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distributed computing Theory for Molecular Robot Systems

NEW GENERATION computing 2020年第2期38卷 325-340页

作者： Yamauchi, Yukiko 744 Motooka Nishi Ku Fukuoka 8190395 Japan

distributed computation consists of local computations and their interactions. We consider a molecular robot system as a distributed system composed of mobile computing entities with very weak capabilities, i.e., computing entities are anonymous (indistinguishable), oblivious (memory-less), and uniform (following a common local computation rule). The key property of such a distributed system is self-organization. In this survey, we first introduce shape formation by mobile computing entities and present characterizations of formable shapes. We then consider global behavior realized by shapes of a distributed system. We demonstrate general computational power of mobile computing entities in terms of computing languages and predicates. Finally, we demonstrate dynamic behavior, such as locomotion and search, realized by a sequence of shapes.

关键词： distributed computing Self-organization Shape formation Computation Search

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Fingerprint feature data matching algorithm based on distributed computing

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED computing 2021年 1-10页

作者： Jiang, Wenguang Ma, Jiasai Shandong Womens Univ Off Educ Adm Jinan Peoples R China Shandong Womens Univ Sch Date Sci & Comp Jinan Peoples R China

In order to effectively reduce the running time of fingerprint feature data matching algorithm and improve the matching accuracy. This paper designs and proposes a fingerprint feature data equivalent matching algorithm based on distributed computing. In the process of de-counterfeiting, the method of combining distance de-counterfeiting with structure de-counterfeiting is adopted. The information of minutiae feature points for subsequent matching, sampling information of corresponding ridge and direction of minutiae points are obtained by ridge tracking method. On this basis, use the fingerprint matching algorithm based on the congruent triangle to match the extracted minutiae feature points, so as to achieve the purpose of matching fingerprint feature data equally. The simulation results show that the proposed algorithm can effectively reduce the running time and improve the matching accuracy, which fully verifies the practical application value of the algorithm.

关键词： distributed computing Fingerprint features Data equivalent matching Ridge tracking method Marine oil spill

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New Results on the Computation-Communication Tradeoff for Heterogeneous Coded distributed computing

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第4期69卷 2254-2270页

作者： Xu, Fan Shao, Shuo Tao, Meixia Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

Coded distributed computing (CDC) can alleviate the communication load in distributed computing systems by leveraging coding opportunities via redundant computation. While the optimal computation-communication tradeoff has been well studied for homogeneous systems, it remains largely unknown for heterogeneous systems where workers have different computation capabilities. This paper characterizes the upper and lower bounds of the optimal communication load as two linear programming problems for a general heterogeneous CDC system using the MapReduce framework. Our achievable scheme first designs a parametric data shuffling strategy for any given mapping strategy, and then jointly optimizes the mapping strategy and the data shuffling strategy to obtain the upper bound. The parametric data shuffling strategy allows adjusting the size of the multicast message intended for each worker set, so that it can largely decrease the number of unicast messages and hence increase the communication efficiency. Numerical results show that our achievable communication load is lower than those achieved in existing works. Our lower bound is established by unifying an improved cut-set bound and a peeling method. The obtained upper and lower bounds degenerate to the existing result in homogeneous systems, and coincide with each other when the system is approximately homogeneous or grouped homogeneous.

关键词： distributed computing Multicast communication Linear programming Upper bound Optimization Wireless communication Unicast Coded distributed computing heterogeneous system computation-communication tradeoff

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Incentive-Based Coded distributed computing Management for Latency Reduction in IoT Services-A Game Theoretic Approach

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IEEE INTERNET OF THINGS JOURNAL 2021年第10期8卷 8259-8278页

作者： Kim, Nakyoung Kim, Daejin Lee, Joohyung Niyato, Dusit Choi, Jun Kyun Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea Samsung Elect Dept Network Business Suwon 16677 South Korea Gachon Univ Sch Comp Seongnam 13120 South Korea Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

This article studies distributed computing (DC) mechanisms on heterogeneous mobile devices (MDs) for latency reduction (LR) in Internet-of-Things (IoT) services by mitigating the effect of straggling MDs. We propose novel coded DC mechanisms with two different incentive distributions that consider the time-discounting value of processed results and the amount of the workload computed by MDs. Specifically, we consider distributed gradient descent computing with coding when a task publisher (TP) with a limited amount of budget offers incentives to encourage MDs' participation in the computation. To analyze a hierarchical decision-making structure of the TP and MDs, we formulate a strategic competition between them as a Stackelberg game. In the case that the MDs are the leaders, we design a CPU-cycle frequency control scheme to balance each MD's computing speed and energy consumption for obtaining its maximum utility with the incentive mechanisms. As the follower, the TP aims at minimizing latency of the DC, and it follows the MDs' decisions to determine the load allocation for each MD. Then, we design an algorithm achieving the Stackelberg equilibrium, which is shown to be a unique Nash equilibrium of the game. The performance evaluation results show that the proposed mechanisms achieve 39% of LR on average compared to the benchmark mechanism. Furthermore, the results corroborate the efficiency of the proposed mechanisms in terms of the MDs' social welfare.

关键词： distributed computing Encoding Task analysis Internet of Things Games Resource management distributed databases Coded distributed computing (DC) incentive mechanism load management Stackelberg game

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distributed Decoding for Coded distributed computing

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IEEE INTERNET OF THINGS JOURNAL 2021年第14期9卷 12555-12562页

作者： Yazdanialahabadi, Arash Ardakani, Masoud Univ Alberta Elect & Comp Engn Dept Edmonton AB T6G 2R3 Canada

In distributed computing, when a large number of helper nodes assist a master node to finish a large task, the main challenge is when some of these helpers straggle. Several coded distributed computing schemes exist that resolve the straggling problem. In these solutions, however, the decoding remains a responsibility of the master node. The complexity of decoding can be significant and increase the execution time considerably. In this work, we propose a multilayer coding strategy that allows some helpers to assist with the decoding. With this multilayer structure, the original decoding is performed by some decoding helpers, and the master only minimally participates in decoding to make every layer reliable. The optimization problem to minimize the overall completion time is also studied and shown to have a simple solution in almost all practical scenarios.

关键词： Decoding Task analysis Codes Encoding distributed computing Internet of Things Complexity theory distributed coding distributed computing edge computing heterogeneous network matrix multiplication

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The evolution of distributed computing systems: from fundamental to new frontiers

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computing 2021年第8期103卷 1859-1878页

作者： Lindsay, Dominic Gill, Sukhpal Singh Smirnova, Daria Garraghan, Peter Univ Lancaster Sch Comp & Commun Lancaster England Queen Mary Univ London Sch Elect Engn & Comp Sci London England

distributed systems have been an active field of research for over 60 years, and has played a crucial role in computer science, enabling the invention of the Internet that underpins all facets of modern life. Through technological advancements and their changing role in society, distributed systems have undergone a perpetual evolution, with each change resulting in the formation of a new paradigm. Each new distributed system paradigm-of which modern prominence include cloud computing, Fog computing, and the Internet of Things (IoT)-allows for new forms of commercial and artistic value, yet also ushers in new research challenges that must be addressed in order to realize and enhance their operation. However, it is necessary to precisely identify what factors drive the formation and growth of a paradigm, and how unique are the research challenges within modern distributed systems in comparison to prior generations of systems. The objective of this work is to study and evaluate the key factors that have influenced and driven the evolution of distributed system paradigms, from early mainframes, inception of the global inter-network, and to present contemporary systems such as edge computing, Fog computing and IoT. Our analysis highlights assumptions that have driven distributed systems appear to be changing, including (1) an accelerated fragmentation of paradigms driven by commercial interests and physical limitations imposed by the end of Moore's law, (2) a transition away from generalized architectures and frameworks towards increasing specialization, and (3) each paradigm architecture results in some form of pivoting between centralization and decentralization coordination. Finally, we discuss present day and future challenges of distributed research pertaining to studying complex phenomena at scale and the role of distributed systems research in the context of climate change.

关键词： distributed computing computing systems Evolution Green computing

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A Comprehensive Survey on Coded distributed computing: Fundamentals, Challenges, and Networking Applications

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2021年第3期23卷 1800-1837页

作者： Ng, Jer Shyuan Lim, Wei Yang Bryan Luong, Nguyen Cong Xiong, Zehui Asheralieva, Alia Niyato, Dusit Leung, Cyril Miao, Chunyan Nanyang Technol Univ Alibaba Grp & Alibaba NTU Joint Res Inst Singapore Singapore PHENIKAA Univ Fac Comp Sci Hanoi 12116 Vietnam PHENIKAA Univ PHENIKAA Res & Technol Inst Hanoi 12116 Vietnam Singapore Univ Technol Design Pillar Informat Syst Technol & Design Singapore Singapore Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Univ British Columbia Joint NTU UBC Res Ctr Excellence Act Living Elder Vancouver BC V6T 1Z4 Canada

distributed computing has become a common approach for large-scale computation tasks due to benefits such as high reliability, scalability, computation speed, and cost-effectiveness. However, distributed computing faces critical issues related to communication load and straggler effects. In particular, computing nodes need to exchange intermediate results with each other in order to calculate the final result, and this significantly increases communication overheads. Furthermore, a distributed computing network may include straggling nodes that run intermittently slower. This results in a longer overall time needed to execute the computation tasks, thereby limiting the performance of distributed computing. To address these issues, coded distributed computing (CDC), i.e., a combination of coding theoretic techniques and distributed computing, has been recently proposed as a promising solution. Coding theoretic techniques have proved effective in WiFi and cellular systems to deal with channel noise. Therefore, CDC may significantly reduce communication load, alleviate the effects of stragglers, provide fault-tolerance, privacy and security. In this survey, we first introduce the fundamentals of CDC, followed by basic CDC schemes. Then, we review and analyze a number of CDC approaches proposed to reduce the communication costs, mitigate the straggler effects, and guarantee privacy and security. Furthermore, we present and discuss applications of CDC in modern computer networks. Finally, we highlight important challenges and promising research directions related to CDC.

关键词： distributed computing Task analysis Encoding Security Internet of Things Resource management Privacy distributed computing communication minimization straggler effects mitigation security coded distributed computing

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Compressed Coded distributed computing

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第5期69卷 2773-2783页

作者： Elkordy, Ahmed Roushdy Li, Songze Maddah-Ali, Mohammad Ali Avestimehr, A. Salman Univ Southern Calif Dept Elect Engn Los Angeles CA 90089 USA Sharif Univ Technol Dept Elect Engn Tehran 111558639 Iran

Communication overhead is one of the major performance bottlenecks in large-scale distributed computing systems, in particular for machine learning applications. Conventionally, compression techniques are used to reduce the load of communication by combining intermediate results of the same computation task as much as possible. Recently, via the development of coded distributed computing (CDC), it has been shown that it is possible to enable coding opportunities across intermediate results of different computation tasks to further reduce the communication load. We propose a new scheme, named compressed coded distributed computing (in short, compressed CDC), which jointly exploits the above two techniques (i.e., combining the intermediate results of the same computation and coding across the intermediate results of different computations) to significantly reduce the communication load for computations with linear aggregation (reduction) of intermediate results in the final stage that are prevalent in machine learning (e.g., distributed training algorithms where partial gradients are computed distributedly and then averaged in the final stage). In particular, compressed CDC first compresses/combines several intermediate results for a single computation, and then utilizes multiple such combined packets to create a coded multicast packet that is simultaneously useful for multiple computations. We characterize the achievable communication load of compressed CDC and show that it substantially outperforms both combining methods and CDC scheme. Based on the compressed CDC technique, we then study a distributed training problem as one of its application. We characterize the communication load for this distributed training problem and show that it is asymptotically optimal.

关键词： Task analysis Training distributed computing Encoding Bandwidth Multicast communication Machine learning MapReduce distributed training gradient aggregation coded multicasting

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Parallel and distributed computing for Anomaly Detection From Hyperspectral Remote Sensing Imagery

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PROCEEDINGS OF THE IEEE 2021年第8期109卷 1306-1319页

作者： Du, Qian Tang, Bo Xie, Weiying Li, Wei Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

Anomaly detection from remote sensing images is to detect pixels whose spectral signatures are different from their background. Anomalies are often man-made targets. With such target signatures being unknown, anomaly detection has many important applications, such as water quality monitoring, crop stress surveying, and law enforcement-related uses, where prior information of targets is often unavailable. The key to success is accurate background modeling. Anomaly detection from remote sensing images is challenging because spatial coverage is very large and the background is highly heterogeneous. For pixel-based anomaly detection, computing cost in background modeling and a spatial-convolution-type detection process is very expensive. Thus, parallel and distributed computing is critical in reducing execution time, which can fit the need for real-time or near real-time detection from airborne and spaceborne platforms in support of immediate decision-making. This article reviews the recent advances in anomaly detection from hyperspectral remote sensing images and their implementation using parallel and distributed systems. The classical methods, i.e., the Reed-Xiaoli (RX) algorithm and its variants, including its real-time processing version, are illustrated in commodity graphic processing units (GPUs), cloud, and field-programmable gate array (FPGA) implementations. Practical issues and future development trends are also discussed.

关键词： Anomaly detection Hyperspectral imaging Feature extraction Real-time systems distributed databases Detectors Principal component analysis Anomaly detection distributed computing field-programmable gate array (FPGA) hyperspectral remote sensing large-scale data parallel computing real-time processing

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A centralized vehicle-to-grid scheme with distributed computing capacity engaging internet of smart charging points: Case study

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第1期45卷 841-863页

作者： Shang, Yitong Liu, Man Shao, Ziyun Jian, Linni Harbin Inst Technol Harbin Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Elect Direct Drive Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Univ Key Lab Adv Wireless Commun Guangdong Prov Shenzhen Peoples R China Guangzhou Univ Sch Elect & Commun Engn Guangzhou Peoples R China

In order to accommodate additional plug-in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle-to-grid (V2G) technology has been regarded as a cost-effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users' privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub-problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak-shaving and valley-filling scheduling algorithm is built up. The proposed algorithm can be conducted in real-time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak-shaving and valley-filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak-shaving and valley-filling strategy is near-linear, which verifies the proposed scheme can be carried out very efficiently.

关键词： distributed computing internet of smart charging points (ISCP) load peak-shaving and valley-filling plug-in electric vehicle vehicle-to-grid

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