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Cloud service providers can operate several execution instances on a single physical server using virtualization technology, which improves resource utilization. In recent years, container-based virtualization has been developed as a remarkably lightweight alternative to virtual machines. Containers consume less memory than virtual machines, enabling faster setup and portability. Cloud-based applications require dynamic resource allocation in response to fluctuations in the number of incoming requests. Most articles on proactive autoscaling in cloud computing have shortcomings in two ways. 1) During feature extraction, the temporal patterns of the data are ignored, and the historical sequences are assigned equal weight. 2) Existing research omits cool down time (CDT) from the planning phase. 3) Scaling operations can be performed at any time depending only on the current input workload, resulting in a large number of contradicting scaling actions. In response to the above shortcomings, this paper presents a proactive autoscaling method for web applications in Kubernetes using an attention-based gated recurrent unit (GRU) encoder-decoder (K-AGRUED), which predicts the resource usage of several future steps based on CDT. The results demonstrate that the proposed method reduces prediction error by 2–25% compared to state of the art methods. Our approach significantly reduces scaling operations and under-provisioning compared to the standard horizontal pod autoscaler (HPA) of Kubernetes and two previous studies. The K-AGRUED increases the scaling speedup by a factor of up to five in a real environment. [ABSTRACT FROM AUTHOR]

The utilization of cloud computing in Internet of Things (IoT) applications has become widespread. However, it presents challenges for latency-sensitive scenarios due to data transmission to the centralized cloud structure, which leads to increased network traffic and service delays. To address this, fog computing has emerged as an intermediary layer between the cloud and IoT, ensuring low-latency interactions. A pivotal challenge within the fog computing paradigm is the service placement problem, involving assigning services to appropriate nodes, which is recognized as NP-hard. Recognizing the intricate nature of service placement, this study introduces a multi-objective optimization approach tailored for dynamic service placement within container-based fog computing environments. Considering multiple objectives is imperative due to the complex interplay of performance metrics in fog computing scenarios. A two-tier resource management framework based on Kubernetes is proposed to manage these diverse yet interrelated objectives effectively. The framework harnesses the power of the multi-objective, non-dominated sorting genetic algorithm II (NSGA-II) to reconcile conflicting objectives and facilitate optimal service placement decisions. Incorporating multi-objective optimization enables a comprehensive evaluation of service placement solutions, ensuring a balanced trade-off between latency, cost-efficiency, and energy consumption. Empirical evaluations demonstrate that the proposed method improves cost, average latency time, and energy consumption by 8% to 36% compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]