research paper on distributed computing system

Distributed Systems and Parallel Computing

No matter how powerful individual computers become, there are still reasons to harness the power of multiple computational units, often spread across large geographic areas. Sometimes this is motivated by the need to collect data from widely dispersed locations (e.g., web pages from servers, or sensors for weather or traffic). Other times it is motivated by the need to perform enormous computations that simply cannot be done by a single CPU.

From our company’s beginning, Google has had to deal with both issues in our pursuit of organizing the world’s information and making it universally accessible and useful. We continue to face many exciting distributed systems and parallel computing challenges in areas such as concurrency control, fault tolerance, algorithmic efficiency, and communication. Some of our research involves answering fundamental theoretical questions, while other researchers and engineers are engaged in the construction of systems to operate at the largest possible scale, thanks to our hybrid research model .

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A curated list to learn about distributed systems

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Awesome-distributed-systems.

A (hopefully) curated list on awesome material on distributed systems, inspired by other awesome frameworks like awesome-python . Most links will tend to be readings on architecture itself rather than code itself.

Read things here before you start.

• CAP Theorem , Also plain english explanation
• Fallacies of Distributed Computing , expect things to break, everything
• Distributed systems theory for the distributed engineer , most of the papers/books in the blog might reappear in this list again. Still a good BFS approach to distributed systems.
• FLP Impossibility Result (paper) , an easier blog post to follow along
• An Introduction to Distributed Systems @aphyr's excellent introduction to distributed systems
• Distributed Systems for fun and profit [Free]
• Distributed Systems Principles and Paradigms, Andrew Tanenbaum [Free with registration]
• Scalable Web Architecture and Distributed Systems [Free]
• Principles of Distributed Systems [Free] [ETH Zurich University]
• Making reliable distributed systems in the presence of software errors , [Free] Joe Amstrong's (Author of Erlang) PhD thesis
• Designing Data Intensive Applications [Amazon Link]
• Distributed Machine Learning Patterns, Yuan Tang , Practical patterns for scaling machine learning from your laptop to a distributed cluster
• Distributed Computing, Hagit Attiya and Jennifer Welch
• Distributed Algorithms, Nancy Lynch [Amazon Link]
• Impossibility Results for Distributed Computing [paywall]
• Designing Distributed Systems, Brendan Burns [Free with registration]
• Distributed Systems: Concepts and Design, George Coulouris [Amazon Link]
• Akka in Action, Second Edition
• Systemantics: how systems work and especially how they fail
• Think Distributed Systems [Free with subscription]

Must read papers on distributed systems. While nearly all of Lamport's work should feature here, just adding a few that must be read.

• Times, Clocks and Ordering of Events in Distributed Systems Lamport's paper, the Quintessential distributed systems primer
• Session Guarantees for Weakly Consistent Replicated Data a '94 paper that talks about various recommendations for session guarantees for eventually consistent systems, many of this would be standard vocabulary in reading other dist. sys papers, like monotonic reads, read your writes etc.

Storage & Databases

• Dynamo: Amazon's Highly Available Key Value Store Paraphrasing @fogus from their blog , it is very rare for a paper describing an active production system to influence the state of active research in any industry; this is one of those seminal distributed systems paper that solves the problem of a highly available and fault tolerant database in an elegant way, later paving the way for systems like Cassandra, and many other AP systems using a consistent hashing.
• Bigtable: A Distributed Storage System for Structured Data
• The Google File System
• Cassandra: A Decentralized Structured Storage System Inspired heavily by Dynamo, an now an open source
• CRUSH: Controlled, Scalable, Decentralized Placement of Replicated Data , the algorithm for the basis of Ceph distributed storage system, for the architecture itself read RADOS

Messaging systems

• The Log: What every software engineer should know about real-time data's unifying abstraction , a somewhat long read, but covers brilliantly on logs, which are at the heart of most distributed systems
• Kafka: a Distributed Messaging System for Log Processing

Distributed Consensus and Fault-Tolerance

• Practical Byzantine Fault Tolerance
• The Byzantine Generals Problem
• Impossibility of Distributed Consensus with One Faulty Process
• The Part Time Parliament Paxos, Lamport's original Paxos paper, a bit difficult to understand, may require multiple passes
• Paxos Made Simple , a more terse readable Paxos paper by Lamport himself. Shorter and more easier compared to the original.
• The Chubby Lock Service for loosely coupled distributed systems Google's lock service used for loosely coupled distributed systems. Sort of Paxos as a Service for building other distributed systems. Primary inspiration behind other Service Discovery & Coordination tools like Zookeeper, etcd, Consul etc.
• Paxos made live - An engineering perspective Google's learning while implementing systems atop of Paxos. Demonstrates various practical issues encountered while implementing a theoretical concept.
• Raft Consensus Algorithm An alternative to Paxos for distributed consensus, that is much simpler to understand. Do checkout an interesting visualization of raft
• Conflict-free Replicated Data Types presents an approach for Strong Eventual Consistency which as been applied in projects such as Riak , Redis and Akka . A great talk on the subject by Martin Kleppmann can be found here
• Speculative algorithms for global state synchronizations Azos.Sky.Server.Locking uses probability based QOS (Quality of Service)/Trust measure to ensure probability-based consensus. The approach avoids distributed state machine/phase synchronization and is very simple to understand and implement

Testing, monitoring and tracing

While designing distributed systems are hard enough, testing them is even harder.

• Dapper , Google's large scale distributed-systems tracing infrastructure, this was also the basis for the design of open source projects such as Zipkin , Apache SkyWalking , Pinpoint and HTrace .

Programming Models

• Distributed Programming Model
• PSync: a partially synchronous language for fault-tolerant distributed algorithms Video: Conference Video
• Programming Models for Distributed Computing
• Logic and Lattices for Distributed Programming

Verification of Distributed Systems

• Curated list of resources on testing distributed systems includes links to materials on testing by various companies (Google, Amazon, Netflix, Microsoft, Dropbox, etc) and research papers.
• Jepsen A framework for distributed systems verification, with fault injection @aphyr has featured enough times in this list already, but Jepsen and the blog posts that go with are a quintessntial addition to any distributed systems reading list.
• Verdi A Framework for Implementing and Formally Verifying Distributed Systems Paper
• Distributed Deep Dive interview series by Ably Relatime .
• Distributed Systems in One Lesson Distributed Systems in One Lesson by Tim Berglund
• Reliable Distributed Algorithms, Part 1 , KTH Sweden
• Reliable Distributed Algorithms, Part 2 , KTH Sweden
• Cloud Computing Concepts , University of Illinois
• CMU: Distributed Systems in Go Programming Language
• Software Defined Networking , Georgia Tech.
• ETH Zurich: Distributed Systems
• ETH Zurich: Distributed Systems Part 2 , covers Distributed control algorithms, communication models, fault-tolerance among other things. In particular fault tolerance issues (models, consensus, agreement) and replication issues (2PC,3PC, Paxos), which are critical in understanding distributed systems are explained in great detail.
• Distributed Systems Course , A beginner course on distributed system by Chris Colohan, A google employee who contributed to SUIF, MapReduce, TCMalloc, Percolator, Caffeine, Borg, Omega, and Piper.
• MIT 6.824 , Youtube-playlist MIT distributed system lectures, in each video they discuss papers like GFS, Zookeeper, RAFT, Spanner...
• Distributed Systems , Lectures 9 to 16 of the Cambridge University lecture "Concurrent and Distributed Systems", given by Dr. Martin Kleppmann. Youtube-playlist . A computer science entrance course, covered basic models and algorithms in distributed systems, also discussed CRDT, collaboration software and google's spanner.

Blogs and other reading links

• Amazon Builder's Library , a collection of Amazon's learnings on distributed systems
• How we implemented consistent hashing efficiently
• Notes on Distributed Systems for Young Bloods
• High Scalability Several architectures of huge internet services, for eg twitter , whatsapp
• There is No Now , Problems with simultaneity in distributed systems
• Turing Lecture: The Computer Science of Concurrency: The Early Years , An article by Leslie Lamport on concurrency
• The Paper Trail blog, a very readable blog covering various aspects of distributed systems
• aphyr , Posts on jepsen series are pretty awesome
• All Things Distributed - Wernel Vogel's (Amazon CTO) blog on distributed systems
• Distributed Systems: Take Responsibility for Failover
• The C10K problem
• On Designing and Deploying Internet-Scale Services
• Files are hard A blog post on filesystem consistency, pretty important to read if you are into distributed storage or databases.
• Distributed Systems Testing: The Lost World Testing distributed systems are hard enough, a well researched blog post which again covers a lot of links to various approaches and other papers
• SWIM Protocol explained A blog post on popular SWIM failure detector
• ACM Symposium on Principles of Distributed Computing (PODC) and International Symposium on Distributed Computing (DISC) , a list of resources from PODC–DISC community including conference series, mailing lists, youtube, twitter, etc.
• IEEE International Parallel & Distributed Processing Symposium (IPDPS) , an international forum for engineers and scientists to present their latest research findings.
• Springer Distributed Computing Journal , a journal about theory, design, specification, and implementation of distributed systems.

Other lists like this one

• Readings in distributed systems
• Distributed Systems meta list
• List of required readings for Distributed Systems Part of CMU's Engineering Distributed Systems course
• The Distributed Reader
• A Distributed Systems Reading List , A collection of material, mostly papers on Distributed Systems Theory as well as seminal industry papers
• Distributed Systems Readings , A comprehensive list of online courses related to distributed systems
• Awesome Distributed Consensus , Another list of materials on distributed consensus protocols
• Beginner's Guide to Distributed Systems A blog post with some useful getting started links for distributed systems

Contributors 31

A brief introduction to distributed systems

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• Vrije Universiteit Amsterdam

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A Comparative Study of Consensus Algorithms for Distributed Systems

• Conference paper
• First Online: 12 April 2022
• Cite this conference paper

• Kelsi Rado Van Dame 8 ,
• Thomas Bronson Bergmann 8 ,
• Mohamed Aichouri 8 &
• Maria Pantoja   ORCID: orcid.org/0000-0002-1942-9769 8  

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1540))

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• Latin American High Performance Computing Conference

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Distributed Systems (DS) where multiple computers share a workload across a network, are used everywhere, from data intensive computations to storage and machine learning. DS provide a relatively cheap and efficient solution that allows stability with improved performance for computational intensive applications. Fundamental to DS is the consensus algorithm, necessary to agree on which server is the master, who has a lock and many other applications. Consensus algorithms are sometimes very difficult to understand and therefore implement correctly. In this paper we chose to complete a comparative study between three different consensus algorithms Raft, Paxos, and pBFT. We provided our implementation for the three algorithms with details of the assumptions taken. The goal of this study is to better understand the differences between the systems in terms of performance and assess the advantages and disadvantages of each. To test the performance of each program, we recorded consensus latency vs. node count and we present a summary of our results in this paper.

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On Performance Evaluation of Distributed System Size Estimation Executed by Average Consensus Weights

Castro, M., Liskov, B.: Practical Byzantine Fault Tolerance. Massachusetts Institute of Technology (2019). http://pmg.csail.mit.edu/papers/osdi99.pdf 6 Dec 2020

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Van Dame, K.R., Bergmann, T.B., Aichouri, M., Pantoja, M. (2022). A Comparative Study of Consensus Algorithms for Distributed Systems. In: Gitler, I., Barrios Hernández, C.J., Meneses, E. (eds) High Performance Computing. CARLA 2021. Communications in Computer and Information Science, vol 1540. Springer, Cham. https://doi.org/10.1007/978-3-031-04209-6_9

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Innovative load forecasting models and intelligent control strategy for enhancing distributed load levelling techniques in resilient smart grids.

1. Introduction

2. related work, 3. materials and methods, 3.1. dataset collection, 3.1.1. data source, 3.1.2. feature descriptions, 3.2. data pre-processing, 3.2.1. data normalization, 3.2.2. min–max scaling, 3.3. proposed model, 3.3.1. model architectures, 3.3.2. predicting dynamic loads, 3.3.3. intelligent control strategy for load levelling, 3.4. mathematical model, 3.4.1. mathematical model for gated recurrent unit (gru).

• Mathematical Model of LSTM

3.4.2. Optimization Model

3.5. intelligent control strategy, 3.6. evaluation metrics, 3.6.1. mean squared error, 3.6.2. mean absolute percentage error, 4. results and discussion, 4.1. performance of lstm, 4.2. performance of gru, 4.3. impact of intelligent control strategy, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Share and Cite

Fangzong, W.; Nishtar, Z. Innovative Load Forecasting Models and Intelligent Control Strategy for Enhancing Distributed Load Levelling Techniques in Resilient Smart Grids. Electronics 2024 , 13 , 3552. https://doi.org/10.3390/electronics13173552

Fangzong W, Nishtar Z. Innovative Load Forecasting Models and Intelligent Control Strategy for Enhancing Distributed Load Levelling Techniques in Resilient Smart Grids. Electronics . 2024; 13(17):3552. https://doi.org/10.3390/electronics13173552

Fangzong, Wang, and Zuhaib Nishtar. 2024. "Innovative Load Forecasting Models and Intelligent Control Strategy for Enhancing Distributed Load Levelling Techniques in Resilient Smart Grids" Electronics 13, no. 17: 3552. https://doi.org/10.3390/electronics13173552

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