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Multi-Access Edge computing for Real-Time Applications With Sporadic DAG Tasks - A Graphical Game Approach

IEEE TRANSACTIONS ON MOBILE computing 2024年第10期23卷 9167-9190页

作者： Asheralieva, Alia Niyato, Dusit Loughborough Univ Dept Comp Sci Loughborough LE11 3TU Leics England Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

We consider a multi-operator multi-access edge computing (MEC) network for applications with dependent tasks. Each task includes jobs executed based on logical precedence modelled as a directed acyclic graph, where each vertex is a job, each edge - precedence constraint, such that the job can be started only after its preceding jobs are completed. Tasks are executed by MEC servers with the assistance of workers - nearby edge devices. Each MEC server acts as a master deciding on jobs assigned to its workers. The master's decision problem is complex, as its workers can be associated with other masters in proximity. Thus, the available workers' resources depend on job assignments of all neighboring masters. Yet, as masters select their decisions simultaneously, no master knows concurrent decisions of its neighbors. Besides, some masters can belong to competing operators that have no incentives to exchange information about their decisions. To address these challenges, we formulate a novel framework based on the graphical stochastic Bayesian game, where masters play under uncertainty about their neighbors' decisions. We prove that the game admits a perfect Bayesian equilibrium (PBE), and develop new Bayesian reinforcement learning and Bayesian deep reinforcement learning algorithms enabling each master to reach the PBE independently.

关键词： Task analysis Servers Games Bayes methods distributed computing Computational modeling Optimization 5G New Radio Bayesian methods distributed computing game theory graph theory incomplete information machine learning multi-access edge computing

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Geography-Aware Task Scheduling for Profit Maximization in distributed Green Data Centers

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IEEE TRANSACTIONS ON CLOUD computing 2022年第3期10卷 1864-1874页

作者： Yuan, Haitao Bi, Jing Zhou, MengChu New Jersey Inst Technol Dept Elect & Comp Sci Newark NJ 07102 USA Beijing Univ Technol Sch Software Engn Fac Informat Technol Beijing 100124 Peoples R China

Infrastructure in distributed Green Data Centers (DGDCs) is concurrently shared by multiple different applications to flexibly provide a growing number of services to global users in a cost-effective way. A highly challenging problem is how to maximize the total profit of the DGDC provider in a market where Internet Service Provider (ISP) bandwidth price, availability of green energy, price of power grid, and revenue brought by the execution of tasks all vary with geographical locations. Unlike existing studies, this article proposes a Geography-Aware Task Scheduling (GATS) approach by considering spatial variations in DGDCs to maximize the total profit of the DGDC provider by intelligently scheduling tasks of all applications. In each time slot, the formulated profit maximization problem is solved as a convex optimization one via the interior point method. Trace-driven simulations show that GATS achieves larger total profit and higher throughput than two typical task scheduling approaches.

关键词： Green data centers distributed computing task scheduling profit maximization convex optimization

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Large-scale response-aware online ANN search in dynamic datasets

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第3期27卷 3499-3519页

作者： Andrade, Guilherme Barreiros Jr, Willian Rocha, Leonardo Ferreira, Renato Teodoro, George Univ Fed Minas Gerais Dept Comp Sci BR-31270 Belo Horizonte MG Brazil Univ Fed Sao Joao Rei Dept Comp Sci Rodovia BR-494 BR-36301 Sao Joao Del Rei MG Brazil

Similarity search is a key operation in content-based multimedia retrieval (CBMR) applications. Online CBMR applications, which are the focus of this work, perform a large number of search operations on dynamic datasets, which are updated at run-time. Additionally, the rates of search and data insertion (updated) operations vary during the execution. Such applications that rely on similarity search are required to fulfill these demands while also offering low response times. Thus, it is common for the computing demands in such applications to exceed the processing power of a single computer, motivating the usage of large-scale compute systems. As such, we propose in this work a distributed memory parallelization of similarity search that addresses these challenges. Our solution employs the efficient Inverted File System with Asymmetric Distance Computation algorithm (IVFADC) as the baseline, which is extended to support dynamic datasets. A dynamic resource management algorithm, called Multi-Stream Adaptation (MS-ADAPT) is proposed. It allows run-time changes on resource assignment with the goal of reducing response times. We evaluate our solution with multiple data partitioning strategies using up to 160 compute nodes and a dataset with 344 billion multimedia descriptors. Our experiments demonstrate superlinear scalability and MS-ADAPT outperforms the best static approach (oracle) by improving the response times up to 32x on high-load cases.

关键词： Online multimedia similarity search Approximate nearest neighbors search Product quantization ANN distributed computing

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Edge-Disjoint Tree Allocation for Multi-Tenant Cloud Security in Datacenter Topologies

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第4期32卷 2858-2874页

作者： Rottenstreich, Ori Yallouz, Jose Mellanox Technol Nvidia Roskilde Denmark Technion IL-3200003 Haifa Israel

Resource sharing with its implied mutual interference has been considered a major concern for running applications of multiple tenants in shared cloud datacenters. Besides its security benefits, the isolation of traffic might ensure a quality of service (QoS) performance guarantee avoiding interference among tenants. Traffic isolation can be achieved by dedicating the usage of link resources in the network to a single tenant preventing its sharing among others. Accordingly, tenants should be connected through an edge-disjoint tree to enable isolated communication among its hosts. In this paper, we study the problem of establishing edge-disjoint trees in common datacenter topologies. We show that the availability of such trees is highly affected by the mapping of the tenants to hosts of the topology. Specifically, with the flexibility to map tenants in the datacenter topology, we describe a mapping algorithm and an optimal tree establishment for the optimization problem. Given the mapping of the tenants, we prove the problem turns out to be NP-Hard and provide comprehensive heuristics for the problem. Finally, we conduct experiments using real workloads to examine tree availability under various scenarios.

关键词： Datacenter distributed computing scheduling algorithms

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vCubeChain: A scalable permissioned blockchain

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AD HOC NETWORKS 2024年 158卷

作者： Freitas, Allan Edgard Silva Rodrigues, Luiz Antonio Duarte Jr, Elias Procopio Fed Inst Bahia IFBA Rua Emidio dos Santos s-n BR-40301015 Salvador Ba Brazil Western Parana State Univ UNIOESTE Rua Univ 2069 BR-85819110 Cascavel PR Brazil Fed Univ Parana UFPR Rua Evaristo F Ferreira da Costa 383-291 BR-82590300 Curitiba PR Brazil

This work presents vCubeChain, a scalable permissioned blockchain based on the vCube virtual topology. vCube is a virtual hierarchical topology that presents several logarithmic properties. vCubeChain employs a leader election algorithm that relies on the failure detection information that vCube provides. The leader employs a vCube-based autonomic reliable broadcast algorithm to disseminate blocks, each consisting of multiple transactions. In case multiple leaders end up concurrently elected due to false suspicions, vCubeChain is proven to recover to a consistent state upon the discovery of contradictory blocks. vCubeChain is described, specified, and correctness and liveness draft proofs are presented. The blockchain was implemented on the Blocksim simulator, and a set of experiments are presented, including comparisons with Bitcoin, Ethereum and Hyperledge Fabric. Results demonstrate the scalability of the solution.

关键词： distributed computing Consensus Blocksim vCube

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Efficient spatial data partitioning for distributed kNN joins

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JOURNAL OF BIG DATA 2022年第1期9卷 1-42页

作者： Zeidan, Ayman Vo, Huy T. CUNY Grad Ctr Dept Comp Sci New York NY 10016 USA CUNY City Coll Dept Comp Sci New York NY 10031 USA

Parallel processing of large spatial datasets over distributed systems has become a core part of modern data analytic systems like Apache Hadoop and Apache Spark. The general-purpose design of these systems does not natively account for the data's spatial attributes and results in poor scalability, accuracy, or prolonged runtimes. Spatial extensions remedy the problem and introduce spatial data recognition and operations. At the core of a spatial extension, a locality-preserving spatial partitioner determines how to spatially group the dataset's objects into smaller chunks using the distributed system's available resources. Existing spatial extensions rely on data sampling and often mismanage non-spatial data by either overlooking their memory requirements or excluding them entirely. This work discusses the various challenges that face spatial data partitioning and proposes a novel spatial partitioner for effectively processing spatial queries over large spatial datasets. For evaluation, the proposed partitioner is integrated with the well-known k-Nearest Neighbor (kNN) spatial join query. Several experiments evaluate the proposal using real-world datasets. Our approach differs from existing proposals by (1) accounting for the dataset's unique spatial traits without sampling, (2) considering the computational overhead required to handle non-spatial data, (3) minimizing partition shuffles, (4) computing the optimal utilization of the available resources, and (5) achieving accurate results. This contributes to the problem of spatial data partitioning through (1) providing a comprehensive discussion of the problems facing spatial data partitioning and processing, (2) the development of a novel spatial partitioning technique for in-memory distributed processing, (3) an effective, built-in, load-balancing methodology that reduces spatial query skews, and (4) a Spark-based implementation of the proposed work with an accurate kNN spatial join query. Experimental tests show

关键词： Big data Spatial data Spatial query Indexing Partitioning Technique Parallel processing Load balancing distributed computing Spark NoSQL kNN query All kNN query

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Verification of distributed Quantum Programs

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ACM TRANSACTIONS ON COMPUTATIONAL LOGIC 2022年第3期23卷 1–40页

作者： Feng, Yuan Li, Sanjiang Ying, Mingsheng Univ Technol Sydney Ctr Quantum Software & Informat 15 Broadway Ultimo NSW 2007 Australia Chinese Acad Sci Inst Software 4 Zhongguancun South Fourth St Beijing 100190 Peoples R China Tsinghua Univ Dept Comp Sci 30 Shuangqing Rd Beijing 100190 Peoples R China

distributed quantum systems and especially the Quantum Internet have the ever-increasing potential to fully demonstrate the power of quantum computation. This is particularly true given that developing a general-purpose quantum computer is much more difficult than connecting many small quantum devices. One major challenge of implementing distributed quantum systems is programming them and verifying their correctness. In this paper, we propose a CSP-like distributed programming language to facilitate the specification and verification of such systems. After presenting its operational and denotational semantics, we develop a Hoare-style logic for distributed quantum programs and establish its soundness and (relative) completeness with respect to both partial and total correctness. The effectiveness of the logic is demonstrated by its applications in the verification of quantum teleportation and local implementation of non-local CNOT gates, two important algorithms widely used in distributed quantum systems.

关键词： Quantum programming distributed computing formal verification Hoare logic

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Research and optimization of task scheduling algorithm based on heterogeneous multi-core processor

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 13435-13453页

作者： Liu, Junnan Liu, Yifan Ding, Yongkang Henan Univ Sch Software Kaifeng 475001 Peoples R China

Heterogeneous multi-core processor has the ability to switch between different types of cores to perform tasks, which provides more space and possibility for realizing efficient operation of computer system and improving computer computing power. Current research focuses on heterogeneous multiprocessor systems with high performance or low power consumption to reduce system energy consumption. However, some studies have shown that excessive voltage reduction may lead to an increase in transient failure rates, reducing system reliability. This paper studies the energy optimal scheduling problem of HMSS with DVFS under the constraints of minimum time and reliability, and proposes an improved wild horse optimization algorithm (OIWHO), which improves the efficiency of heterogeneous task scheduling and shortens the task completion time. The algorithm uses the learning and chaos perturbation strategies based on opposition and crossover strategies to balance the search and utilization capabilities, and can further improve the performance of OIWHO. Compared with previous work, our proposed algorithm has more advantages than existing algorithms. Experimental results show that the average computing time of OIWHO algorithm is 12.58%, 11.42%, 7.53%, 4.20% and 3.21% faster than DRNN-BWO, PSO, GWO-GA, GACSH and OIWOAH, respectively. Especially when solving large-scale problems, our algorithm takes less time than other algorithms.

关键词： distributed computing Heterogeneous multi-core processors High performance computing Task scheduling Swarm intelligence optimization algorithms

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Group-Wise Verifiable Coded computing Under Byzantine Attacks and Stragglers

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 4344-4357页

作者： Hong, Sangwoo Yang, Heecheol Yoon, Youngseok Lee, Jungwoo Seoul Natl Univ Dept Elect & Comp Engn Commun & Machine Learning Lab Seoul 08826 South Korea Chungnam Natl Univ Div Comp Convergence Distributed Networks & Comp Lab Daejeon 34134 South Korea Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

distributed computing has emerged as a promising solution for accelerating machine learning training processes on large-scale datasets by leveraging the parallel processing capabilities of multiple workers. However, there remain two major issues that still need to be addressed: 1) Byzantine attacks from malicious workers;and 2) the effect of slow workers, commonly referred to as stragglers. In this paper, we address both issues concurrently by introducing Group-wise Verifiable Coded computing (GVCC), a novel approach that combines coding techniques and group-wise verification to enhance robustness against Byzantine attacks and resilience to straggler effects in distributed computing. The key idea of GVCC is to verify a group of computation results from workers at a time, while providing resilience to stragglers through encoding tasks assigned to workers with Group-wise Verifiable Codes. We evaluate the performance of GVCC through experiments conducted on Amazon EC2 clouds and the results show that GVCC outperforms the existing methods in terms of overall processing time and verification time while maintaining the verification performance. This study highlights the potential of GVCC as an effective solution for overcoming the challenges of Byzantine attacks and stragglers in distributed computing for executing matrix multiplication.

关键词： Task analysis distributed computing Encoding Codes Resilience Machine learning Decoding Byzantine attacks straggler effect coded computing

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Logical Synchrony and the Bittide Mechanism

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2024年第11期35卷 1936-1948页

作者： Lall, Sanjay Cascaval, Calin Izzard, Martin Spalink, Tammo Google Mountain View CA 94043 USA

We introduce logical synchrony, a framework that allows distributed computing to be coordinated as tightly as in synchronous systems without the distribution of a global clock or any reference to universal time. We develop a model of events called a logical synchrony network, in which nodes correspond to processors and every node has an associated local clock which generates the events. We construct a measure of logical latency and develop its properties. A further model, called a multiclock network, is then analyzed and shown to be a refinement of the logical synchrony network. We present the bittide mechanism as an instantiation of multiclock networks, and discuss the clock control mechanism that ensures that buffers do not overflow or underflow. Finally we give conditions under which a logical synchrony network has an equivalent synchronous realization.

关键词： Computer networks distributed computing Computer networks distributed computing

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