Computer Systems & Networks

They are less capable of handling large volumes of transactions.

They don't allow for real-time access to data for users worldwide.

They often require simpler, less secure authentication methods.

They may have more points of vulnerability due to numerous nodes.

Developing a distributed system that processes data in smaller batches close to sensor locations before aggregation.

Designing a centralized database system that collects all sensor data for batch processing every hour.

Creating a single-threaded queue that processes incoming data sequentially from all sensors.

Using round-robin scheduling on one server to handle requests from all sensors equally.

Higher resource consumption due to complex hashing computations.

Increased server storage demands from longer hash strings.

Possibility of hash inversion attacks recovering original passwords.

Reduced performance from frequent password hash validation checks.

Recursion in functional languages

Encapsulation in procedural languages

Inheritance in object-oriented languages

Concurrency control mechanisms

A single processor handles all tasks in order.

Tasks are sent to different networks to be processed later.

Tasks are processed simultaneously.

Each task waits for the previous one to finish.

Cost efficiency as more hardware resources are used for storage needs

Availability and reliability are often improved with distributed databases

Simplicity as management is spread across many different systems

Accessibility from multiple devices rather than just locally stored information

Solely rely on user feedback sessions identifying unexpected behaviors

Utilize static code analysis tools expecting predictable outcomes every execution run

Perform deterministic algorithm comparisons ignoring inherent non-determinism characteristics

Conduct probabilistic analysis combined with extensive simulation runs across various nodes

To simplify the steps required to solve a problem.

To increase error rates for better testing conditions.

To speed up processing time by performing multiple calculations simultaneously.

To reduce the number of tasks an algorithm must perform.

Fault tolerance through redundancy

Higher consistency and less replication needs

Uniform load distribution on a single server

Decreased complexity in database management

Batch scheduling

Sequential processing

Singular tasking

Parallel processing