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Chapter: Distributed Systems - Communication in Distributed System

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Performance of communication channels

The communication channels in our model are realized in a variety of ways in distributed systems – for example, by an implementation of streams or by simple message passing over a computer network.

Performance of communication channels

Performance of communication channels • The communication channels in our model are realized in a variety of ways in distributed systems – for example, by an implementation of streams or by simple message passing over a computer network. Communication over a computer network has the following performance characteristics relating to latency, bandwidth and jitter:

 

The delay between the start of a message’s transmission from one process and the beginning of its receipt by another is referred to as latency. The latency includes:

 

The time taken for the first of a string of bits transmitted through a network to reach its destination. For example, the latency for the transmission of a message through a satellite link is the time for a radio signal to travel to the satellite and back.

 

The delay in accessing the network, which increases significantly when the network is heavily loaded. For example, for Ethernet transmission the sending station waits for the network to be free of traffic.

 

The time taken by the operating system communication services at both the sending and the receiving processes, which varies according to the current load on the operating systems.

 

The bandwidth of a computer network is the total amount of information that can be transmitted over it in a given time. When a large number of communication channels are using the same network, they have to share the available bandwidth.

 

Jitter is the variation in the time taken to deliver a series of messages. Jitter is relevant to multimedia data. For example, if consecutive samples of audio data are played with differing time intervals, the sound will be badly distorted.

 

Computer clocks and timing events • Each computer in a distributed system has its own internal clock, which can be used by local processes to obtain the value of the current time. Therefore two processes running on different computers can each associate timestamps with their events. However, even if the two processes read their clocks at the same time, their local clocks may supply different time values. This is because computer clocks drift from perfect time and, more importantly, their drift rates differ from one another. The term clock drift rate refers to the rate at which a computer clock deviates from a perfect reference clock. Even if the clocks on all the computers in a distributed system are set to the same time initially, their clocks will eventually vary quite significantly unless corrections are applied.

 

Two variants of the interaction model • In a distributed system it is hard to set limits on the time that can be taken for process execution, message delivery or clock drift. Two opposing extreme positions provide a pair of simple models – the first has a strong assumption of time and the second makes no assumptions about time:

 

Synchronous distributed systems: Hadzilacos and Toueg define a synchronous distributed system to be one in which the following bounds are defined:

 

·        The time to execute each step of a process has known lower and upper bounds.

·        Each message transmitted over a channel is received within a known bounded time.

·        Each process has a local clock whose drift rate from real time has a known bound.

 

Asynchronous distributed systems: Many distributed systems, such as the Internet, are very useful without being able to qualify as synchronous systems. Therefore we need an alternative model. An asynchronous distributed system is one in which there are no bounds on:

 

·        Process execution speeds – for example, one process step may take only a picosecond and another a century; all that can be said is that each step may take an arbitrarily long time.

 

·        Message transmission delays – for example, one message from process A to process B may be delivered in negligible time and another may take several years. In other words, a message may be received after an arbitrarily long time.

 

·        Clock drift rates – again, the drift rate of a clock is arbitrary.

 

 

Event ordering • In many cases, we are interested in knowing whether an event (sending or receiving a message) at one process occurred before, after or concurrently with another event at another process. The execution of a system can be described in terms of events and their ordering despite the lack of accurate clocks. For example, consider the following set of exchanges between a group of email users, X, Y, Z and A, on a mailing list:

 

1.     User X sends a message with the subject Meeting.

2.     Users Y and Z reply by sending a message with the subject Re: Meeting.

 

In real time, X’s message is sent first, and Y reads it and replies; Z then reads both X’s message and Y’s reply and sends another reply, which references both X’s and Y’s

 

messages. But due to the independent delays in message delivery, the messages may be delivered as shown in the following figure and some users may view these two messages in the wrong order.

 


 

Failure model

 

In a distributed system both processes and communication channels may fail – that is, they may depart from what is considered to be correct or desirable behaviour. The failure model defines the ways in which failure may occur in order to provide an understanding of the effects of failures. Hadzilacos and Toueg provide a taxonomy that distinguishes between the failures of processes and communication channels. These are presented under the headings omission failures, arbitrary failures and timing failures.

 

Omission failures • The faults classified as omission failures refer to cases when a process or communication channel fails to perform actions that it is supposed to do.

 

Process omission failures: The chief omission failure of a process is to crash. When, say that a process has crashed we mean that it has halted and will not execute any further steps of its program ever. The design of services that can survive in the presence of faults can be simplified if it can be assumed that the services on which they depend crash cleanly – that is, their processes either function correctly or else stop. Other processes may be able to detect such a crash by the fact that the process repeatedly fails to respond to invocation messages. However, this method of crash detection relies on the use of timeouts – that is, a method in which one process allows a fixed period of time for

 

something to occur. In an asynchronous system a timeout can indicate only that a process is not responding – it may have crashed or may be slow, or the messages may not have arrived.

 

Communication omission failures: Consider the communication primitives send and receive. A process p performs a send by inserting the message m in its outgoing message buffer. The communication channel transports m to q’s incoming message buffer. Process q performs a receive by taking m from its incoming message buffer and delivering it. The outgoing and incoming message buffers are typically provided by the operating system.

 

Arbitrary failures • The term arbitrary or Byzantine failure is used to describe the worst possible failure semantics, in which any type of error may occur. For example, a process may set wrong values in its data items, or it may return a wrong value in response to an invocation.

 

An arbitrary failure of a process is one in which it arbitrarily omits intended processing steps or takes unintended processing steps. Arbitrary failures in processes cannot be detected by seeing whether the process responds to invocations, because it might arbitrarily omit to reply.

 

Communication channels can suffer from arbitrary failures; for example, message contents may be corrupted, nonexistent messages may be delivered or real messages may be delivered more than once. Arbitrary failures of communication channels are rare because the communication software is able to recognize them and reject the faulty

 

messages. For example, checksums are used to detect corrupted messages, and message sequence numbers can be used to detect nonexistent and duplicated messages.

 


 

Timing failures • Timing failures are applicable in synchronous distributed systems where time limits are set on process execution time, message delivery time and clock drift rate. Timing failures are listed in the following figure. Any one of these failures may result in responses being unavailable to clients within a specified time interval.

 

In an asynchronous distributed system, an overloaded server may respond too slowly, but we cannot say that it has a timing failure since no guarantee has been offered. Real-time operating systems are designed with a view to providing timing guarantees, but they are more complex to design and may require redundant hardware.

 

Most general-purpose operating systems such as UNIX do not have to meet real-time constraints.

 

Masking failures • Each component in a distributed system is generally constructed from a collection of other components. It is possible to construct reliable services from components that exhibit failures. For example, multiple servers that hold replicas of data can continue to provide a service when one of them crashes. A knowledge of the failure characteristics of a component can enable a new service to be designed to mask the failure of the components on which it depends. A service masks a failure either by hiding it altogether or by converting it into a more acceptable type of failure. For an example of the latter, checksums are used to mask corrupted messages, effectively converting an arbitrary failure into an omission failure. The omission failures can be hidden by using a protocol that retransmits messages that do not arrive at their destination. Even process crashes may be masked, by replacing the process and restoring its memory from information stored on disk by its predecessor.

 


 

Reliability of one-to-one communication • Although a basic communication channel can exhibit the omission failures described above, it is possible to use it to build a communication service that masks some of those failures.

 

The term reliable communication is defined in terms of validity and integrity as follows:

 

Validity: Any message in the outgoing message buffer is eventually delivered to the incoming message buffer.

 

Integrity: The message received is identical to one sent, and no messages are delivered twice.

 

The threats to integrity come from two independent sources:

 

Any protocol that retransmits messages but does not reject a message that arrives twice. Protocols can attach sequence numbers to messages so as to detect those that are delivered twice.

 

Malicious users that may inject spurious messages, replay old messages or tamper with messages. Security measures can be taken to maintain the integrity property in the face of such attacks.

 

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