Prior studies on traditional centralized clouds independently optimizing static resource utilization and dynamic bandwidth cost are not applicable to edge scenarios, where edge sites are interconnected by wide area networks (WAN) rather than local area networks (LAN) as within clouds. Due to the lack of knowledge about the actual status of public edge platforms and real-world edge datasets, existing influential literature on edge scenarios demonstrates significant disparities in optimization objectives and perspectives. To bridge this gap, we collaborate with a public edge platform and perform a comprehensive measurement, which reveals limitations of the status quo VM scheduling schemes and potential opportunities for improvement. However, resolving VM scheduling considering static resource utilization, dynamic bandwidth cost, and end users’ QoE in a cost-efficient manner faces several challenges, including coupled objectives, exponentially increased complexity, and spatiotemporal dynamics. To address the above challenges, in this work, we propose a holistic online framework that integrates combinatorial bandit-based VM migration and seasonality-aware VM request allocation at two distinct time granularities. Large-scale experiments based on a real-world dataset confirm that our online framework achieves near-offline bandwidth cost and resource utilization while significantly lowering time consumption.

With the rapid development of edge computing in the post-COVID19 pandemic period, precise workload forecasting is considered the basis for making full use of the edge-limited resources, and both edge service providers (ESPs) and edge service consumers (ESCs) can benefit significantly from it. Existing paradigms of workload forecasting (i.e., edge-only or cloud-only) are improper, due to failing to consider the inter-site correlations and might suffer from significant data transmission delays. With the increasing adoption of edge platforms by web services, it is critical to balance both accuracy and efficiency in workload forecasting. In this paper, we propose XELASTIC, which offers three key improvements over the conference version. First, we redesigned the aggregation and disaggregation layers using GCNs to capture more complex relationships among workload series. Second, we introduced a supervised contrastive loss to enhance robustness against outliers, particularly for handling missing or abnormal data in real-world scenarios. Finally, we expanded the evaluation with additional baselines and larger datasets. Extensive experiments on realistic edge workload datasets collected from China’s largest edge service provider (Alibaba ENS) show that XELASTIC outperforms state-of-the-art methods, decreases time consumption, and reduces communication costs.

With the rapid development of edge computing in the post-COVID19 pandemic period, precise workload forecasting is considered the basis for making full use of the edge limited resources, and both edge service providers (ESPs) and edge service consumers (ESCs) can benefit significantly from it. Existing paradigms of workload forecasting (i.e., edge-only or cloud-only) are improper, due to failing to consider the inter-site correlations and might suffer from significant data transmission delays. With the increasing adoption of edge platforms by web services, it is critical to balance both accuracy and efficiency in workload forecasting. In this paper, we propose ELASTIC, which is the first study that leverages a cloud-edge collaborative paradigm for edge workload forecasting with multi-view graphs. Specifically, at the global stage, we design a learnable aggregation layer on each edge site to reduce the time consumption while capturing the inter-site correlation. Additionally, at the local stage, we design a disaggregation layer combining both the intra-site correlation and inter-site correlation to improve the prediction accuracy. Extensive experiments on realistic edge workload datasets collected from China’s largest edge service provider show that ELASTIC outperforms state-of-the-art methods, decreases time consumption, and reduces communication cost.

Public edge platforms have drawn increasing attention from both academia and industry. In this study, we perform a first-of-its-kind measurement study on a leading public edge platform that has been densely deployed in China. Based on this measurement, we quantitatively answer two critical yet unexplored questions. First, from end users' perspective, what is the performance of commodity edge platforms compared to cloud, in terms of the end-to-end network delay, throughput, and the application QoE. Second, from the edge service provider's perspective, how are the edge workloads different from cloud, in terms of their VM subscription, monetary cost, and resource usage. Our study quantitatively reveals the status quo of today's public edge platforms, and provides crucial insights towards developing and operating future edge services.