Identity Management and Resource Allocation in the Network Virtualization Environment

Due to the existence of multiple stakeholders with conflicting goals and policies, alterations to the existing Internet architecture are now limited to simple incremental updates; deployment of any new, radically different technology is next to impossible. To fend off this ossification, network virtualization has been propounded as a diversifying attribute of the future inter-networking paradigm. In this talk, we provide an overview of the network virtualization environment (NVE) and address two basic problems in this emerging field of networking research. The identity management problem is primarily concerned with ensuring interoperability across heterogeneous identifier spaces for locating and identifying end hosts in different virtual networks. We describe the architectural and the functional components of a novel identity management framework (iMark) that enables end-to-end connectivity across heterogeneous virtual networks in the NVE without revoking their autonomy. The virtual network embedding problem deals with the mapping of virtual nodes and links onto physical network resources. We argue that the separation of the node mapping and the link mapping phases in the existing algorithms considerably reduces the solution space and degrades embedding quality. We propose coordinated node and link mapping to devise two algorithms (D-ViNE and R-ViNE) for the online version of the problem under realistic assumptions and compare their performance with the existing heuristics

Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

Training deep neural networks (DNNs) is becoming increasingly more resource- and energy-intensive every year. Unfortunately, existing works primarily focus on optimizing DNN training for faster completion, often without considering the impact on energy efficiency. In this paper, we observe that common practices to improve training performance can often lead to inefficient energy usage. More importantly, we demonstrate that there is a tradeoff between energy consumption and performance optimization. To this end, we propose Zeus, an optimization framework to navigate this tradeoff by automatically finding optimal job- and GPU-level configurations for recurring DNN training jobs. Zeus uses an online exploration-exploitation approach in conjunction with just-in-time energy profiling, averting the need for expensive offline measurements, while adapting to data drifts over time. Our evaluation shows that Zeus can improve the energy efficiency of DNN training by 15.3%-75.8% for diverse workloads.Comment: NSDI 2023 | Homepage https://ml.energy/zeu

FLINT: A Platform for Federated Learning Integration

Cross-device federated learning (FL) has been well-studied from algorithmic, system scalability, and training speed perspectives. Nonetheless, moving from centralized training to cross-device FL for millions or billions of devices presents many risks, including performance loss, developer inertia, poor user experience, and unexpected application failures. In addition, the corresponding infrastructure, development costs, and return on investment are difficult to estimate. In this paper, we present a device-cloud collaborative FL platform that integrates with an existing machine learning platform, providing tools to measure real-world constraints, assess infrastructure capabilities, evaluate model training performance, and estimate system resource requirements to responsibly bring FL into production. We also present a decision workflow that leverages the FL-integrated platform to comprehensively evaluate the trade-offs of cross-device FL and share our empirical evaluations of business-critical machine learning applications that impact hundreds of millions of users.Comment: Preprint for MLSys 202

A survey of network virtualization

a b s t r a c t Due to the existence of multiple stakeholders with conflicting goals and policies, alterations to the existing Internet architecture are now limited to simple incremental updates; deployment of any new, radically different technology is next to impossible. To fend off this ossification, network virtualization has been propounded as a diversifying attribute of the future inter-networking paradigm. By introducing a plurality of heterogeneous network architectures cohabiting on a shared physical substrate, network virtualization promotes innovations and diversified applications. In this paper, we survey the existing technologies and a wide array of past and state-of-the-art projects on network virtualization followed by a discussion of major challenges in this area