The increasing interconnectivity of HPC systems has highlighted the need for efficient application migration across different environments. Containers, widely adopted for this purpose, simplify deployment but often fail to deliver optimal performance due to the separated build and execution container workflow. This leads to generic container images that miss out on system-specific software stack advantages, a challenge we define as the adaptability issue.

Web APIs are the primary communication form for Web services, with RESTful design being the predominant paradigm. However, RESTful APIs are typically fixed once defined, causing data under- or over-fetching as they can't meet clients' varying Web service needs. While semantic enriched API query languages like GraphQL mitigates this problem, they still face expressiveness limitations for logical operations such as indirect queries and loop traversals. To address this, we propose ORFA (One Request For All), the first in literature that employs WebAssembly (Wasm) as a Web API query language to achieve complete expressiveness of client requests. ORFA's key advantage lies in its use of Wasm's Turing completeness to allow clients to compose arbitrary operations within a single request, thus significantly eliminating redundant data transmission and boosting communication efficiency. Technically, ORFA provides a runtime for executing Wasm query programs and incorporates new module splitting strategies and a caching mechanism customized for integrating Wasm into Web API services, which can enable lightweight code transfer and fast request responses. Experimental results on a realistic testbed and popular Web applications show that ORFA effectively reduces latency by 18.4% and network traffic by 24.5% on average, compared to the state-of-the-art GraphQL.

With the popularity of IoT (Internet of Things) applications, edge computing has received lots of attention. To meet data privacy protection requirements of edge nodes and cope with their unbalanced data distribution , federated learning (FL), a distributed learning framework, is widely used in intelligent edge computing applications. However, recent studies have shown that FL still suffers from privacy leakage problems, including membership inference, data reconstruction, etc. However, these studies mainly focus on the feature information of private data. In this paper, we concern the user-level label privacy in FL. We propose LDIA, a label distribution inference attack against FL in edge computing, exploring the possibility that an honest but curious cloud server can infer the proportions of samples per label in the edge user’s private data. LDIA is inspired by the observation that parameter changes in the output layer of a model can reflect the label distribution of training data . We use a neural network to learn individual features of the output layer updates over different label distributions, and then perform inference from local models uploaded by users. Our comprehensive evaluation shows that LDIA is effective on various datasets in different settings, demonstrating the severe privacy leakage in FL-based edge computing.

In current routing algorithms based on encounter history in Delay-Tolerant Networks (DTNs), packets are always forwarded to nodes with highest probability to reach destination node. However, to the best of our knowledge, no analytical node transient contact pattern detection to achieve high performance, is reported in the literature. In this letter, DTN-Knca - a novel routing which detects frequently encountered nodes' transient contact patterns by correlation analysis is proposed. The trace-driven simulations demonstrate the higher throughput of DTN-Knca in comparison to the state-of-the-art DTN typical routing algorithms based on the encounter history knowledge.

Accurate and timely internet traffic information is important for many applications, such as bandwidth allocation, anomaly detection, congestion control and admission control. Over the last few years, internet flow data have been exploding, and we have truly entered the era of big data. Existing traffic flow prediction methods mainly use simple traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the internet traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, we propose a novel deep-learning-based internet traffic flow prediction method, which is called SDAPM. It consider the spatial and temporal correlations inherently and internet flow data character. A stacked denoising autoencoder prediction model (SDA) is used to learn generic internet traffic flow features, and it is trained in a greedy layer-wise fashion. Moreover, experiments demonstrate that the SDAPM for traffic flow prediction has effective performance. Our prediction model is in production as part of the traffic scheduling system at China Unicom, one of the largest Internet companies in China, helping improving the network bandwidth utilization.

GPUs have been becoming an indispensable computing platform in data centers, and co-locating multiple applications on the same GPU is widely used to improve resource utilization. However, performance interference due to uncontrolled resource contention severely degrades the performance of co-locating applications and fails to deliver satisfactory user experience. In this paper, we present SMGuard, a software approach to flexibly manage the GPU resource usage of multiple applications under co-location. We also propose a capacity based GPU resource model CapSM, which provisions the GPU resources in a fine-grained granularity among co-locating applications. When co-locating latency-sensitive applications with batch applications, SMGuard can prevent batch applications from occupying resources without constraint using quota based mechanism, and guarantee the resource usage of latency-sensitive applications with reservation based mechanism. In addition, SMGuard supports dynamic resource adjustment through evicting the running thread blocks of batch applications to release the occupied resources and remapping the uncompleted thread blocks to the remaining resources, which avoids the relaunch of the preempted kernel. The SMGuard is a pure software solution that does not rely on special GPU hardware or programming model, which is easy to adopt on commodity GPUs in data centers. Our evaluation shows that SMGuard improves the average performance of latency-sensitive applications by 9.8× when co-located with batch applications. In the meanwhile, the GPU utilization can be improved by 35 percent on average.