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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Zou, Yuelin Shen, Jia Yin, Rui Guo, Jiangtao Guo, Yi State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi830002 China State Grid Xinjiang Electric Power Co. Ltd. Urumqi830002 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fuzhou350001 China

ISBN: (纸本)9798350341546

With the establishment and development of integrated monitoring platforms and communication information platforms for intelligent substations, the data volume of the power system is showing explosive growth. However, existing data centers have poor processing capabilities for massive amounts of data, making it difficult to meet the multiple service quality requirements of users. How to effectively manage massive and complex data, fully utilize the value of data, improve service quality, reduce operational costs, and achieve rapid response to users is an urgent problem to be solved. Therefore, based on the actual needs of power enterprises, this article proposed a cloud computing power data middleware architecture based on distributed technology, which effectively managed, cleaned, converted, and evaluated power data sources through cloud computing platforms. This article mainly analyzed the shortcomings of traditional power data processing platforms and the feasibility of cloud computing based power data platforms. Finally, through load balancing bias comparison and pressure testing experiments under different task numbers, it was proven that cloud computing systems outperformed traditional power data processing systems in terms of utility value, completion time, and load balancing. Moreover, in the pressure testing experiment, under the condition of 10 concurrent users and 673 system request transactions, 91.3% of the interface response time in the cloud computing system was within 50ms. Therefore, cloud computing systems have certain advantages in processing power data. © 2023 IEEE.

关键词： Cloud computing Data Center System Data Processing distributed computing Electricity Data

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A game theoretic framework for distributed computing with dynamic set of agents

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ANNALS OF OPERATIONS RESEARCH 2024年第3期336卷 1871-1904页

作者： Dhamal, Swapnil Ben-Ameur, Walid Chahed, Tijani Altman, Eitan Sunny, Albert Poojary, Sudheer Telecom SudParis Inst Polytech Paris Evry France INRIA Sophia Antipolis Mediterranee Valbonne France Indian Inst Technol Palakkad India Qualcomm India Pvt Ltd Bengaluru India Avignon Univ LIA Avignon France

We consider a distributed computing setting wherein a central entity seeks power from computational providers by offering a certain reward in return. The computational providers are classified into long-term stakeholders that invest a constant amount of power over time and players that can strategize on their computational investment. In this paper, we model and analyze a stochastic game in such a distributed computing setting, wherein players arrive and depart over time. While our model is formulated with a focus on volunteer computing, it equally applies to certain other distributed computing applications such as mining in blockchain. We prove that, in Markov perfect equilibrium, only players with cost parameters in a relatively low range which collectively satisfy a certain constraint in a given state, invest. We infer that players need not have knowledge about the system state and other players' parameters, if the total power that is being received by the central entity is communicated to the players as part of the system's protocol. If players are homogeneous and the system consists of a reasonably large number of players, we observe that the total power received by the central entity is proportional to the offered reward and does not vary significantly despite the players' arrivals and departures, thus resulting in a robust and reliable system. We then study by way of simulations and mean field approximation, how the players' utilities are influenced by their arrival and departure rates as well as the system parameters such as the reward's amount and dispensing rate. We observe that the players' expected utilities are maximized when their arrival and departure rates are such that the average number of players present in the system is typically between 1 and 2, since this leads to the system being in the condition of least competition with high probability. Further, their expected utilities increase almost linearly with the offered reward and converge to a cons

关键词： Game theory Stochastic game Markov perfect equilibrium Stackelberg game distributed computing Volunteer computing

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A Worst-Case Latency and Age Analysis of Coded distributed computing With Unreliable Workers and Periodic Tasks

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 5874-5889页

作者： Chiariotti, Federico Soret, Beatriz Popovski, Petar Univ Padua Dept Informat Engn I-35131 Padua Italy Univ Malaga Telecommun Res Inst Malaga 29010 Spain Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark

Over the past decade, the deep learning revolution has led to ever-increasing demands for computing power and working memory to support larger and larger neural networks. As this coincided with the end of Moore's law, distributed solutions have emerged as a natural answer: in particular, the novel Coded distributed computing (CDC) paradigm exploits results from coding theory to divide large tasks into redundant sets of smaller subtasks to be processed across multiple workers, making the computation more robust to stragglers and malicious worker nodes. Optimizing the use of these distributed computing resources is critical, as excessive redundancy might impact on performance and energy consumption. This work considers a CDC system receiving periodic tasks, deriving the full distribution of the latency, reliability, and Peak Age of Information (PAoI) under worker diversity and random failures. The CDC system is modeled as a fork-join D/M/(K, N)/L queue, where only K of the coded N subtasks are necessary to solve the overall task, and workers can hold up to L subtasks in their queues. Our results are useful for resource optimization, showing the relationship between system load, redundancy, and latency, as well as the trade-off between latency, reliability, and age performance.

关键词： Computational modeling distributed computing Reliability Queueing analysis Probability density function Solid modeling Optimization Coded distributed computing latency analysis age of information fork-join queues

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A Fundamental Tradeoff Among Storage, Computation, and Communication for distributed computing Over Star Network

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第10期71卷 5686-5697页

作者： Yan, Qifa Tang, Xiaohu Tao, Meixia Huang, Qin Southwest Jiaotong Univ CSNMT Int Coop Res Ctr MoST Informat Coding & Transmiss Key Lab Sichuan Prov Chengdu 611756 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

Coded distributed computing can alleviate the communication load by leveraging the redundant storage and computation resources with coding techniques in distributed computing. In this paper, we study a MapReduce-type distributed computing framework over star topological network, where all the workers exchange information through a common access point. The optimal tradeoff among the normalized number of stored files (storage load), computed intermediate values (computation load), and transmitted bits in the uplink and downlink (communication loads) is characterized. A coded computing scheme is proposed to achieve the Pareto-optimal tradeoff surface, in which the access point only needs to perform simple chain coding between the signals it receives, and information- theoretical bound matching the surface is also provided.

关键词： distributed computing Stars Computational modeling Uplink Encoding Downlink Wireless networks Storage coded computing communication MapReduce star network

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Handoff-Aware distributed computing in High Altitude Platform Station (HAPS)--Assisted Vehicular Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第12期22卷 8814-8827页

作者： Ren, Qiqi Abbasi, Omid Kurt, Gunes Karabulut Yanikomeroglu, Halim Chen, Jian Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Carleton Univ Dept Syst & Comp Engn Nonterr Networks NTN Lab Ottawa ON K1S 5B6 Canada Polytech Montreal Dept Elect Engn Montreal PQ H3T 1J4 Canada

distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks because of its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. On this basis, we formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation-based method are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner, it also takes delay performance into account and maintains the delay stability.

关键词： Task analysis Servers Delays Computational modeling Wireless communication distributed databases Autonomous aerial vehicles Internet of Vehicles (IoV) high altitude platform station (HAPS) distributed computing edge network handoff

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FLCD: A Flexible Low Complexity Design of Coded distributed computing

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IEEE TRANSACTIONS ON CLOUD computing 2023年第1期11卷 470-483页

作者： Woolsey, Nicholas Wang, Xingyue Chen, Rong-Rong Ji, Mingyue Univ Utah Dept Elect Engn Salt Lake City UT 84112 USA

We propose a flexible low complexity design (FLCD) of coded distributed computing (CDC) with empirical evaluation on Amazon Elastic Compute Cloud (Amazon EC2). CDC can expedite MapReduce like computation by trading increased map computations to reduce communication load and shuffle time. A main novelty of FLCD is to utilize the design freedom in defining map and reduce functions to develop asymptotic homogeneous systems to support varying intermediate values (IV) sizes under a general MapReduce framework. Compared to existing designs with constant IV sizes, FLCD offers greater flexibility in adapting to network parameters and significantly reduces the implementation complexity by requiring fewer input files and shuffle groups. The FLCD scheme is the first proposed low-complexity CDC design that can operate on a network with an arbitrary number of nodes and computation load. We perform empirical evaluations of the FLCD by executing the TeraSort algorithm on an Amazon EC2 cluster. This is the first time that theoretical predictions of the CDC shuffle time are validated by empirical evaluations. The evaluations demonstrate a 2.0 to 4.24x speedup compared to conventional uncoded MapReduce, a 12 to 52 percent reduction in total time, and a wider range of operating network parameters compared to existing CDC schemes.

关键词： Complexity theory Computational modeling Cloud computing distributed computing Indexes Benchmark testing Urban areas Coded distributed computing communication load computation load coded multicasting heterogeneity low-complexity implementation Amazon EC2

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Elastic Resource Allocation for Coded distributed computing Over Heterogeneous Wireless Edge Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第4期22卷 2636-2649页

作者： Nguyen, Cong T. Nguyen, Diep N. Hoang, Dinh Thai Phan, Khoa Tran Niyato, Dusit Pham, Hoang-Anh Dutkiewicz, Eryk Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia Vietnam Natl Univ Ho Chi Minh City VNU HCM Ho Chi Minh City Univ Technol HCMUT Fac Comp Sci & Engn Ho Chi Minh City 700000 Vietnam La Trobe Univ Sch Engn & Math Sci Melbourne VIC 3086 Australia Nanyang Technol Univ Sch Comp Sci & Engn Singapore City 639798 Singapore

Coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. By assigning redundant workloads to the computing nodes, CDC can significantly enhance the performance of the whole system. However, since the core idea of CDC is to introduce redundancies to compensate for uncertainties, it may lead to a large amount of wasted energy at the edge nodes. It can be observed that the more redundant workload added, the less impact the straggling effects have on the system. However, at the same time, the more energy is needed to perform redundant tasks. In this work, we develop a novel framework, namely CERA, to elastically allocate computing resources for CDC processes. Particularly, CERA consists of two stages. In the first stage, we model a joint coding and node selection optimization problem to minimize the expected processing time for a CDC task. Since the problem is NP-hard, we propose a linearization approach and a hybrid algorithm to quickly obtain the optimal solutions. In the second stage, we develop a smart online approach based on Lyapunov optimization to dynamically turn off straggling nodes based on their actual performance. As a result, wasteful energy consumption can be significantly reduced with minimal impact on the total processing time. Simulations using real-world datasets have shown that our proposed approach can reduce the system's total processing time by more than 200% compared to that of the state-of-the-art approach, even when the nodes' actual performance is not known in advance. Moreover, the results have shown that CERA's online optimization stage can reduce the energy consumption by up to 37.14% without affecting the total processing time.

关键词： Task analysis Servers Optimization distributed computing Wireless communication Energy consumption Codes Coded distributed computing maximum distance separable code resource allocation INLP Lyapunov optimization edge computing straggling effects

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Edge Intelligence-Research Opportunities for distributed computing Continuum Systems

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IEEE INTERNET computing 2023年第4期27卷 53-74页

作者： Pujol, Victor Casamayor Donta, Praveen Kumar Morichetta, Andrea Murturi, Ilir Dustdar, Schahram Tech Univ Wien Distributed Syst Grp A-1040 Vienna Austria Tech Univ Wien Div Distributed Syst A-1040 Vienna Austria

Edge intelligence and, by extension, any distributed computing continuum system will bring to our future society a plethora of new and useful applications, which will certainly revolutionize our way of living. Nevertheless, managing these systems challenges all previously developed technologies for Internet-distributed systems. In this regard, this article presents a set of techniques and concepts that can help manage these systems;these are framed in the main paradigm for autonomic computing, the well-known monitor-analyze-plan-execute over shared knowledge, or MAPE-K. All in all, this article aims at unveiling research opportunities for these new systems, encouraging the community to work together toward new technologies to make edge intelligence a reality.

关键词： Manifolds Cloud computing Roads Monitoring Edge computing distributed computing

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Dynamic two-side matching of tasks and resources in wide-area distributed computing environments

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JOURNAL OF SUPERcomputing 2023年第9期79卷 10208-10231页

作者： Song, Yao Wang, Liang Xiao, Limin Shen, Runnan Wang, Jinquan Zhang, Chenhao Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

Recently, wide-area distributed computing environments have become popular due to its huge resource capacities. In the wide-area distributed computing environment, matching between tasks and resources plays an important role in improving system performance. However, the geographically distribution of diverse resources complicates the matching problems, thus degrading the system performance. To achieve an efficient matching between task requirements and resource capacities, this study proposes a dynamic two-side matching of tasks and resources in wide-area distributed computing environments. First, the proposed method establishes the task requirement model and the resource capacity model using a uniform description based on the characteristic analysis to mitigate the impact of diversity and simplify the matching process. Then, a two-side matching degree metric is designed to comprehensively quantify the matching quality between the task requirements and resource capacities. Finally, a resource selection strategy is raised to guide the scheduling based on the matching degree. The experimental results indicate that compared with the state-of-the-art matching methods, the proposed method reduces the overall completion time and response delay by up to 35.60% and 29.28%, respectively.

关键词： distributed computing Two-side matching Task scheduling Collaborative scheduling Resource allocation

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Worker Assignment for Multiple Masters to Speed Up Coded distributed computing in Heterogeneous Clusters

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IEEE TRANSACTIONS ON SERVICES computing 2023年第3期16卷 2283-2298页

作者： Kim, Daejin Park, Hyegyeong Niyato, Dusit Choi, Junkyun Samsung Elect Suwon South Korea Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA 15213 USA Korea Adv Inst Sci & Technol Sch Electr Engn Daejeon 34141 South Korea Nanyang Technol Univ Sch Comp Sci & Engn SCSE Singapore 639798 Singapore Korea Adv Inst Sci & Technol KAIST Sch Elect Engn Daejeon South Korea

In distributed computing systems, coding has played an important role to robustify the system against the effect of noise, e.g., stragglers, system failures and communication bottlenecks. Most of the existing work has focused on a simple master-worker model with one master and homogeneous workers. However, real-world systems are typically configured with heterogeneous workers distributed to computing nodes and serve multiple tasks in parallel. In this study, we consider the scenario in which multiple masters perform matrix multiplications using the workers having group heterogeneity. The group heterogeneity models that homogeneous workers are located in the same location and regarded as a group;the workers deployed in the different locations are potentially heterogeneous. We propose an asymptotically optimal worker assignment to multiple masters for coded distributed computing in the presence of heterogeneous groups of workers. Specifically, we present a lower bound for the expected latency in terms of the numbers of workers assigned to the masters and the amount of tasks allocated to workers. Adding the concentration constraints on the number of workers allocated to masters, we can obtain the minimum of the lower bound by taking the optimal worker assignment. We find the optimal worker assignment by converting the problem at hand into a linear programming problem. From both numerical simulations and experiments on Amazon EC2 clusters, we confirm that the effect of the proposed worker assignment is significant in various scenarios.

关键词： Task analysis Resource management distributed computing Computational modeling Encoding Load modeling Codes Coded distributed computing heterogeneous clusters optimal worker assignment optimal load allocation

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