While spot instances offer a cost-effective alternative to on-demand cloud resources, they introduce reliability challenges for latency-sensitive microservices due to preemption risks and unpredictable provisioning delays. Conventional resource management systems, which often rely on assumptions of immediate instance availability, fail to account for these operational realities—resulting in increased risk of SLO violations when deployed in spot-based environments.

In a unified resource scheduling architecture, containers within the same microservice often encounter temporal and spatial performance imbalance when deployed in large-scale shared clusters. As a result, the commonly employed load-balancing approach often leads to substantial resource wastage as applications are frequently over-provisioned to meet service level agreements (SLAs).

Lung cancer is a leading cause of cancer-related deaths globally. PET-CT is crucial for imaging lung tumors, providing essential metabolic and anatomical information, while it faces challenges such as poor image quality, motion artifacts, and complex tumor morphology. Deep learning-based models are expected to address these problems, however, existing small-scale and private datasets limit significant performance improvements for these methods. Hence, we introduce a large-scale PET-CT lung tumor segmentation dataset, termed PCLT20K, which comprises 21, 930 pairs of PET-CT images from 605 patients. Furthermore, we propose a cross-modal interactive perception network with Mamba (CIPA) for lung tumor segmentation in PET-CT images. Specifically, we design a channel-wise rectification module (CRM) that implements a channel state space block across multi-modal features to learn correlated representations and helps filter out modality-specific noise. A dynamic cross-modality interaction module (DCIM) is designed to effectively integrate position and context information, which employs PET images to learn regional position information and serves as a bridge to assist in modeling the relationships between local features of CT images. Extensive experiments on a comprehensive benchmark demonstrate the effectiveness of our CIPA compared to the current state-of-the-art segmentation methods. We hope our research can provide more exploration opportunities for medical image segmentation. The dataset and code are available at https://github.com/mj129/CIPA.

Microservice applications are commonly deployed alongside other services to enhance resource utilization. However, this practice also leads to notable resource contention. While existing studies primarily focus on scaling critical microservices responsible for performance degradation to mitigate violations of SLAs regarding end-to-end latency in highly interfered environments, they often overlook the potential advantages of scaling non-critical microservices for optimized resource efficiency. In this paper, we introduce Grad, an intelligent microservice scaling framework by harnessing resource fungibility between critical and non-critical microservices. Addressing the challenges posed by the dynamic nature of resource fungibility during scaling, Grad incorporates three key components. First, Grad employs a modular learning approach to profile individual microservice latency in relation to environmental conditions. Utilizing gradient extracts from this profile, Grad designs a scalable optimization module to dynamically select the optimal set of microservices for scaling. To rapidly mitigate SLA violations, Grad also deploys an accurate end-to-end latency predictor, serving as an simulator to obtain real-time feedback. We evaluate Grad in our cluster using real microservice benchmarks and production traces, demonstrating its ability to reduce resource usage by $\mathbf{4 9. 1 \%}$ and lower the probability of SLA violations by $3.7 \times$ when compared to state-of-the-art solutions.

This paper proposes a traffic sign small object detection algorithm based on lightweight structural design to address the existing problems of low detection accuracy for small objects, high complexity, and unsuitability for deployment on mobile in-vehicle devices in current traffic sign detection algorithms. Firstly, the algorithm optimizes YOLOv8n. By reducing the number of backbone network layers and replacing the large object detection layer with the small object detection layer, the number of parameters is greatly reduced, and the detection performance of small traffic signs is significantly improved. Secondly, we design a lightweight detection head (LDH), which greatly reduces the number of parameters and calculation amount of the algorithm while maintaining the original detection accuracy, thus significantly improving the inference speed of the algorithm. Finally, a slice non-parametric attention module (SimAMWithSlicing) is introduced. The module does not need to introduce additional parameters, and only generates attention weights by calculating local self-similarity of feature graphs, thus effectively enhancing the feature representation ability of small objects. Our algorithm was tested on the TT100K2021 and CCTSDB2021 datasets. Versus the original YOLOv8n, the average precision was increased by 2.6% and 0.9%, respectively, while the number of parameters was reduced by 80.3% and 81%. The experimental results demonstrate that our algorithm achieves a balance between accuracy and speed with lower parameters and computational load in traffic sign small object detection.

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Existing object detection models are mainly trained on large-scale labeled datasets. However, annotating data for novel aerial object classes is expensive since it is time-consuming and may require expert knowledge. Thus, it is desirable to study label-efficient object detection methods on aerial images. In this work, we propose a zero-shot method for aerial object detection named visual Description Regularization, or DescReg. Concretely, we identify the weak semantic-visual correlation of the aerial objects and aim to address the challenge with prior descriptions of their visual appearance. Instead of directly encoding the descriptions into class embedding space which suffers from the representation gap problem, we propose to infuse the prior inter-class visual similarity conveyed in the descriptions into the embedding learning. The infusion process is accomplished with a newly designed similarity-aware triplet loss which incorporates structured regularization on the representation space. We conduct extensive experiments with three challenging aerial object detection datasets, including DIOR, xView, and DOTA. The results demonstrate that DescReg significantly outperforms the state-of-the-art ZSD methods with complex projection designs and generative frameworks, e.g., DescReg outperforms best reported ZSD method on DIOR by 4.5 mAP on unseen classes and 8.1 in HM. We further show the generalizability of DescReg by integrating it into generative ZSD methods as well as varying the detection architecture. Codes will be released at https://github.com/zq-zang/DescReg.

Ensuring efficient resource allocation while providing service level agreement (SLA) guarantees for end-to-end (E2E) latency is crucial for microservice applications. Although existing studies have made significant contributions towards achieving this objective, they primarily concentrate on static graphs. However, microservice graphs are inherently dynamic during runtime in production environments, necessitating more effective and scalable resource management solutions.In this paper, we present Derm, a new resource management system designed for microservice applications with highly dynamic graphs. Our principal finding is that prioritizing different microservice graphs can lead to a substantial reduction in resource allocation. To take advantage of this opportunity, we develop three main components. The first is a performance model that describes uncertainties of microservice latency through a conditional exponential distribution. The second is a probabilistic quantification of the dynamics of microservice graphs. The third is an optimization method for adjusting the resource allocation of microservices to minimize resource usage. We evaluate Derm in our cluster using real microservice benchmarks and production traces. The results highlight that Derm reduces the resource usage by $68.4 \%$ and lowers SLA violation probability by $6.7 \times$, compared to existing approaches.

Recent studies have confirmed that GPS trajectory can effectively assist in achieving more accurate road extraction from remote sensing images. Therefore, lots of efforts focus on designing effective multi-modal fusion strategies for GPS trajectory and remote sensing image modalities. However, there are still some limitations,e.g., the fusion structures are complex and hinder further improvements. Moreover, the negative impact of redundant information in various modalities is commonly ignored. This paper aims to design a simple yet effective fusion strategy for GPS trajectory and remote sensing image modalities to address the above issues. Inspired by the network pruning algorithm, we design a Cross-Modal Information Propagation (CMIP) mechanism. CMIP utilizes the scaling factors and sparse constraint to distinguish the redundant information of a certain modality that is directly replaced with corresponding information of another modality. We improve the widely-usedL1sparse constraint and propose a novel information balanced constraint which is added on the scaling factors to better identify and prune redundant channels. Embedded in the CMIP mechanism, a multi-modal information propagation network (CMIPNet) is proposed, which can fully explore the complementarities between different modalities to accurately locate roads, especially roads with noise or incomplete information of a certain modality. Since the CMIP is parameter-free and self-adaptive, CMIPNet is lightweight and easy to deploy. The parameter number of CMIPNet can be comparable to single-modal models, which is about 1/3 of the existing multi-modal models. Extensive experiments are performed to demonstrate that CMIPNet outperforms the previous single- and multi-modal road extraction methods.

A common approach to improving resource utilization in data centers is to adaptively provision resources based on the actual workload. One fundamental challenge of doing this in microservice management frameworks, however, is that different components of a service can exhibit significant differences in their impact on end-to-end performance. To make resource management more challenging, a single microservice can be shared by multiple online services that have diverse workload patterns and SLA requirements. We present an efficient resource management system, namely Erms, for guaranteeing SLAs with high probability in shared microservice environments. Erms profiles microservice latency as a piece-wise linear function of the workload, resource usage, and interference. Based on this profiling, Erms builds resource scaling models to optimally determine latency targets for microservices with complex dependencies. Erms also designs new scheduling policies at shared microservices to further enhance resource efficiency. Experiments across microservice benchmarks as well as trace-driven simulations demonstrate that Erms can reduce SLA violation probability by 5× and more importantly, lead to a reduction in resource usage by 1.6×, compared to state-of-the-art approaches.