquisition). This scheme can satisfy soundness, enabling ciphertexts carrying multiple keywords and receivers representing multiple interests to match each other simultaneously. We conducted a security analysis that confirms the security of DMSA against chosen-plaintext attacks. Our experimental results demonstrate a significant efficiency improvement, with a 67% increase over single-keyword data-sharing schemes and a 16% enhancement compared to the existing multi-keyword data-sharing solution.

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud provider.To address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

With the rapid growth of large language models, cloud computing has become an indispensable component of the AI industry. Cloud service providers(CSPs) are establishing AI data centers to service AI workloads. In the face of this surging need for AI computing power, building a connected computing environment across various clouds and forming a JointCloud presents an attractive solution. However, scheduling AI tasks across multiple AI data centers within a JointCloud environment presents a significant challenge: how to balance users’ demands while ensuring CSPs’ fairness in scheduling. Existing research primarily focuses on optimizing scheduling quality with limited consideration for fairness. Therefore, this paper proposes a Fairness-Aware AI-Workloads Allocation method (F3A), a fair cross-cloud allocation technique for AI tasks. F3A utilizes Point and Token to reflect both the resource status and historical task allocations of AI data centers, enabling the consideration of users’ multidimensional demands and facilitating fair task allocation across multiple centers. In order to better assess the fairness of scheduling, we also devised a fairness indicator(FI), based on the Gini coefficient to measure the fairness of task allocation. The experimental results demonstrate that F3A consistently maintains FI within 0.1 across various cluster sizes and different task quantities, representing an improvement of 76.45% compared to classical fair scheduling algorithms round-robin. F3A exhibits commendable performance in ensuring fairness in task allocation while also demonstrating effectiveness in cost reduction and enhancing user satisfaction.

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

We present an exploratory study to improve the performance of a knowledge push system in product design. We focus on the domain of knowledge matching, where traditional matching algorithms need repeated calculations that result in a long response time and where accuracy needs to be improved. The goal of our approach is to meet designers’ knowledge demands with a quick response and quality service in the knowledge push system. To improve the previous work, two methods are investigated to augment the limited training set in practical operations, namely, oscillating the feature weight and revising the case feature in the case feature vectors. In addition, we propose a multi-classification radial basis function neural network that can match the knowledge from the knowledge base once and ensure the accuracy of pushing results. We apply our approach using the training set in the design of guides by computer numerical control machine tools for training and testing, and the results demonstrate the benefit of the augmented training set. Moreover, experimental results reveal that our approach outperforms other matching approaches.