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Profit-driven Optimization of Server Deployment and Service Placement in Multi-User Mobile Edge computing 29

Profit-driven Optimization of Server Deployment and Service ...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Fang, Juan Wu, Shen Lu, Shuaibing Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9798350330717

Edge computing has emerged as a promising paradigm to fulfill the escalating demands of latency-sensitive and computationally intensive applications. In this context, efficient server deployment and service placement have become imperative to optimize performance and increase platform profit. In this paper, we investigate the problem of server deployment and service placement in a multi-user scenario, aiming to enhance the profit of Mobile Network Operators (MNOs) while considering constraints related to distance thresholds, resource limitations, and connectivity requirements. Then, we propose a novel two-stage method to decouple the problem, breaking down server deployment and service placement into two distinct yet interconnected stages. In stage I, the server deployment is formulated as a combinatorial optimization problem within the framework of a Markov Decision Process (MDP), where the state space, action space, and penalty function are defined to effectively model the issue. We propose the SDQ algorithm to establish a relatively stable server deployment strategy. In stage II, the service placement is formulated as a constrained integer linear programming problem. We propose the SPIB-TDB algorithm to optimize service placement. Extensive experimentation validates the exceptional performance of our proposed algorithms in enhancing the profit of MNOs. © 2023 IEEE.

关键词： integer linear programming mobile edge computing profit-driven optimization reinforcement learning

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Deterministic parallel Hypergraph Partitioning 28th

Deterministic Parallel Hypergraph Partitioning

引用

28th international conference on parallel and distributed computing (Euro-Par)

作者： Gottesbueren, Lars Hamann, Michael Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9783031125973;9783031125966

Balanced hypergraph partitioning is a classical NP-hard optimization problem with applications in various domains such as VLSI design, simulating quantum circuits, optimizing data placement in distributed databases or minimizing communication volume in high performance computing. Engineering parallel partitioning heuristics is a topic of recent research, yet most of them are non-deterministic. In this work, we design and implement a highly scalable deterministic algorithm in the parallel partitioning framework Mt-KaHyPar. On our extensive set of benchmark instances, it achieves similar partition quality and performance as a comparable but non-deterministic configuration of Mt-KaHyPar and outperforms the only other parallel deterministic algorithm BiPart regarding partition quality, running time and speedups.

关键词： Optimization

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Improved DQN-Based Computation Offloading Algorithm in MEC Environment 28

Improved DQN-Based Computation Offloading Algorithm in MEC E...

引用

IEEE 28th international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Zhao, Zheyu Cheng, Hao Xu, Xiaohua Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Delft Univ Technol Dept Elect Engn Math & Comp Sci Delft Netherlands

ISBN: (纸本)9781665473156

Massive terminal users have brought explosive need of data residing at edge of overall network. Multiple Mobile Edge computing (MEC) servers are built in/near base station to meet this need. However, optimal distribution of these servers to multiple users in real time is still a problem. Reinforcement Learning (RL) as a framework to solve interaction problem is a promising solution. In order to apply RL based algorithm into a multi-agent environment, we propose an iterative scheme: select individual users with priorities to interact with the environment iteratively one at a time. Furthermore, we tried to optimize the overall system performance based on this scheme. Hence, we construct three objective system performance indicators: average processing cost, delay and energy consumption, improve the existing Deep Q-learning Network (DQN) by using the cost as reward function, changing the fixed exploitation rate into dynamic one that associated with reward and episode time. In order to explore the performance potential of the proposed algorithm, we have simulated the proposed algorithm, DQN algorithm and greedy algorithm under different users and data sizes. The results show that the proposed algorithm had reduced at least 12% of system average processing cost comparing to the greedy algorithm. It also outperform the greedy algorithm and DQN algorithm in delay and energy consumption significantly.

关键词： Mobile Edge computing Computation Offloading Reinforcement Learning Deep Q-Learning Network

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parallelized Multi-Agent Bayesian Optimization in Lava

Parallelized Multi-Agent Bayesian Optimization in Lava

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2024 international conference on Neuromorphic Systems, ICONS 2024

作者： Snyder, Shay Gobin, Derek Clerico, Victoria Risbud, Sumedh R. Parsa, Maryam George Mason University Electrical and Computer Engineering Fairfax United States Intel Labs Neuromorphic Computing Lab Santa Clara United States

ISBN: (纸本)9798350368659

In parallel with the continuously increasing parameter space dimensionality, search and optimization algorithms should support distributed parameter evaluations to reduce cumulative runtime. Intel's neuromorphic optimization library, Lava-Optimization, was introduced as an abstract optimization system compatible with neuromorphic systems developed in the broader Lava software framework. In this work, we introduce Lava Multi-Agent Optimization (LMAO) with native support for distributed parameter evaluations communicating with a central Bayesian optimization system. LMAO provides an abstract framework for deploying distributed optimization and search algorithms within the Lava software framework. Moreover, LMAO introduces support for random and grid search along with process connections across multiple levels of mathematical precision. We evaluate the algorithmic performance of LMAO with a traditional non-convex optimization problem, a fixed-precision transductive spiking graph neural network for citation graph classification, and a neuromorphic satellite scheduling problem. Our results highlight LMAO's efficient scaling to multiple processes, reducing cumulative runtime and minimizing the likelihood of converging to local optima. © 2024 IEEE.

关键词： Convex optimization

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Secure CNN Training and Inference based on Multi-key Fully Homomorphic Encryption 29

Secure CNN Training and Inference based on Multi-key Fully H...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Qin, Hong He, Debiao Feng, Qi Luo, Min Wuhan University School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350330717

Convolutional neural network (CNN) has attracted increasing attention and been widely used in imaging processing, bioinformatics and so on. As the cloud computing and multiparty computing are booming, the training and inference data of convolutional neural network often comes from diverse users. These users tend to jointly perform the computation but reluctantly share original data with others. Multi-key fully homomorphic encryption (MKFHE) supports homomorphic computation on ciphertexts encrypted with different keys, which is especially suitable for this scenario. In this paper, we firstly propose secure convolution, matrix multiplication, comparison and maximum protocols based on MKFHE. Then we design the secure CNN training and inference framework, outsourcing almost all computations to cloud server. To improve the efficiency, we use key switching technique for ciphertext transformation. We prove that the proposed frameworks are secure and feasible. The theoretical and experimental analysis show that our framework achieves the trade-off between security, efficiency and scalability. © 2023 IEEE.

关键词： Cloud computing Convolutional neural network Multi-key fully homomorphic encryption Privacy protection

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Group-based Hierarchical Federated Learning: Convergence, Group Formation, and Sampling 23

Group-based Hierarchical Federated Learning: Convergence, Gr...

引用

52nd international conference on parallel Processing (ICPP)

作者： Liu, Jiyao Wei, Xinliang Liu, Xuanzhang Gao, Hongchang Wang, Yu Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9798400708435

Hierarchical federated learning has been studied as a more practical approach to federated learning in terms of scalability, robustness, and privacy protection, particularly in edge computing. To achieve these advantages, operations are typically conducted in a grouped manner at the edge, which means that the formation of client groups can affect the learning performance, such as the benefits gained and costs incurred by group operations. This is especially true for edge and mobile devices, which are more sensitive to computation and communication overheads. The formation of groups is critical for group-based federated edge learning but has not been studied in detail, and even been overlooked by researchers. In this paper, we consider a group-based federated edge learning framework that leverages the hierarchical cloud-edge-client architecture and probabilistic group sampling. We first theoretically analyze the convergence rate with respect to the characteristics of the client groups, and find that group heterogeneity plays an important role in the convergence. Then, on the basis of this key observation, we propose new group formation and group sampling methods to reduce data heterogeneity within groups and to boost the convergence and performance of federated learning. Finally, our extensive experiments show that our methods outperform current algorithms in terms of prediction accuracy and training cost.

关键词： Hierarchical federated learning non-IID group formation group sampling distributed learning edge computing

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A Symmetric Nonconvex Method for MRI 4

A Symmetric Nonconvex Method for MRI

引用

4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Luo, Zhijun Zhu, Zhibin School of Mathematics and Finance Hunan University of Humanities Science and Technology Loudi China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350337181

Magnetic resonance imaging is an essential tool for clinical diagnosis, but acquisition time and reconstruction errors have always been bottleneck issues limiting the development of this field. In this paper, we use SCAD nonconvex function to construct a new MRI reconstruction nonconvex model, which can overcome the shortcomings of the traditional convex model. In model solving, considering the symmetry ADMM method improves the image reconstruction performance while increasing the reconstruction speed. The experiment shows that the method proposed in this paper has significant improvements over traditional TV methods. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Hybrid parallel Inference for Large Model on Heterogeneous Clusters for High Throughput 29

Hybrid Parallel Inference for Large Model on Heterogeneous C...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Xu, Chaonong Kong, Weiming Liu, Min Zhang, Mingming Li, Chao Gong, Luqi Beijing Key Lab of Petroleum Data Mining Beijing China Institute of Intelligent Computing Zhejiang Lab Hangzhou China Bupt School of Computer Science National Pilot Software Engineering School Beijing China

ISBN: (纸本)9798350330717

In high-throughput intelligent computing scenarios, multi-device parallelism strategies based on data parallelism or pipeline parallelism have been extensively utilized to accelerate large deep neural network model inference. Data parallelism offers nearly linear improvement in inference speed, but it is limited by the memory capacity of a single device which constrains the model size. On the other hand, pipeline parallelism can support larger models, but the total communication of the activations among devices is high, which limits the improvement of the inference speed. To address the demand for efficient model inference in high-throughput heterogeneous scenarios, we proposes a hybrid parallelism strategy that combines data parallelism and pipeline parallelism. The strategy involves grouping heterogeneous device clusters and then employing inter-group data parallelism along with intra-group pipeline parallelism. Moreover, we propose an algorithm to find an optimal hybrid parallel inference strategy with maximum throughput. The control variables of the strategy includes the number of groups, group-device assignments and model partition ratios. Our experimental evaluation demonstrates that compared to PipeEdge, a pipeline parallel inference framework for heterogeneous cluster, our strategy can achieve 1.7× -3.4× acceleration in an 8-device heterogeneous cluster without loss of accuracy. © 2023 IEEE.

关键词： data parallelism heterogeneous cluster hybrid parallelism model inference pipeline parallelism

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Optimizing Pointwise Convolutions on Multi-core DSPs 1

引用

23rd international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Wang, Yang Wang, Qinglin Pei, Xiangdong Mei, Songzhu Liu, Jie Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Beijing Inst Astronaut Syst Engn Beijing 100076 Peoples R China

ISBN: (数字)9789819708628

ISBN: (纸本)9789819708611;9789819708628

Pointwise convolutions are widely used in various convolutional neural networks, due to low computation complexity and parameter requirements. However, pointwise convolutions are still time-consuming like regular convolutions. As a result of increasing power consumption, low-power embedded processors have been brought into high-performance computing field, such as multi-core digital signal processors (DSPs). In this paper, we propose a high-performance multi-level parallel direct implementation of pointwise convolutions on multi-core DSPs in FT-M7032, a CPU-DSP heterogeneous prototype processor. The main optimizations include on-chip memory blocking, loop ordering, vectorization, register blocking, and multi-core parallelization. The experimental results show that the proposed direct implementation achieves much better performance than GEMM-based ones on FT-M7032, and a speedup of up to 79.26 times is achieved.

关键词： CNNs Pointwise Convolution Direct Convolution DSPs parallel algorithm

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HPPython: Extending Python with HPspmd for Data parallel Programming 2

HPPython: Extending Python with HPspmd for Data Parallel Pro...

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2nd international Symposium on computing and Artificial Intelligence, ISCAI 2023

作者： Nava, Judah Josiah Lee, Jaden Jinu Lee, Hanku Computer Science and Information Systems Department University Moorhead MN United States

ISBN: (纸本)9798400708954

In light of previous endeavors and trends in the realm of parallel programming, HPPython emerges as an essential superset that enhances the accessibility of parallel programming for developers, facilitating scalability across multiple nodes. Despite Python's popularity as a programming language in scientific and engineering applications and its native support for executing various processes, HPPython brings substantial simplification to the development of parallel programs and empowers program distribution across heterogeneous clusters consisting of multiple physical computers. HPPython leverages the MPI standard for its underlying communication, thereby harnessing the benefits of the SPMD model. Additionally, HPPython introduces novel syntax and constructs, such as parallel loops and distributed lists, while endeavoring to retain the natural essence of the original language. This paper delves into the distinct components of HPPython and elucidates their integration, establishing HPPython as a viable solution for parallel programming in today's data-driven world. © 2023 ACM.

关键词： parallel programming

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