Federated learning (FL) protects data privacy by enabling distributed model training without direct access to client data. However, its distributed nature makes it vulnerable to model and data poisoning attacks. While numerous defenses filter malicious clients using statistical metrics, they overlook the role of model redundancy, where not all parameters contribute equally to the model and attack performance. Current attacks manipulate all model parameters uniformly, making them more detectable, while defenses focus on the overall statistics of client updates, leaving gaps for more sophisticated attacks. We propose an attack-agnostic augmentation method to enhance the stealthiness and effectiveness of existing poisoning attacks in FL, exposing flaws in current defenses and highlighting the need for fine-grained FL security. Our three-stage methodology, including pill construction, pill poisoning, and pill injection, injects poison into a compact subnet (i.e., pill) of the global model during the iterative FL training. Experimental results show that FL poisoning attacks enhanced by our method can bypass 8 state-of-the-art (SOTA) defenses, gaining an up to 7x error rate increase, as well as on average a more than 2x error rate increase on both IID and non-IID data, in both cross-silo and cross-device FL systems.

Backdoor attacks in data outsourcing settings pose severe risks to deep neural networks. Specifically, adversaries can manipulate externally sourced training data to implant hidden behaviors in target models (e.g., incorrect predictions on triggered samples). Existing defenses are either pre-processing or post-processing. Since the two approaches are orthogonal and either one can independently strengthen real-world defenses, we focus on the latter in this paper. Yet current post-processing defenses face one or more of the following issues: overemphasis on output logits while overlooking rich information in intermediate layers, injection of uncertain new triggers while requiring alignment with the original triggers, and underuse of poisoned model representations. To overcome the aforementioned limitations, we propose ATClean, an adaptive post-processing defense based on feature collisions in latent space. Specifically, it leverages all layers rather than only output logits to capture backdoor-affected regions using an adaptive loss function, relaxes the need for exact trigger reconstruction by generating adversarial samples that only enforce feature collisions with a theoretical guarantee, and fully exploits poisoned representations with feature-collision-based fine-tuning. Experiments across benchmark datasets, multiple architectures, and seven representative attacks show that ATClean achieves state-of-the-art defense effectiveness with the lowest drop on clean data, including about a 20% improvement in DER, which measures the accuracy-defense trade-off.

LLM serving is increasingly dominated by decode attention, which is a memory-bound operation due to massive KV cache loading from global memory. Meanwhile, real-world workloads exhibit substantial, hierarchical shared prefixes across requests (e.g., system prompts, tools/templates, RAG). Existing attention implementations fail to fully exploit prefix sharing: one-query-per-CTA execution repeatedly loads shared prefix KV cache, while one-size-fits-all tiling leaves on-chip resources idle and exacerbates bubbles for uneven KV lengths. These choices amplify memory bandwidth pressure and stall memory-bound decode attention.

Large Language Models (LLMs) have gained immense success in revolutionizing various applications, including content generation, search and recommendation, and AI-assisted operations. To reduce high training costs, Mixture-of-Experts (MoE) architecture has become a popular backbone for modern LLMs. However, despite the benefits, serving MoE-based LLMs experience severe memory inefficiency due to sparsely activated experts. Recent studies propose to offload inactive experts from GPU memory to CPU memory to improve the serving efficiency of MoE models. However, they either incur high inference latency or high model memory footprints due to coarse-grained designs.

Modern deep neural network (DNN) and large language model (LLM) applications integrate multiple models into inference pipelines with stringent latency requirements for customized tasks. To mitigate extensive request timeouts caused by accumulation, systems for inference pipelines commonly drop a subset of requests so the remaining ones can satisfy latency constraints. Since it is commonly believed that request dropping adversely affects goodput, existing systems only drop requests when they have to, which we call reactive dropping. However, this reactive policy can not maintain high goodput, as it neither makes timely dropping decisions nor identifies the proper set of requests to drop, leading to issues of dropping requests too late or dropping the wrong set of requests.

Few-shot fine-tuning of Diffusion Models (DMs) is a key advancement, significantly reducing training costs and enabling personalized AI applications. However, we explore the training dynamics of DMs and observe an unanticipated phenomenon: during the training process, image fidelity initially improves, then unexpectedly deteriorates with the emergence of noisy patterns, only to recover later with severe overfitting. We term the stage with generated noisy patterns as corruption stage. To understand this corruption stage, we begin by heuristically modeling the one-shot fine-tuning scenario, and then extend this modeling to more general cases. Through this modeling, we identify the primary cause of this corruption stage: a narrowed learning distribution inherent in the nature of few-shot fine-tuning. To tackle this, we apply Bayesian Neural Networks (BNNs) on DMs with variational inference to implicitly broaden the learned distribution, and present that the learning target of the BNNs can be naturally regarded as an expectation of the diffusion loss and a further regularization with the pretrained DMs. This approach is highly compatible with current few-shot fine-tuning methods in DMs and does not introduce any extra inference costs. Experimental results demonstrate that our method significantly mitigates corruption, and improves the fidelity, quality and diversity of the generated images in both object-driven and subject-driven generation tasks.

Federated Learning (FL) allows edge clients to collaborate in model training with data privacy preserved, yet it is known to suffer low training efficiency and model accuracy. Given that efficiency and accuracy are usually conflicting objectives, existing practices increasingly employ an adaptive scheme that changes the FL configurations (e.g., quantization or sparsification level) based on runtime training status, for which accurate training diagnosis—used for guiding the optimization actions—is crucial. However, while training diagnosis is a common task shared by different optimization schemes, existing works propose their diagnosis methods in an ad-hoc manner, which yield multiple limitations. First, the diagnosis metric in an optimization scheme may sometimes be less accurate than others; second, existing schemes fail to fully exploit the diagnosis result by applying it for only one optimization action; third, existing methods usually do not perceive cross-client data heterogeneity, failing to simultaneously enhance FL accuracy. To tackle those limitations, we make a systematical study on the training diagnosis methods of multiple optimization schemes, and propose metric grafting—replacing a scheme's diagnosis metric with a better one to improve the training performance. Moreover, to fully exploit the potential of training diagnosis, we build a system platform that supports flexible combinations of training diagnosis and optimization actions (i.e., single-diagnosis-multiple actions and multiple-diagnosis-multiple-actions). Evaluation on testbeds show that, with metric grafting and advanced diagnosis action combinations, we can substantially improve the efficiency and accuracy performance of FL.

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices’ computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing and communications capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices’ communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. We provide a theoretical convergence analysis for WHALE-FL with heterogeneous subnetwork assignment, based on which subnetwork structures can be dynamically optimized to reduce the resulting gap to standard full-model FL. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy.

Serverless computing has emerged as a novel paradigm in cloud computing, characterized by its agile scalability, cost-effective pay-as-you-go billing, and user-friendly capabilities for Machine Learning (ML) inference tasks. Developers wrap their ML algorithms into serverless functions and run them in containers. However, the well-known cold-start problem significantly slows down the response time of functions. To address cold-starts, the technique of pre-warming, which proactively maintains containers in a warm state, has gained widespread adoption across both research and industry. Nevertheless, we observed that pre-warming does not address the distinct delays caused by the loading of ML artifacts. According to our analysis, in ML inference functions, the time required to load libraries and models significantly exceeds the time needed to warm containers. Thus, relying solely on pre-warming is insufficient for mitigating cold-starts. This paper presentsTyche, an opportunistic pre-loading approach designed to eliminate the latency associated with loading ML artifacts, enabling near-instant inference and minimizing function execution time.Tychefully leverages the idle memory in warmed containers and GPUs to pre-load required libraries and models, striking an optimal balance between acceleration and resource efficiency. Additionally,Tycheis tailored for large-scale serverless platforms, incorporating cluster-wide scheduling and lightweight locality-aware load balancing to enhance performance. We designTycheto be transparent to providers and compatible with existing pre-warming solutions. Experiments on OpenWhisk with real-world workloads show thatTychereduces up to 93% loading latency and achieves up to 8× speedup compared to state-of-the-art pre-warming solutions. Compared with the state-of-the-art serverless pre-loading solution,Tychealso achieves up to 1.9× speedup.

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices' computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices' communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy.

Multi-agent reinforcement learning (MARL) has emerged as a promising approach for tasks requiring multiple agents for cooperation or competition, such as scientific simulation, multi-robot collaboration, and traffic control. Serverless computing, with its dynamic and flexible resource allocation, has demonstrated potential for improving training efficiency and cost-efficiency in RL workloads. However, existing serverless RL training systems focus primarily on single-agent scenarios, overlooking the unique characteristics and inherent complexities of MARL—such as dynamic inter-agent relationships and heterogeneous policy requirements across agents—leaving inefficient and even infeasible support to diverse and complex MARL algorithms.

Circuit cutting is a promising technique that leverages both quantum and classical computational resources, enabling the practical execution of large quantum circuits on noisy intermediate-scale quantum (NISQ) hardware. Recent approaches typically focus exclusively on either gate cuts or wire cuts, modeling quantum circuits as graphs. However, identifying optimal cutting locations using this representation often results in prohibitively high computational complexity, especially under realistic hardware constraints. In this paper, we introduce CiFold, a novel graph-based framework that exploits repetitive modular structures inherent in quantum algorithms, significantly enhancing the scalability and efficiency of circuit cutting. Our approach systematically folds quantum circuits into compact meta-graphs by identifying and merging common gate sequences across entangled qubits, dramatically simplifying subsequent partitioning tasks. We define folding factor and variance to quantify circuit compression and ensure balanced folding. Using these condensed representations, CiFold precisely identifies cut locations without exhaustive global graph searches. We perform extensive experiments, comparing CiFold with state-of-the-art circuit-cutting techniques. Results demonstrate that CiFold achieves superior partition quality and computational efficiency, reducing the number of required cuts by an average of 31.6% and lowering the sampling overhead substantially by 3.55 × 109. Our findings illustrate that CiFold represents a significant advancement toward scalable quantum circuit cutting.

Advances in deep neural networks (DNNs) have significantly contributed to the development of real-time video processing applications. Efficient scheduling of DNN workloads in cloud-hosted inference systems is crucial to minimizing serving costs while meeting application latency constraints. However, existing systems suffer from excessive module latency during request dispatching, low execution throughput during module scheduling, and wasted latency budget during latency splitting for multi-DNN applications, which undermines their capability to minimize the serving cost. In this paper, we design a DNN inference system called Harpagon, which minimizes the serving cost under latency constraints with a three-level design. It first maximizes the batch collection rate with a batch-aware request dispatch policy to minimize the module latency. It then maximizes the module throughput with multi-tuple configurations and proper amount of dummy requests. It also carefully splits the end-to-end latency into per-module latency budget to minimize the total serving cost for multi-DNN applications. Evaluation shows that Harpagon outperforms the state of the art by 1.49 to 2.37 times in serving cost while satisfying the latency objectives. Additionally, compared to the optimal solution using brute force search, Harpagon derives the lower bound of serving cost for 91.5% workloads with millisecond level runtime.

Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm that allows collaborative learning with data privacy protection, personalized FL (pFL) has gained significant prominence as a research direction within the FL domain. Whereas traditional FL (tFL) focuses on jointly learning a global model, pFL aims to balance each client's global and personalized goals in FL settings. To foster the pFL research community, we started and built PFLlib, a comprehensive pFL library with an integrated benchmark platform. In PFLlib, we implemented 37 state-of-the-art FL algorithms (8 tFL algorithms and 29 pFL algorithms) and provided various evaluation environments with three statistically heterogeneous scenarios and 24 datasets. At present, PFLlib has gained more than 1600 stars and 300 forks on GitHub.

Federated Learning (FL) enables edge devices to collaboratively train a global model using local data. However, the increasing prevalence of watermarks in datasets presents a new challenge to efficient FL. While watermarks assert data ownership and copyright, they introduce complexities that can lead to shortcut learning problems and mislead utility measurements for client selection. These issues are further exacerbated by batch size variations in efficient FL frameworks, ultimately undermining their time-to-accuracy performance. We introduceLotusFL, an FL system designed to address the challenges posed by watermarked datasets in efficient FL. Specifically, it tackles the increased time-to-accuracy due to erroneous client selection and the accuracy degradation observed with larger batch sizes.LotusFLfirst estimates the characteristics of watermarks through statistical estimation and then adjusts the batch size using this estimated watermark information to balance the negative impact of the watermark against device idle waiting time. Additionally, its client selection mechanism, based on historical information, avoids the misleading utility signals from watermarks. This mechanism, working in conjunction with batch size adjustment, aims to accurately predict device runtime and identify potentially valuable devices. We evaluatedLotusFLthrough a real-world deployment on 40 edge devices. Compared to state-of-the-art efficient FL frameworks,LotusFLachieves superior performance, enhancing accuracy by up to 8.2% and reducing training time by 1.97×.

Deep reinforcement learning (DRL) has gained immense success in many applications, including gaming AI, robotics, and system scheduling. Distributed algorithms and architectures have been vastly proposed (e.g., actor-learner architecture) to accelerate DRL training with large-scale server-based clusters. However, training on-policy algorithms with the actor-learner architecture unavoidably induces resource wasting due to synchronization between learners and actors, thus resulting in significantly extra billing. As a promising alternative, serverless computing naturally fits on-policy synchronization and alleviates resource wasting in distributed DRL training with pay-as-you-go pricing. Yet, none has leveraged serverless computing to facilitate DRL training. This paper proposes MinionsRL, the first serverless distributed DRL training framework that aims to accelerate DRL training- and cost-efficiency with dynamic actor scaling. We prototype MinionsRL on top of Microsoft Azure Container Instances and evaluate it with popular DRL tasks from OpenAI Gym. Extensive experiments show that MinionsRL reduces total training time by up to 52% and training cost by 86% compared to latest solutions.

Serverless computing has grown rapidly as a new cloud computing paradigm that promises ease-of-management, cost-efficiency, and auto-scaling by shipping functions via self-contained virtualized containers. Unfortunately, serverless computing suffers from severe cold-start problems---starting containers incurs non-trivial latency. Full container caching is widely applied to mitigate cold-starts, yet has recently been outperformed by two lines of research: partial container caching and container sharing. However, either partial container caching or container sharing techniques exhibit their drawbacks. Partial container caching effectively deals with burstiness while leaving cold-start mitigation halfway; container sharing reduces cold-starts by enabling containers to serve multiple functions while suffering from excessive memory waste due to over-packed containers.

Serverless computing has rapidly prospered as a new cloud computing paradigm with agile scalability, pay-as-you-go pricing, and ease-to-use features for Machine Learning (ML) inference tasks. Users package their ML code into lightweight serverless functions and execute them using containers. Unfortunately, a notorious problem, called cold-starts, hinders serverless computing from providing low-latency function executions. To mitigate cold-starts, pre-warming, which keeps containers warm predictively, has been widely accepted by academia and industry. However, pre-warming fails to eliminate the unique latency incurred by loading ML artifacts. We observed that for ML inference functions, the loading of libraries and models takes significantly more time than container warming. Consequently, pre-warming alone is not enough to mitigate the ML inference function's cold-starts.

Diffusion Models (DMs) have evolved into advanced image generation tools, especially for few-shot generation where a pretrained model is fine-tuned on a small set of images to capture a specific style or object. Despite their success, concerns exist about potential copyright violations stemming from the use of unauthorized data in this process. In response, we present Contrasting Gradient Inversion for Diffusion Models (CGI-DM), a novel method featuring vivid visual representations for digital copyright authentication. Our approach involves removing partial information of an image and recovering missing details by exploiting conceptual differences between the pretrained and fine-tuned models. We formulate the differences as KL divergence between latent variables of the two models when given the same input image, which can be maximized through Monte Carlo sampling and Projected Gradient Descent (PGD). The similarity between original and recovered images serves as a strong indicator of potential infringements. Extensive experiments on the WikiArt and Dream-booth datasets demonstrate the high accuracy of CGI-DM in digital copyright authentication, surpassing alternative validation techniques. Code implementation is available at https://github.com/Nicholas0228/Revelio.

Federated learning (FL) has been widely deployed to enable machine learning training on sensitive data across distributed devices. However, the decentralized learning paradigm and heterogeneity of FL further extend the attack surface for backdoor attacks. Existing FL attack and defense methodologies typically focus on the whole model. None of them recognizes the existence of backdoor-critical (BC) layers-a small subset of layers that dominate the model vulnerabilities. Attacking the BC layers achieves equivalent effects as attacking the whole model but at a far smaller chance of being detected by state-of-the-art (SOTA) defenses. This paper proposes a general in-situ approach that identifies and verifies BC layers from the perspective of attackers. Based on the identified BC layers, we carefully craft a new backdoor attack methodology that adaptively seeks a fundamental balance between attacking effects and stealthiness under various defense strategies. Extensive experiments show that our BC layer-aware backdoor attacks can successfully backdoor FL under seven SOTA defenses with only 10% malicious clients and outperform the latest backdoor attack methods.

Federated Learning (FL) is increasingly vulnerable to model poisoning attacks, where malicious clients degrade the global model's accuracy with manipulated updates. Unfortunately, most existing defenses struggle to handle the scenarios when multiple adversaries exist, and often rely on historical or validation data, rendering them ill-suited for the dynamic and diverse nature of real-world FL environments. Exacerbating these limitations is the fact that most existing defenses also fail to account for the distinctive contributions of Deep Neural Network (DNN) layers in detecting malicious activity, leading to the unnecessary rejection of benign updates. To bridge these gaps, we introduce FedRoLa, a cutting-edge similarity-based defense method optimized for FL. Specifically, FedRoLa leverages global model parameters and client updates independently, moving away from reliance on historical or validation data. It features a unique layer-based aggregation with dynamic layer selection, enhancing threat detection, and includes a dynamic probability method for balanced security and model performance. Through comprehensive evaluations using different DNN models and real-world datasets, FedRoLa demonstrates substantial improvements over the status quo approaches in global model accuracy, achieving up to 4% enhancement in terms of accuracy, reducing false positives to 6.4%, and securing an 92.8% true positive rate.

Federated learning is a promising privacy-preserving learning paradigm in which multiple clients can collaboratively learn a model with their image data kept local. For protecting data ownership, personalized watermarks are usually added to the image data by each client. However, the introduced watermarks can lead to a shortcut learning problem, where the learned model performs predictions over-rely on the simple watermark-related features and represents a low accuracy on real-world data. Existing works assume the central server can directly access the predefined shortcut features during the training process. However, these may fail in the federated learning setting as the shortcut features of the heterogeneous watermarked data are difficult to obtain. In this paper, we propose a federated Morozov regularization technique, where the regularization parameter can be adaptively determined based on the watermark knowledge of all the clients in a privacy-preserving way, to eliminate the shortcut learning problem caused by the watermarked data. Specifically, federated Morozov regularization firstly performs lightweight local watermark mask estimation in each client to obtain the locations and intensities knowledge of local watermarks. Then, it aggregates the estimated local watermark masks to generate the global watermark knowledge with a weighted averaging. Finally, federated Morozov regularization determines the regularization parameter for each client by combining the local and global watermark knowledge. With the regularization parameter determined, the model is trained as normal federated learning. We implement and evaluate federated Morozov regularization based on a real-world deployment of federated learning on 40 Jetson devices with real-world datasets. The results show that federated Morozov regularization improves model accuracy by 11.22% compared to existing baselines.

Most existing model poisoning attacks in federated learning (FL) control a set of malicious clients and share a fixed number of malicious gradients with the server in each FL training round, to achieve a desired tradeoff between the attack impact and the attack budget. In this paper, we show that such a tradeoff is not fundamental and an adaptive attack budget not only improves the impact of attack <?TeX $\mathcal {A}$?> Math 1 but also makes it more resilient to defenses. However, adaptively determining the number of malicious clients that share malicious gradients with the central server in each FL training round has been less investigated. This is due to the fact that most existing model poisoning attacks mainly focus on FL optimization itself to maximize the damage to the global model, and largely ignore the impact of the underlying deep neural networks that are used to train FL models. Inspired by recent findings on critical learning periods (CLP), where small gradient errors have irrecoverable impact on model accuracy, we advocate CLP augmented model poisoning attacks <?TeX $\mathcal {A}$?> Math 2 -CLP in this paper. <?TeX $\mathcal {A}$?> Math 3 -CLP merely augments an existing model poisoning attack <?TeX $\mathcal {A}$?> Math 4 with an adaptive attack budget scheme. Specifically, <?TeX $\mathcal {A}$?> Math 5 -CLP inspects the changes in federated gradient norms to identify CLP and adaptively adjusts the number of malicious clients that share their malicious gradients with the server in each round, leading to dramatically improved attack impact compared to <?TeX $\mathcal {A}$?> Math 6 by up to 6.85 ×, with a smaller attack budget. This in turn improves the resilience of <?TeX $\mathcal {A}$?> Math 7 by up to 2 ×. Since <?TeX $\mathcal {A}$?> Math 8 -CLP is orthogonal to the attack <?TeX $\mathcal {A}$?> Math 9 , it also crafts malicious gradients by solving a difficult optimization problem. To tackle this challenge and based on our understandings of <?TeX $\mathcal {A}$?> Math 10 -CLP, we further relax the inner attack subroutine <?TeX $\mathcal {A}$?> Math 11 in <?TeX $\mathcal {A}$?> Math 12 -CLP and design GraSP, a lightweight CLP augmented similarity-based attack. We show that GraSP not only is more flexible but also achieves an improved attack impact compared to the strongest of existing model poisoning attacks.

Deep reinforcement learning (DRL) has achieved remarkable success in diverse areas, including gaming AI, scientific simulations, and large-scale (HPC) system scheduling. DRL training, which involves a trial-and-error process, demands considerable time and computational resources. To overcome this challenge, distributed DRL algorithms and frameworks have been developed to expedite training by leveraging large-scale resources. However, existing distributed DRL solutions rely on synchronous learning with serverful infrastructures, suffering from low training efficiency and overwhelming training costs. This paper proposes Stellaris, the first to introduce a generic asynchronous learning paradigm for distributed DRL training with serverless computing. We devise an importance sampling truncation technique to stabilize DRL training and develop a staleness-aware gradient aggregation method tailored to the dynamic staleness in asynchronous serverless DRL training. Experiments on AWS EC2 regular testbeds and HPC clusters show that Stellaris outperforms existing state-of-the-art DRL baselines by achieving $2.2 \times$ higher rewards (i.e., training quality) and reducing 41% training costs.

Deep reinforcement learning (DRL) has demonstrated significant potential in various applications, including gaming AI, robotics, and system scheduling. DRL algorithms produce, sample, and learn from training data online through a trial-and-error process, demanding considerable time and computational resources. To address this, distributed DRL algorithms and paradigms have been developed to expedite training using extensive resources. Through carefully designed experiments, we are the first to observe that strategically increasing the actor-environment interactions by spawning more concurrent actors at certain training rounds within ephemeral time frames can significantly enhance training efficiency. Yet, current distributed DRL solutions, which are predominantly server-based (or serverful), fail to capitalize on these opportunities due to their long startup times, limited adaptability, and cumbersome scalability. This paper proposes Nitro , a generic training engine for distributed DRL algorithms that enforces timely and effective boosting with concurrent actors instantaneously spawned by serverless computing. With serverless functions, Nitro adjusts data sampling strategies dynamically according to the DRL training demands. Nitro seizes the opportunity of real-time boosting by accurately and swiftly detecting an empirical metric. To achieve cost efficiency, we design a heuristic actor scaling algorithm to guide Nitro for cost-aware boosting budget allocation. We integrate Nitro with state-of-the-art DRL algorithms and frameworks and evaluate them on AWS EC2 and Lambda. Experiments with Mujoco and Atari benchmarks show that Nitro improves the final rewards ( i.e. , training quality) by up to 6× and reduces training costs by up to 42%.

Federated learning (FL), an emerging machine learning paradigm that trains a global model across distributed clients without violating data privacy, has recently attracted significant attention. However, FL?s distributed nature and iterative training extensively increase the attacking surface for Byzantine and inference attacks. Existing FL defense methods can hardly protect FL from both Byzantine and inference attacks due to their fundamental conflicts. The noise injected to defend against inference attacks interferes with model weights and training data, obscuring model analysis that Byzantine-robust methods utilize to detect attacks. Besides, the practicability of existing Byzantine-robust methods is limited since they heavily rely on model analysis. In this paper, we present SIREN+, a new robust FL system that defends against a wide spectrum of Byzantine attacks and inference attacks by jointly utilizing a proactive alarming mechanism and local differential privacy (LDP). The proactive alarming mechanism orchestrates clients and the FL server to collaboratively detect attacks using distributed alarms, which is free from the noise interference injected by LDP. Compared with the state-of-the-art defense methods, SIREN+can protect FL from Byzantine and inference attacks from a higher proportion of malicious clients in the system while keeping the global model performing normally. Extensive experiments with diverse settings and attacks on real-world datasets show that SIREN+outperforms existing defense methods when attacked by Byzantine and inference attacks.

With the rapid development of big data and the Internet of Things (IoT), traffic data from an Intelligent Transportation System (ITS) is becoming more and more accessible. To understand and simulate the traffic patterns from the traffic data, Multimedia Cognitive Computing (MCC) is an efficient and practical approach. Distributed Machine Learning (DML) has been the trend to provide sufficient computing resources and efficiency for MCC tasks to handle massive data and complex models. DML can speed up computation with those computing resources but introduces communication overhead. Gradient collaborative management or gradient aggregation in DML for MCC tasks is a critical task. An efficient managing algorithm of the communication schedules for gradient aggregation in ITS can improve the performance of MCC tasks. However, existing communication schedules typically rely on specific physical connection matrices, which have low robustness when a malfunction occurs. In this article, we propose Robust Searching-based Gradient Collaborative Management (RSGCM) in Intelligent Transportation System, a practical ring-based gradient managing algorithm for communication schedules across devices to deal with ITS malfunction. RSGCM provides solutions of communication schedules to various kinds of connection matrices with an acceptable amount of training time. Our experimental results have shown that RSGCM can deal with more varieties of connection matrices than existing state-of-the-art communication schedules. RSGCM also increases the robustness of ITS since it can restore the system’s functionality in an acceptable time when device or connection breakdown happens.

The increasing prevalence of smart devices spurs the development of emerging indoor localization technologies for supporting diverse personalized applications at home. Given marked drawbacks of popular chirp signal-based approaches, we aim at developing a novel device-free localization system via the continuous wave of the inaudible frequency. To achieve this goal, solutions are developed for fine-grained analyses, able to precisely locate moving human traces in the room-scale environment. In particular, a smart speaker is controlled to emit continuous waves at inaudible 20kHz , with a co-located microphone array to record their Doppler reflections for localization. We first develop solutions to remove potential noises and then propose a novel idea by slicing signals into a set of narrowband signals, each of which is likely to include at most one body segment’s reflection. Different from previous studies, which take original signals themselves as the baseband, our solutions employ the Doppler frequency of a narrowband signal to estimate the velocity first and apply it to get the accurate baseband frequency, which permits a precise phase measurement after I-Q (i.e., in-phase and quadrature) decomposition. A signal model is then developed, able to formulate the phase with body segment’s velocity, range, and angle. We next develop novel solutions to estimate the motion state in each narrowband signal, cluster the motion states for different body segments corresponding to the same person, and locate the moving traces while mitigating multi-path effects. Our system is implemented with commodity devices in room environments for performance evaluation. The experimental results exhibit that our system can conduct effective localization for up to three persons in a room, with the average errors of 7.49 cm for a single person, with 24.06 cm for two persons, with 51.15 cm for three persons.

Serverless computing has revolutionized online service development and deployment with ease-to-use operations, auto-scaling, fine-grained resource allocation, and pay-as-you-go pricing. However, a gap remains in configuring serverless functions—the actual resource consumption may vary due to function types, dependencies, and input data sizes, thus mismatching the static resource configuration by users. Dynamic resource consumption against static configuration may lead to either poor function execution performance or low utilization. This paper proposesFreyr$^+$, a novel resource manager (RM) that dynamically harvests idle resources from over-provisioned functions to accelerate under-provisioned functions for serverless platforms.Freyr$^+$monitors each function's resource utilization in real-time and detects the mismatches between user configuration and actual resource consumption. We design deep reinforcement learning (DRL) algorithms with attention-enhanced embedding, incremental learning, and safeguard mechanism forFreyr$^+$to harvest idle resources safely and accelerate functions efficiently. We have implemented and deployed aFreyr$^+$prototype in a 13-node Apache OpenWhisk cluster using AWS EC2.Freyr$^+$is evaluated on both large-scale simulation and real-world testbed. Experimental results show thatFreyr$^+$harvests 38% of function invocations’ idle resources and accelerates 39% of invocations using harvested resources.Freyr$^+$reduces the 99th-percentile function response latency by 26% compared to the baseline RMs.

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.

Federated learning (FL) is known to be susceptible to model poisoning attacks in which malicious clients hamper the accuracy of the global model by sending manipulated model updates to the central server during the FL training process. Existing defenses mainly focus on Byzantine-robust FL aggregations, and largely ignore the impact of the underlying deep neural network (DNN) that is used to FL training. Inspired by recent findings on critical learning periods (CLP) in DNNs, where small gradient errors have irrecoverable impact on the final model accuracy, we propose a new defense, called a CLP-aware defense against poisoning of FL (DeFL). The key idea of DeFL is to measure fine-grained differences between DNN model updates via an easy-to-compute federated gradient norm vector (FGNV) metric. Using FGNV, DeFL simultaneously detects malicious clients and identifies CLP, which in turn is leveraged to guide the adaptive removal of detected malicious clients from aggregation. As a result, DeFL not only mitigates model poisoning attacks on the global model but also is robust to detection errors. Our extensive experiments on three benchmark datasets demonstrate that DeFL produces significant performance gain over conventional defenses against state-of-the-art model poisoning attacks.

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy. Code is available at https://github.com/TsingZ0/FedALA.

Semi-supervised learning, which learns with only a small amount of labeled data while collecting voluminous unlabeled data to aid its training, has achieved promising performance lately, but it also raises a serious privacy concern: Whether a user’s data has been collected for use without authorization. In this paper, we propose a novel membership inference method against semi-supervised learning, serving to protect user data privacy. Due to involving both the labeled and unlabeled data, the membership patterns of semi-supervised learning’s training data cannot be well captured by the existing membership inference solutions. To this end, we propose two new metrics, i.e., inter-consistency and intra-entropy, tailored specifically to the semi-supervised learning paradigm, able to respectively measure the similarity and calculate the cross-entropy among prediction vectors from the perturbed versions. By exploiting the two metrics for membership inference, our method can dig out membership patterns imprinted on prediction outputs of semi-supervised learning models, thus facilitating effective membership inference. Extensive experiments have been conducted for comparing our method with five rectified baseline inference techniques across four datasets on six semi-supervised learning algorithms. Experimental results exhibit that our inference method achieves over 80% accuracy under each experimental setting, substantially outperforming all baseline techniques.

Bayesian neural networks have drawn extensive interest because of their distinctive probabilistic representation framework. However, despite its recent success, little work focuses on the information-theoretic understanding of Bayesian neural networks. In this paper, we propose Information Bound as a metric of the amount of information in Bayesian neural networks. Different from mutual information on deterministic neural networks where modification of network structure or specific input data is usually necessary, Information Bound can be easily estimated on current Bayesian neural networks without any modification of network structures or training processes. By observing the trend of Information Bound during training, we demonstrate the existence of the “critical period” in Bayesian neural networks. Besides, we show that the Information Bound can be used to judge the confidence of the model prediction and to detect out-of-distribution datasets. Based on these observations of model interpretation, we propose Information Bound regularization and Information Bound variance regularization methods. The Information Bound regularization encourages models to learn the minimum necessary information and improves the model generality and robustness. The Information Bound variance regularization encourages models to learn more about complex samples with low Information Bound. Extensive experiments on KMNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 verify the effectiveness of the proposed regularization methods.

Serverless computing has been favored by users and infrastructure providers from various industries, including online services and scientific computing. Users enjoy its auto-scaling and ease-of-management, and providers own more control to optimize their service. However, existing serverless platforms still require users to pre-define resource allocations for their functions, leading to frequent misconfiguration by inexperienced users in practice. Besides, functions' varying input data further escalate the gap between their dynamic resource demands and static allocations, leaving functions either over-provisioned or under-provisioned. This paper presents Libra, a safe and timely resource harvesting framework for multi-node serverless clusters. Libra makes precise harvesting decisions to accelerate function invocations with harvested resources and jointly improve resource utilization by profiling dynamic resource demands and availability proactively. Experiments on OpenWhisk clusters with real-world workloads show that Libra reduces response latency by 39% and achieves 3X resource utilization compared to state-of-the-art solutions.

Federated Learning (FL) is popular for its privacy-preserving and collaborative learning capabilities. Recently, personalized FL (pFL) has received attention for its ability to address statistical heterogeneity and achieve personalization in FL. However, from the perspective of feature extraction, most existing pFL methods only focus on extracting global or personalized feature information during local training, which fails to meet the collaborative learning and personalization goals of pFL. To address this, we propose a new pFL method, named GPFL, to simultaneously learn global and personalized feature information on each client. We conduct extensive experiments on six datasets in three statistically heterogeneous settings and show the superiority of GPFL over ten state-of-the-art methods regarding effectiveness, scalability, fairness, stability, and privacy. Besides, GPFL mitigates overfitting and outperforms the baselines by up to 8.99% in accuracy.

Federated learning (FL) is a distributed optimization paradigm that learns from data samples distributed across a number of clients. Adaptive client selection that is cognizant of the training progress of clients has become a major trend to improve FL efficiency but not yet well-understood. Most existing FL methods such as FedAvg and its state-of-the-art variants implicitly assume that all learning phases during the FL training process are equally important. Unfortunately, this assumption has been revealed to be invalid due to recent findings on critical learning periods (CLP), in which small gradient errors may lead to an irrecoverable deficiency on final test accuracy. In this paper, we develop CriticalFL, a CLP augmented FL framework to reveal that adaptively augmenting exiting FL methods with CLP, the resultant performance is significantly improved when the client selection is guided by the discovered CLP. Experiments based on various machine learning models and datasets validate that the proposed CriticalFL framework consistently achieves an improved model accuracy while maintains better communication efficiency as compared to state-of-the-art methods, demonstrating a promising and easily adopted method for tackling the heterogeneity of FL training.

Recently, personalized federated learning (pFL) has attracted increasing attention in privacy protection, collaborative learning, and tackling statistical heterogeneity among clients, e.g., hospitals, mobile smartphones, etc. Most existing pFL methods focus on exploiting the global information and personalized information in the client-level model parameters while neglecting that data is the source of these two kinds of information. To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively. FedCP is more fine-grained to consider personalization in a sample-specific manner than existing pFL methods. Extensive experiments in computer vision and natural language processing domains show that FedCP outperforms eleven state-of-the-art methods by up to 6.69%. Furthermore, FedCP maintains its superiority when some clients accidentally drop out, which frequently happens in mobile settings. Our code is public at https://github.com/TsingZ0/FedCP.

Recently, federated learning (FL) is popular for its privacy-preserving and collaborative learning abilities. However, under statistically heterogeneous scenarios, we observe that biased data domains on clients cause a representation bias phenomenon and further degenerate generic representations during local training, i.e., the representation degeneration phenomenon. To address these issues, we propose a general framework Domain Bias Eliminator (DBE) for FL. Our theoretical analysis reveals that DBE can promote bi-directional knowledge transfer between server and client, as it reduces the domain discrepancy between server and client in representation space. Besides, extensive experiments on four datasets show that DBE can greatly improve existing FL methods in both generalization and personalization abilities. The DBE-equipped FL method can outperform ten state-of-the-art personalized FL methods by a large margin. Our code is public at https://github.com/TsingZ0/DBE.

Model data-driven ontology and knowledge presentation for evolving semantic Asian social networks (OK-ASN) is a critical strategy for web of things (WoT) services. Meanwhile, Deep Neural Network (DNN)-based OK-ASN service in WoT is growing rapidly. However, most DNN-based services cannot utilize the potential of WoT fully, as heterogeneity exists in WoT. Therefore, this article proposes a novel framework called Web-based Heterogeneous Hierarchical Distributed Deep Neural Network ( WH 2 D 2 N 2 ) to deploy the DNNs for OK-ASN services on WoT, overcoming the heterogeneity. The architecture of the system and the designed Edge-Cloud-Joint execute scheme utilize heterogeneous devices to make DNN inference ubiquitous and output two types of results to meet various requirements. To bring robustness to OK-ASN services, a global scheduling is designed to arrange the workflow dynamically. The results of our experiments prove the efficiency of the execute scheme and the global scheduling in the system.

Federated learning (FL) is a popular technique to train machine learning (ML) models with decentralized data. Extensive works have studied the performance of the global model; however, it is still unclear how the training process affects the final test accuracy. Exacerbating this problem is the fact that FL executions differ significantly from traditional ML with heterogeneous data characteristics across clients, involving more hyperparameters. In this work, we show that the final test accuracy of FL is dramatically affected by the early phase of the training process, i.e., FL exhibits critical learning periods, in which small gradient errors can have irrecoverable impact on the final test accuracy. To further explain this phenomenon, we generalize the trace of the Fisher Information Matrix (FIM) to FL and define a new notation called FedFIM, a quantity reflecting the local curvature of each clients from the beginning of the training in FL. Our findings suggest that the initial learning phase plays a critical role in understanding the FL performance. This is in contrast to many existing works which generally do not connect the final accuracy of FL to the early phase training. Finally, seizing critical learning periods in FL is of independent interest and could be useful for other problems such as the choices of hyperparameters including but not limited to the number of client selected per round, batch size, so as to improve the performance of FL training and testing.

Bayesian neural networks (BNNs) have drawn extensive interest due to the unique probabilistic representation framework. However, Bayesian neural networks have limited publicized deployments because of the relatively poor model performance in real-world applications. In this paper, we argue that the randomness of sampling in Bayesian neural networks causes errors in the updating of model parameters during training and some sampled models with poor performance in testing. To solve this, we propose to train Bayesian neural networks with Adversarial Distribution as a theoretical solution. To avoid the difficulty of calculating Adversarial Distribution analytically, we further present the Adversarial Sampling method as an approximation in practice. We conduct extensive experiments with multiple network structures on different datasets, e.g., CIFAR-10 and CIFAR-100. Experimental results validate the correctness of the theoretical analysis and the effectiveness of the Adversarial Sampling on improving model performance. Additionally, models trained with Adversarial Sampling still keep their ability to model uncertainties and perform better when predictions are retained according to the uncertainties, which further verifies the generality of the Adversarial Sampling approach.

Serverless computing automates fine-grained resource scaling and simplifies the development and deployment of online services with stateless functions. However, it is still non-trivial for users to allocate appropriate resources due to various function types, dependencies, and input sizes. Misconfiguration of resource allocations leaves functions either under-provisioned or over-provisioned and leads to continuous low resource utilization. This paper presents Freyr, a new resource manager (RM) for serverless platforms that maximizes resource efficiency by dynamically harvesting idle resources from over-provisioned functions to under-provisioned functions. Freyr monitors each function’s resource utilization in real-time, detects over-provisioning and under-provisioning, and learns to harvest idle resources safely and accelerates functions efficiently by applying deep reinforcement learning algorithms along with a safeguard mechanism. We have implemented and deployed a Freyr prototype in a 13-node Apache OpenWhisk cluster. Experimental results show that 38.8% of function invocations have idle resources harvested by Freyr, and 39.2% of invocations are accelerated by the harvested resources. Freyr reduces the 99th-percentile function response latency by 32.1% compared to the baseline RMs.

With the popularity of machine learning on many applications, data privacy has become a severe issue when machine learning is applied in the real world. Federated learning (FL), an emerging paradigm in machine learning, aims to train a centralized model while distributing training data among a large number of clients in order to avoid data privacy leaking, which has attracted great attention recently. However, the distributed training scheme in FL is susceptible to different kinds of attacks. Existing defense systems mainly utilize model weight analysis to identify malicious clients with many limitations. For example, some defense systems must know the exact number of malicious clients beforehand, which can be easily bypassed by well-designed attack methods and become impractical for real-world scenarios.

Vessel segmentation is critically essential for diagnosing a series of diseases, e.g., coronary artery disease and retinal disease. However, annotating vessel segmentation maps of medical images is notoriously challenging due to the tiny and complex vessel structures, leading to insufficient available annotated datasets for existing supervised methods and domain adaptation methods. The subtle structures and con-fusing background of medical images further suppress the efficacy of unsupervised methods. In this paper, we propose a self-supervised vessel segmentation method via adversarial learning. Our method learns vessel representations by training an attention-guided generator and a segmentation generator to simultaneously synthesize fake vessels and segment vessels out of coronary angiograms. To support the research, we also build the first X-ray angiography coronary vessel segmentation dataset, named XCAD. We evaluate our method extensively on multiple vessel segmentation datasets, including the XCAD dataset, the DRIVE dataset, and the STARE dataset. The experimental results show our method suppresses unsupervised methods significantly and achieves competitive performance compared with supervised methods and traditional methods.

It has become increasingly thorny for computer vision competitions to preserve fairness when participants intentionally fine-tune their models against the test datasets to improve their performance. To mitigate such unfairness, competition organizers restrict the training and evaluation process of participants' models. However, such restrictions introduce massive computation overheads for organizers and potential intellectual property leakage for participants. Thus, we propose Themis, a framework that trains a noise generator jointly with organizers and participants to prevent intentional fine-tuning by protecting test datasets from surreptitious manual labeling. Specifically, with the carefully designed noise generator, Themis adds noise to perturb test sets without twisting the performance ranking of participants' models. We evaluate the validity of Themis with a wide spectrum of real-world models and datasets. Our experimental results show that Themis effectively enforces competition fairness by precluding manual labeling of test sets and preserving the performance ranking of participants' models.

The widespread deployment of machine learning applications in ubiquitous environments has sparked interests in exploiting the vast amount of data stored on mobile devices. To preserve data privacy, Federated Learning has been proposed to learn a shared model by performing distributed training locally on participating devices and aggregating the local models into a global one. However, due to the limited network connectivity of mobile devices, it is not practical for federated learning to perform model updates and aggregation on all participating devices in parallel. Besides, data samples across all devices are usually not independent and identically distributed (IID), posing additional challenges to the convergence and speed of federated learning. In this paper, we propose Favor, an experience-driven control framework that intelligently chooses the client devices to participate in each round of federated learning to counterbalance the bias introduced by non-IID data and to speed up convergence. Through both empirical and mathematical analysis, we observe an implicit connection between the distribution of training data on a device and the model weights trained based on those data, which enables us to profile the data distribution on that device based on its uploaded model weights. We then propose a mechanism based on deep Q-learning that learns to select a subset of devices in each communication round to maximize a reward that encourages the increase of validation accuracy and penalizes the use of more communication rounds. With extensive experiments performed in PyTorch, we show that the number of communication rounds required in federated learning can be reduced by up to 49% on the MNIST dataset, 23% on FashionMNIST, and 42% on CIFAR-10, as compared to the Federated Averaging algorithm.

Big data analytics are practiced in many fields to extract insights from massive amounts of data. With exponential growth in both the volume and variety of data, analytic queries have expanded from those executed in a single datacenter to those requiring inputs from multiple datacenters that are geographically separate or even globally distributed. Unfortunately, the software stack that supports data analytics is designed originally for a cluster environment and is not tailored to execute global analytic queries, where resources such as inter-datacenter networks may vary on the fly. Existing optimization strategies that determine the query execution plan before its execution are not able to adapt to resource variations at query runtime. In this article, we present Turbo, a lightweight and non-intrusive global analytics system that can dynamically adjust query execution plans for geo-distributed analytics in the presence of time-varying resources and network bandwidth across datacenters. Turbo uses machine learning to accurately predict the time cost of a query execution plan so that dynamic adjustments can be made to it when necessary. Turbo is non-intrusive in the sense that it does not require modifications to the existing software stack for data analytics. We have implemented a real-world prototype of Turbo, and evaluated it on a cluster of 33 instances across eight regions in the Google Cloud Platform. Our experimental results have shown that Turbo can achieve an accuracy of 95 percent for estimating time costs, and can reduce the query completion time by 41 percent.

The need to scale up machine learning, in the presence of a rapid growth of data both in volume and in variety, has sparked broad interests to develop distributed machine learning systems, typically based on parameter servers. However, since these systems are based on a dedicated cluster of physical or virtual machines, they have posed non-trivial cluster management overhead to machine learning practitioners and data scientists. In addition, there exists an inherent mismatch between the dynamically varying resource demands during a model training job and the inflexible resource provisioning model of current cluster-based systems.In this paper, we propose SIREN, an asynchronous distributed machine learning framework based on the emerging serverless architecture, with which stateless functions can be executed in the cloud without the complexity of building and maintaining virtual machine infrastructures. With SIREN, we are able to achieve a higher level of parallelism and elasticity by using a swarm of stateless functions, each working on a different batch of data, while greatly reducing system configuration overhead. Furthermore, we propose a scheduler based on Deep Reinforcement Learning to dynamically control the number and memory size of the stateless functions that should be used in each training epoch. The scheduler learns from the training process itself, in pursuit for the minimum possible training time given a cost. With our real-world prototype implementation on AWS Lambda, extensive experimental results have shown that SIREN can reduce model training time by up to 44%, as compared to traditional machine learning training benchmarks on AWS EC2 at the same cost.

Operating at a large scale, data analytics has become an essential tool for gaining insights from operational data, such as user online activities. With the volume of data growing exponentially, data analytic jobs have expanded from a single datacenter to multiple geographically distributed datacenters. Unfortunately, designed originally for a single datacenter, the software stack that supports data analytics is oblivious to on-the-fly resource variations on inter-datacenter networks, which negatively affects the performance of analytic queries. Existing solutions that optimize query execution plans before their execution are not able to quickly adapt to resource variations at query runtime.

As increasingly large volumes of raw data are generated at geographically distributed datacenters, they need to be efficiently processed by data analytic jobs spanning multiple datacenters across wide-area networks. Designed for a single datacenter, existing data processing frameworks, such as Apache Spark, are not able to deliver satisfactory performance when these wide-area analytic jobs are executed. As wide-area networks interconnecting datacenters may not be congestion free, there is a compelling need for a new system framework that is optimized for wide-area data analytics. In this paper, we design and implement a new proactive data aggregation framework based on Apache Spark, with a focus on optimizing the network traffic incurred in shuffle stages of data analytic jobs. The objective of this framework is to strategically and proactively aggregate the output data of mapper tasks to a subset of worker datacenters, as a replacement to Spark's original passive fetch mechanism across datacenters. It improves the performance of wide-area analytic jobs by avoiding repetitive data transfers, which improves the utilization of inter-datacenter links. Our extensive experimental results using standard benchmarks across six Amazon EC2 regions have shown that our proposed framework is able to reduce job completion times by up to 73%, as compared to the existing baseline implementation in Spark.

Many existing data center network (DCN) flow scheduling schemes, that minimize flow completion times (FCT) assume prior knowledge of flows and custom switch functions, making them superior in performance but hard to implement in practice. By contrast, we seek to minimize FCT with no prior knowledge and existing commodity switch hardware. To this end, we present PIAS, a DCN flow scheduling mechanism that aims to minimize FCT by mimicking shortest job first (SJF) on the premise that flow size is not knowna priori. At its heart, PIAS leverages multiple priority queues available in existing commodity switches to implement a multiple level feedback queue, in which a PIAS flow is gradually demoted from higher-priority queues to lower-priority queues based on the number of bytes it has sent. As a result, short flows are likely to be finished in the first few high-priority queues and thus be prioritized over long flows in general, which enables PIAS to emulate SJF without knowing flow sizes beforehand. We have implemented a PIAS prototype and evaluated PIAS through both testbed experiments and ns-2 simulations. We show that PIAS is readily deployable with commodity switches and backward compatible with legacy TCP/IP stacks. Our evaluation results show that PIAS significantly outperforms existing information-agnostic schemes, for example, it reduces FCT by up to 50% compared to DCTCP[11]and L2DCT[32]; and it only has a 1.1% performance gap to an ideal information-aware scheme, pFabric[13], for short flows under a production DCN workload.

Inter-data center wide area networks (inter-DC WANs) carry a significant amount of data transfers that require to be completed within certain time periods, or deadlines. However, very little work has been done to guarantee such deadlines. The crux is that the current inter-DC WAN lacks an interface for users to specify their transfer deadlines and a mechanism for provider to ensure the completion while maintaining high WAN utilization. In this paper, we address the problem by introducing a deadline-based network abstraction (DNA) for inter-DC WANs. DNA allows users to explicitly specify the amount of data to be delivered and the deadline by which it has to be completed. The malleability of DNA provides flexibility in resource allocation. Based on this, we develop a system calledAmoebathat implements DNA. Our simulations and test bed experiments show thatAmoeba, by harnessing DNA’s malleability, accommodates 15% more user requests with deadlines, while achieving 60% higher WAN utilization than prior solutions.

Many data center network DCN applications require explicit routing path control over the underlying topologies. In this paper, we present XPath, a simple, practical and readily-deployable way to implement explicit path control, using existing commodity switches. At its core, XPath explicitly identifies an end-to-end path with a path ID and leverages a two-step compression algorithm to pre-install all the desired paths into IP TCAM tables of commodity switches. Our evaluation and implementation show that XPath scales to large DCNs and is readily-deployable. Furthermore, on our testbed, we integrate XPath into four applications to showcase its utility.

In this paper, we present our effort towards comprehensive traffic forecasting for big data applications using external, light-weighted file system monitoring. Our idea is motivated by the key observations that rich traffic demand information already exists in the log and meta-data files of many big data applications, and that such information can be readily extracted through run-time file system monitoring. As the first step, we use Hadoop as a concrete example to explore our methodology and develop a system called HadoopWatch to predict traffic demands of Hadoop applications. We further implement HadoopWatch in a small-scale testbed with 10 physical servers and 30 virtual machines. Our experiments over a series of MapReduce applications demonstrate that HadoopWatch can forecast the traffic demand with almost 100% accuracy and time advance. Furthermore, it makes no modification on the Hadoop framework, and introduces little overhead to the application performance. Finally, to showcase the utility of accurate traffic prediction made by HadoopWatch, we design and implement a simple HadoopWatch-enabled network optimization module into the HadoopWatch controller, and with realistic Hadoop job benchmarks we find that even a simple algorithm can leverage the forecasting results provided by HadoopWatch to significantly improve the Hadoop job completion time by up to 14.72%.

Inter-datacenter wide area networks (inter-DC WAN) carry a significant amount of data transfers that require to be completed within certain time periods, or deadlines. However, very little work has been done to guarantee such deadlines. The crux is that the current inter-DC WAN lacks an interface for users to specify their transfer deadlines and a mechanism for provider to ensure the completion while maintaining high WAN utilization.

Data-parallel computing frameworks (DCF) such as MapReduce, Spark, and Dryad etc. Have tremendous applications in big data and cloud computing, and throw tons of flows into data center networks. In this paper, we design and implement FLOW PROPHET, a general framework to predict traffic flows for DCFs. To this end, we analyze and summarize the common features of popular DCFs, and gain a key insight: since application logic in DCFs is naturally expressed by directed acyclic graphs (DAG), DAG contains necessary time and data dependencies for accurate flow prediction. Based on the insight, FLOW PROPHET extracts DAGs from user applications, and uses the time and data dependencies to calculate flow information 4-tuple, (source, destination, flow size, establish time), ahead-of-time for all flows. We also provide generic programming interface to FLOW PROPHET, so that current and future DCFs can deploy FLOW PROPHET readily. We implement FLOW PROPHET on both Spark and Hadoop, and perform extensive evaluations on a testbed with 37 physical servers. Our implementation and experiments demonstrate that, with time in advance and minimal cost, FLOW PROPHET can achieve almost 100% accuracy in source, destination, and flow size predictions. With accurate prediction from FLOW PROPHET, the job completion time of a Hadoop TeraSort benchmark is reduced by 12.52% on our cluster with a simple network scheduler.

This paper aims at systematically analyzing the pricing schemes in data center network. The interaction between a monopolistic operator and customers in the network is modeled as Stackelberg game. In this model, both homogeneous- and heterogeneous-customer scenarios are analyzed. In homogeneous customer case, a special scenario is that only a single customer exists in the network. In this scenario, we observe that the Stackelberg equilibrium will lead to a Pareto-inefficient outcome. To address this problem, a two-part pricing scheme is proposed to derive a Pareto efficient outcome and benefit both the operator and customers. When there are an infinite number of homogeneous customers in the network, our analysis shows that customers' selfish action may incur zero utility to them and operator can achieve all the utility by announcing an appropriate price. As to the heterogeneous customer case, we not only analyse how the operator should price the network resources, but also introduce Paris Metro Pricing (PMP) scheme to further increase operator's profit. Since the operator's profit is not a concave function of the resource price, these studies are conducted by simulation.

Forming coalitions for a group of tasks efficiently in a distributed setting is a practical problem and a hot research topic in agent research. Most of current solutions for this problem are based on the assumption that there is no relationship between these tasks. However, logical interdependent relationships may exist between tasks so that transfer costs will be incurred when interdependent tasks are assigned to different agents. In order to solve this problem, a coalition formation strategy is proposed. In this strategy, it is allowed that agents disclose some personal cost information to compete for tasks in a controlled way. Experimental results show that, under the labor-based profit distribution framework, the information disclosure strategy is faster in forming coalitions and increases coalition net profit compared to the traditional information non-disclosure strategy.

Modern software model checkers are usually used to find safety violations. However, checking liveness properties can offer a more natural and effective way to detect errors, particularly in complex concurrent and distributed e-business systems. Specifying global liveness properties which should always eventually be true proves to be more desirable, but it is hard for existing software model checkers to verify liveness in real codes because doing so requires finding an infinite execution. For solving such a challenge, this paper proposes an online checking tool to verify the safety and liveness properties of complex systems. We adopt the linear temporal logic to describe the semantics of the finite model checking, use binary instrumentation to obtain the distribute states and apply a checking engine to dynamically verify the finite trace linear temporal logic properties. At last, we demonstrate the method in a distributed system using distributed protocol Paxos and achieve good results by experiments.