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DISTRIBUTED SYSTEMS

COMPSCI 739
과목 설명

Large-scale distributed systems have become pervasive, underlying virtually all widely used computing services. Explore prevalent issues in designing and implementing distributed systems. Topics include fault tolerance, scalability, replication, distributed storage, consensus, reliability, performance, and correctness.

선수과목
충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점
3.88

2.99% 과거 데이터 대비

수료율
100%

0.5% 과거 데이터 대비

A 비율
79.17%

16.03% 과거 데이터 대비

학급 규모
24

-27.76% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

BIG DATA SYSTEMS

COMPSCI 744

Issues in the design and implementation of big data processing systems, including: an overview of cluster architecture, key design goals (flexibility, performance and fault tolerance), popular execution frameworks, basic abstractions, and applications (e.g., batch analytics, stream processing, graph processing, and machine learning).

ADVANCED OPERATING SYSTEMS

COMPSCI 736

Advanced topics in operating systems, including process communication, resource allocation, multiprocess and network operating systems, kernel philosophies, fault-tolerant systems, virtual machines, high-level language systems, verifiability and proof techniques. Comp Sci 537 or cons inst

CORE TOPICS IN COMPUTING

COMPSCI 839

Topics selected from advanced areas.

TOPICS IN DATABASE MANAGEMENT SYSTEMS

COMPSCI 764

Implementation of database management systems, the impact of new technology on database management systems, back-end database computers, distributed database management systems, concurrency control, and query execution in both distributed and centralized systems, implementation of multiple user views, roll-back and recovery mechanisms, database translation. Students are strongly encouraged to have knowledge of database design (e.g.,COMP SCI 564).

INTRODUCTION TO BIG DATA SYSTEMS

COMPSCI 544

Deploy and use distributed systems to store and analyze large datasets. Unstructured and structured approaches to storage will be covered. Analysis will involve learning new query languages, processing streaming data, and training machine learning models. Most programming will be done in Python.