We have a production need to reduce the time for customers to live migrate their application virtual machine (VM) in the cloud. A single customer of ours migrates their nested, cloud-based, user virtual machines tens of thousands of times a month.

In response to the issues of noise and redundant information affecting the accuracy and robustness of existing multimodal sentiment analysis models, this paper suggests a multimodal sentiment analysis model based on TCN (Time-Causal Convolutional Network) and cross-modal interaction feedback networks. It is primarily composed of two modules: a modality fusion module and a feedback module. Firstly, the text, audio, and video feature vectors are encoded through TCN, followed by the generation of unimodal representations via dot-product attention. To capture interaction information across different modalities, a cross-modal fusion module comprising three symmetric multi-head attention mechanisms is introduced. To eliminate noise and redundant information generated from modality fusion, a feedback module consisting of TCN, fully connected layers, and sigmoid layers is designed to apply its generated masked features in the cross-modal fusion process. Finally, a fully connected layer is employed to integrate the concatenated vector of unimodal representations and cross-modal features, which is then fed into softmax for sentiment classification. Experimental results demonstrate that compared to existing models, this model exhibits superior performance in sentiment classification tasks.

Social platforms allow individuals to express their opinions and viewpoints using multiple information modes. The effective fusion of these various types of information can enhance the accuracy of predicting users’ emotional tendencies. However, existing multimodal sentiment analysis does not fully consider the emoticon information contained in the text and the semantic irrelevance between the text and the image, resulting in poor sentiment analysis. To address this problem, we propose an image-text multimodal emotion analysis model (ITMEA-FE) that incorporates emoji features and text features into feature vectors to enhance the utilization rate of features. The correlation between image information and text information is identified, reducing the influence of emoji information and image-text semantic irrelevance on sentiment analysis. Finally, sentiment analysis is performed through a network of multi-head attention mechanisms. Experimental results show that the proposed method achieves an accuracy rate of 75.32% and a Macro-F1 value of 75.11%, outperforming the benchmark model.

Infrastructure-as-a-Service cloud providers sell virtual machines that are only specified in terms of number of CPU cores, amount of memory, and I/O throughput. Performance-critical aspects such as cache sizes and memory latency are missing or reported in ways that make them hard to compare across cloud providers. It is difficult for users to adapt their application’s behavior to the available resources. In this work, we aim to increase the visibility that cloud users have into shared resources on public clouds. Specifically, we present CacheInspector , a lightweight runtime that determines the performance and allocated capacity of shared caches on multi-tenant public clouds. We validate CacheInspector ’s accuracy in a controlled environment, and use it to study the characteristics and variability of cache resources in the cloud, across time, instances, availability regions, and cloud providers. We show that CacheInspector ’s output allows cloud users to tailor their application’s behavior, including their output quality, to avoid suboptimal performance when resources are scarce.

"Cloud-native" container platforms, such as Kubernetes, have become an integral part of production cloud environments. One of the principles in designing cloud-native applications is called Single Concern Principle, which suggests that each container should handle a single responsibility well. In this paper, we propose X-Containers as a new security paradigm for isolating single-concerned cloud-native containers. Each container is run with a Library OS (LibOS) that supports multi-processing for concurrency and compatibility. A minimal exokernel ensures strong isolation with small kernel attack surface. We show an implementation of the X-Containers architecture that leverages Xen paravirtualization (PV) to turn Linux kernel into a LibOS. Doing so results in a highly efficient LibOS platform that does not require hardware-assisted virtualization, improves inter-container isolation, and supports binary compatibility and multi-processing. By eliminating some security barriers such as seccomp and Meltdown patch, X-Containers have up to 27X higher raw system call throughput compared to Docker containers, while also achieving competitive or superior performance on various benchmarks compared to recent container platforms such as Google's gVisor and Intel's Clear Containers.

The "cloud paradigm" can provide a wealth of sophisticated emergency communication services that are gamechangers in emergency response, but its current implementation is not suitable to the challenging environments in which these responses often take place. The networking infrastructure may be all but unavailable, and access to centralized datacenters may be impossible.

Infrastructure-as-a-Service (IaaS) cloud providers hide available interfaces for virtual machine (VM) placement and migration, CPU capping, memory ballooning, page sharing, and I/O throttling, limiting the ways in which applications can optimally configure resources or respond to dynamically shifting workloads. Given these interfaces, applications could migrate VMs in response to diurnal workloads or changing prices, adjust resources in response to load changes, and so on. This article proposes a new abstraction that we call a Library Cloud and that allows users to customize the diverse available cloud resources to best serve their applications. We built a prototype of a Library Cloud that we call the Supercloud . The Supercloud encapsulates applications in a virtual cloud under users’ full control and can incorporate one or more availability zones within a cloud provider or across different providers. The Supercloud provides virtual machine, storage, and networking complete with a full set of management operations, allowing applications to optimize performance. In this article, we demonstrate various innovations enabled by the Library Cloud.

Global cloud services have to respond to workloads that shift geographically as a function of time-of-day or in response to special events. While many such services have support for adding nodes in one region and removing nodes in another, we demonstrate that such mechanisms can lead to significant performance degradation. Yet other services do not support application-level migration at all. Live VM migration between availability zones or even across cloud providers would be ideal, but cloud providers do not support this flexible mechanism.

Infrastructure as a Service (IaaS) clouds provide users with the ability to easily and quickly provision servers. A recent study found that one in three data center servers continues to consume resources without producing any useful work. A number of techniques have been proposed to identify such unproductive instances. However, those approaches adopt the strategy to identify idle cloud instances based on resource utilization. Resource utilization as indicator alone could be misleading, which is especially true for enterprise cloud environment. In this paper, we present Pleco, a tool that detects unproductive instances in IaaS clouds. Pleco captures dependency information between users and cloud instances by constructing a weighted reference model based on application knowledge. To handle cases of insufficient application knowledge, Pleco also supplements its dependency results with a machine learning model trained on resource utilization data. Pleco gives a confidence level and justification for each identified unproductive instances. Cloud administrators can then take different actions according to the information provided by Pleco. Pleco is lightweight and requires no modification to existing applications.

The Hopfield neural network (HNN) has been demonstrated to be an effective tool for the spectral mixture unmixing of hyperspectral images. However, it is extremely time consuming for such per-pixel algorithm to be utilized in real-world applications. In this letter, the implementation of a multichannel structure of HNN (named as MHNN) on a graphics processing unit (GPU) platform is proposed. According to the unmixing procedure of MHNN, three levels of parallelism, including thread, block, and stream, are designed to explore the peak computing capacity of a GPU device. In addition, constant and texture memories are utilized to further improve its computational performance. Experiments on both synthetic and real hyperspectral images demonstrated that the proposed GPU-based implementation works on the peak computing ability of a GPU device and obtains several hundred times of acceleration versus the CPU-based implementation while its unmixing performance remains unchanged.

Distributed applications running inside cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. One big challenge for diagnosing an abnormal distributed application is to pinpoint the faulty components. In this paper, we present a black-box online fault localization system called FChain that can pinpoint faulty components immediately after a performance anomaly is detected. FChain first discovers the onset time of abnormal behaviors at different components by distinguishing the abnormal change point from many change points caused by normal workload fluctuations. Faulty components are then pinpointed based on the abnormal change propagation patterns and inter-component dependency relationships. FChain performs runtime validation to further filter out false alarms. We have implemented FChain on top of the Xen platform and tested it using several benchmark applications (RUBiS, Hadoop, and IBM System S). Our experimental results show that FChain can quickly pinpoint the faulty components with high accuracy within a few seconds. FChain can achieve up to 90% higher precision and 20% higher recall than existing schemes. FChain is non-intrusive and light-weight, which imposes less than 1% overhead to the cloud system.

Nonnegative Matrix Factorization (NMF) has been successfully employed to address the mixed-pixel problem of hyperspectral remote sensing images. However, minimizing the representation error by NMF is not sufficient for SMA since the unmixing results of NMF are not unique. Therefore, in this paper, a neighborhood preserving regularization, which preserves the local structure of the hyperspectral data on a low-dimensional manifold, is proposed to constrain NMF for unique solution in SMA. As a result, a Neighborhood Preserving constrained NMF (NP-NMF) algorithm is proposed for SMA of highly mixed hyperspectral data. Finally, experimental results on AVIRIS data demonstrate the effectiveness of our proposed NP-NMF algorithm for SMA applications.

The popularity of cloud-based interactive computing services (e.g., virtual desktops) brings new management challenges. Each interactive user leaves abundant but fluctuating residual resources while being intolerant to latency, precluding the use of aggressive VM consolidation. In this paper, we present the Resource Harvester for Interactive Clouds (RHIC), an autonomous management framework that harnesses dynamic residual resources aggressively without slowing the harvested interactive services. RHIC builds ad-hoc clusters for running throughput-oriented "background" workloads using a hybrid of residual and dedicated resources. These hybrid clusters offer significant gains over normal dedicated clusters: 20-40% cost and 20-29% energy savings in our test bed. For a given background job, RHIC intelligently discovers and maintains the ideal cluster size and composition, to meet user-specified goals such as cost/energy minimization or deadlines. RHIC employs black-box workload performance modeling, requiring only system-level metrics and incorporating techniques to improve modeling accuracy with bursty and heterogeneous residual resources. We demonstrate the effectiveness and adaptivity of our RHIC prototype with two parallel data analytics frameworks, Hadoop and HBase. Our results show that RHIC finds near-ideal cluster sizes and compositions across a wide range of workload/goal combinations.

Virtualized cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. In this paper, we present a novel Predictive Performance Anomaly Prevention (PREPARE) system that provides automatic performance anomaly prevention for virtualized cloud computing infrastructures. PREPARE integrates online anomaly prediction, learning-based cause inference, and predictive prevention actuation to minimize the performance anomaly penalty without human intervention. We have implemented PREPARE on top of the Xen platform and tested it on the NCSU's Virtual Computing Lab using a commercial data stream processing system (IBM System S) and an online auction benchmark (RUBiS). The experimental results show that PREPARE can effectively prevent performance anomalies while imposing low overhead to the cloud infrastructure.

SUMMARY Reducing the time that a user has to occupy resources for completing cloud tasks can improve cloud efficiency and lower user cost. Such a time, called cloud time, consists of cloud deployment time and application running time. In this work, we design Jump‐start cloud, under which an efficient cloud deployment scheme is proposed for minimizing cloud time. It is especially beneficial to minimize cloud time for clouds serving multimedia applications, as they occupy much more significant cloud resources, such as disk I/O and networking bandwidth and computational costs in processing or transcoding multimedia contents. In particular, we have implemented virtual machine cloning based on disk image sharing for fast virtual machine and application deployment. For applications with heavy disk visits, the post‐deployment QoS may suffer from image sharing, and consequently, application running time will increase. To solve this problem, different image distribution schemes have been designed. We test Jump‐start cloud through a Hadoop‐based benchmark and MapReduce applications. Experiment studies show that our design saves application installation time and, meanwhile, keeps application running time reasonably low, thus makes cloud time shorter. Copyright © 2011 John Wiley & Sons, Ltd.

Elastic resource scaling lets cloud systems meet application service level objectives (SLOs) with minimum resource provisioning costs. In this paper, we present CloudScale, a system that automates fine-grained elastic resource scaling for multi-tenant cloud computing infrastructures. CloudScale employs online resource demand prediction and prediction error handling to achieve adaptive resource allocation without assuming any prior knowledge about the applications running inside the cloud. CloudScale can resolve scaling conflicts between applications using migration, and integrates dynamic CPU voltage/frequency scaling to achieve energy savings with minimal effect on application SLOs. We have implemented CloudScale on top of Xen and conducted extensive experiments using a set of CPU and memory intensive applications (RUBiS, Hadoop, IBM System S). The results show that CloudScale can achieve significantly higher SLO conformance than other alternatives with low resource and energy cost. CloudScale is non-intrusive and light-weight, and imposes negligible overhead (< 2% CPU in Domain 0) to the virtualized computing cluster.