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Cloud computing and data center design lab

01DSSBG, 01DSSOQ

A.A. 2023/24

Course Language

Inglese

Degree programme(s)

Master of science-level of the Bologna process in Communications Engineering - Torino
Master of science-level of the Bologna process in Ingegneria Elettronica (Electronic Engineering) - Torino

Course structure
Teaching Hours
Lezioni 30
Esercitazioni in laboratorio 30
Lecturers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Giaccone Paolo Professore Ordinario ING-INF/03 10 0 30 0 1
Co-lectuers
Espandi
Context
SSD CFU Activities Area context
ING-INF/01
ING-INF/03		 2
4		 D - A scelta dello studente
D - A scelta dello studente		 A scelta dello studente
A scelta dello studente
Statistiche superamento esami
2023/24
Data centers are the computing infrastructure supporting cloud computing applications. The class is focused on understanding the cloud computing approach and how the data centers are designed. The course focuses on cloud computing and on the different technologies for virtualization, from virtual machines to containers. Furthermore, it focuses also on the design of the data center, in particular on the computing resources, the interconnection infrastructure and the power management. State-of-the-art architectures for the design of large data centers will be considered, based on Clos topologies. Finally, the hardware architectures for advanced computing will be presented, highlighting the role of accelerators for practical relevant applications, as Artificial Intelligence (AI). A practical approach will be adopted, with virtual laboratories on containers, on data center design and on hardware acceleration. The involved professors are experts in computer networks and in electronic design, providing a complementary view of all the main technological issues involved in the design of data centers.
Data centers are the computing infrastructure supporting cloud computing applications. The class is focused on understanding the cloud computing approach and how the data centers are designed. The course focuses on cloud computing and on the different technologies for virtualization, from virtual machines to containers. Furthermore, it focuses also on the design of the data center, in particular on the computing resources, the interconnection infrastructure and the power management. State-of-the-art architectures for the design of large data centers will be considered, based on Clos topologies. Finally, the hardware architectures for advanced computing will be presented, highlighting the role of accelerators for practical relevant applications, as Artificial Intelligence (AI). A practical approach will be adopted, with virtual laboratories on containers, on data center design and on hardware acceleration. The involved professors are experts in computer networks and in electronic design, providing a complementary view of all the main technological issues involved in the design of data centers.
- Knowledge of cloud computing and virtualization (computing, memory and network) - Knowledge of the theory of Clos topologies for data centers - Knowledge of different virtualization techniques (virtual machines, containers) - Knowledge of probabilistic data structures for fast packet processing - Knowledge of P4 abstraction for fast packet processing - Ability to design the interconnection network of a data center - Ability to design a software to compute the total number of devices in a data center - Ability to experiment Docker containers and configure the networking layer - Ability to design the power distribution network in a large data center - Ability to run an AI application on hardware accelerators
- Knowledge of cloud computing and virtualization (computing, memory and network) - Knowledge of the theory of Clos topologies for data centers - Knowledge of different virtualization techniques (virtual machines, containers) - Knowledge of probabilistic data structures for fast packet processing - Knowledge of P4 abstraction for fast packet processing - Ability to design the interconnection network of a data center - Ability to design a software to compute the total number of devices in a data center - Ability to experiment Docker containers and configure the networking layer - Ability to design the power distribution network in a large data center - Ability to run an AI application on hardware accelerators
- Main concepts of signal processing and computer networks - Python programming - Linux bash commands (useful)
- Main concepts of signal processing and computer networks - Python programming - Linux bash commands (useful)
The class is divided in two parts, the first (Part A) on cloud computing and design of data centers (4CFU) and the second (Part B) on digital architectures and advanced computing architectures (2CFU). Lectures topics and corresponding credits: A.1 Cloud computing (2CFU) - Service models. Web services. - Virtual machines and containers. Docker engine and container networking - Lab on Docker engine and networking A.2 Design of data centers (2CFU) - Clos-based design of data centers. Recursive construction - Optical interconnections and switching - Lab on data center design - Software Defined Networking (SDN) and programmable data planes. P4 switches. - Traffic monitoring. Probabilistic data structures (hash tables, bloom filters, sketches) - Lab on probabilistic data structures B.1 Basic digital hardware architectures (0.68CFU) - Von Neumann architectures and fundamental components of a computing system - CPU/GPU architectures, pipelining and parallel processing, power consumption model and voltage scaling - Types of memories: static/dynamic/non-volatile - High-speed buses and transmission lines: the PCIexpress case - Power supply and power management in high-power data centers - Lab on practical optimization of a PCIexpress bus in a GPU-based server
The class is divided in two parts, the first (Part A) on cloud computing and design of data centers (4CFU) and the second (Part B) on digital architectures and advanced computing architectures (2CFU). Lectures topics and corresponding credits: A.1 Cloud computing (2CFU) - Service models. Web services. - Virtual machines and containers. Docker engine and container networking - Lab on Docker engine and networking A.2 Design of data centers (2CFU) - Clos-based design of data centers. Recursive construction - Optical interconnections and switching - Lab on data center design - Software Defined Networking (SDN) and programmable data planes. P4 switches. - Traffic monitoring. Probabilistic data structures (hash tables, bloom filters, sketches) - Lab on probabilistic data structures B.1 Basic digital hardware architectures (0.68CFU) - Von Neumann architectures and fundamental components of a computing system - CPU/GPU architectures, pipelining and parallel processing, power consumption model and voltage scaling - Types of memories: static/dynamic/non-volatile - High-speed buses and transmission lines: the PCIexpress case - Power supply and power management in high-power data centers - Lab on practical optimization of a PCIexpress bus in a GPU-based server B.2 Architectures for advanced computing (1.42CFU) - Mathematical model and hardware implementation of a neural network - Optimization of matrix-vector multiplication for neural networks: quantization; pruning - Hardware accelerators for neural networks and in-memory computing - Lab on GPU-based accelerated neural network
The course comprises theoretical classes which are need for the main laboratory and practical activities. All the laboratories are mandatory and will be carried out in the classroom, using the student laptop, eventually connecting to remote servers or cloud. The students will be supposed to deliver some homeworks, which will be used for self-evaluation through a peer grading system available on Politecnico Moodle portal. Furthermore, the student will document part of their practical activities on lab reports. Group interaction will be fostered, even if all homeworks and lab reports will be individual.
The course comprises theoretical classes which are need for the main laboratory and practical activities. All the laboratories are mandatory and will be carried out in the classroom, using the student laptop, eventually connecting to remote servers or cloud. The students will be supposed to deliver some homeworks, which will be used for self-evaluation through a peer grading system available on Politecnico Moodle portal. Furthermore, the student will document part of their practical activities on lab reports. Group interaction will be fostered, even if all homeworks and lab reports will be individual.
The teaching material (handouts and reference scientific papers) will be made available by the class teachers on the Politecnico courses web portal and is sufficient to cover all the topics taught in the class.
The teaching material (handouts and reference scientific papers) will be made available by the class teachers on the Politecnico courses web portal and is sufficient to cover all the topics taught in the class.
Lucidi delle lezioni; Esercitazioni di laboratorio; Video lezioni dell’anno corrente; Strumenti di simulazione; Strumenti di auto-valutazione;
Lecture slides; Lab exercises; Video lectures (current year); Simulation tools; Self-assessment tools;
Modalità di esame: Prova orale obbligatoria; Elaborato scritto individuale;
Exam: Compulsory oral exam; Individual essay;
... The final evaluation is based on the oral discussion on the delivered reports. The report will be evaluated based on correctness, original contribution and quality of presentation. The oral will be aimed at discussing the reports and at assessing the level of understanding of the theoretical topics covered in the class. Let GA be the grade of the oral of Part A and let GB be the grade of the oral of Part B, the final grade G of the exam will be computed based on the credit-weighted average of the two parts as follows: G=nearest-integer(4/6*GA+2/6*GB) Both GA and GB must be >=18 to be considered valid.
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Compulsory oral exam; Individual essay;
The final evaluation is based on the oral discussion on the delivered reports. The report will be evaluated based on correctness, original contribution and quality of presentation. The oral will be aimed at discussing the reports and at assessing the level of understanding of the theoretical topics covered in the class. Let GA be the grade of the oral of Part A and let GB be the grade of the oral of Part B, the final grade G of the exam will be computed based on the credit-weighted average of the two parts as follows: G=nearest-integer(4/6*GA+2/6*GB) Both GA and GB must be >=18 to be considered valid.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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