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Service meshes play a central role in the modern application ecosystem by providing an easy and flexible way to connect different services that form a distributed application. However, because of the way they interpose on application traffic, they can substantially increase application latency and resource consumption. We develop a decompositional approach and a tool, called MeshInsight, to systematically characterize the overhead of service meshes and to help developers quantify overhead in deployment scenarios of interest. Using MeshInsight, we confirm that service meshes can have high overhead -- up to 185% higher latency and up to 92% more virtual CPU cores for our benchmark applications -- but the severity is intimately tied to how they are configured and the application workload. The primary contributors to overhead vary based on the configuration too. IPC (inter-process communication) and socket writes dominate when the service mesh operates as a TCP proxy, but protocol parsing dominates when it operates as an HTTP proxy. MeshInsight also enables us to study the end-to-end impact of optimizations to service meshes. We show that not all seemingly-promising optimizations lead to a notable overhead reduction in realistic settings.

We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research. FedScale datasets encompass a wide range of critical FL tasks, ranging from image classification and object detection to language modeling and speech recognition. Each dataset comes with a unified evaluation protocol using real-world data splits and evaluation metrics. To reproduce realistic FL behavior, FedScale contains a scalable and extensible runtime. It provides high-level APIs to implement FL algorithms, deploy them at scale across diverse hardware and software backends, and evaluate them at scale, all with minimal developer efforts. We combine the two to perform systematic benchmarking experiments and highlight potential opportunities for heterogeneity-aware co-optimizations in FL. FedScale is open-source and actively maintained by contributors from different institutions at http://fedscale.ai. We welcome feedback and contributions from the community.

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. Despite having the same end goals as traditional ML, FL executions differ significantly in scale, spanning thousands to millions of participating devices. As a result, data characteristics and device capabilities vary widely across clients. Yet, existing efforts randomly select FL participants, which leads to poor model and system efficiency. In this paper, we propose Oort to improve the performance of federated training and testing with guided participant selection. With an aim to improve time-to-accuracy performance in model training, Oort prioritizes the use of those clients who have both data that offers the greatest utility in improving model accuracy and the capability to run training quickly. To enable FL developers to interpret their results in model testing, Oort enforces their requirements on the distribution of participant data while improving the duration of federated testing by cherry-picking clients. Our evaluation shows that, compared to existing participant selection mechanisms, Oort improves time-to-accuracy performance by 1.2x-14.1x and final model accuracy by 1.3%-9.8%, while efficiently enforcing developer-specified model testing criteria at the scale of millions of clients.

Ying Zeng,1,2 Xiangfeng Zhu,2 Yali Wang,1 Bo Liu,1 Xin Yang,2 Qiushi Wang,2 Juan Du,2 Yu Ma,2 Li Lin,2 Ping Fu,2 Hualiang Xiao,2 Qiao-Nan Guo1 1Department of Pathology, Second Affiliated Hospital, Amy Medical University (Third Military Medical University), Chongqing 400037, People’s Republic of China; 2Department of Pathology, Daping Hospital, Amy Medical University (Third Military Medical University), Chongqing 400042, People’s Republic of ChinaCorrespondence: Qiao-Nan Guo; Hualiang Xiao Email qiaonan85@263.net; dpbl_xhl@126.comPurpose: To observe the clinicopathological, immunohistochemical, and molecular genetic features of epithelioid glioblastoma (E-GBM), and identify tumor-associated prognostic factors.Patients and Methods: The clinical and radiological data of fifteen cases of E-GBM were collected, and their pathological, immunohistochemical, and molecular features were examined. A 1p/19q analysis via FISH, MGMT promoter methylation by MS-PCR, and IDH1 and BRAF V600E mutation analysis by HRM-PCR were performed. The level of EZH2 expression was valuated by immunohistochemistry in 15 E-GBM cases, and the prognostic factors were analyzed in E-GBM patients. Fifteen non-E-GBM cases were used as a control.Results: The fifteen cases of E-GBM included twelve males and three females, with fourteen cases supratentorially located. Headache was the main symptom. Microscopy revealed that the tumors were composed of epithelioid cells and some rhabdoid cells. The epithelioid and rhabdoid cells displayed focal discohesion, scant intervening neuropil, a distinct cell membrane, eosinophilic cytoplasm, and a laterally positioned nucleus. Most tumors showed high mitosis, zonal necrosis, and microvascular hyperplasia. Immunohistochemical findings included epithelioid cells positive for GFAP, vimentin, nestin, S-100, and INI-1. The molecular findings included no deletions of 1p/19q, EGFR amplifications, or IDH1 mutations in any case, a methylated MGMT promoter