MIDDLEWARE

Serverless computing has emerged as a popular paradigm in modern cloud environments, offering flexibility and scalability to tenants. A serverless function might handle sensitive tenant data. Employing Trusted Execution Environment (TEE) techniques to ...

Stream Processing Engines (SPEs) extract value from data streams in the Edge-to-Cloud continuum through graphs of operators that progressively transform data.

State-of-the-art SPEs are bridged into shared models based on their overlapping APIs. The ...

Serverless computing has received growing interest in recent years for supporting large-scale machine learning tasks. However, training a machine learning model in a serverless environment is a nontrivial procedure and several challenges still need to be ...

Ensuring the highest training throughput to maximize resource efficiency, while maintaining fairness among users, is critical for deep learning (DL) training in heterogeneous GPU clusters. However, current DL schedulers provide only limited fairness ...

Microservice architectures and service meshes have become highly popular and face increasingly stringent scalability and dependability requirements. To achieve low-latency service execution and maximize performance, service providers of large-scale ...

Multi-Variant Execution (MVX) is an effective way to detect memory corruption vulnerabilities, intrusions, or live software updates. A traditional MVX system concurrently runs multiple copies of functionally identical, layout-different program variants. ...

As demand for cloud computing in bioinformatics increases, various studies have explored options for running large-scale workloads with reduced costs, often leveraging spot instances in multi-region deployments. For example, spot instances offer lower ...

Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. Heterogeneous hardware, unreliable client devices, and energy constraints often characterize edge computing systems. In ...

We investigate the problem of getting hints on the effects of virtualization system (aka hypervisor) updates impact on virtual machines (VMs). System administrators can be reluctant to apply updates due to vague hints regarding the updates' impact on ...

Leveraging serverless platforms for the efficient execution of distributed data analytics frameworks, such as Apache Spark [3], has gained substantial interest since early 2022. The elasticity, free-of-management, and on-demand scalability of serverless ...

We investigate the issue of deploying applications on a set of loosely coupled compute devices, connected through a wireless mesh, typical in community networks. Wireless mesh networks experience significant temporal and spatial variations in link ...

Serverless computing has disrupted how computation is performed in the Cloud. The ability to write Functions, and not care about infrastructure brings many benefits, including significantly lower deployment costs, improved developer workflow, scalability,...

Blockchain technology sparked renewed interest in planetary-scale Byzantine fault-tolerant (BFT) state machine replication (SMR). While recent works predominantly focused on improving the scalability and throughput of these protocols, few of them ...

In Function-as-a-Service (FaaS) serverless, large applications are split into short-lived stateless functions. Deploying functions is mutually profitable: users need not be concerned with resource management, while providers can keep their servers at ...

Fine-tuning of pre-trained large language models has become increasingly popular, yet existing fine-tuning methods are typically centralized, requiring users to send local data to centralized servers, or model owners to open-source their models. However, ...

Partitioning a multi-threaded application between a secure and a non-secure memory zone remains a challenge. The current tools rely on data flow analysis techniques, which are unable to handle multi-threaded C or C++ applications. To avoid this ...

Serverless platforms are supporting an increasing variety of applications (apps). Among these, apps such as Machine Learning (ML) inference serving can benefit significantly from leveraging accelerators like GPUs. Yet, major serverless providers, despite ...

System call filtering is widely used to secure programs in multi-tenant environments, and to sandbox applications in modern desktop software deployment and package management systems. Filtering rules are hard to write and maintain manually, hence ...

Training a proprietary Neural Network (NN) model with a proprietary dataset on the cloud comes at the risk of exposing the model architecture and the dataset to the cloud service provider. To tackle this problem, in this paper, we present an NN ...

In stream processing, managing sensitive information in a timely manner while ensuring trust remains a significant challenge. When parties without a priori trust cooperate to execute a streaming application, it is difficult to ensure that sensitive data ...

Federated Unlearning (FU) aims to delete specific training data from an ML model trained using Federated Learning (FL). However, existing FU methods suffer from inefficiencies due to the high costs associated with gradient recomputation and storage. This ...

Edge AI aims to enable distributed machine learning (DML) on edge resources to fulfill the need for data privacy and low latency. Meanwhile, the challenge of device heterogeneity and discrepancy in data distribution requires more sophisticated DML ...

Data deduplication is critical to cloud storage providers and is widely employed to conserve server-side storage space. Data chunking is an important aspect of deduplication, being directly responsible for storage space savings and end-to-end system ...

Adjusting batch sizes and adaptively tuning other hyperparameters can significantly speed up deep neural network (DNN) training. Despite the ubiquity of heterogeneous clusters, existing adaptive DNN training techniques solely consider homogeneous ...

Modern GPU applications, such as machine learning (ML), can only partially utilize GPUs, leading to GPU underutilization in cloud environments. Sharing GPUs across multiple applications from different tenants can improve resource utilization and ...

When put under stress, traditional state-machine replication protocols typically exhibit response times that by far exceed the average level of normal-case operation. The common way to mitigate such overload-induced tail latency is to overprovision ...

State-machine replication (SMR) is a popular fault-tolerance technique for building highly-available services. Usually, consensus protocols are used to enforce a deterministic service-request ordering among replicas, in order to prevent their state from ...

Microservice architectures backed by container technology have been widely used in many real-world cloud-native applications. By enabling customers to manage their services and configurations in the cloud in a centralized, externalized, and dynamic ...

Federated learning (FL) systems enable multiple clients to train a machine learning model iteratively through synchronously exchanging the intermediate model weights with a single server. The scalability of such FL systems can be limited by two factors: ...

This paper explores a practical means to employ Data Plane Development Kit (DPDK), a kernel-bypassing framework for packet processing, in resource-limited multi-tenant edge data centers. The problem is that the traditional virtual CPU (vCPU) schedulers ...