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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

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Reinforcement Learning-Based Dynamic Voltage and Frequency Scaling for Energy-Efficient computing 3

Reinforcement Learning-Based Dynamic Voltage and Frequency S...

引用

3rd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Panda, Prasanta Tripathy, Aruna Charan Bhuyan, Kanhu Tcs Bhubaneswar India Bhubaneswar India

ISBN: (纸本)9798350318609

The surge in demand for energy-efficient computing has spurred the exploration of cutting-edge techniques to optimize power consumption in modern computing systems. Though the traditional implementation of Dynamic Voltage and Frequency Scaling (DVFS) has proven effective, their traditional implementations lack adaptability, limiting their ability to fully exploit dynamic workload variations. This research presents an innovative solution by integrating proposed Reinforcement Learning (RL) algorithms into DVFS, addressing the limitations of conventional methods. The proposed RL algorithm employs Q-Iearning, a model-free RL technique, to iteratively learn the optimal policy for adjusting CPU voltage and frequency. Our customized algorithm enables autonomous real-time adjustments of voltage and frequency levels, showcasing a remarkable 20% power saving compared to conventional DVFS. The model's adaptability is evident in its capacity to achieve optimal configurations across diverse workloads, emphasizing RL's potential for enhancing energy efficiency in computing systems. As energy efficiency gains paramount importance, this research underscores the pivotal role of RL, in achieving sustainable and optimal performance in computing systems. © 2024 ieee.

关键词： Energy efficiency

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Stash: A comprehensive stall-centric characterization of public cloud VMs for distributed deep learning 43

Stash: A comprehensive stall-centric characterization of pub...

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Sharma, Aakash Bhasi, Vivek M. Singh, Sonali Jain, Rishabh Gunasekaran, Jashwant Raj Mitra, Subrata Kandemir, Mahmut Taylan Kesidis, George Das, Chita R. Penn State Univ Comp Sci & Engn University Pk PA 16802 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9798350339864

Deep neural networks (DNNs) are increasingly popular owing to their ability to solve complex problems such as image recognition, autonomous driving, and natural language processing. Their growing complexity coupled with the use of larger volumes of training data (to achieve acceptable accuracy) has warranted the use of GPUs and other accelerators. Such accelerators are typically expensive, with users having to pay a high upfront cost to acquire them. For infrequent use, users can, instead, leverage the public cloud to mitigate the high acquisition cost. However, with the wide diversity of hardware instances (particularly GPU instances) available in public cloud, it becomes challenging for a user to make an appropriate choice from a cost/performance standpoint. In this work, we try to address this problem by (i) introducing a comprehensive distributed deep learning (DDL) profiler Stash, which determines the various execution stalls that DDL suffers from, and (ii) using Stash to extensively characterize various public cloud GPU instances by running popular DNN models on them. Specifically, it estimates two types of communication stalls, namely, interconnect and network stalls, that play a dominant role in DDL execution time. Stash is implemented on top of prior work, DS-analyzer, that computes only the CPU and disk stalls. Using our detailed stall characterization, we list the advantages and shortcomings of public cloud GPU instances for users to help them make an informed decision(s). Our characterization results indicate that the more expensive GPU instances may not be the most performant for all DNN models and that AWS can sometimes sub-optimally allocate hardware interconnect resources. Specifically, the intra-machine interconnect can introduce communication overheads of up to 90% of DNN training time and the network-connected instances can suffer from up to 5x slowdown compared to training on a single instance. Furthermore, (iii) we also model the impact of DNN m

关键词： Characterization Cloud computing distributed deep learning Public cloud GPU

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TopoCommit: A Topological Commit Protocol for Cross-Ledger Transactions in Scientific computing 25

TopoCommit: A Topological Commit Protocol for Cross-Ledger T...

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25th ieee international conference on Cluster computing (CLUSTER)

作者： Tawose, Olamide Timothy Yang, Lei Zhao, Dongfang Lincoln Univ Lincoln Univ PA 19352 USA Univ Nevada Reno NV 89557 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798350307924

While increasingly more applications are tempted to manage their data in decentralized systems, such as blockchains or distributed ledgers, the data exchange across multiple, potentially heterogeneous, decentralized systems remains an open problem: State-of-the-art protocols cannot meet one or more of the core requirements, such as atomicity, liveness, and scalability. Specifically, in the field of scientific computing, although a blockchain service was recently developed for scientific computing environments, the data exchanges and transactions among distinct ledgers are not supported. Observing that many modern scientific applications are collaborated on by multiple teams and the increasingly complicated (in-situ) workflows thereof, we argue that there is a pressing need to realize an efficient and scalable protocol for distinct ledgers to exchange data in scientific computing. This paper proposes a topological approach to enabling atomic, nonblocking, and scalable data exchanges among an arbitrary number of scientific ledgers in the context of collaborative scientific computing. Specifically, we construct a topological space formed by these ledgers-abstracting those nodes in a cross-ledger transaction as topological objects such as abstract simplex and simplicial complex. These topological objects, in turn, serve as the building blocks of a topological protocol, namely TopoCommit, under practical assumptions. We implement TopoCommit and integrate it into SciChain, a recently published distributed ledger for tracking scientific data provenance. The extensive evaluation of up to 1,008 nodes and 144 distinct ledgers on CloudLab shows that TopoCommit outperforms state-of-the-art protocols by up to 70x.

关键词： Scientific data management distributed ledgers applied topology

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An MDP-Based Approach for Distribution System Control with PV Generation and Battery Storage

An MDP-Based Approach for Distribution System Control with P...

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2024 international conference on Communications Control and computing Technologies for Smart Grids

作者： Sosnowskit, Robert Baszyrnski, Marcin Konstantinou, Charalambos AGH Univ Sci & Technol AGH UST Dept Power Elect & Energy Control Syst Krakow Poland King Abdullah Univ Sci & Technol KAUST CEMSE Div King WI USA

ISBN: (纸本)9798350318562;9798350318555

This paper proposes a decision-making approach for the control of distribution systems with distributed energy resources (DERs) equipped with photovoltaic (PV) units and battery energy storage systems (BESS). The objective is to minimize the total operational cost of the distribution system while satisfying the system operating constraints. The method is based on the discrete-time finite-horizon Markov Decision Process (MDP) framework. Different aspects of the distribution system operation are considered, such as the possibilities of curtailment of PV generation, managing battery storage, reactive power injection, load shedding, and providing a flexibility service for the transmission system. The model is tested for the ieee 33-bus system with two added DERs and the study cases involve various unexpected events. The experimental results show that this method enables the attainment of relatively low total cost values compared to the reference deterministic approach. The benefits of applying this approach are particularly evident when there is a significant difference between the predicted and actual PV power generation.

关键词： Distribution systems Markov Decision Process (MDP) distributed energy resources (DERs) PV battery energy storage systems (BESS)

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A Service for Resilient Manufacturing 9

A Service for Resilient Manufacturing

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ieee international conference on Smart computing (SMARTCOMP)

作者： Soderi, Mirco Breslin, John Gerard Univ Galway Data Sci Inst Galway Ireland

ISBN: (纸本)9798350322811

In modern industry, adaptation to market changes, as well as prompt reaction to a variety of predictable and unpredictable events, is a key requirement. Ubiquitous computing, real-time analytics, reconfigurable hardware/software components, often coexist in the complex, internally variegated, and often proprietary systems that are traditionally deployed to meet such requirement. However, such tailor-made systems meet only in part the requirements of openness, security, monitorability, geographical distribution, and most of all, remote extendability and changeability, which are crucial for prompt reaction to unforeseen circumstances. In this work, a containerized service application named Network Factory is presented. It enables the remote construction, configuration and operation of resilient computation systems that meet the above-mentioned requirements, and distinguish for their logical simplicity and for the uniform addressing of elaborations and human-computer interfaces, which are achieved through few reconfigurable components and communication mechanisms that are used from the production line up to the Cloud. Source code, documentation, and step-by-step introductory guides are publicly available in a dedicated GitHub repository, and distributed under the CC-BY-4.0 license.

关键词： Resiliency Reconfigurable manufacturing Ubiquitous computing Real-time analytics Open architecture Security Monitorability Changeable software Geo-distributed system Containerization

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distributed Discovery of Causal Networks in Pervasive Environments

Distributed Discovery of Causal Networks in Pervasive Enviro...

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ieee international conference on Pervasive computing and Communications (PerCom)

作者： Mariani, Stefano Zambonelli, Franco Univ Modena & Reggio Emilia Dept Sci & Methods Engn Reggio Emilia Italy

ISBN: (纸本)9798350304367;9798350304374

In pervasive computing environments, learning the causal network of relationships between environmental variables is crucial to support situation recognition and planning. However, this may be impossible when computing nodes have only partial observability and control, as in the case of multiple fog nodes each sensing only a local portion of variables and controlling a local portion of actuators. In fact, the causal network learnt by individual nodes may not suffice to give them full awareness and control over the environment. In this paper, we propose a protocol for distributed causal discovery, where fog nodes in an environment cooperate with each other to expand their individual local knowledge of the causal network and acquire the minimal knowledge of the causal network needed to achieve awareness and control. We evaluate our approach in a smart home scenario, showing its superior performance with respect to global causal learning with full observability.

关键词： Observability

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Thinking out of replication for geo-distributing applications: the sharding case 8

Thinking out of replication for geo-distributing application...

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8th ieee international conference on Fog and Edge computing (ICFEC)

作者： Antony, Geo Johns Delavergne, Marie Lebre, Adrien Rainimangavelo, Matthieu Rakotojaona INRIA STACK Res Team Nantes France IMT Atlantique STACK Res Team Nantes France

ISBN: (纸本)9798350361360;9798350361353

To promote the adoption of the edge paradigm, our community needs innovative approaches for geo-distributing cloud applications across multiple locations without modifying existing business logic. While recent efforts propose using external services to orchestrate REST operations and achieve geo-distribution, relying solely on resource sharing and replication has limitations in finely distributing manipulated resources. This paper introduces a novel collaboration method that extends resources across multiple instances, going beyond simple replication. Our approach employs a shard-like strategy, enabling the creation of a distributed resource with a unified state view while mitigating a synchronization overhead. The effectiveness of our mechanism is demonstrated through a proof-of-concept implemented on top of the Kubernetes ecosystem.

关键词： Sharding Replication Geo-distributed system Edge application Cloud application Micro-services

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FaaSBatch: Enhancing the Efficiency of Serverless computing by Batching and Expanding Functions 43

FaaSBatch: Enhancing the Efficiency of Serverless Computing ...

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Wu, Zhaorui Deng, Yuhui Zhou, Yi Li, Jie Pang, Shujie Jinan Univ Dept Comp Sci Guangzhou 510632 Guangdong Peoples R China Wuhan Natl Lab Optoelect Wuhan Peoples R China Columbus State Univ TSYS Sch Comp Sci Columbus GA USA

ISBN: (纸本)9798350339864

With high scalability and flexibility, serverless computing is becoming the most promising computing model. Existing serverless computing platforms initiate a container for each function invocation, which leads to a huge waste of computing resources. Our examinations reveal that (i) executing invocations concurrently within a single container can provide comparable performance to that provided by multiple containers (i.e., traditional approaches);(ii) redundant resources generated within a container result in memory resource waste, which prolongs the execution time of function invocations. Motivated by these insightful observations, we propose FaaSBatch - a serverless framework that reduces invocation latency and saves scarce computing resources. In particular, FaaSBatch first classifies concurrent function requests into different function groups according to the invocation information. Next, FaaSBatch batches the invocations of each group, aiming to minimize resource utilization. Then, FaaSBatch utilizes an inline parallel policy to map each group of batched invocations into a single container. Finally, FaaSBatch expands and executes invocations of containers in parallel. To further reduce invocation latency and resource utilization, within each container, FaaSBatch reuses redundant resources created during function execution. We conduct extensive experiments based on Azure traces to evaluate the effectiveness and performance of FaaSBatch. We compare FaaSBatch with three state-of-the-art schedulers Vanilla, SFS, and Kraken. Our experimental results show that FaaSBatch effectively and remarkably slashes invocation latency and resource overhead. For instance, when executing I/O functions, FaaSBatch cuts back the invocation latency of Vanilla, SFS, and Kraken by up to 92.18%, 89.54%, and 90.65%, respectively;FaaSBatch also slashes the resource overhead of Vanilla, SFS, and Kraken by 58.89% to 94.77%, 43.72% to 90.39%, and 42.99% to 78.88%, respectively.

关键词： Cloud computing serverless computing batching request resource management

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Building a First Prototype of a Multi-scale Modular distributed Display 17

Building a First Prototype of a Multi-scale Modular Distribu...

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ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social computing, CPSCom 2024, 10th ieee international conference on Smart Data, SmartData 2024

作者： Lassabe, Frédéric Dhoutaut, Dominique Piranda, Benoît Kouchnarenko, Olga Bourgeois, Julien Cnrs Utbm FEMTO-ST Institute Belfort France Cnrs Univ. Franche-Comté FEMTO-ST Institute Montbéliard France Cnrs Univ. Franche-Comté FEMTO-ST Institute Besançon France

ISBN: (纸本)9798350351637

Blinky Blocks are cubic modular robots, which communicate with their neighbors through their faces, and change color using LEDs. We previously used sets of Blinky Blocks to display images on the basis of one Blinky Block being one pixel. In this paper, we build a multi-resolution screen with modular robots. This screen benefits from its distributed architecture: being able to work if some nodes fail, and being completely customizable. We propose a hardware architecture and related protocols to attach a 8 × 8 LED matrix to a Blinky Block. The architecture allows us to build distributed multi-resolution screens by mixing regular and enhanced Blinky Blocks. We demonstrate the usage of our integrated screen through experiments with continuously fed Blinky Blocks as well as an autonomous scrolling. © 2024 ieee.

关键词： Modular robots

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Intent-based Management for the distributed computing Continuum 17

Intent-based Management for the Distributed Computing Contin...

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17th ieee international Congress on Intelligent and Service-Oriented systems Engineering (CISOSE)

作者： Morichetta, Andrea Spring, Nikolaus Raith, Philipp Dustdar, Schahram TU Wien Distributed Syst Grp Vienna Austria

ISBN: (纸本)9798350322392

Managing digital and connected systems has become increasingly challenging in the past decade due to their scale and complexity. A new perspective is required to manage these systems, considering the infrastructure and components from edge to cloud, i.e., in the distributed computing continuum. Serverless computing offers improved scalability and cost efficiency, but balancing and coordinating serverless systems remain complex. Intent-based systems, popular in networking, can provide a solution by translating stakeholder inputs into actions that meet Service Level Objectives (SLOs). Their application in the computing continuum can be highly beneficial, but it has yet to be deeply explored. To bridge this gap, we propose a methodology for deploying an intent-based system for the computing continuum. We implement an architectural framework leveraging the serverless paradigm. Furthermore, we focus on defining and implementing the main components for translating the management requirements into actions executed by serverless functions inspired by a threelayer model. Through a Proof of Concept (PoC) deployed in Amazon's AWS cloud and detailed simulations, we showcase how such an approach can resolve conflicts in a complex system, i.e., balancing efficiency and availability. Our work aims to contribute to effectively managing the computing continuum and highlight the potential of intent-based systems in this domain. The experiments' results show our framework's ability to make appropriate scaling decisions, fulfilling both objectives.

关键词： computing continuum intents PoC serverless SLO

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