Reviewerly/exHarmony huggingface dataset:
Plexus

Modern applications are increasingly generating and persisting data across geo-distributed data centers or edge clusters rather than a single cloud. This paradigm introduces challenges for traditional query execution due to increased latency when transferring data over wide-area network links. Join queries in particular are heavily affected, due to their large output size and amount of data that must be shuffled over the network. Join sampling---computing a uniform sample from the join results---is a useful technique for reducing resource requirements. However, applying it to a geo-distributed setting is challenging, since acquiring independent samples from each location and joining on the samples does not produce uniform and independent tuples from the join result. To address these challenges, we first generalize an existing join sampling algorithm to the geo-distributed setting. We then present our system, Plexus, which introduces three additional optimizations to further reduce the network overhead and handle network and data heterogeneity: (i) weight approximation, (ii) heterogeneity awareness and (iii) sample prefetching. We evaluate Plexus on a geo-distributed system deployed across multiple AWS regions, with an implementation based on Apache Spark. Using three real-world datasets, we show that Plexus can reduce query latency by up to 80% over the default Spark join implementation on a wide class of join queries without substantially impacting sample uniformity.
Federated Secure Computing

Privacy-preserving computation (PPC) enables encrypted computation of private data. While advantageous in theory, the complex technology has steep barriers to entry in practice. Here, we derive design goals and principles for a middleware that encapsulates the demanding cryptography server side and provides a simple-to-use interface to client-side application developers. The resulting architecture, “Federated Secure Computing”, offloads computing-intensive tasks to the server and separates concerns of cryptography and business logic. It provides microservices through an Open API 3.0 definition and hosts multiple protocols through self-discovered plugins. It requires only minimal DevSecOps capabilities and is straightforward and secure. Finally, it is small enough to work in the internet of things (IoT) and in propaedeutic settings on consumer hardware. We provide benchmarks for calculations with a secure multiparty computation (SMPC) protocol, both for vertically and horizontally partitioned data. Runtimes are in the range of seconds on both dedicated workstations and IoT devices such as Raspberry Pi or smartphones. A reference implementation is available as free and open source software under the MIT license.
The Gap Between Serverless Research and Real-world Systems

With the emergence of the serverless computing paradigm in the cloud, researchers have explored many challenges of serverless systems and proposed solutions such as snapshot-based booting. However, we have noticed that some of these optimizations are based on oversimplified assumptions that lead to infeasibility and hide real-world issues. This paper aims to analyze the gap between current serverless research and real-world systems from a perspective of industry, and present new observations, challenges, opportunities, and insights that may address the discrepancies.
Gödel

Over the last few years, at ByteDance, our compute infrastructure scale has been expanding significantly due to expedited business growth. In this journey, to meet hyper-scale growth, some business groups resorted to managing their own compute infrastructure stack running different scheduling systems such as Kubernetes, YARN which created two major pain points: the increasing resource fragmentation across different business groups and the inadequate resource elasticity between workloads of different business priorities. Isolation across different business groups (and their compute infrastructure management) leads to inefficient compute resource utilization and prevents us from serving the business growth needs in the long run.
CAMEO

Modern computer systems are highly configurable, with hundreds of configuration options that interact, resulting in an enormous configuration space. As a result, optimizing performance goals (e.g., latency) in such systems is challenging due to frequent uncertainties in their environments (e.g., workload fluctuations). Lately, there has been a utilization of transfer learning to tackle this issue, leveraging information obtained from configuration measurements in less expensive source environments, as opposed to the costly or sometimes impossible interventions required in the target environment. Recent empirical research showed that statistical models can perform poorly when the deployment environment changes because the behavior of certain variables in the models can change dramatically from source to target. To address this issue, we propose Cameo---a method that identifies invariant causal predictors under environmental changes, allowing the optimization process to operate in a reduced search space, leading to faster optimization of system performance. We demonstrate significant performance improvements over state-of-the-art optimization methods in MLperf deep learning systems, a video analytics pipeline, and a database system.
Towards GPU Memory Efficiency for Distributed Training at Scale

The scale of deep learning models has grown tremendously in recent years. State-of-the-art models have reached billions of parameters and terabyte-scale model sizes. Training of these models demands memory bandwidth and capacity that can only be accommodated distributively over hundreds to thousands of GPUs. However, large-scale distributed training suffers from GPU memory inefficiency, such as memory under-utilization and out-of-memory events (OOMs). There is a lack of understanding of actual GPU memory behavior of distributed training on terabyte-size models, which hinders the development of effective solutions to such inefficiency. In this paper, we present a systematic analysis of GPU memory behavior of large-scale distributed training jobs in production at Meta. Our analysis is based on offline training jobs of multi-terabyte Deep Learning Recommendation Models from one of Meta's largest production clusters. We measure GPU memory inefficiency; characterize GPU memory utilization, and provide fine-grained GPU memory usage analysis. We further show how to build on the understanding to develop a practical GPU provisioning system in production.
Is Machine Learning Necessary for Cloud Resource Usage Forecasting?

Robust forecasts of future resource usage in cloud computing environments enable high efficiency in resource management solutions, such as autoscaling and overcommitment policies. Production-level systems use lightweight combinations of historical information to enable practical deployments. Recently, Machine Learning (ML) models, in particular Long Short Term Memory (LSTM) neural networks, have been proposed by various works, for their improved predictive capabilities. Following this trend, we train LSTM models and observe high levels of prediction accuracy, even on unseen data. Upon meticulous visual inspection of the results, we notice that although the predicted values seem highly accurate, they are nothing but versions of the original data shifted by one time step into the future. Yet, this clear shift seems to be enough to produce a robust forecast, because the values are highly correlated across time. We investigate time series data of various resource usage metrics (CPU, memory, network, disk I/O) across different cloud providers and levels, such as at the physical or virtual machine-level and at the application job-level. We observe that resource utilization displays very small variations in consecutive time steps. This insight can enable very simple solutions, such as data shifts, to be used for cloud resource forecasting and deliver highly accurate predictions. This is the reason why we ask whether complex machine learning models are even necessary to use. We envision that practical resource management systems need to first identify the extent to which simple solutions can be effective, and resort to using machine learning to the extent that enables its practical use.
TMC

Main memory dominates data center server cost, and hence data center operators are exploring alternative technologies such as CXL-attached and persistent memory to improve cost without jeopardizing performance. Introducing multiple tiers of memory introduces new challenges, such as selecting the appropriate memory configuration for a given workload mix. In particular, we observe that inefficient configurations increase cost by up to 2.6× for clients, and resource stranding increases cost by 2.2× for cloud operators. To address this challenge, we introduce TMC, a system for recommending cloud configurations according to workload characteristics and the dynamic resource utilization of a cluster. Whereas prior work utilized extensive simulation or costly machine learning techniques, incurring significant search costs, our approach profiles applications to reveal internal properties that lead to fast and accurate performance estimations. Our novel configuration-selection algorithm incorporates a new heuristic, packing penalty, to ensure that recommended configurations will also achieve good resource efficiency. Our experiments demonstrate that TMC reduces the search cost by up to 4× over the state-of-the-art, while improving resource utilization by up to 17% as compared to a naive policy that requests optimal tiered memory allocations in isolation.
Cryonics

Recent research has proposed the use of trusted execution environments (TEEs), such as SGX, in serverless computing to safeguard against threats from insecure system software, malicious co-located tenants, or suspicious cloud operators. However, integrating SGX, one of the most mature TEE, with serverless computing results in significant performance degradation due to the function startup latency caused by enclave creation. This performance degradation arises because SGX is not designed with serverless function startup procedures in mind, where numerous application codes, libraries, and data are re-initialized upon each function invocation. The inherent limitations of SGX contribute to significant performance degradation, whether through the addition of every page into the enclave, or the restriction of page permissions, which ultimately cause TLB flushes, context switches, and re-entering the enclave. In this paper, we first take key observations resident in the intrinsic features of the server-less function and propose Cryonics, a method of serving snapshot-based enclave that accelerates the startup time of the function instance by creating a future-proof working set of that. We consider the page locality and obsolete pages of the enclaved function instance to create a lightweight working set used for serving requests. Our evaluation shows that Cryonics achieves up to 100x outperformed startup time compared to existing cold-start-based methods and reveals the stability of the startup time.
Parrotfish

Serverless computing is a new paradigm that aims to remove the burdens of cloud management from developers. Yet rightsizing serverless functions remains a pain point for developers. Choosing the right memory configuration is necessary to ensure cost and/or performance optimality for serverless workloads. In this work, we identify that using parametric regression can significantly simplify function rightsizing compared to black-box optimization techniques currently available. With this insight, we build a tool, called Parrotfish, which finds optimal configurations through an online learning process. It also allows users to communicate constraints on execution time, or to relax cost optimality to gain performance. Parrotfish achieves substantially lower exploration costs (1.81-9.96×) compared with the state-of-the-art tools, while delivering similar or better recommendations.
Chitu

Serverless workflows are characterized as multi-stage computing, while downstream functions require accessing intermediate states or the output of upstream functions for running. The workflow's performance can be easily affected due to the inefficiency of data access. Studies accelerate data access with various policies, such as direct and indirect methods. However, these methods may fail due to various limitations such as resource availability.
Multivariate Anomaly Detection with Domain Clustering

Existing time-series anomaly detection (AD) pipelines for cloud monitoring at scale commonly rely on isolated training per cloud service or cloud infrastructure component. However, with the increasing volume of data generated from thousands of services and components, there is an untapped opportunity for a more effective approach to detect key performance indicator (KPI) anomalies by capitalizing on the abundance of data available. In this paper, we propose MADDoC, an unsupervised transfer learning framework for reconstruction based anomaly detection on multivariate time-series data. We show how to efficiently leverage available KPIs in the realm of cloud infrastructure monitoring to generalize unsupervised time-series AD across infrastructure components. Compared to state-of-the-art approaches relying on isolated component-wise training, the MADDoC framework achieves superior Precision and F1 scores on public and internal time-series AD datasets, by learning a strong reconstruction backbone on the time-series data across many components, before fine-tuning to a specific component. Moreover, MADDoC achieves substantial cost savings in model training, with reductions of 60% to 75% when monitoring thousands of storage infrastructure components. Further, the framework overcomes the trade-off between training efficiency and AD performance of previous AD transfer learning approaches.
Optimized Quantum Circuit for Quantum Security Strength Analysis of Argon2

This paper explores the optimization of quantum circuits for Argon2, a memory-hard function used in password hashing applications. With the rise of quantum computers, the security of classical cryptographic systems is at risk. This paper emphasizes the need to accurately measure the quantum security strength of cryptographic schemes through highly optimized quantum circuits for the target cryptography algorithm. The proposed method focuses on two perspectives: qubit reduction (qubit-optimized quantum circuit) and depth reduction (depth-optimized quantum circuit). The qubit-optimized quantum circuit was designed to find a point where an appropriate inverse is possible and reuses the qubit through the inverse to minimize the number of qubits. The start and end points of the inverse are determined by identifying a point where qubits can be reused with minimal computation. The depth-optimized quantum circuit reduces the depth of the quantum circuit by using the minimum number of qubits necessary without performing an inverse operation. The trade-off between qubit and depth is confirmed by modifying the internal structure of the circuits and the quantum adders. The qubit optimization achieved up to a 12,229 qubit reduction, while the depth optimization resulted in an approximately 196,741 (approximately 69.02%) depth reduction. In conclusion, this research demonstrates the importance of implementing and analyzing quantum circuits from multiple optimization perspectives. The results contribute to the post-quantum strength analysis of Argon2 and provide valuable insights for future research on optimized quantum circuit design, considering the appropriate trade-offs of quantum resources in response to advancements in quantum computing technology.
Applying Image Analysis to Build a Lightweight System for Blind Obstacles Detecting of Intelligent Wheelchairs

Intelligent wheelchair blind spot obstacle detection is an important issue for semi-enclosed special environments in elderly communities. However, the LiDAR- and 3D-point-cloud-based solutions are expensive, complex to deploy, and require significant computing resources and time. This paper proposed an improved YOLOV5 lightweight obstacle detection model, named GC-YOLO, and built an obstacle dataset that consists of incomplete target images captured in the blind spot view of the smart wheelchair. The feature extraction operations are simplified in the backbone and neck sections of GC-YOLO. The backbone network uses GhostConv in the GhostNet network to replace the ordinary convolution in the original feature extraction network, reducing the model size. Meanwhile, the CoordAttention is applied, aiming to reduce the loss of location information caused by GhostConv. Further, the neck stem section uses a combination module of the lighter SE Attention module and the GhostConv module to enhance the feature extraction capability. The experimental results show that the proposed GC-YOLO outperforms the YOLO5 in terms of model parameters, GFLOPS and F1. Compared with the YOLO5, the number of model parameters and GFLOPS are reduced by 38% and 49.7%, respectively. Additionally, the F1 of the proposed GC-YOLO is improved by 10% on the PASCAL VOC dataset. Moreover, the proposed GC-YOLO achieved mAP of 90% on the custom dataset.
Golgi

This paper introduces Golgi, a novel scheduling system designed for serverless functions, with the goal of minimizing resource provisioning costs while meeting the function latency requirements. To achieve this, Golgi judiciously over-commits functions based on their past resource usage. To ensure overcommitment does not cause significant performance degradation, Golgi identifies nine low-level metrics to capture the runtime performance of functions, encompassing factors like request load, resource allocation, and contention on shared resources. These metrics enable accurate prediction of function performance using the Mondrian Forest, a classification model that is continuously updated in real-time for optimal accuracy without extensive offline training. Golgi employs a conservative exploration-exploitation strategy for request routing. By default, it routes requests to non-overcommitted instances to ensure satisfactory performance. However, it actively explores opportunities for using more resource-efficient overcommitted instances, while maintaining the specified latency SLOs. Golgi also performs vertical scaling to dynamically adjust the concurrency of overcommitted instances, maximizing request throughput and enhancing system robustness to prediction errors. We have prototyped Golgi and evaluated it in both EC2 cluster and a small production cluster. The results show that Golgi can meet the SLOs while reducing the resource provisioning cost by 42% (30%) in EC2 cluster (our production cluster).
A Digital Painting Learning Model Using Mixed-Reality Technology to Develop Practical Skills in Character Design for Animation

This research aims to (1) synthesize the digital painting learning process by using mixed-reality technology to develop practical skills in character design for animation and (2) develop and evaluate digital painting learning models using mixed-reality technology to develop practical skills in character design for animation. This research was conducted by studying documentation and related research and designing digital painting learning models using mixed-reality technology to develop practical skills in character design for animation. Using a questionnaire, a total of 9 experts evaluated the model’s suitability. The statistics used in the research were the mean and standard deviation. The study found that a component of the digital painting learning process involves using mixed-reality technology to develop practical skills in character design for animation. It consists of the following four components: (1) input data, (2) the digital painting learning process, (3) output assessment, and (4) feedback. The expert’s evaluation of the model’s suitability found that the digital painting learning model using mixed-reality technology to develop practical skills in character design for animation was at the highest level (mean ± SD = 4.38 ± 0.21). This research is beneficial to faculty who teach character design and development for animation. The results of the study can be applied to teaching and learning.
Metaverse as a Service

We present a vision for the future of an emerging category of cloud service: the metaverse of 3D virtual worlds. Today, hundreds of millions of users are active daily in such worlds, but they are partitioned into small groups of at most a few hundred players. Each group joins a different virtual world instance, and players can only interact in 3D with others players in the same group during that session. Current platforms are designed in ways that simply cannot scale much further, and solutions from other cloud services do not generalize to the more interactive, bidirectional, and latency-sensitive interactive 3D domain. We outline some of the technical challenges that currently stand in the way of a metaverse without inherent technical limitations on the number of users in a shared experience. We argue that, although these obviously touch on many other areas of Computer Science such as computer graphics and numerical simulation, the core challenges lie squarely within the systems domain.
Maximizing VMs' IO Performance on Overcommitted CPUs with Fairness

To improve resource utilization and reduce costs many Cloud providers adopt virtual machines (VMs) overcommitment. While effective, this strategy may lead to adverse outcomes, significantly affecting a VM IO performance when one virtual CPU (vCPU) is preempted by another vCPU within the same runqueue of the VM scheduler -- i.e., same physical CPU (pCPU). Additionally, the responsiveness of a VM is reduced during the inactive time of the vCPU, and it necessitates an extra schedule timeslice to react to any IO event. While such problems have been studied in academia and industry, no previous solution has been deployed in production. This is because for example certain solutions require modifications of the guest VM, which is in contrast with industry requirements.
Yama

The sharing of clusters with various on-NIC offloads by high-level entities (users, containers, etc.) has become increasingly common. Performance isolation across these entities is desired because the offloads can become bottlenecks due to the limited capacity of hardware. However, the existing works that provide scheduling and resource management to NIC offloads all require customization of the NIC or offloads, while commodity off-the-shelf NICs and offloads with proprietary implementation have been widely deployed in datacenters. This paper presents Yama, the first solution to enable per-entity isolation in the sharing of such black-box NIC offloads. Yama provides a generic framework that captures a common abstraction to the operation of most offloads, which allows operators to incorporate existing offloads. The framework proactively probes for the performance of the offloads with auxiliary workload and enforces isolation at the initiator side. Yama also accommodates chained offloads. Our evaluation shows that 1) Yama achieves per-entity max-min fairness for various types of offloads and in complicated offload chaining scenarios; 2) Yama quickly converges to changes in equilibrium and 3) Yama adds negligible overhead to application workload.