![]() ![]() This study highlights the recent contributions in three inter‐related main topics that were published within the year 2017 to 2022, namely, microservice, verification, and autoscaling. Therefore, our review complements the existing by focusing on autoscaling with verification perspectives. Meanwhile, a few reviews have been published concerning microservices from different aspects. Hence, it is significant to gather and summarize these approaches to foster future innovation. Many research works have proposed autoscaling approaches for microservices, however, less likely concerned with the correctness guarantee of the proposed algorithms. ![]() The process of scaling microservices is a challenging task, especially in maintaining optimum resource provisioning while respecting QoS constraints and SLA. Compared with the existing methods, our proposed method reduces the energy consumption of the hosts and SLAV and reduces the total cost by 26.1~39.3%. Simulations driven by the VM real workload dataset validate the effectiveness of our proposed method. A heuristic algorithm based on the complementary use of multiple resources in space and time is proposed to solve the placement problem. Based on the predicted results, we trigger VM migration before the host enters an overloaded state to reduce the occurrence of SLAV. We employ a convolutional autoencoder-based filter to preprocess the VM historical workload and use an attention-based RNN method to predict the computing resource usage of the VMs in future periods. To minimize this cost, we design the following solution. ![]() To solve this problem, this paper establishes a cost model based on multiple computing resources in CDC, which comprehensively considers the hosts’ energy cost, virtual machine (VM) migration cost, and SLAV penalty cost. Guaranteeing the QoS of multiple users while reducing the operating cost of the cloud data center (CDC) is a major problem that needs to be solved urgently. The surge in the number of users presents cloud service providers with severe challenges in managing computing resources. The recent COVID-19 pandemic has accelerated the use of cloud computing. ![]()
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