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B.Sc. (Information Technology) Cloud Computing Fundamentals Syllabus - Mumbai University 2026

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This is the TY BSc IT syllabus under NEP 2020, in force from the academic year 2026-27. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.

Cloud Computing Fundamentals Syllabus.pdf
Minor · Semester 6 · TY BSc IT · 2 credits · 50 marks

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Syllabus for Cloud Computing Fundamentals

Minor · Semester 6 · TY BSc IT · 2 credits · 50 marks

This is a paper of the Information Technology Minor, for students whose college runs that Minor. In Semester 6 you sit one pair: Cloud Computing Fundamentals and Digital Forensics Practical, or AI Ethics and Green IT Practices and Sustainability. From 2026-27 your college sets and marks Minor papers itself.

Module I

  • The vision of cloud computing — computing as a utility (analogous to water, electricity, gas, telephony); dynamic provisioning model
  • Defining a cloud: the XaaS (Everything as a Service) paradigm; Buyya's formal definition — 'a type of parallel and distributed system consisting of a collection of interconnected and virtualised computers...'
  • The Cloud Computing Reference Model: IaaS (virtualised servers, storage, networking), PaaS (runtime environments, development platforms), SaaS (end-user applications, CRM, social networking)
  • Characteristics and benefits of cloud computing — elasticity, pay-per-use billing, multi-tenancy, on-demand provisioning
  • Challenges ahead: interoperability, scalability, fault tolerance, security, trust, and privacy
  • Historical developments: evolution from distributed systems → virtualisation → Web 2.0 → service-oriented computing → utility-oriented computing → cloud
  • Building cloud computing environments: application development, infrastructure development, computing platforms and technologies
  • Eras of computing: mainframe era, client-server era, internet era, cloud era
  • Parallel vs. distributed computing — definitions, distinctions, and complementary roles
  • Elements of parallel computing: parallel processing, hardware architectures (SIMD/MIMD), levels of parallelism, Amdahl's Law and Gustafson's Law
  • Elements of distributed computing: general concepts, components of a distributed system, architectural styles (client-server, peer-to-peer, three- tier, n-tier)
  • Models for interprocess communication: message passing, shared memory, remote procedure call (RPC)
  • Technologies for distributed computing: RPC mechanisms, distributed object frameworks (CORBA, Java RMI), service-oriented computing (SOAP, WSDL, REST, web services)
  • Introduction to virtualisation: why it is the backbone of cloud computing infrastructure
  • Characteristics of virtualised environments: increased security, managed execution, portability
  • Taxonomy of virtualisation techniques: execution virtualisation (full virtualisation, para-virtualisation, hardware-assisted), and other types (OS- level, application-level, storage, network)
  • Virtualisation and cloud computing: the relationship between VM lifecycle management and cloud elasticity
  • Pros and cons of virtualisation: resource consolidation, isolation, overhead, complexity
  • Technology examples: Xen (para-virtualisation), VMware (full virtualisation — vSphere, ESX, Workstation), Microsoft Hyper-V Unit 1.4 — Cloud Computing Architecture [Chapter 4]
  • The cloud reference model in depth: layered architecture and service stack
  • Infrastructure- and Hardware-as-a-Service (IaaS/HaaS): virtual servers, storage provisioning, networking — example: Amazon EC2, Rightscale, vCloud
  • Platform-as-a-Service (PaaS): runtime environments, development and data processing platforms — example: Windows Azure, Hadoop, Google AppEngine, Aneka
  • Software-as-a-Service (SaaS): end-user applications, office automation, CRM, social networking — example: Google Docs, Facebook, Flickr, Salesforce
  • Types of clouds: Public Clouds (third-party, multi-tenant, subscription), Private/Enterprise Clouds (internal data center, high security), Hybrid Clouds (mixed use), Community Clouds
  • Economics of the cloud: pay-per-use model, capital expenditure vs. operational expenditure (CapEx vs. OpEx), pricing strategies, billing models
  • Open challenges: cloud definition ambiguity, interoperability and standards, scalability and fault tolerance, security/trust/privacy, and organisational aspects

Module II

  • What is data-intensive computing? Characteristics of data-intensive workloads; historical perspective (from data warehousing to Big Data)
  • Challenges in data-intensive computing: data volume, velocity, variety, veracity — the 4Vs of Big Data in cloud context
  • Technologies for data-intensive computing — Storage Systems: distributed file systems (GFS/HDFS), key-value stores, NoSQL databases, object storage
  • Programming platforms for data-intensive computing: Google File System, Bigtable, Hadoop HDFS; conceptual overview
  • The MapReduce programming model: principles, Map and Reduce phases, shuffling and sorting, fault tolerance in distributed MapReduce
  • Conceptual walkthrough of a MapReduce application — word count and similar examples Cloud Platforms in Industry: AWS, Google AppEngine, Microsoft Azure
  • Amazon Web Services (AWS): Compute Services (EC2 — instance types, AMIs, Auto Scaling, Elastic Load Balancer), Storage Services (S3 — buckets and objects, EBS, Glacier), Communication Services (SQS, SNS, SES), Additional Services overview
  • Google AppEngine: architecture and core concepts (sandbox model, datastore, memcache), application life cycle, cost model and scaling behaviour, observations and limitations
  • Microsoft Azure: Azure core concepts (compute, storage, fabric controller), SQL Azure (relational DB as a service), Windows Azure Platform Appliance, observations
  • Comparative analysis of the three platforms: pricing models, geographic availability, service depth, developer experience, and appropriate use cases
  • Energy efficiency in clouds: motivations (rising data center energy costs, carbon emissions), energy-efficient and green cloud computing architecture, dynamic voltage and frequency scaling (DVFS), VM consolidation strategies
  • Market-based management of clouds: market-oriented cloud computing (MOCC) — why market mechanisms?, a reference model for MOCC, technologies and initiatives supporting MOCC, observations
  • Federated clouds / InterCloud: characterisation and definition of cloud federation, cloud federation stack (Service Layer, Resource Layer, Network Layer), aspects of interest (interoperability, SLA negotiation, portability), technologies for cloud federations
  • Third-party cloud services: MetaCDN (content delivery as a third-party cloud service), SpotCloud (spot market for excess cloud capacity)
  • Future directions: trends in cloud research — autonomic cloud management, cloud standards, hybrid and multi-cloud strategies

Text Books

  • 7 Mastering Cloud Computing Foundations and Applications Programming- Rajkumar Buyya
  • 8 CLOUD COMPUTING Principles and Paradigms-Rajkumar Buyya
  • 9 Cloud Computing Concepts, Technology & Architecture Thomas Erl

Reproduced from the University of Mumbai syllabus for B.Sc. (Information Technology) under NEP 2020, in force from the academic year 2026-27. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

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