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Carbon-Aware distributed Energy Management for Data Center Microgrids Based on Blockchain 22

Carbon-Aware Distributed Energy Management for Data Center M...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Si, Xiaowei Wan, Jianxiong Liu, Chuyi Li, Leixiao Wang, Xiaolei Lu, Xinyu Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Hohhot China Ministry of Education of the People's Republic of China Research Center of Large-Scale Energy Storage Technologies China

ISBN: (纸本)9798331509712

In this poster, the energy and carbon management problem in Data Center Microgrid (DCMG) is modeled as a Decentralized Partially Observable Markov Decision Process, and the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is adopted to learn the optimal operating policy. In addition, the system uses blockchain and smart contract to ensure data security and auditability in the power trading process. Finally, simulation results show that the proposed system significantly reduces the overall cost of the DCMGs compared with traditional systems. © 2024 IEEE.

关键词： Blockchain Data Center Microgrids (DCMG) Energy and carbon management MADDPG

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Coded parallelism for distributed Deep Learning

Coded Parallelism for Distributed Deep Learning

引用

2023 IEEE international symposium on Information Theory, ISIT 2023

作者： Ji, Songting Zhang, Zhaoyang Yang, Zhaohui Jin, Richeng Yang, Qianqian College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China Zhejiang University International Joint Innovation Center Haining314400 China

ISBN: (纸本)9781665475549

With the rapid development of deep learning, the parameters of modern neural network models, especially in the field of Natural Language Processing (NLP) are extremely huge. When the parameters of the model are larger even than the storage memory of a single device, it is necessary to split the original big learning model into different parts with each part assigned to one device, thus realizing joint model training over different devices (i.e., distributed training). In this paper, we aim to introduce the advanced coding scheme into the distributed parallel framework, which leads to the perfect combination of coding and the underlying calculation of neural networks. The proposed scheme is not only able to avoid the impact of poor computing power or low bandwidth and even dropped devices (stragglers) on system performance but also reduce the communication load between different devices, thereby greatly improving the performance of distributed parallel systems. © 2023 IEEE.

关键词： Deep learning

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RIMR: Reverse Influence Maximization Rank

RIMR: Reverse Influence Maximization Rank

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1st international Conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Vap, Jay Kogge, Peter Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9798350364613;9798350364606

The ubiquitous usage of social media and other internet platforms has led to the widespread proliferation of naturally occurring social networks. This has caused a revolution in marketing and information dissemination due to the rise of influencers, and their innate ability to broadcast information to large audiences. Influence maximization, a key problem in identifying optimal influential individuals, can be used to identify influencers in a social network, or can be superimposed on other settings which exhibit relational information. Popular methods, such as greedy algorithms, face scalability issues, performing poorly on larger networks, and incurring a large memory overhead. To address these challenges, this paper introduces a novel methodology, RIMR, which leverages statistical modeling and parallel programming to circumvent the need for storing large collections of data, achieving scalability and efficiency. Experimental results demonstrate the effectiveness and scalability of the proposed approach compared to state-of-the-art methods, showcasing significant speedups. This paper provides insights into the development of the proposed statistical process, the heterogeneous algorithm implementation, and a prototype demonstrating its efficacy.

关键词： GPUs Heterogeneous Architectures Influence Maximization parallel Algorithms

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A Methodology to Build Decision Analysis Tools Applied to distributed Reinforcement Learning 36

A Methodology to Build Decision Analysis Tools Applied to Di...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Prigent, Cedric Cudennec, Loic Costan, Alexandru Antoniu, Gabriel Univ Rennes INRIA CNRS IRISA Rennes France DGA Maitrise Informat Rennes France

ISBN: (纸本)9781665497473

As Artificial Intelligence-based applications become more and more complex, speeding up the learning phase (which is typically computation-intensive) becomes more and more necessary. distributed machine learning (ML) appears adequate to address this problem. Unfortunately, ML also brings new development frameworks, methodologies and high-level programming languages that do not fit to the regular high-performance computing design flow. This paper introduces a methodology to build a decision making tool that allows ML experts to arbitrate between different frameworks and deployment configurations, in order to fulfill project objectives such as the accuracy of the resulting model, the computing speed or the energy consumption of the learning computation. The proposed methodology is applied to an industrial-grade case study in which reinforcement learning is used to train an autonomous steering model for a cargo airdrop system. Results are presented within a Pareto front that lets ML experts choose an appropriate solution, a framework and a deployment configuration, based on the current operational situation. While the proposed approach can effortlessly be applied to other machine learning problems, as for many decision making systems, the selected solutions involve a trade-off between several antagonist evaluation criteria and require experts from different domains to pick the most efficient solution from the short list. Nevertheless, this methodology speeds up the development process by clearly discarding, or, on the contrary, including combinations of frameworks and configurations, which has a significant impact for time and budget-constrained projects.

关键词： Machine Learning distributed Computing Decision Making

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Consistent and Efficient Batch Operations for NoSQL databases with Hybrid Timestamp 19th

Consistent and Efficient Batch Operations for NoSQL Database...

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19th international Conference on Network and parallel Computing (NPC)

作者： Yu, Qianmian Zhou, Jing Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9783031213946;9783031213953

NoSQL databases, such as HBase or Cassandra employ weak consistency models to provide good scalability and availability. However, they often lack functionality that would help programmers reason about the correctness of their applications. Notably, they do not support consistent batch operations that could be used for important tasks, such as batch updates or maintaining secondary indexes. Some systems add transaction support to NoSQL databases. However, they often bring much overhead to existing single-row operations. This paper proposes an efficient algorithm for supporting batch operations on existing NoSQL databases. It reuses the existing local timestamp and adds a global timestamp to ensure batch operations' consistency. Our implementation based on HBase shows that compared to transactional systems, our algorithm improves the throughput of batch operations by up to 2x. Meanwhile, the latency of single-row operations only increases by around 12%. In comparison, other transactional systems increase their latency by over 3x.

关键词： Concurrency control NoSQL databases distributed systems

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GIM (Ghost In the Machine): A Coarse -Grained Reconfigurable Compute -In -Memory Platform for Exploring Machine -Learning Architectures

GIM (Ghost In the Machine): A Coarse -Grained Reconfigurable...

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1st international Conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Borowicz, Maya Ding, James Fan, Winnie Gao, Zhongqi Jackson, Davis Lu, Ares Rohlfsen, Sophia Simar, Ray Rice Univ Houston TX 77005 USA

ISBN: (纸本)9798350364613;9798350364606

Machine-learning (ML) algorithms are finding wide adoption across a rich spectrum of application domains with diverse requirements in terms of performance, power, and cost. These diverse requirements are making it necessary to explore a large space of ML architectures and reexamine fundamental computational structures, a process of exploration that is very expensive. To get around the costly computations associated with large data sets and long training times, there have been increasing investments in specialized fixed-function hardware. However, this specialized hardware is expensive and hard to generalize to address the spectrum of applications. For our experiments, we focus on a novel, highly parallel, superset ML architecture, and use it to test the capabilities of new coarse-grained FPGAs containing hundreds and thousands of DSP slices with dedicated local storage. These new coarse-grained architectures allow us to achieve ASIC-like clock rate and reductions in power while exploring novel and common ML architectures.

关键词： AMD architecture back propagation coarse grained compute in memory DSP FPGA machine learning neural network parallel processing Rice University RISC V Simulink systolic UltraScale Zync

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ZPaxos: An Asynchronous BFT Paxos with a Leaderless Synchronous Group 21

ZPaxos: An Asynchronous BFT Paxos with a Leaderless Synchron...

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21st IEEE international symposium on parallel and distributed Computing (ISPDC)

作者： Amarasekara, D. D. Ranasinghe, D. N. Typefi Syst Pty Ltd Res & Dev Maroochydore Australia Univ Colombo Sch Comp Colombo Sri Lanka

ISBN: (数字)9781665488020

ISBN: (纸本)9781665488020

Increased resource overhead and low throughput are major issues associated with current BFT consensus systems. EPaxos [48], a leaderless crash fault-tolerant protocol can perform commits quickly within a single phase using a fast quorum provided that the commands are independent. If the commands have dependencies, EPaxos needs to run an additional phase on a majority quorum. As a result, it would lose its throughput due to the number of command instances it needs to keep in memory and the high volume of messages it needs to exchange with other replicas to reach consensus. Alternatively, XPaxos [43] which is a leader-based system solves practical BFT with minimal resources while assuring high throughput. This paper describes an improved consensus mechanism, ZPaxos, which is an enhanced EPaxos embedding two key features of XPaxos i.e., a synchronous group with fault detection, and a recovery protocol under weak asynchrony assumptions. The new protocol achieves consensus in a single round with a leaderless synchronous group which allows the removal and addition of a node at a time whenever it sees a crashed or a byzantine replica while the system continues servicing requests without any significant interruptions throughout view synchrony. Transaction throughput and latency of ZPaxos which can handle both byzantine and crash failures exhibit superior performance to that of EPaxos which handles crash failures only, which are two leaderless core group architectures.

关键词： paxos consensus distributed ledgers bft

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Data-parallel Actors: A Programming Model for Scalable Query Serving systems 19

Data-Parallel Actors: A Programming Model for Scalable Query...

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19th USENIX symposium on Networked systems Design and Implementation (NSDI)

作者： Kraft, Peter Kazhamiaka, Fiodar Bailis, Peter Zaharia, Matei Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781939133274

We present data-parallel actors (DPA), a programming model for building distributed query serving systems. Query serving systems are an important class of applications characterized by low-latency data-parallel queries and frequent bulk data updates;they include data analytics systems like Apache Druid, full-text search engines like ElasticSearch, and time series databases like InfluxDB. They are challenging to build because they run at scale and need complex distributed functionality like data replication, fault tolerance, and update consistency. DPA makes building these systems easier by allowing developers to construct them from purely single-node components while automatically providing these critical properties. In DPA, we view a query serving system as a collection of stateful actors, each encapsulating a partition of data. DPA provides parallel operators that enable consistent, atomic, and fault-tolerant parallel updates and queries over data stored in actors. We have used DPA to build a new query serving system, a simplified data warehouse based on the single-node database MonetDB, and enhance existing ones, such as Druid, Solr, and MongoDB, adding missing user-requested features such as load balancing and elasticity. We show that DPA can distribute a system in <1K lines of code (>10x less than typical implementations in current systems) while achieving state-of-the-art performance and adding rich functionality.

关键词： Data warehouses

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Curator - A system for creating data sets for behavioral malware detection 20

Curator - A system for creating data sets for behavioral mal...

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20th international symposium on parallel and distributed Computing (ISPDC)

作者： Portase, Radu-Marian Colesa, Adrian Tech Univ Cluj Napoca Bitdefender Cluj Napoca Romania Tech Univ Cluj Napoca Cluj Napoca Romania

ISBN: (纸本)9781665432818

Detecting malware during execution using machine learning models presents some hard-to-solve problems relating to data set construction seldom discussed in the literature. We identify, name these problems and show our solutions to them in the form of Curator, a specialized distributed system for detonating potentially malicious programs, extracting behavior information, and correctly labeling said behavior to construct an accurate, consistent, and reliable data set. We demonstrate Curator's need by using generated data sets to train machine learning models based on Naive Bayes, Logistic Regression, and Random Forests. Our work is currently focused on the Windows operating system.

关键词： Operating systems distributed databases Malware Data models Windows Reliability Labeling

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A parallel Novelty Search Metaheuristic Applied to a Wildfire Prediction System 36

A Parallel Novelty Search Metaheuristic Applied to a Wildfir...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Strappa, Jan Caymes-Scutari, Paola Bianchini, German Consejo Nacl Invest Cient & Tecn CONICET Mendoza Argentina Univ Tecnol Nacl Fac Reg Mendoza Buenos Aires DF Argentina

ISBN: (纸本)9781665497473

Wildfires are a highly prevalent multi-causal environmental phenomenon. The impact of this phenomenon includes human losses, environmental damage and high economic costs. To mitigate these effects, several computer simulation systems have been developed in order to predict fire behavior based on a set of input parameters, also called a scenario (wind speed and direction;temperature;etc.). However, the results of a simulation usually have a high degree of error due to the uncertainty in the values of some variables, because they are not known, or because their measurement may be imprecise, erroneous, or impossible to perform in real time. Previous works have proposed the combination of multiple results in order to reduce this uncertainty. State-of-the-art methods are based on parallel optimization strategies that use a fitness function to guide the search among all possible scenarios. Although these methods have shown improvements in the quality of predictions, they have some limitations related to the algorithms used for the selection of scenarios. To overcome these limitations, in this work we propose to apply the Novelty Search paradigm, which replaces the objective function by a measure of the novelty of the solutions found, which allows the search to continuously generate solutions with behaviors that differ from one another. This approach avoids local optima and may be able to find useful solutions that would be difficult or impossible to find by other algorithms. As with existing methods, this proposal may also be adapted to other propagation models (floods, avalanches or landslides).

关键词： wildfire propagation prediction evolutionary algorithms novelty search uncertainty reduction

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