Developing Distributed Systems Quiz

Developing Distributed Systems: Pitfalls

• Many distributed systems are unnecessarily complex due to mistakes requiring later corrections.
• Common false assumptions often contribute to this complexity.
• Network reliability is not always guaranteed.
• Network security is sometimes an issue.
• Networks aren't always homogeneous (consistent).
• Network topology may change.
• Latency is often not zero.
• Bandwidth is often not infinite.
• Transport costs may not be negligible.
• There might not be a single administrator.

Three Types of Distributed Systems

• High-performance distributed computing systems
• Distributed information systems
• Distributed systems for pervasive computing

Parallel Computing

• High-performance distributed computing began with parallel computing.
• Multiprocessor/multicore systems vs. Multicomputers:
  • Shared memory systems use an interconnect to share memory among processors.
  • Private memory systems have individual memory for each processor using an interconnect.

Distributed Shared Memory Systems

• Multiprocessors are easier to program than multicomputers.
• A shared-memory model can be implemented on multicomputers.
• Virtual-memory techniques allow mapping pages from multiple processors to a virtual address space.
• If a process needs a page from another processor, the OS can fetch it.
• Distributed shared memory systems encountered performance limitations.
• They are now widely abandoned due to better methods.

Types of Multicomputer High-Disturbed Systems (Cluster Computing)

• Cluster computing involves interconnected high-end systems on a LAN.
• Systems are typically homogeneous (same OS, similar hardware).
• A single managing node coordinates the cluster.
• Compute nodes perform tasks under management from a master node.
• Components of applications are run on the cluster nodes, and libraries are often used in parallel.
• Supercomputers are often examples of clusters.

Grid Computing

• Grid computing extends cluster computing to a wider area.
• Grids use many nodes from various locations—making them heterogeneous.
• Grid computing spans several organizations.
• Virtual organizations enable collaboration by allowing authorized users access to resources.

Architecture for Grid Computing

• Grid computing has four layers: Fabric, Connectivity, Resource and Collective.
• Fabric: interfaces to the physical resources.
• Connectivity: communication and transaction protocols, authenticating resources.
• Resource: supports single resource management, processes and data.
• Collective: manages access to multiple resources.
• Application layer: grid applications run in an organization.

Cloud Computing

• Cloud computing is a layered architecture.
• Software, Web services, multimedia, business apps at the highest layer.
• Application frameworks (like Java, .Net, Python) are in the next layer, sitting on top of the layer below.
• Storage (databases) are on the next layer, alongside platforms.
• Computation (virtual machines), storage, the next layer.
• At the bottom, fundamental hardware resources: CPU, memory, disk, bandwidth.
• Datacenters hold the physical hardware.

Cloud Computing Layers (More Detail)

• Hardware: Includes processors, routers, power, and cooling, not usually seen by customers directly.
• Infrastructure: Uses virtualization, allocates and manages virtual storage devices, and virtual servers.
• Platform: Provides higher-level abstractions for storage; for instance, Amazon S3 provides an API, organizing files into buckets.
• Application: Contains actual applications (like office suites).

Distributed Pervasive Systems

• These are the newest generation of distributed systems.
• Nodes are small, mobile, and often embedded in larger systems.
• Ubiquitous computing systems seamlessly interact with the user's environment.
• Mobile computing focuses on mobility.
• Sensor networks use sensors in various locations.

Ubiquitous Systems (Core Elements)

• Distribution: Devices are networked, distributed, and accessible in a transparent manner.
• Interaction: Interaction between users and devices is highly unobtrusive.
• Context awareness: The system is aware of a user's context to optimize interaction.
• Autonomy: Devices operate independently.
• Intelligence: The system handles many actions.

Mobile Computing

• Mobile computing involves many different mobile devices.
• Devices change locations; their local services and reachability change.
• Communication issues occur due to these changes in location.
• Discovery systems deal with this change of local services.
• Disruptions in networking may arise from mobile devices changing location.

Sensor Networks

• Sensor networks consist of many connected sensors.
• Sensors have small memory, compute, and communication capabilities.
• Sensors are often powered by batteries.
• Wireless communication is often critical.

Sensor Networks as Distributed Databases

• Sensor data is stored and reported to an operator in one possible configuration.
• Sensors can process and store data—then send data to the operator.
• Querying sensors for information is another extreme configuration.

Duty-cycled Networks

• Many sensor networks need to operate on strict energy budgets.
• Duty cycling is used to save energy.
• Sensor nodes only use energy to transmit data and respond to queries periodically.