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optimization decomposition approach for layered QoS scheduling in grid computing

JOURNAL OF SYSTEMS ARCHITECTURE 2007年第11期53卷 816-832页

作者： Chunlin, Li Layuan, Li Wuhan Univ Technol Dept Comp Sci Wuhan 430063 Peoples R China

The paper presents optimization decomposition based layered Quality of Service (QoS) scheduling for computational grid. Cross layer joint QoS scheduling is studied by considering the global problem as decomposed into three sub-problems: resource allocation at the fabric layer, service composing at the collective layer, and user satisfaction degree at the application layer. The paper proposes a complete solution from optimization modeling, Lagrange relaxation based decomposition, to solutions for each sub-problem Lagrange relaxation based decomposition. These aspects match the vertical organization of the considered grid system: each layer trade with adjacent layers to find a global optimum of the whole grid system. Through multi-layer QoS joint optimization approach, grid global QoS optimization can be achieved. The cross layer policy produces an optimal set of grid resources, service compositions, and user's payments at the fabric layer, collective layer and application layer, respectively, to maximize global grid QoS. The owner of each layer obtains inputs from other layers, tries to maximize its own utility and provides outputs back to other layers. This iterative process lasts until presumably all layers arrive at the same solution. (c) 2007 Elsevier B.V. All rights reserved.

关键词： optimization decomposition layered QoS scheduling grid computing optimization modeling

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DONE: Distributed Approximate Newton-type Method for Federated Edge Learning

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第11期33卷 2648-2660页

作者： Dinh, Canh T. Tran, Nguyen H. Nguyen, Tuan Dung Bao, Wei Balef, Amir Rezaei Zhou, Bing B. Zomaya, Albert Y. Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia Australian Natl Univ Sch Comp Canberra ACT 2601 Australia Sharif Univ Technol Tehran *** Iran

There is growing interest in applying distributed machine learning to edge computing, forming federated edge learning. Federated edge learning faces non-i.i.d. and heterogeneous data, and the communication between edge workers, possibly through distant locations and with unstable wireless networks, is more costly than their local computational overhead. In this work, we propose DONE, a distributed approximate Newton-type algorithm with fast convergence rate for communication-efficient federated edge learning. First, with strongly convex and smooth loss functions, DONE approximates the Newton direction in a distributed manner using the classical Richardson iteration on each edge worker. Second, we prove that DONE has linear-quadratic convergence and analyze its communication complexities. Finally, the experimental results with non-i.i.d. and heterogeneous data show that DONE attains a comparable performance to Newton's method. Notably, DONE requires fewer communication iterations compared to distributed gradient descent and outperforms DANE, FEDL, and GIANT, state-of-the-art approaches, in the case of non-quadratic loss functions.

关键词： Distributed machine learning federated learning optimization decomposition

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Risk aware decomposition of online scheduling for large flexible consumers considering the age of information

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ENERGY REPORTS 2023年 9卷 409-418页

作者： You Situ Chen, Fengchao Zhang, Xin Su, Junni Jiang, Wenqian Guangdong Power Grid Corp Dongguan Power Supply Bur Dongguan 523000 Guangdong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 519172 Guangdong Peoples R China

The stability and security of the power system is significantly influenced by large electricity consumers, especially those with flexible demand, whose electricity purchase scheduling has been extensively studied. However, the high penetration of renewable energy and various widespread distributed storage systems have increased uncertainty of the power system, making it difficult to plan effective day-ahead electricity purchase for major electrical consumers. Online scheduling is therefore required so that the surplus can be sold to and the insufficient can be purchased from the real-time market at the lowest electricity cost. To this end, we formulate a multi-stage online scheduling model for large power consumers with flexible demand. To improve the efficiency of online scheduling, we decompose the problem into multiple single-stage sub-problems. Aimed at each single-stage scheduling, an online algorithm is designed to ensure the performance of optimal scheduling strategies maintains at a level comparable to the offline fixed strategies obtained from an oracle who has complete knowledge of the problem's settings. We prove that our proposed online energy schedule algorithm is able to achieve O(v T) regret bound for the single-stage online scheduling problem. To further highlight the significance of data and improve the multi-stage performance of our online scheduling algorithm, we also involve the notion of age of information combined with regret bound to help determine the optimal interval length. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Online scheduling Large power consumers Demand flexibility Age of information optimization decomposition

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Cooperative and joint video multicast over MIMO-OFDM networks

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DIGITAL SIGNAL PROCESSING 2014年 33卷 98-115页

作者： Oh, Taegeun Lee, Sanghoon Yonsei Univ Dept Elect & Elect Engn Seoul 120749 South Korea

Systems that broadcast/multicast over cellular networks have recently been intensively investigated. When compared to conventional terrestrial or satellite broadcasting systems, the Quality of Service (QoS) experienced by edge users is an important issue due to the inevitable inter-cell interference (ICI) that occurs within multi-cell environments. In order to resolve this issue, we have developed cooperative sub-band allocation (CSA) and CSA-joint transmission (CSA-JT) techniques that operate as a function of defined visual importance levels assigned to multi-layer videos, where the number of service users is limited over the macro/micro cell environment. To ensure that an acceptable level of video quality is delivered to edge users, an adaptive sub-band allocation scheme for layered video is designed to enhance the overall experience for all users based on maintaining program fairness. In a multiple-input/multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system, CSA can effectively mitigate ICI at the cell border via base station (BS) cooperation. Moreover, CSA-JT can improve the QoS for cell border users via joint transmission among the cooperating BSs. In order to achieve the delivery of optimal visual quality, an optimization problem is formulated that seeks to maximize the sum experience of the multicast users. A dual decomposition technique is applied in order to reduce the computational complexity of the system. Simulation results show that the CSA and CSA-JT algorithms exhibit a remarkable reduction of outage probability. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Cooperative video transmission Utility maximization Adaptive resource allocation MIMO optimization decomposition Multicast service

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Cross-layer design in wireles mesh networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2008年第2期57卷 1061-1076页

作者： Akyildiz, Ian F. Wang, Xudong Georgia Inst Technol Sch Elect & Comp Engn Broadband Wireless Networking Lab Atlanta GA 30332 USA Kiyon Inc San Diego CA 92121 USA

The conventional layered-protocol architecture does not provide optimal performance for wireless mesh networks (WMNs). The method of optimization decomposition of the protocol stack can achieve optimal network performance. This method usually results in a clean-slate protocol architecture that is different from the protocol architecture of WMNs. Such a difference actually demonstrates the need for a cross-layer design. Specific features pertaining to WMNs also show the need for cross-layer optimization across different protocol layers. In this paper, motivations for cross-layer design in WMNs are stated first. Moreover, cross-layer optimization schemes and algorithms between different protocol layers are investigated with an objective of shedding light on open research problems and new approaches. Guidelines for carrying out cross-layer design in WMNs are also provided in this paper.

关键词： control cross-layer design medium access optimization decomposition routing wireless mesh networks

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Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

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IEEE-ACM TRANSACTIONS ON NETWORKING 2021年第1期29卷 398-409页

作者： Dinh, Canh T. Tran, Nguyen H. Nguyen, Minh N. H. Hong, Choong Seon Bao, Wei Zomaya, Albert Y. Gramoli, Vincent Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia Kyung Hee Univ Dept Comp Sci & Engn Seoul 02447 South Korea Univ Da Nang Vietnam Korea Univ Informat & Commun Technol Da Nang 550000 Vietnam Ecole Polytech Fed Lausanne Distributed Comp Lab CH-1015 Lausanne Switzerland

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advantages such as preserving data privacy, FL still has challenges of heterogeneity across UEs' data and physical resources. To address these challenges, we first propose FEDL, a FL algorithm which can handle heterogeneous UE data without further assumptions except strongly convex and smooth loss functions. We provide a convergence rate characterizing the trade-off between local computation rounds of each UE to update its local model and global communication rounds to update the FL global model. We then employ FEDL in wireless networks as a resource allocation optimization problem that captures the trade-off between FEDL convergence wall clock time and energy consumption of UEs with heterogeneous computing and power resources. Even though the wireless resource allocation problem of FEDL is non-convex, we exploit this problem's structure to decompose it into three sub-problems and analyze their closed-form solutions as well as insights into problem design. Finally, we empirically evaluate the convergence of FEDL with PyTorch experiments, and provide extensive numerical results for the wireless resource allocation sub-problems. Experimental results show that FEDL outperforms the vanilla FedAvg algorithm in terms of convergence rate and test accuracy in various settings.

关键词： Convergence Computational modeling Training Data models Resource management Wireless communication Wireless networks Distributed machine learning federated learning optimization decomposition

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User-Centric Networks Selection With Adaptive Data Compression for Smart Health

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IEEE SYSTEMS JOURNAL 2018年第4期12卷 3618-3628页

作者： Abdellatif, Alaa Awad Mohamed, Amr Chiasserini, Carla-Fabiana Qatar Univ Dept Comp Sci & Engn Doha 2713 Qatar Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy

The increasing demand for intelligent and sustainable healthcare services has prompted the development of smart health systems. Rapid advances in wireless access technologies and in-network data reduction techniques can significantly assist in implementing such smart systems through providing seamless integration of heterogeneous wireless networks, medical devices, and ubiquitous access to data. Utilization of the spectrum across diverse radio technologies is expected to significantly enhance network capacity and quality of service (QoS) for emerging applications such as remote monitoring over mobile-health (m-health) systems. However, this imposes an essential need to develop innovative networks selection mechanisms that account for energy efficiency while meeting application quality requirements. In this context, this paper proposes an efficient networks selection mechanism with adaptive compression for improving medical data delivery over heterogeneous m-health systems. We consider different performance aspects, as well as networks characteristics and application requirements, so as to obtain an efficient solution that grasps the conflicting nature of the various users' objectives and addresses their inherent tradeoffs. The proposed methodology advocates a user-centric approach towards leveraging heterogeneous wireless networks to enhance the performance of m-health systems. Simulation results show that our solution significantly outperforms state-of-the-art techniques.

关键词： Heterogeneous wireless environment mobile health multi-RAT architectures network selection optimization decomposition

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Joint source adaptation and resource allocation for multi-user wireless video streaming

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2008年第5期18卷 582-595页

作者： Huang, Jianwei Li, Zhu Chiang, Mung Katsaggelos, Aggelos K. Chinese Univ Hong Kong Dept Informat Engn Shatin Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong Peoples R China Princeton Univ Dept Elect Engn Princeton NJ 08544 USA NW Univ Dept Elect Engn & Comp Sci Evanston IL 60260 USA

Multi-user video streaming over wireless channels is a challenging problem, where the demand for better video quality and small transmission delays needs to be reconciled with the limited and often time-varying communication resources. This paper presents a framework for joint network optimization, source adaptation, and deadline-driven scheduling for multi-user video streaming over wireless networks. We develop a joint adaptation, resource allocation and scheduling (JARS) algorithm, which allocates the communication resource based on the video users' quality of service, adapts video sources based on smart summarization, and schedules the transmissions to meet the frame delivery deadlines. The proposed algorithm leads to near full utilization of the network resources and satisfies the delivery deadlines for all video frames. Substantial performance improvements are achieved compared with heuristic schemes that do not take the interactions between multiple users into consideration.

关键词： collaborative video streaming optimization decomposition pricing control rate-distortion modeling video adaptation

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Dice: a Game Theoretic Framework for Wireless Multipath Network Coding

<i>Dice</i>: a Game Theoretic Framework for Wireless Multipa...

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9th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)

作者： Zhang, Xinyu Li, Baochun Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 1A1 Canada

ISBN: (纸本)9781605580739

Network coding has emerged as a promising approach that enables reliable and efficient end-to-end transmissions in lossy wireless mesh networks. Existing protocols have demonstrated its resilience to packet losses, as well as the ability to integrate naturally with multipath opportunistic routing. However, these heuristics do not take into account the inherent resource competition in wireless networks, thereby compromising the coding advantages. In this paper, we take a game-theoretic perspective towards optimized resource allocation for network coding based unicast protocols. We design decentralized mechanisms that achieve better efficiency-fairness tradeoff, for both cooperative and selfish users. Our framework features a modularized optimization of two sub-problems: the multipath routing of coded information flows for each player, and the broadcast and coding rate allocation among competing players. We have implemented the framework on a wireless emulation testbed and demonstrated its high performance in terms of throughput and fairness.

关键词： Network coding multipath opportunistic routing wireless mesh networks flow control optimization decomposition game theory

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Federated Learning over Wireless Networks: optimization Model Design and Analysis

Federated Learning over Wireless Networks: Optimization Mode...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Tran, Nguyen H. Bao, Wei Zomaya, Albert Nguyen, Minh N. H. Hong, Choong Seon Univ Sydney Sch Comp Sci Sydney NSW Australia Kyung Hee Univ Dept Comp Sci & Engn Yongin South Korea

ISBN: (纸本)9781728105154

There is an increasing interest in a new machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), and each UE contributes to the learning model by independently computing the gradient based on its local training data. Federated Learning has several benefits of data privacy and potentially a large amount of UE participants with modern powerful processors and low-delay mobile-edge networks. While most of the existing work focused on designing learning algorithms with provable convergence time, other issues such as uncertainty of wireless channels and UEs with heterogeneous power constraints and local data size, are under-explored. These issues especially affect to various trade-offs: (i) between computation and communication latencies determined by learning accuracy level, and thus (ii) between the Federated Learning time and UE energy consumption. We fill this gap by formulating a Federated Learning over wireless network as an optimization problem FEDL that captures both trade-offs. Even though FEDL is non-convex, we exploit the problem structure to decompose and transform it to three convex sub-problems. We also obtain the globally optimal solution by charactering the closed-form solutions to all sub-problems, which give qualitative insights to problem design via the obtained optimal FEDL learning time, accuracy level, and UE energy cost. Our theoretical analysis is also illustrated by extensive numerical results.

关键词： Distributed Machine Learning over Wireless Networks Federated Learning optimization decomposition

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