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AI workloads, often hosted in multi-tenant cloud environments, require vast computational resources but suffer inefficiencies due to limited tenant-provider coordination. Tenants lack infrastructure insights, while providers lack workload details to optimize tasks like partitioning, scheduling, and fault tolerance. We propose the HarmonAIze project to redefine cloud abstractions, enabling cooperative optimization for improved performance, efficiency, resiliency, and sustainability. This paper outlines key opportunities, challenges, and a research agenda to realize this vision.

In today's digital landscape, the importance of timely and accurate vulnerability detection has significantly increased. This paper presents a novel approach that leverages transformer-based models and machine learning techniques to automate the identification of software vulnerabilities by analyzing GitHub issues. We introduce a new dataset specifically designed for classifying GitHub issues relevant to vulnerability detection. We then examine various classification techniques to determine their effectiveness. The results demonstrate the potential of this approach for real-world application in early vulnerability detection, which could substantially reduce the window of exploitation for software vulnerabilities. This research makes a key contribution to the field by providing a scalable and computationally efficient framework for automated detection, enabling the prevention of compromised software usage before official notifications. This work has the potential to enhance the security of open-source software ecosystems.

Large Language Models (LLMs) have demonstrated their exceptional performance in various complex code generation tasks. However, their broader adoption is limited by significant computational demands and high resource requirements, particularly memory and processing power. To mitigate such requirements, model pruning techniques are used to create more compact models with significantly fewer parameters. However, current approaches do not focus on the efficient extraction of programming-language-specific sub-models. In this work, we explore the idea of efficiently deriving coding-specific sub-models through unstructured pruning (i.e., Wanda). We investigate the impact of different domain-specific calibration datasets on pruning outcomes across three distinct domains and extend our analysis to extracting four language-specific sub-models: Python, Java, C++, and JavaScript. We are the first to efficiently extract programming-language-specific sub-models using appropriate calibration datasets while maintaining acceptable accuracy w.r.t. full models. We are also the first to provide analytical evidence that domain-specific tasks activate distinct regions within LLMs, supporting the creation of specialized sub-models through unstructured pruning. We believe that this work has significant potential to enhance LLM accessibility for coding by reducing computational requirements to enable local execution on consumer-grade hardware, and supporting faster inference times critical for real-time development feedback.

Battery-free sensor tags are devices that leverage backscatter techniques to communicate with standard IoT devices, thereby augmenting a network's sensing capabilities in a scalable way. For communicating, a sensor tag relies on an unmodulated carrier provided by a neighboring IoT device, with a schedule coordinating this provisioning across the network. Carrier scheduling--computing schedules to interrogate all sensor tags while minimizing energy, spectrum utilization, and latency--is an NP-Hard optimization problem. Recent work introduces learning-based schedulers that achieve resource savings over a carefully-crafted heuristic, generalizing to networks of up to 60 nodes. However, we find that their advantage diminishes in networks with hundreds of nodes, and degrades further in larger setups. This paper introduces RobustGANTT, a GNN-based scheduler that improves generalization (without re-training) to networks up to 1000 nodes (100x training topology sizes). RobustGANTT not only achieves better and more consistent generalization, but also computes schedules requiring up to 2x less resources than existing systems. Our scheduler exhibits average runtimes of hundreds of milliseconds, allowing it to react fast to changing network conditions. Our work not only improves resource utilization in large-scale backscatter networks, but also offers valuable insights in learning-based scheduling., Comment: 15 Pages, 12 Figures. Pre-print, under review

Could information about future incoming packets be used to build more efficient CPU-based packet processors? Can such information be obtained accurately? This paper studies novel packet processing architectures that receive external hints about which packets are soon to arrive, thus enabling prefetching into fast cache memories of the state needed to process them, just-in-time for the packets' arrival. We explore possible approaches to (i) obtain such hints either from network devices or the end hosts in the communication and (ii) use these hints to better utilize cache memories. We show that such information (if accurate) can improve packet processing throughput by at least 50%.

Detecting anomalies in electrocardiogram data is crucial to identifying deviations from normal heartbeat patterns and providing timely intervention to at-risk patients. Various AutoEncoder models (AE) have been proposed to tackle the anomaly detection task with ML. However, these models do not consider the specific patterns of ECG leads and are unexplainable black boxes. In contrast, we replace the decoding part of the AE with a reconstruction head (namely, FMM-Head) based on prior knowledge of the ECG shape. Our model consistently achieves higher anomaly detection capabilities than state-of-the-art models, up to 0.31 increase in area under the ROC curve (AUROC), with as little as half the original model size and explainable extracted features. The processing time of our model is four orders of magnitude lower than solving an optimization problem to obtain the same parameters, thus making it suitable for real-time ECG parameters extraction and anomaly detection., Comment: 23 pages, 14 figures

This paper explores opportunities to utilize Large Language Models (LLMs) to make network configuration human-friendly, simplifying the configuration of network devices and minimizing errors. We examine the effectiveness of these models in translating high-level policies and requirements (i.e., specified in natural language) into low-level network APIs, which requires understanding the hardware and protocols. More specifically, we propose NETBUDDY for generating network configurations from scratch and modifying them at runtime. NETBUDDY splits the generation of network configurations into fine-grained steps and relies on self-healing code-generation approaches to better take advantage of the full potential of LLMs. We first thoroughly examine the challenges of using these models to produce a fully functional & correct configuration, and then evaluate the feasibility of realizing NETBUDDY by building a proof-of-concept solution using GPT-4 to translate a set of high-level requirements into P4 and BGP configurations and run them using the Kathar\'a network emulator., Comment: 6 pages,3 figures

In cross-device Federated Learning (FL), clients with low computational power train a common\linebreak[4] machine model by exchanging parameters via updates instead of potentially private data. Federated Dropout (FD) is a technique that improves the communication efficiency of a FL session by selecting a \emph{subset} of model parameters to be updated in each training round. However, compared to standard FL, FD produces considerably lower accuracy and faces a longer convergence time. In this paper, we leverage \textit{coding theory} to enhance FD by allowing different sub-models to be used at each client. We also show that by carefully tuning the server learning rate hyper-parameter, we can achieve higher training speed while also achieving up to the same final accuracy as the no dropout case. For the EMNIST dataset, our mechanism achieves 99.6\% of the final accuracy of the no dropout case while requiring $2.43\times$ less bandwidth to achieve this level of accuracy., Comment: 6 pages plus references and appendix, 6 figures. Accepted and presented at FL-IJCAI22 (https://federated-learning.org/fl-ijcai-2022/)

Novel backscatter communication techniques enable battery-free sensor tags to interoperate with unmodified standard IoT devices, extending a sensor network's capabilities in a scalable manner. Without requiring additional dedicated infrastructure, the battery-free tags harvest energy from the environment, while the IoT devices provide them with the unmodulated carrier they need to communicate. A schedule coordinates the provision of carriers for the communications of battery-free devices with IoT nodes. Optimal carrier scheduling is an NP-hard problem that limits the scalability of network deployments. Thus, existing solutions waste energy and other valuable resources by scheduling the carriers suboptimally. We present DeepGANTT, a deep learning scheduler that leverages graph neural networks to efficiently provide near-optimal carrier scheduling. We train our scheduler with relatively small optimal schedules obtained from a constraint optimization solver, achieving a performance within 3% of the optimal scheduler. Without the need to retrain, DeepGANTT generalizes to networks 6x larger in the number of nodes and 10x larger in the number of tags than those used for training, breaking the scalability limitations of the optimal scheduler and reducing carrier utilization by up to 50% compared to the state-of-the-art heuristic. Our scheduler efficiently reduces energy and spectrum utilization in backscatter networks., Comment: 11 pages (excluding references). Submitted version that was accepted to IPSN 2023 (not the camera-ready version). Camera-ready version available here: https://doi.org/10.1145/3583120.3586957

It is becoming increasingly popular for distributed systems to exploit offload to reduce load on the CPU. Remote Direct Memory Access (RDMA) offload, in particular, has become popular. However, RDMA still requires CPU intervention for complex offloads that go beyond simple remote memory access. As such, the offload potential is limited and RDMA-based systems usually have to work around such limitations. We present RedN, a principled, practical approach to implementing complex RDMA offloads, without requiring any hardware modifications. Using self-modifying RDMA chains, we lift the existing RDMA verbs interface to a Turing complete set of programming abstractions. We explore what is possible in terms of offload complexity and performance with a commodity RDMA NIC. We show how to integrate these RDMA chains into applications, such as the Memcached key-value store, allowing us to offload complex tasks such as key lookups. RedN can reduce the latency of key-value get operations by up to 2.6x compared to state-of-the-art KV designs that use one-sided RDMA primitives (e.g., FaRM-KV), as well as traditional RPC-over-RDMA approaches. Moreover, compared to these baselines, RedN provides performance isolation and, in the presence of contention, can reduce latency by up to 35x while providing applications with failure resiliency to OS and process crashes., Comment: Updated to NSDI 2022 version

The 15th European Conference on Computer Systems (EuroSys'20) was organized as a virtual (online) conference on April 27-30, 2020. The main EuroSys'20 track took place April 28-30, 2020, preceded by five workshops (EdgeSys'20, EuroDW'20, EuroSec'20, PaPoC'20, SPMA'20) on April 27, 2020. The decision to hold a virtual (online) conference was taken in early April 2020, after consultations with the EuroSys community and internal discussions about potential options, eventually allowing about three weeks for the organization. This paper describes the choices we made to organize EuroSys'20 as a virtual (online) conference, the challenges we addressed, and the lessons learned.

The adoption of very low latency persistent memory modules (PMMs) upends the long-established model of disaggregated file system access. Instead, by colocating computation and PMM storage, we can provide applications much higher I/O performance, sub-second application failover, and strong consistency. To demonstrate this, we built the Assise distributed file system, based on a persistent, replicated coherence protocol for managing a set of server-colocated PMMs as a fast, crash-recoverable cache between applications and slower disaggregated storage, such as SSDs. Unlike disaggregated file systems, Assise maximizes locality for all file IO by carrying out IO on colocated PMM whenever possible and minimizes coherence overhead by maintaining consistency at IO operation granularity, rather than at fixed block sizes. We compare Assise to Ceph/Bluestore, NFS, and Octopus on a cluster with Intel Optane DC PMMs and SSDs for common cloud applications and benchmarks, such as LevelDB, Postfix, and FileBench. We find that Assise improves write latency up to 22x, throughput up to 56x, fail-over time up to 103x, and scales up to 6x better than its counterparts, while providing stronger consistency semantics. Assise promises to beat the MinuteSort world record by 1.5x.