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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]

Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. Processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Numerous experiments have been designed to demonstrate the efficacy of the proposed method. The experimental results show that the proposed method outperforms the baseline methods by 11% in the F1-measure when the number of workers is two and by 25% when the number of workers is equal to 32. [ABSTRACT FROM AUTHOR]

The resources required to service cloud computing applications are dynamic and fluctuate over time in response to variations in the volume of incoming requests. Proactive autoscaling techniques attempt to predict future resource demand and assign resources before the request. Thus, establishing accurate prediction methods optimizes resource allocation and avoids service-level agreement violations (SLA). Most existing methods for predicting resource usage in cloud computing rely on univariate time series prediction models. These models consider only a single resource usage metric, make future predictions based on historical data for the target metric, and provide predictions for a single step ahead. This paper provides a hybrid method for multivariate time series workload prediction of host machines in cloud data centers that predicts the workload for the next few steps. First, a statistical analysis is used to construct the training set. Then, a convolutional neural network (CNN) is employed to extract the hidden spatial features between all correlated variables. Finally, the spatial features extracted by the CNN are fed into a GRU network optimized with the attention mechanism in order to extract the temporal correlation features. Two experiments employing Google cluster data were conducted to evaluate the proposed method. Experimental results reveal that our method improves prediction accuracy by 2% to 28% compared to baseline methods and previous research. [ABSTRACT FROM AUTHOR]

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]