Shuaishuai Liu

The Computing Power Network (CPN) is a distributed system that integrates computing resources to optimize utilization, but ensuring Quality of Service (QoS) is challenging due to high demand and complex heterogeneous connections. Accurate workload prediction is essential for maintaining QoS, yet the diverse and complex user requirements in CPN make prediction difficult. To address this challenge, we propose an ensemble workload prediction model with fluctuation division control for workload prediction in CPN, comprising three key components. First, we use the Three-Way Decision (3WD) approach to partition workload fluctuations, controlling granularity thickness and applying clustering to capture dynamic workload characteristics. Second, we develop tailored prediction methods for each of the three partitioned regions and ensembles them to enhance overall prediction performance. Third, the ensemble prediction method is applied to each region to obtain the final predicted values. The proposed method introduces an innovative fluctuation division control strategy for characteristic mining to capture dynamic workload fluctuation patterns and designs the effective ensemble workload prediction model deal with the problem of non-stationary workload prediction in CPN. Experimental results on trace datasets from Alibaba and Dinda demonstrate that the proposed model improves the higher average prediction accuracy by up to 26.06%$\sim$66.4% than the comparison methods.

Although Computing Power Network (CPN) as the new network computing paradigm which can fully improve the utilization rate of decentralized computing power resources, the dynamic and heterogeneous characteristics of multivariate workloads present significant challenges to maintaining the Quality of Service (QoS) under dynamic resource scheduling. Therefore, workload prediction should be considered to ensure elastic demand services. However, the existing workload prediction methods mainly focus on (1) a single-granularity perspective, and (2) struggle to adapt to dynamic and heterogeneous multivariate workload environments, such as traditional LSTM-based or CNN-based methods that fail to capture cross-granularity dependencies under varying workload patterns. To consider above problems, we propose Multi-Granularity Workload Ensemble and Feature Inference for Multivariate Computing Power Prediction (MG-WEP), which address the problem from a multi-granularity perspective. First, we develop a mutual information feature selection method using a variational inference network to identify key features, facilitating a comprehensive exploration of the relationships among workload variables from an attribute perspective. Then, the clustering method is used to cluster similar workloads, effectively capturing the relationships among them. Furthermore, a combined ensemble prediction method is applied on all clustered workloads to improve prediction accuracy by leveraging the distinctive characteristics of each cluster from object perspective. Finally, we have fully compared the proposed algorithm with eleven comparison methods and four evaluation metrics on three real-world workload trace datasets. The results show that the proposed method has superior prediction performance.

Cloud computing, an emerging computing paradigm, has been widely concerned due to its high scalability and availability. An essential stage of cloud computing is cloud resource management. Currently, the existing research about cloud computing technology has two prevalent disadvantages: high energy consumption and low resource utilization. Considering greedy scheduling is an effective strategy for cloud resource management technology in cloud computing, particularly in improving resource utilization and reducing energy consumption, we consider the heterogeneous characteristics of resources to save energy consumption of datacenter when tasks are the fundamental element of cloud datacenter. Meanwhile, granular computing is a complex problem‐solving strategy through a granulation method. Thus, we introduce granular computing theory into cloud task scheduling and propose a greedy scheduling strategy based on different information granules, dividing the tasks into three types (i.e., CPU, memory, and hybrid type). Finally, we assign various scheduling strategies for cloud tasks with different characteristics. All the numerical experiments on the CloudSim platform show that our method has significant effects on energy consumption optimization and is a practical task scheduling algorithm.