Related papers: Serverless End Game: Disaggregation enabling Trans…
In the contemporary business landscape, collaboration across multiple organizations offers a multitude of opportunities, including reduced operational costs, enhanced performance, and accelerated technological advancement. The application…
As computational infrastructure extends to the edge, it will increasingly offer the same fine-grained resource provisioning mechanisms used in large-scale cloud datacenters, and advances in low-latency, wireless networking technology will…
Today's data centers have an abundance of computing resources, hosting server clusters consisting of as many as tens or hundreds of thousands of machines. To execute a complex computing task over a data center, it is natural to distribute…
Public Cloud Computing has become a fundamental part of modern IT infrastructure as its adoption has transformed the way businesses operate. However, cloud security concerns introduce new risks and challenges related to data protection,…
Cloud Computing offers virtualized computing, storage, and networking resources, over the Internet, to organizations and individual users in a completely dynamic way. These cloud resources are cheaper, easier to manage, and more elastic…
Elastic scaling is one of the central benefits provided by serverless platforms, and requires that they scale resource up and down in response to changing workloads. Serverless platforms scale-down resources by terminating previously…
Serverless computing has become a major trend among cloud providers. With serverless computing, developers fully delegate the task of managing the servers, dynamically allocating the required resources, as well as handling availability and…
As an emerging application paradigm, serverless computing attracts attention from more and more attackers. Unfortunately, security tools for conventional applications cannot be easily ported to serverless, and existing serverless security…
The traditional cloud-centric approach for Deep Learning (DL) requires training data to be collected and processed at a central server which is often challenging in privacy-sensitive domains like healthcare. Towards this, a new learning…
Current serverless offerings give users a limited degree of flexibility for configuring the resources allocated to their function invocations by either coupling memory and CPU resources together or providing no knobs at all. These…
The future of computing systems is inevitably embracing a disaggregated and composable pattern: from clusters of computers to pools of resources that can be dynamically combined together and tailored around applications requirements.…
Serverless computing with cloud functions is quickly gaining adoption, but constrains programmers with its limited support for state management. We introduce a shared file system for cloud functions. It offers familiar POSIX semantics while…
Many applications benefit from computations over the data of multiple users while preserving confidentiality. We present a solution where multiple mutually distrusting users' data can be aggregated with an acceptable overhead, while…
We present Memtrade, the first memory disaggregation system for public clouds. Public clouds introduce a set of unique challenges for resource disaggregation across different tenants, including security, isolation and pricing. Memtrade…
In the era of Web3, decentralized technologies have emerged as the cornerstone of a new digital paradigm. Backed by a decentralized blockchain architecture, the Web3 space aims to democratize all aspects of the web. From data-sharing to…
Confidential computing has gained prominence due to the escalating volume of data-driven applications (e.g., machine learning and big data) and the acute desire for secure processing of sensitive data, particularly, across distributed…
The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume {\it all the data are already in one place}, and…
Distributed Machine Learning refers to the practice of training a model on multiple computers or devices that can be called nodes. Additionally, serverless computing is a new paradigm for cloud computing that uses functions as a…
Serverless computing is an emerging cloud paradigm with serverless functions at its core. While serverless environments enable software developers to focus on developing applications without the need to actively manage the underlying…
The modern datacenter's computing capabilities have far outstripped the applications running within and have become a hidden cost of doing business due to how software is architected and deployed. Resources are over-allocated to monolithic…