The principle summary for each of the seven categories consists of a series of top-level principle statements together with brief points that clarify and deepen the statements. Here is a compilation of the top-level statements.
Representations hold information.
Computation is a sequence of representations.
Representations can be compressed, but not too much.
Computations can be open or closed.
Computations have characteristic speeds of resolution.
Complexity measures the time or space essential to complete computations.
Finite representations of real processes always contain errors.
Information can be encoded into messages.
Data communication always takes place in a system consisting of a message source, an encoder, a channel, and a decoder.
Information in a message source places a hard lower bound on channel capacity for accurate reception (Shannon Capacity Theorem).
Messages corrupted during transmission can be recovered during reception (Error Correction).
Messages can be compressed.
Messages can hide information.
A coordination system is a set of agents interacting within a finite or infinite game toward a common objective.
Action loop is the foundational element of all coordination protocols.
Coordination tasks can be delegated to computational processes.
The protocols of coordination systems manage dependencies of flow, sharing, and fit among activities.
It is impossible to select one of several simultaneous or equally attractive alternatives within a preset deadline (Choice Uncertainty Principle)
All coordination systems depend on solutions to the concurrency control problems of arbitration, synchronization, serialization, determinacy, and deadlock.
All computations take place in storage systems.
Storage systems comprise hierarchies with volatile (fast) storage at the top and persistent (slower) storage at the bottom.
The principle of locality dynamically identifies the most useful data, which can be cached at the top of the hierarchy.
Thrashing is a severe performance degradation caused when parallel computations overload the storage system.
Access to stored objects is controlled by dynamic bindings between names, handles, addresses, and locations.
Hierarchical naming systems allow local authorities to assign names that are globally unique in very large name spaces.
Handles enable sharing by providing unique-for-all-time object identifiers that are independent of all address spaces.
Data can be retrieved by name or by content.
Physical automation maps hard computational tasks to physical systems that perform them acceptably well.
Artificial intelligence maps human cognitive tasks to physical systems that perform them acceptably well.
Artificial intelligence maps tasks to systems through models, search, deduction, induction, and collective intelligence.
Models represent processes by which intelligent beings generate their behavior.
Search finds the subsets of states of a complex system that must participate in the final outcome of a task.
Deduction locates the outcome of a task by applying rules of logic to move from axioms to provable statements.
Induction builds models by generalizing from data about a complex task's behavior.
Collective intelligence exploits large scale aggregation and coordination in networks to produce new knowledge.
The principal tools of evaluation are modeling, simulation, experiment, and statistical analysis of data.
Computing systems can be represented as sets of equations balancing transition flows among states.
Network of servers is a common, efficient representation of computing systems.
Network-of-server systems obey fundamental laws on their utilizations, throughputs, queueing, response times, and bottlenecks.
Resource sharing, when feasible, is always more efficient than partitioning.
Design principles are conventions for planning and building correct, fast, fault tolerant, and fit software systems.
Error confinement and recovery are much harder in the virtual worlds of software than in the real world of physical objects.
The four base principles of software design are hierarchical aggregation, levels, virtual machines, and objects.
Abstraction, information hiding, and decomposition are complementary aspects of modularity.
Levels organize the functions of a system into hierarchies that allow downward invocations and upward replies.
Virtual machines organize software as simulations of computing machines.
Objects organize software into networks of shared entities that activate operations in each other by exchanging signals.
In a distributed system, it is more efficient to implement a function in the communicating applications than in the network itself (end-to-end principle).