arXiv subjects

Chien-Sheng Yang

Publications and source records attributed to Chien-Sheng Yang.

9 recordsLinked to original sources

arXiv2025

6G EdgeAI: Performance Evaluation and Analysis

Generative AI (GenAI) services powered by large language models (LLMs) increasingly deliver real-time interactions, yet existing 5G multi-access edge computing (MEC) architectures often treat communication and computing as separate domains, limiting their ability to meet stringent latency requirements. To address this challenge, we introduce an Integrated Communication and Computing (ICC) framework where computing capabilities are enabled to reside directly in radio access network (RAN) nodes and jointly manage bandwidth and computing resources. Our queueing-theoretic analysis shows that ICC outperforms 5G MEC, achieving higher service capacity (defined as the maximum arrival rate that maintains a specified fraction of jobs completed within a given delay budget) by 98%. We corroborate these gains through system-level simulations that account for transformer-based LLM workloads, realistic GPU specifications, and a priority-based scheduling scheme. The simulations show that ICC improves service capacity by 60%, demonstrating its potential to enable efficient, cost-effective real-time GenAI services in 6G.

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arXiv2023

Carbon-Neutralized Joint User Association and Base Station Switching for Green Cellular Networks

Mitigating climate change and its impacts is one of the sustainable development goals (SDGs) required by United Nations for an urgent action. Increasing carbon emissions due to human activities is the root cause to climate change. Telecommunication networks that provide service connectivity to mobile users contribute great amount of carbon emissions by consuming lots of non-renewable energy sources. Beyond the improvement on energy efficiency, to reduce the carbon footprint, telecom operators are increasing their adoption of renewable energy (e.g., wind power). The high variability of renewable energy in time and location; however, creates difficulties for operators when utilizing renewables for the reduction of carbon emissions. In this paper, we consider a heterogeneous network consisted of one macro base station (MBS) and multiple small base stations (SBSs) where each base station (BS) is powered by both of renewable and non-renewable energy. Different from the prior works that target on the total power consumption, we propose a novel scheme to minimize the carbon footprint of networks by dynamically switching the ON/OFF modes of SBSs and adjusting the association between users and BSs to access renewables as much as possible. Our numerical analysis shows that the proposed scheme significantly reduces up to 86% of the nonrenewable energy consumption compared to two representative baselines.

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arXiv2022

Carbon-Neutralized Task Scheduling for Green Computing Networks

Climate change due to increasing carbon emissions by human activities has been identified as one of the most critical threat to Earth. Carbon neutralization, as a key approach to reverse climate change, has triggered the development of new regulations to enforce the economic activities toward low carbon solutions. Computing networks that enable users to process computation-intensive tasks contribute huge amount of carbon emissions due to rising energy consumption. To analyze the achievable reduction of carbon emissions by a scheduling policy, we first propose a novel virtual queueing network model that captures communication and computing procedures in networks. To adapt to highly variable and unpredictable nature of renewable energy utilized by computing networks (i.e., carbon intensity of grid varies by time and location), we propose a novel carbon-intensity based scheduling policy that dynamically schedules computation tasks over clouds via the drift-plus-penalty methodology in Lyapunov optimization. Our numerical analysis using real-world data shows that the proposed policy achieves 54% reduction on the cumulative carbon emissions for AI model training tasks compared to the queue-length based policy.

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arXiv2022

LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning

Secure model aggregation is a key component of federated learning (FL) that aims at protecting the privacy of each user's individual model while allowing for their global aggregation. It can be applied to any aggregation-based FL approach for training a global or personalized model. Model aggregation needs to also be resilient against likely user dropouts in FL systems, making its design substantially more complex. State-of-the-art secure aggregation protocols rely on secret sharing of the random-seeds used for mask generations at the users to enable the reconstruction and cancellation of those belonging to the dropped users. The complexity of such approaches, however, grows substantially with the number of dropped users. We propose a new approach, named LightSecAgg, to overcome this bottleneck by changing the design from "random-seed reconstruction of the dropped users" to "one-shot aggregate-mask reconstruction of the active users via mask encoding/decoding". We show that LightSecAgg achieves the same privacy and dropout-resiliency guarantees as the state-of-the-art protocols while significantly reducing the overhead for resiliency against dropped users. We also demonstrate that, unlike existing schemes, LightSecAgg can be applied to secure aggregation in the asynchronous FL setting. Furthermore, we provide a modular system design and optimized on-device parallelization for scalable implementation, by enabling computational overlapping between model training and on-device encoding, as well as improving the speed of concurrent receiving and sending of chunked masks. We evaluate LightSecAgg via extensive experiments for training diverse models on various datasets in a realistic FL system with large number of users and demonstrate that LightSecAgg significantly reduces the total training time.

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arXiv2021

Edge Computing in the Dark: Leveraging Contextual-Combinatorial Bandit and Coded Computing

With recent advancements in edge computing capabilities, there has been a significant increase in utilizing the edge cloud for event-driven and time-sensitive computations. However, large-scale edge computing networks can suffer substantially from unpredictable and unreliable computing resources which can result in high variability of service quality. Thus, it is crucial to design efficient task scheduling policies that guarantee quality of service and the timeliness of computation queries. In this paper, we study the problem of computation offloading over unknown edge cloud networks with a sequence of timely computation jobs. Motivated by the MapReduce computation paradigm, we assume each computation job can be partitioned to smaller Map functions that are processed at the edge, and the Reduce function is computed at the user after the Map results are collected from the edge nodes. We model the service quality (success probability of returning result back to the user within deadline) of each edge device as function of context (collection of factors that affect edge devices). The user decides the computations to offload to each device with the goal of receiving a recoverable set of computation results in the given deadline. Our goal is to design an efficient edge computing policy in the dark without the knowledge of the context or computation capabilities of each device. By leveraging the \emph{coded computing} framework in order to tackle failures or stragglers in computation, we formulate this problem using contextual-combinatorial multi-armed bandits (CC-MAB), and aim to maximize the cumulative expected reward. We propose an online learning policy called \emph{online coded edge computing policy}, which provably achieves asymptotically-optimal performance in terms of regret loss compared with the optimal offline policy for the proposed CC-MAB problem.

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arXiv2021

Coded Computing for Secure Boolean Computations

The growing size of modern datasets necessitates splitting a large scale computation into smaller computations and operate in a distributed manner. Adversaries in a distributed system deliberately send erroneous data in order to affect the computation for their benefit. Boolean functions are the key components of many applications, e.g., verification functions in blockchain systems and design of cryptographic algorithms. We consider the problem of computing a Boolean function in a distributed computing system with particular focus on \emph{security against Byzantine workers}. Any Boolean function can be modeled as a multivariate polynomial with high degree in general. However, the security threshold (i.e., the maximum number of adversarial workers can be tolerated such that the correct results can be obtained) provided by the recent proposed Lagrange Coded Computing (LCC) can be extremely low if the degree of the polynomial is high. We propose three different schemes called \emph{coded Algebraic normal form (ANF)}, \emph{coded Disjunctive normal form (DNF)} and \emph{coded polynomial threshold function (PTF)}. The key idea of the proposed schemes is to model it as the concatenation of some low-degree polynomials and threshold functions. In terms of the security threshold, we show that the proposed coded ANF and coded DNF are optimal by providing a matching outer bound.

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arXiv2020

PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously

Today's blockchain designs suffer from a trilemma claiming that no blockchain system can simultaneously achieve decentralization, security, and performance scalability. For current blockchain systems, as more nodes join the network, the efficiency of the system (computation, communication, and storage) stays constant at best. A leading idea for enabling blockchains to scale efficiency is the notion of sharding: different subsets of nodes handle different portions of the blockchain, thereby reducing the load for each individual node. However, existing sharding proposals achieve efficiency scaling by compromising on trust - corrupting the nodes in a given shard will lead to the permanent loss of the corresponding portion of data. In this paper, we settle the trilemma by demonstrating a new protocol for coded storage and computation in blockchains. In particular, we propose PolyShard: ``polynomially coded sharding'' scheme that achieves information-theoretic upper bounds on the efficiency of the storage, system throughput, as well as on trust, thus enabling a truly scalable system. We provide simulation results that numerically demonstrate the performance improvement over state of the arts, and the scalability of the PolyShard system. Finally, we discuss potential enhancements, and highlight practical considerations in building such a system.

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arXiv2019

Timely-Throughput Optimal Coded Computing over Cloud Networks

In modern distributed computing systems, unpredictable and unreliable infrastructures result in high variability of computing resources. Meanwhile, there is significantly increasing demand for timely and event-driven services with deadline constraints. Motivated by measurements over Amazon EC2 clusters, we consider a two-state Markov model for variability of computing speed in cloud networks. In this model, each worker can be either in a good state or a bad state in terms of the computation speed, and the transition between these states is modeled as a Markov chain which is unknown to the scheduler. We then consider a Coded Computing framework, in which the data is possibly encoded and stored at the worker nodes in order to provide robustness against nodes that may be in a bad state. With timely computation requests submitted to the system with computation deadlines, our goal is to design the optimal computation-load allocation scheme and the optimal data encoding scheme that maximize the timely computation throughput (i.e, the average number of computation tasks that are accomplished before their deadline). Our main result is the development of a dynamic computation strategy called Lagrange Estimate-and Allocate (LEA) strategy, which achieves the optimal timely computation throughput. It is shown that compared to the static allocation strategy, LEA increases the timely computation throughput by 1.4X - 17.5X in various scenarios via simulations and by 1.27X - 6.5X in experiments over Amazon EC2 clusters

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arXiv2018

Communication-Aware Scheduling of Serial Tasks for Dispersed Computing

There is a growing interest in development of in-network dispersed computing paradigms that leverage the computing capabilities of heterogeneous resources dispersed across the network for processing massive amount of data is collected at the edge of the network. We consider the problem of task scheduling for such networks, in a dynamic setting in which arriving computation jobs are modeled as chains, with nodes representing tasks, and edges representing precedence constraints among tasks. In our proposed model, motivated by significant communication costs in dispersed computing environments, the communication times are taken into account. More specifically, we consider a network where servers are capable of serving all task types, and sending the results of processed tasks from one server to another server results in some communication delay that makes the design of optimal scheduling policy significantly more challenging than classical queueing networks. As the main contributions of the paper, we first characterize the capacity region of the network, then propose a novel virtual queueing network encoding the state of the network. Finally, we propose a Max-Weight type scheduling policy, and considering the virtual queueing network in the fluid limit, we use a Lyapunov argument to show that the policy is throughput-optimal.

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