Heart sounds play a crucial role in assessing Coronary Artery Disease (CAD). The advancement of Artificial Intelligence (AI) technologies has given rise to Computer Audition (CA)-based methods for CAD detection. However, previous research has focused primarily on analyzing and modeling heart sound data, overlooking practical application scenarios. In this work, we design a pervasive heart sound collection device used for high-quality heart sound data acquisition. Moreover, we introduce an on-board executable lightweight network tailored for the designed portable device, referred to as TYKDModel. Further, heart sound data from 41 CAD patients and 22 non-CAD healthy controls are collected using the developed device. Experimental results show that the TYKDModel exhibits low-computational complexity, with 52.16 K parameters and 5.03 M Floating-Point Operations (FLOPs). When deployed on the board, it requires only 1.10 MB of Random Access Memory (RAM) and 236.27 KB of Read-Only Memory (ROM), and takes around 1.72 seconds to perform a classification. Despite the low computational and spatial complexity, the TYKDModel achieves a notable classification accuracy of 85.2%, specificity of 88.6%, and sensitivity of 82.8% on the board. These results indicate the promising potential of AI-assisted all-in-one integrated system for the diagnosis of heart sound-assisted CAD.

With the recent advances in vision transformers and large language models (LLMs),finetuning costly large mod-els on downstream learning tasks poses significant chal-lenges under limited computational resources. This pa-per presents a REsource and ComputAtion-efficient Pruning framework (RECAP) for the finetuning of transformer-based large models. RECAP by design bridges the gap between efficiency and performance through an iterative process cycling between pruning, finetuning, and updating stages to explore different chunks of the given large-scale model. At each iteration, we first prune the model with Taylor-approximation-based importance estimation and then only update a subset of the pruned model weights based on the Fisher-information criterion. In this way, RE-CAP achieves two synergistic and yet conflicting goals: re-ducing the GPU memory footprint while maintaining model performance, unlike most existing pruning methods that re-quire the model to be finetuned beforehand for better preser-vation of model performance. We perform extensive exper-iments with a wide range of large transformer-based archi-tectures on various computer vision and natural language understanding tasks. Compared to recent pruning techniques, we demonstrate that RECAP offers significant im-provements in GPU memory efficiency, capable of reducing the footprint by up to 65%.

Federated learning (FL) is an attractive distributed learning paradigm supporting real-time continuous learning and client privacy by default. In most FL approaches, all edge clients are assumed to have sufficient computation capabilities to participate in the learning of a deep neural network (DNN) model. However, in real-life applications, some clients may have severely limited resources and can only train a much smaller local model. This paper presents ScaleFL, a novel FL approach with two distinctive mechanisms to handle resource heterogeneity and provide an equitable FL framework for all clients. First, ScaleFL adaptively scales down the DNN model along width and depth dimensions by leveraging early exits to find the best-fit models for resource-aware local training on distributed clients. In this way, ScaleFL provides an efficient balance of preserving basic and complex features in local model splits with various sizes for joint training while enabling fast inference for model deployment. Second, ScaleFL utilizes self-distillation among exit predictions during training to improve aggregation through knowledge transfer among subnetworks. We conduct extensive experiments on benchmark CV (CIFAR-10/100, ImageNet) and NLP datasets (SST-2, AgNews). We demonstrate that ScaleFL outperforms existing representative heterogeneous FL approaches in terms of global/local model performance and provides inference efficiency, with up to 2x latency and 4x model size reduction with negligible performance drop below 2%.

Federated learning (FL) is a distributed learning framework that inherently provides data privacy and parallel computation capability over a set of participating devices (clients). In real-life applications, these clients can have a great variety in terms of resources (storage, RAM, CPU/GPU speed, network speed, etc.). However, most previous FL studies do not consider this scenario with system heterogeneity and assume that all clients can operate on the same full-size deep neural network (DNN) model. In this work, we demonstrate a scalable FL approach, ScaleFL, which tackles system heterogeneity through hierarchically downscaling the DNN model for clients with limited resources. ScaleFL utilizes early exits to form multi-exit DNN models by injecting early exit networks into the given DNN. During FL, the model is adaptively split along depth (exits) and width (hidden dimensions) based on the resource budget of each participating client. A proof-of-concept demonstration is provided with interactive features, demonstrating the system flow on image classification and NLP benchmark workloads.

This paper presents a transparent Container Network Memory storage device, coined as CNetMem, aiming to address the open problem of unpredictable performance degradation of containers when the working set of an application no longer fits in container memory. First, CNetMem will enable application tenants running in a container to park their working set memory/file to a faster network memory storage by organizing a group of remote memory nodes as remote memory donors. This allows CNetMem to take advantage of remote idle memory on a cluster before resorting to a slow local I/O subsystem like local disk without any modification of host OS or application. Second, CNetMem provides a hybrid batching technique to remove or alleviate performance bottlenecks in the I/O performance critical path for remote memory read/write with replication or disk backup for fault tolerance. Third, CNetMem introduces a rank-based node selection algorithm to find the optimal node for placing remote memory blocks across cluster. This helps CNetMem to reduce the performance impact due to remote memory eviction. Extensive experiments are conducted on three big data applications and four machine learning workloads. The results show that CNetMem achieves up to 172× throughput improvements compared to vanilla Linux and up to 5.9× completion time improvements over existing approaches in big data and ML workload.

An efficient approach to managing big data workloads is to enable applications to work directly with huge pages. This can effectively avoid or reduce the memory fragmentation problem due to high frequent memory allocation and deallocation and significantly minimize the performance degradation of big data applications. This paper presents XPage, a huge page memory management framework, with three novel features. First, XPage by design can provide automated huge page managements with transparency to both OS and applications. Second, Xpage represents a memory management redesign that brings performance and memory saving to memory intensive applications by supporting dynamic huge page memory management without resorting to splitting huge pages for memory fragmentation. Third but not the least, XPage can efficiently minimize the internal fragmentation without impacting performance of applications. We conduct extensive experiments to evaluate the effectiveness of XPage in minimizing internal memory fragmentation in the presence of dynamic memory intensive big data workloads, by comparing XPage with vanilla Linux using 4KB base page and Linux with 2MB huge page.

Federated learning(FL) is an emerging distributed learning paradigm with default client privacy because clients can keep sensitive data on their devices and only share local training parameter updates with the federated server. However, recent studies reveal that gradient leakages in FL may compromise the privacy of client training data. This paper presents a gradient leakage resilient approach to privacy-preserving federated learning with per training example-based client differential privacy, coined as Fed-CDP. It makes three original contributions. First, we identify three types of client gradient leakage threats in federated learning even with encrypted client-server communications. We articulate when and why the conventional server coordinated differential privacy approach, coined as Fed-SDP, is insufficient to protect the privacy of the training data. Second, we introduce Fed-CDP, the per example-based client differential privacy algorithm, and provide a formal analysis of Fed-CDP with the (∊,δ) differential privacy guarantee, and a formal comparison between Fed-CDP and Fed-SDP in terms of privacy accounting. Third, we formally analyze the privacy-utility tradeoff for providing differential privacy guarantee by Fed-CDP and present a dynamic decay noise-injection policy to further improve the accuracy and resiliency of Fed-CDP. We evaluate and compare Fed-CDP and Fed-CDP(decay) with Fed-SDP in terms of differential privacy guarantee and gradient leakage resilience over five benchmark datasets. The results show that the Fed-CDP approach outperforms conventional Fed-SDP in terms of resilience to client gradient leakages while offering competitive accuracy performance in federated learning.

The volume of trajectory data has become tremendously large in recent years. How to efficiently maintain and compute such trajectory data becomes a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road networks. By treating the mined patterns as dictionary items, we have the chance to encode a long trajectory by shorter paths, thus leading to smaller space cost. Meanwhile, we design an error-bounded temporal compression on top of the identified spatial patterns for much low space cost. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of both space saving and runtime of trajectory compression.

Ballooning is a popular solution for dynamic memory balancing. However, existing solutions may perform poorly in the presence of heavy guest swapping. Furthermore, when the host has sufficient free memory, guest virtual machines (VMs) under memory pressure is not be able to use it in a timely fashion. Even after the guest VM has been recharged with sufficient memory via ballooning, the applications running on the VM are unable to utilize the free memory in guest VM to quickly recover from the severe performance degradation. To address these problems, we present MemFlex, a shared memory swapper for improving guest swapping performance in virtualized environment with three novel features: (1) MemFlex effectively utilizes host idle memory by redirecting the VM swapping traffic to the host-guest shared memory area. (2) MemFlex provides a hybrid memory swapping model, which treats a fast but small shared memory swap partition as the primary swap area whenever it is possible, and smoothly transits to the conventional disk-based VM swapping on demand. (3) Upon ballooned with sufficient VM memory, MemFlex provides a fast swap-in optimization, which enables the VM to proactively swap in the pages from the shared memory using an efficient batch implementation. Instead of relying on costly page faults, this optimization offers just-in-time performance recovery by enabling the memory intensive applications to quickly regain their runtime momentum. Performance evaluation results are presented to demonstrate the effectiveness of MemFlex when compared with existing swapping approaches.

Dynamic VM memory management via the balloon driver is a common strategy to manage the memory resources of VMs under changing workloads. However, current approaches rely on kernel instrumentation to estimate the VM working set size, which usually result in high run-time overhead. Thus system administrators have to tradeoff between the estimation accuracy and the system performance. This paper presents iBalloon, a light-weight, accurate and transparent prediction based mechanism to enable more customizable and efficient ballooning policies for rebalancing memory resources among VMs. Experiment results from well known benchmarks such as Dacapo and SPECjvm show that iBalloon is able to quickly react to the VM memory demands, provide up to 54% performance speedup for memory intensive applications running in the VMs, while incurring less than 5% CPU overhead on the host machine as well as the VMs.

Nowadays large enterprises maintain a huge amount of data in multiple backend systems including traditional database systems and recently popular big data systems. In an example of telecom providers, the key business data (e.g., billing information) is maintained in database systems whereas the huge signaling log data is on HDFS with Hive. How to integrate such data and provide a consolidate query and analytic becomes a challenging task. Neither traditional database warehouse nor recent Big Data system (e.g. Apache Spark and Hadoop) can fully leverage the power of each backend system. In this paper, we build a hybrid data processing engine, called Octopus, to fully integrate backend systems. Given the backend systems, data is distributed at multiple locations. Octopus focuses on the optimization of the amount of data movement. To this end, Octopus proposes a technique of query pushdown for such optimization. A proof-of-concept prototype of Octopus successfully verifies that Octopus can achieve much faster running time than Spark. For example, Octopus outperforms the recent Spark version 1.4.0 by 5.25 X faster running time to process an aggregation query.

In this paper we present our experiences constructing and testing in-memory data structures designed to be disjoint enough for transactional memory to be profitable as a serialization mechanism with no fallback to traditional locking. Our goal was to restrict memory conflicts to actual contention situations so that transactional memory techniques could be used as efficiently as possible. We describe the hardware transactional execution facility in the IBM enterprise EC12 server. We present an order preserving hashed structure that permits insertion, deletion, and traversal operations typically supported by a sorted linked list. We measure the performance and scalability for these operations on the IBM enterprise EC12 server. Our results show near linear scalability of the insertion and deletion operations for up to 96 CPUs. We also discuss transaction abort frequency and hardware/software interactions.

We present a highly available system for environments such as stock trading, where high request rates and low latency requirements dictate that service disruption on the order of seconds in length can be unacceptable. After a node failure, our system avoids delays in processing due to detecting the failure or transferring control to a back-up node. We achieve this by using multiple primary nodes which process transactions concurrently as peers. If a primary node fails, the remaining primaries continue executing without being delayed at all by the failed primary. Nodes agree on a total ordering for processing requests with a novel low overhead wait-free algorithm that utilizes a small amount of shared memory accessible to the nodes and a simple compare-and-swap like protocol which allows the system to progress at the speed of the fastest node. We have implemented our system and show experimentally that it performs well and can transparently handle node failures without causing delays to transaction processing. The efficient implementation of our algorithm for ordering transactions is a critically important factor in achieving good performance.

We present the design, optimization and analysis of a highly flexible and efficient multi-leg stock trading system. Automated electronic multi-leg trading allows atomic processing of consolidated orders such as "Buy 200 shares of IBM and sell 100 shares of HPQ". While the expressive power of multi-leg trading brings significant value to investors, it also poses major challenges to stock exchange architecture design, due to additional complexities introduced in performance, tradability, and fairness. Performance can be significantly worse due to the need to coordinate transactions among multiple stocks at once. This paper studies the performance of multi-leg trading under different fairness constraints and variability in order price and order quantity. We identify the major performance bottlenecks when using traditional atomic commitment protocols such as 2-Phase Commit (2PC), and propose a new look-ahead algorithm to maximize transaction concurrency and minimize performance degradation. We have implemented a base-line 2PC prototype and a look-ahead optimized prototype on IBM z10 z Series e Server mainframes. Our experimental results show that the look-ahead optimization can improve throughput by 58% and reduce latency by 30%.

We present MobiDesk, a mobile virtual desktop computing hosting infrastructure that leverages continued improvements in network speed, cost, and ubiquity to address the complexity, cost, and mobility limitations of today's personal computing infrastructure. MobiDesk transparently virtualizes a user's computing session by abstracting underlying system resources in three key areas: display, operating system, and network. It provides a thin virtualization layer that decouples a user's computing session from any particular end-user device, and moves all application logic to hosting providers. The virtualization layer decouples a user's computing session from the underlying operating system and server instance, enabling high-availability service by transparently migrating sessions from one server to another during server maintenance or upgrades. We have implemented a prototype in Linux that works with existing unmodified applications and operating system kernels. Our experimental results demonstrate that MobiDesk has very low virtualization overhead, can provide a full featured desktop experience including full-motion video support, and is able to migrate users' sessions efficiently and reliably for high-availability, while maintaining existing network connections.