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Pricing-Based Task Offloading Considering User Energy Consumption in MEC System

Pricing-Based Task Offloading Considering User Energy Consum...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Xiyu Han Zhuofan Liao Xiaoyong Tang School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Multi-access Edge computing (MEC) can find its wide applications in various resource-constrained scenario where the user needs to pay a price to the server for meeting the latency requirements of their own tasks. However, to the best of our knowledge, there is no effective pricing model in MEC taking into account the impact of the user’s own energy consumption on the server’s price scheme. In fact, the transmission energy consumption affects the users’ offloading task decisions, which in turn affects the revenue of the edge servers. To fill in this gap, a novel pricing mechanism for energy-sensitive users is proposed in this paper, specifically: 1) We propose a joint optimization problem of computation offloading decisions and resource pricing that considers user energy consumption. 2) By constructing a two-stage Stackelberg game model, the first stage simplifies the problem by constraint relaxation theory and finds an approximate optimal offloading strategy through the Fast Genetic Algorithm (FGA). The second stage uses the gradient descent method to find the potential optimal prices, thereby achieving a balance between the lowest user cost and the highest server profit. Experimental results show that, compared to traditional algorithms, our approach optimizes the average user cost and user task completion time by 24.65% and 12.37%, respectively.

关键词： Energy consumption Costs Multi-access edge computing Computational modeling Pricing Games Servers Resource management Optimization Genetic algorithms

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Using Pilot Jobs and CernVM File System for Simplified Use of Containers and Software Distribution 21

Using Pilot Jobs and CernVM File System for Simplified Use o...

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30th international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Urs, Namratha Mambelli, Marco Dykstra, Dave Univ North Texas Dept Comp Sci & Engn Denton TX 76203 USA Fermilab Natl Accelerator Lab Sci Comp Div POB 500 Batavia IL 60510 USA

ISBN: (纸本)9781450382175

High Energy Physics (HEP) experiments entail an abundance of computing resources, i.e. sites, to run simulations and analyses by processing data. This requirement is fulfilled by local batch farms, grid sites, private/commercial clouds, and supercomputing centers via High Throughput computing (HTC). The growing needs of such experiments and resources being prone to trends of heterogeneity make it difficult for physicists to handle these resources directly. Additionally, HEP collaborations heavily rely on data and software releases, typically in the order of tens of gigabytes, while conducting simulations and analyses. Hence, aspects of scalability, reliability, and maintenance become crucial with regards to the distribution of the necessary data and software stack. The GlideinWMS [4] framework helps with the resource management problem by using pilot jobs, aka Glideins, to provision reliable elastic virtual clusters. Glideins are submitted to unreliable heterogeneous resources which are validated and customized by the Glideins to make the worker nodes available for end-user job execution. On the other hand, the CernVM File System (CernVM-FS or CVMFS) [1] helps with data distribution. It is a write-once, read-everywhere filesystem used to deploy scientific software to thousands of nodes on a worldwide distributed computing infrastructure. CVMFS is based on the Hyper Text Transfer Protocol and has been widely used within the particle physics community for (1) distributing experiment software and data such as calibrations, and (2) facilitating containerization by efficiently hosting container images along with providing containerization software, especially Singularity [3]. GlideinWMS relies on CVMFS installed locally on the computing resources to satisfy the experiments' software needs. This requires system administrators' effort to install and maintain CVMFS at the sites and limits the use of sites, especially HPC resources, that do not have CVMFS installed. This pos

关键词： high throughput computing distributed computing

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Graph computing on Long Vector Architectures (Yes, It Works!)

Graph Computing on Long Vector Architectures (Yes, It Works!...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Pablo Vizcaino Jesus Labarta Filippo Mantovani Barcelona Supercomputing Center Barcelona Spain

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

The HPC market is witnessing the appearance of emerging architectures exploiting long and variable vector lengths, like the NEC SX-Aurora or the RISC-V vector extension. These new architectures often require rethinking scientific algorithms to leverage their potential, and sparse computations can be the most challenging codes to adapt. Specifically, we showcase the obstacles of vectorizing graph algorithms and propose a graph representation suited for long vector architectures. We evaluate our work implementing three graph algorithms, Breadth-First-Search (BFS), Connected Components (CC), and Page Rank (PR), for a prototype RISC-V core with a long Vector Unit, presenting optimizations that benefit long vector architectures. We also run our implementations on the NEC SX-Aurora and compare our results with other graph libraries for CPUs and vector acceleration. Our BFS and CC implementations outperform CPU approaches by a factor around 4× depending on the algorithm and graph, with PR having a speedup closer to 20×. We also match or outperform the single-core performance of other vector implementations in the NEC SX-Aurora by 2× on average. We exhibit the efficiency of the RISC-V prototype, obtaining more performance out of its available bandwidth than the other evaluated single-core architectures. More generally, we show that adapting data structures and algorithms unleashes the potential of long vector architectures for sparse computations, demonstrating that these emerging technologies strive against traditional accelerated computing.

关键词： Sockets Platinum Prototypes Computer architecture Bandwidth Vectors Libraries

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3rd IEEE international Workshop on Extreme-Scale Storage and Analysis (ESSA 2022)

3rd IEEE International Workshop on Extreme-Scale Storage and...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

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An Oracle for Guiding Large-Scale Model/Hybrid parallel Training of Convolutional Neural Networks 21

An Oracle for Guiding Large-Scale Model/Hybrid Parallel Trai...

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30th international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Njoroge Kahira, Albert Truong Thao Nguyen Bautista Gomez, Leonardo Takano, Ryousei Badia, Rosa M. Wahib, Mohamed Univ Politecn Cataluna Barcelona Supercomp Ctr Barcelona Spain Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan Barcelona Supercomp Ctr Barcelona Spain Natl Inst Adv Ind Sci & Technol RIKEN CCS Tsukuba Ibaraki Japan

ISBN: (纸本)9781450382175

Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence and alleviate memory capacity limitations when training large models and/or using high dimension inputs. With the steady increase in datasets and model sizes, model/hybrid parallelism is deemed to have an important role in the future of distributed training of DNNs. We analyze the compute, communication, and memory requirements of Convolutional Neural Networks (CNNs) to understand the trade-offs between different parallelism approaches on performance and scalability. We leverage our model-driven analysis to be the basis for an oracle utility which can help in detecting the limitations and bottlenecks of different parallelism approaches at scale. We evaluate the oracle on six parallelization strategies, with four CNN models and multiple datasets (2D and 3D), on up to 1024 GPUs. The results demonstrate that the oracle has an average accuracy of about 86.74% when compared to empirical results, and as high as 97.57% for data parallelism.

关键词： Deep Learning Model parallelism Performance Modeling

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Non-deterministic event brokered computing 2022

Non-deterministic event brokered computing

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12th international symposium on Highly Efficient Accelerators and Reconfigurable Technologies (HEART)

作者： Brown, Andrew Thomas, David Vousden, Mark Bragg, Graeme Luk, Wayne Beaumont, Jonny Todman, Tim Yakovlev, Alex Rafiev, Ashur Univ Southampton Southampton Hants England Imperial Coll London London England Univ Newcastle Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781450396608

This paper reviews the massively micro-parallel compute system POETS (Partially Ordered Event Triggered System) and illustrates its potential for speeding up demanding applications. Application domains that benefit from POETS include simulations of physical systems that can be discretised as a mesh. The problem graph is distributed over a large compute mesh;each mesh vertex contains a processor - an FPGA-based RISC-V thread supporting custom instructions in our prototype - and a small amount of local problem state data. There is no central overseer of any sort and processors cannot see memory besides their own. A problem graph vertex interacts with a neighbour to send a state change by sending an asynchronous packet. The packets are fixed size and small - currently 64 bytes - and the hardware communications infrastructure is very fast. Applications can use an asynchronous 'packet storm' approach;run synchronously using a hardware idle barrier or run in a globally asynchronous, locally synchronous manner. Results show significant wallclock speedup and power consumption improvement over conventional systems: for one application we show a 40-fold speedup over a conventional CPU-based system;versus a multi-GPU system, the POETS cluster is 26% faster, 60% more power efficient, and 34% more energy efficient.

关键词： Event-driven Network-on-chip Massively parallel

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Combining Lossy Compression with Multi-Level Caching for Data Staging over Network

Combining Lossy Compression with Multi-Level Caching for Dat...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Rei Aoyagi Keichi Takahashi Yoichi Shimomura Hiroyuki Takizawa Graduate School of Information Sciences Tohoku University Sendai Japan Cyberscience Center Tohoku Uniersity Sendai Japan

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Researchers conduct post-processing on the simulation results by running an interactive data analysis tool on a High-Performance computing (HPC) system installed at an HPC center and retrieving the post-processed results. Certain data analysis scenarios require to transfer the simulation results directly from the center. In such scenarios, a portion of the data would usually be streamed over the network to achieve interactivity. However, there still exist two challenges in maintaining interactivity: (1) limited network bandwidth and (2) long network latency. To tackle these challenges, we propose a system to enable interactive array analysis over the network. We employ error-bounded lossy compression to increase the effective network bandwidth. Furthermore, we employ multilevel caching to hide the network latency and combine prefetching to improve the cache hit ratio. The cache replacement and prefetching policies are designed considering the data access pattern of interactive analysis. We compared our proposed system with TileDB, one of the state-of-the-art array databases, by measuring the average latency for various access patterns. Compared to TileDB, the proposed system reduces the average latency by up to 91.6% by allowing 10% of error because the cache hit ratio was improved by more than 40% due to the proper cache replacement and prefetching policy and network transfer time was reduced more than 75% by using lossy compression.

关键词： Data analysis Databases Prefetching Simulation Heuristic algorithms High performance computing Bandwidth

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Improving the MPI-IO Performance of Applications with Genetic Algorithm based Auto-tuning

Improving the MPI-IO Performance of Applications with Geneti...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Bagbaba, Ayse Wang, Xuan Univ Stuttgart High Performance Comp Ctr Stuttgart HLRS Stuttgart Germany

ISBN: (纸本)9781665435772

parallel I/O is an essential part of scientific applications running on high-performance computing systems. Understanding an application's parallel I/O behavior and identifying sources of performance bottlenecks require a multi-layer view of the I/O. Typical parallel I/O stack layers offer many tunable parameters that can achieve the best possible I/O performance. However, scientific users do often not have the time nor the experience for investigating the proper combination of these parameters for each application use-case. Auto-tuning can help users by automatically tuning I/O parameters at various layers transparently. In auto-tuning, using naive strategy, running an application by trying all possible combinations of tunable parameters for all layers of the I/O stack to find the best settings is an exhaustive search through the huge parameter space. This strategy is infeasible because of the long execution times of trial runs. In this paper, we propose a genetic algorithm-based parallel I/O auto-tuning approach that can hide the complexity of the I/O stack from users and auto-tune a set of parameter values for an application on a given system to improve the I/O performance. In particular, our approach tests a set of parameters and then, modifies the combination of these parameters for further testing based on the I/O performance. We have validated our model using two I/O benchmarks, namely IOR and MPI-Tile-IO. We achieved an increase in I/O bandwidth of up to 7.74xover the default parameters for IOR and 5.59xover the default parameters for MPI-Tile-IO.

关键词： parallel I/O Auto-tuning Genetic Algorithm MPI-IO Lustre

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Container-Aware Service Function Chains Placement and Optimization in Vehicular Edge computing

Container-Aware Service Function Chains Placement and Optimi...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Kaixin Zhang Shihong Hu Zhihao Qu Baoliu Ye School of Computer Science and Software Engineering Hohai University National Key Laboratory for Novel Software Technology Nanjing University

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In the domain of Vehicular Edge computing (VEC), this paper addresses the complex problem of Service Function Chain (SFC) placement, which is crucial for the efficient deployment of cloud applications in vehicular environments. Our approach leverages Virtual Network Function (VNF) technology, transitioning network services from traditional hardware to more flexible, container-based edge computing frameworks. The primary objective is to organize these VNFs into functional SFCs, thereby minimizing service delays in VEC systems. SFC placement faces two significant challenges. The first is the sequential dependency among VNFs within an SFC, which adds substantial complexity to container deployment. The second challenge is the cold start delay of VNF containers, a critical issue in scenarios requiring swift response times, which negatively impacts service quality in VEC applications. To address these challenges, we propose a comprehensive model for SFC placement that considers the deployment status of VNF containers, acknowledging the complexity of this NP-hard problem. The core of our contribution is the development of the Single SFC Placement Algorithm (SSPA), a sophisticated, greedy-based approach designed for the effective placement of individual SFCs. We enhance this algorithm by incorporating a Particle Swarm Optimization (PSO) technique, making it capable of efficiently handling the placement of multiple SFCs. Our solution aims to improve edge resource utilization and mitigate startup delays associated with the initial activation of containers, thereby reducing service delays. Extensive experimental evaluations demonstrate that our algorithm achieves a 34.3% average reduction in service delay compared to four baselines and a 5.8% average reduction compared to other container-aware algorithms.

关键词： Service function chaining NP-hard problem Containers Hardware Delays Time factors Resource management Particle swarm optimization Optimization Edge computing

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Auction-Based Job Scheduling for Smart Manufacturing 17

Auction-Based Job Scheduling for Smart Manufacturing

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17th IEEE international symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Gatial, Emil Balogh, Zoltan Dolatabadi, Sepideh Hassankhani Ghorbel, Hatem Carrino, Stefano Dreyer, Jonathan Montequin, Vicente Rodriguez Gligor, Adrian Iantovics, Laszlo Barna Institute of Informatics Sav Parallel and Distributed Computing Bratislava Slovakia Institute of Informatics Sav Modelling and Control of Discrete Processes Bratislava Slovakia Haute École Arc Ingénierie HES-SO St-Imier Switzerland University of Oviedo Project Engineering Area Oviedo Spain University of Medicine Pharmacy Science and Technology of Targu Mures Electrical Engineering and Information Technology 'George Emil Palade' Targu Mures Romania

ISBN: (纸本)9798350321104

The paper describes a real-time job scheduling method designed for production of goods with different characteristics on machines with different processing parameters. The objective is to maximize the global reward of the factory as a sum of the rewards for each machine job. Traditionally, the task assignment problems deal with the assignment of m-tasks to n-agents and are calculated by analytical methods or heuristics. The proposed method is based on online auctions that distributes the tasks to the machines using the software agents. The method is implemented using the ERTS (Erlang Real-Time System) that allows adopting the features of fault-tolerance and real-time processing. The paper starts with introduction and review of related state-of-the-art. The following chapter briefly describes the problem and the specific requirements. The following chapter describes the software architecture of the online auction system, the use-case and the motivation for developing this method. The proposed method auction-based task assignment and its implementation are described in the next chapters. At the end of the work, the results of the method are presented in comparison with the optimal solution and the performance characteristics are also described. In the conclusion, possible advances and future work are proposed. © 2023 IEEE.

关键词： Fault tolerance

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