Related papers: Syndeo: Portable Ray Clusters with Secure Containe…

Many modern applications require real-time processing of large volumes of high-speed data. Such data processing needs can be modeled as a streaming computation. A streaming computation is specified as a dataflow graph that exposes multiple…

In this work, we detail the design and structure of a Synopses Data Engine (SDE) which combines the virtues of parallel processing and stream summarization towards delivering interactive analytics at extreme scale. Our SDE is built on top…

Service-Oriented Computing is a paradigm that uses services as building blocks for building distributed applications. The primary motivation for orchestrating services in the cloud used to be distributed business processes, which drove the…

Streaming computation plays an important role in large-scale data analysis. The sliding window model is a model of streaming computation which also captures the recency of the data. In this model, data arrives one item at a time, but only…

In this paper, we address the virtualization and the centralization of real-time network functions, notably in the framework of Cloud RAN (C-RAN). We thoroughly analyze the required fronthaul capacity for the deployment of the proposed…

Cloud computing enables users (clients) to outsource large volume of their data to cloud servers. Secure distributed cloud storage schemes ensure that multiple servers store these data in a reliable and untampered fashion. We propose an…

With the ability to use containers at the edge, they pose a unified solution to combat the complexity of distributed multi-host ROS deployments, as well as individual ROS-node and dependency deployment. The bidirectional communication in…

Distributed scientific workflows increasingly span heterogeneous compute clusters, edge resources, and geo-distributed data repositories. In these environments, a centralized orchestrator is an architectural bottleneck -- introducing a…

The increasing volume and complexity of IoT systems demand a transition from the cloud-centric model to a decentralized IoT architecture in the so-called Computing Continuum, with no or minimal reliance on central servers. This paradigm…

Heterogeneous clusters with nodes containing one or more accelerators, such as GPUs, have become common. While MPI provides inter-address space communication, and OpenCL provides a process with access to heterogeneous computational…

This study investigates the potential of WebAssembly as a more secure and efficient alternative to Linux containers for executing untrusted code in cloud computing with Kubernetes. Specifically, it evaluates the security and performance…

Open-source software OSS is widely adopted in enterprise settings, but standalone tools often lack native support for protocols like SAML or OIDC, creating a critical security integration gap. This paper introduces and formalizes the Auth…

Existing blockchain systems scale poorly because of their distributed consensus protocols. Current attempts at improving blockchain scalability are limited to cryptocurrency. Scaling blockchain systems under general workloads (i.e.,…

The paper is devoted to the problem of erasure coding in distributed storage. We consider a model of storage that assumes that nodes are organized into equally sized groups, called racks, that within each group the nodes can communicate…

The increasing availability of cloud computing and scientific super computers brings great potential for making R accessible through public or shared resources. This allows us to efficiently run code requiring lots of cycles and memory, or…

Modern operating systems all support multi-users that users could share a computer simultaneously and not affect each other. However, there are some limitations. For example, privacy problem exists that users are visible to each other in…

Cloud computing enables users to process and store data remotely on high-performance computers and servers by sharing data over the Internet. However, transferring data to clouds causes unavoidable privacy concerns. Here, we present a…

Most neural network scheduling research focuses on optimizing static, end-to-end models of fixed width, overlooking dynamic approaches that adapt to heterogeneous hardware and fluctuating runtime conditions. We present Slim Scheduler, a…

Classical erasure codes, e.g. Reed-Solomon codes, have been acknowledged as an efficient alternative to plain replication to reduce the storage overhead in reliable distributed storage systems. Yet, such codes experience high overhead…

Stream processing is a computing paradigm that supports real-time data processing for a wide variety of applications. At Meta, it's used across the company for various tasks such as deriving product insights, providing and improving user…