Failure Detectors of Strong S and Perfect P Classes for Time Synchronous Hierarchical Distributed Systems

Failure Detectors of Strong S and Perfect P Classes for Time Synchronous Hierarchical Distributed Systems

Anshul Verma (Banaras Hindu University, India), Mahatim Singh (Banaras Hindu University, India) and Kiran Kumar Pattanaik (Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior, India)
DOI: 10.4018/978-1-5225-8295-3.ch010
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Present failure detection algorithms for distributed systems are designed to work in asynchronous or partially synchronous environments on mesh (all-to-all) connected systems and maintain status of every other process. Several real-time systems are hierarchically connected and require working in strict synchronous environments. Use of existing failure detectors for such systems would generate excess computation and communication overhead. The chapter describes two suspicion-based failure detectors of Strong S and Perfect P classes for hierarchical distributed systems working in time synchronous environments. The algorithm of Strong S class is capable of detecting permanent crash failures, omission failures, link failures, and timing failures. Strong completeness and weak accuracy properties of the algorithm are evaluated. The failure detector of Perfect P class is capable of detecting crash failures, crash-recovery failures, omission failures, link failures, and timing failures. Strong completeness and strong accuracy properties of the failure detector are evaluated.
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In distributed systems failure detectors are used to maintain information about the operational states of other processes. Information provided by a failure detector is assumed unreliable because it can suspect a correct process or not suspect a faulty process. The operational status information of a process provided by two failure detectors at different processes may differ (Cortinas, 2011). In such scenarios completeness and accuracy are the two properties to assess the reliability of failure detectors. Completeness has been further defined into two variations: strong and weak; while, accuracy has been defined into four variations: strong, weak, eventual strong, and eventual weak (Chandra & Toueg, 1996). The strong completeness represents that eventually every process that crashes is permanently suspected by every correct process. Whereas, strong accuracy represents that no correct process is suspected by any process. Weak accuracy represents that some correct process is never suspected, means some correct processes can be suspected. The failure detectors that satisfy strong completeness and weak accuracy properties belong to Strong S class. However, those satisfy strong completeness and strong accuracy properties belong to the Perfect P class. Similarly, there are eight pairs, each pair forming a new failure detector class (see Table 1) formed by selecting one of the two completeness properties and one of the four accuracy properties.

Failure detectors adopt mainly two methods for status monitoring of other processes: polling and heartbeat. Polling is basically a query/reply (or pull) based status monitoring technique (Larrea, Arévalo, & Fernández, 1999; Larrea, Fernández, & Arévalo, 2004). Whereas, in heartbeat, every process q periodically sends a heartbeat message to all its neighbours processes p to inform them that q is alive, thus termed as push based. Absence of the heartbeat message implies a fault (Aguilera, Chen, & Toueg, 1997; Soraluze, Cortiñas, Lafuente, Larrea, & Freiling, 2011). Some failure detectors return a list of suspected processes as output fall under suspicion based (Chandra & Toueg, 1996), and those return a list of trusted (correct) processes as output fall under trust based failure detectors (Chandra, Hadzilacos, & Toueg, 1996).

Table 1.
Classification of failure detectors
StrongWeakEventual StrongEventual Weak
StrongPerfect 978-1-5225-8295-3.ch010.m01Strong 978-1-5225-8295-3.ch010.m02Eventually Perfect 978-1-5225-8295-3.ch010.m03Eventually Strong 978-1-5225-8295-3.ch010.m04
Weak978-1-5225-8295-3.ch010.m05Weak 978-1-5225-8295-3.ch010.m06978-1-5225-8295-3.ch010.m07Eventually Weak 978-1-5225-8295-3.ch010.m08

(Chandra & Toueg, 1996)

Key Terms in this Chapter

Failure Detector Classes: Failure detectors are classified according to two properties: completeness and accuracy . There are two variations of the completeness property and four variations of the accuracy property. Failure detectors are classified into eight different classes by combining two variations, one from completeness and another from accuracy properties.

Hierarchical Systems: In hierarchical systems, processes/nodes are arranged in hierarchy or reverse tree like structure. The system is structured using different levels of authority and chain of commands in which higher levels control lower levels of the hierarchy. If a link breaks, the higher authorities do not have control on the levels below the failure.

Distributed Systems: A distributed system is a collection of independent computers and software that appears to its users as a single coherent system. The software components located on networked computers communicate and coordinate with each other by passing messages in order to achieve a common goal.

Synchronous Systems: A synchronous system has a clear time bound for each event occurs in the system. There are clear lower and upper time bound for each event execution and message transmission. Every clock has a known, bounded deviation from the real time. Time bound violation by any event is considered as a failure.

Failure Detectors: Failure detector is an algorithm/module located at each process in the distributed system. It collects operational state information of other processes in the system and provides to its owner process to make him aware of faulty processes in the system. Failure detectors reliability is measured through two properties: completeness and accuracy .

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