Бакалавриат 2020/2021

Распределенные вычисления

Статус: Курс по выбору (Бизнес-информатика)

Направление: 38.03.05. Бизнес-информатика

Кто читает: Департамент бизнес-информатики

Где читается: Высшая школа бизнеса

Когда читается: 3-й курс, 4 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Панфилов Петр Борисович

Язык: английский

Кредиты: 3

Контактные часы: 30

Course Syllabus

Abstract

Distributed computing and application systems have become central concept of how computers are used, from web applications to e-commerce and to content distribution. Distributed systems help programmers aggregate the resources of many networked computers to construct highly available and scalable services. This course teaches the abstractions, design and implementation techniques that enable the building of efficient, scalable, fault-tolerant distributed systems. A course will cover abstractions and implementation techniques for the construction of distributed systems, including client server computing, the web, cloud computing, peer-to-peer systems, and distributed storage systems. Topics will include remote procedure call, preventing and finding errors in distributed programs, maintaining consistency of distributed state, fault tolerance, high availability, multithreading, etc. Case studies of distributed systems will be considered.

Learning Objectives

• To introduce students to the fundamental problems, concepts, and approaches in the design and analysis of distributed computing systems and applications.
• To familiarize students with the stages of the distributed system design cycle, including system architecture, data and processes arrangements, naming, communication and coordination issues, existing distributed computing paradigms, techniques, and tools, and evaluating the effectiveness of distributed application systems for specific data, task, and user types.

Expected Learning Outcomes

• understand the evolution of the distributed computing from its early beginnings as multi-processor and multi-computer systems, to computer networks, to the emerging cloud, edge (fog, dew, mist) and heterogeneous computing environments
• understand the distinction between distributed computing systems, distributed information systems and pervasive systems
• understand the existing distributed computing paradigms and systematic issues; understand some commonly applied architectural styles toward organizing distributed computing systems; know the role of middleware layer in separating applications from underlying platforms
• understand the practical issues and choices that can be made to instantiate and place software components on the real machines; understand the difference between centralized and decentralized architecture
• understand the concept of processes and how the different types of processes play a crucial role in distributed systems; understand threads and their role in obtaining performance in multicore and multiprocessor environments and in structuring clients and servers; know basic principles of virtualization for making applications to run concurrently and independently of the underlying hardware and platforms
• understand client-server organizations in distributed systems; understand typical organizations of both clients and servers; know the design issues for servers including those used in object-based distributed systems
• understand the ways that processes on different machines in distributed system can exchange information; know the widely used models of communication: Remote Procedure Call (RPC), and Message-Oriented Middleware (MOM); know what an application-level routing means for the message-oriented communication; know how to set up multicast facilities for data dissemination in distributed systems
• understand the usage of names in resource sharing, identifying entities, referring to locations, and other uses in distributed systems; understand the difference in implementing naming system in distributed systems and nondistributed systems; know what a flat-naming system is, and what mechanisms are needed to trace the location of entities in distributed system; understand general principles and scalability issues of structured name systems; know the use of Domain Name System (DNS)
• understand the importance of cooperation and synchronization of actions between processes; understand the goal of process coordination, coordination problems and solutions in distributed systems; understand coordination of a group of processes by means of election algorithms
• understand an importance of the replication of data in distributed systems; know consistency models for shared data and their implementation; discuss and explain difference between data-centric and client-centric consistency models; understand the issue of managing replica servers; know the alternatives for implementing strong consistency for replicas

Course Contents

• Introduction: Design goals
  Distributed systems consisit of autonomous computers that work together to give appearance of a single coherent system. Design goals for distributed systems include sharing resources and ensuring openness. In addition designers aim at hiding many of the intricacies related to distribution of processes, data and control.
• Introduction: Types of systems
  Different types of distributed systems exist which can be classified as being oriented towards supporting computations, information processing and pervasiveness. Distributed computing systems are typically deployed for high-performance applications often originating from parallel computing. Cloud computing goes beyond high-performance computing and also supports distributed systems found in traditional office environments. An emerging class of distributed systems is represented by pervasive computing environments, including mobile-computing systems as well as sensor-reach environments.
• Architectures: Architectural styles. Middleware.
  We can make a distinction between software architecture and system architecture. An architectural style reflects the basic principle that is followed in organizing the interaction between the software components comprising a distributed system. Important styles include layering, object-based styles, resource-based styles, and styles in which handling events are prominent.
• Architectures: System architecture.
  There are many different organizations of distributed systems. Client-server architecturesare often highly centralized. In peer-to-peer systems, the processes are organized into an overlaynetwrok, which is a logical network that can be structured using deterministic schemes for routing messages between processes, or unstructured. In hybrid architectures, elements from centralized and decentralized organizations are combined, as is the case in BitTorrent-based systems.
• Processes: Threads. Virtualization.
  Processes play a fundamental role in distributed systems as they form a basis for communication between different machines. Threads in distributed systems are particularly useful to continue using the CPU when a blocking I/O operation is performed. In general, threads are preferred over the use of processes when performance is at stake. Virtualization has since long been an important field of computer science. Popular virtualization schemes allow users to run a suite of applications on top of their favourite operating system and configure complete virtual distributed system in the cloud.
• Processes: Clients. Servers.
  Organizing a distributed application in terms of clients and servers has proven to be useful. Client processes generally implement user interfaces, which may range from very simple displays to advanced interfaces. Client software is furthermore aimed at achieving distribution transparency by hiding details concerning the communication with servers. Servers are often more intricate than clients. They can either be iterative or concurrent, implement one or more services, and can be stateless or stateful.
• Communication: Foundations. RPC.MOM. Multicasting.
  Communication between processes is essential for any distributed system. In traditional network applications, communication is often based on the low-level message-passing primitives offered by the transport layer. One of the most widely used abstractions is the Remote Procedure Call (RPC), that offers synchronous communication facilities, by which a client is blocked until the server has sent a reply. Message-oriented middleware models generally offer persistent asynchronous communication, and are used where RPCs are not approapriate. An important class of communication protocols in distributed systems is multicasting.
• Naming: Names, IDs. Flat naming. Structured naming. Attribute-based naming.
  Names are used to refer to entities. There are three types of names: an address, an identifier, and human-friendly names. Given these types, we make a distinction between flat naming, structured naming, and attribute-basednaming. Systems for flat naming essentially need to resolve an identifier to the address of its associated entity. Structured names are easily organized in a name space that can be represented by a naming graph in which a node represents a named entity and the label on an edge represents the name of the entity. Naming graphs are convenient to organize human-friendly names in a structured way. More problematic are attribute-based naming schemes in which entities are described by a collection of (attribute, value) pairs.
• Coordination: Clock synchronization. Mutual exclusion. Election algorithms.
  There are various ways to synchronize clocks in a distributed system. All methods are based on exchanging clock values, while taking into account the time it takes to send and receive messages. An important class of synchronization algorithms is that of distributed mutual exclusion. These algorithms ensure that in a distributed collection of processes, at most one process at a time has access to a shared resource. Synchronization between processes often requires that one process acts as a coordinator. To decide on who is going to be that coordinator an election algorithm is applied.
• Consistency and replication: Data-centric & Client-centric models. Replica management. Consistency protocols.
  Replicating data is used for improving the reliability of a distributed system and for improving preformance. Replication introduces a consistency problem: whenever a replica is updated, that replica becomes different from the others. To keep replicas consistent we need to propagate updates in such a way that temporary inconsistencies are not noticed. There are different consistency models. Consistent ordering of operations has since long formed the basis for many consistency models. An opposed to data-centric models, researchers in the field of distributed databases for mobile users have defined a number of client-centric consistency models.

Assessment Elements

• in class activity
• homeworks
• home assignment
• final examination
  Written examination in MS Teams. Without proctoring. Technical specifications: web-camera, microphone, speakers / headphones.

Interim Assessment

• Interim assessment (4 module)
  0.2 * final examination + 0.5 * home assignment + 0.2 * homeworks + 0.1 * in class activity

Bibliography

Recommended Core Bibliography

• Distributed Systems. (2017). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsnar&AN=edsnar.oai.ris.utwente.nl.publications.db6a761f.b353.419e.b65a.81e3740bbe53
• Tanenbaum, A. S., & Steen, M. van. (2014). Distributed Systems: Pearson New International Edition : Principles and Paradigms (Vol. 2nd ed). Harlow, Essex: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1418515

Recommended Additional Bibliography

• Distributed systems : concepts and design, Coulouris, G., 2012