EUROSYS

Graph Neural Network (GNN) has emerged as an important workload for learning on graphs. With the size of graph data and the complexity of GNN model architectures increasing, developing an efficient GNN system grows more important. As GNN has heavy neural ...

When evaluating an iterative graph query over a large graph, systems incur significant overheads due to repeated graph transfer across the memory hierarchy coupled with repeated (redundant) propagation of values over the edges in the graph. An approach ...

Streaming graph has been broadly employed across various application domains. It involves updating edges to the graph and then performing analytics on the updated graph. However, existing solutions either suffer from poor data locality and high ...

While graph mining systems employ efficient task-parallel strategies to quickly explore subgraphs of interest (or matches), they remain oblivious to containment constraints like maximality and minimality, resulting in expensive constraint checking on ...

Modern data centers (DCs) employ various traffic load balancers to achieve high bisection bandwidth. Among them, flowlet switching has shown remarkable performance in both load balancing and upper-layer protocol (e.g., TCP) friendliness. However, flowlet-...

Open vSwitch (OvS) has been widely used in cloud networks in view of its programmability and flexibility. However, we observe a huge performance drop when it loads practical cloud networking services (e.g., tunneling and firewalling). Our further ...

Recent years have witnessed a plethora of learning-based solutions for congestion control (CC) that demonstrate better performance over traditional TCP schemes. However, they fail to provide consistently good convergence properties, including fairness, ...

Discrete-event simulation (DES) is a prevalent tool for evaluating network designs. Although DES offers full fidelity and generality, its slow performance limits its application. To speed up DES, many network simulators employ parallel discrete-event ...

Serialization and deserialization play a dominant role in the state transfer time of serverless workflows, leading to substantial performance penalties during workflow execution. We identify the key reason as a lack of ability to efficiently access the (...

The complexity of large networks makes their management a daunting task. State-of-the-art network management tools use workflow systems for automation, but they do not adequately address the substantial challenges in operation reliability. This paper ...

Many parallel mechanisms, including data parallelism, tensor parallelism, and pipeline parallelism, have been proposed and combined together to support training increasingly large deep neural networks (DNN) on massive GPU devices. Given a DNN model and ...

Federated Learning (FL) is an emerging distributed machine learning (ML) technique that enables in-situ model training and inference on decentralized edge devices. We propose Totoro, a novel scalable FL engine, that enables massive FL applications to run ...

Federated Learning (FL) has emerged as a powerful approach that enables collaborative distributed model training without the need for data sharing. However, FL grapples with inherent heterogeneity challenges leading to issues such as stragglers, dropouts,...

Federated learning (FL) relies on a central authority to oversee and aggregate model updates contributed by multiple participating parties in the training process. This centralization of sensitive model updates naturally raises concerns about the ...

In recent years, large-scale models can be easily scaled to trillions of parameters with sparsely activated mixture-of-experts (MoE), which significantly improves the model quality while only requiring a sub-linear increase in computational costs. ...

State-of-the-art Byzantine fault-tolerant (BFT) protocols assuming partial synchrony such as SBFT and HotStuff use regular certificates obtained from 2f + 1 (partial) signatures. We show that one can use weak certificates obtained from only f + 1 ...

State machine replication (SMR) uses consensus as its core component for reaching agreement among a group of processes, in order to provide fault-tolerant services. Most SMR protocols, such as Paxos and Raft, are designed in the partial synchrony model. ...

FaaS (function-as-a-service) is becoming a popular workload in cloud environments due to its virtues such as auto-scaling and pay-as-you-go. High-level languages like JavaScript and Java are commonly used in FaaS for programmability, but their managed ...

Serverless computing allows developers to deploy and scale stateless functions in ephemeral workers easily. As a result, serverless computing has been widely used for many applications, such as computer vision, video processing, and HTML generation. ...

The rise of cloud gaming makes interactive 3D applications an emerging type of data center workload. However, the excessive rendering in current cloud 3D systems leads to large gaps between the cloud and client frame rates (FPS, frames per second), thus ...

We present Draconis, a novel scheduler for workloads in the range of tens to hundreds of microseconds. Draconis challenges the popular belief that programmable switches cannot house the complex data structures, such as queues, needed to support an in-...

In recent years, we have witnessed a growing trend of content hyper-giants deploying server infrastructure and services close to end-users, in "eyeball" networks. Still, one of the services that remained largely unaffected by this trend is online ...

Modern data processing workloads, such as machine learning and graph processing, involve iterative computations to converge generated models into higher accuracy. An effective caching mechanism is vital to expedite iterative computations since the ...

In-memory caches play a pivotal role in optimizing distributed systems by significantly reducing query response times. Correctly sizing these caches is critical, especially considering that prominent organizations use terabytes and even petabytes of DRAM ...

With the proliferation of attribute-rich machine-generated data, emerging real-time monitoring, diagnosis, and visualization tools ingest and analyze such data across multiple attributes simultaneously. Due to the sheer volume of the data, applications ...

While Miss Ratio Curve (MRC) profiling methods based on spatial sampling are effective in modeling cache behaviors, previous MRC studies lack in-depth analysis of profiling errors and primarily target homogeneous object-size scenarios. This has caused ...

In persistent B+ -Tree, random updates of small key-value (KV) pairs will cause severe XPBuffer-induced write amplification (XBI-amplification) because CPU cacheline size is smaller than media access granularity in persistent memory (PM). We observe that ...

Approximate membership query (AMQ) data structures can approximately determine whether an element is in the set with high efficiency. They are widely used in distributed systems, database systems, bioinformatics, IoT applications, data stream mining, ...

Federated learning (FL) is increasingly deployed among multiple clients to train a shared model over decentralized data. To address privacy concerns, FL systems need to safeguard the clients' data from disclosure during training and control data leakage ...

Cross-silo privacy-preserving machine learning (PPML) adopt; Partial Homomorphic Encryption (PHE) for secure data combination and high-quality model training across multiple organizations (e.g., medical and financial). However, PHE introduces significant ...