## Detecting and tolerating faults in distributed systems

##### Abstract

This dissertation presents techniques for detecting and tolerating faults in distributed systems. Detecting faults in distributed or parallel systems is often very difficult. We look at the problem of determining if a property or assertion was true in the computation. We formally define a logic called BTL that can be used to define such properties. Our logic takes temporal properties in consideration as these are often necessary for expressing conditions like safety violations and deadlocks. We introduce the idea of a basis of a computation with respect to a property. A basis is a compact and exact representation of the states of the computation where the property was true. We exploit the lattice structure of the computation and the structure of different types of properties and avoid brute force approaches. We have shown that it is possible to efficiently detect all properties that can be expressed by using nested negations, disjunctions, conjunctions and the temporal operators possibly and always. Our algorithm is polynomial in the number of processes and events in the system, though it is exponential in the size of the property. After faults are detected, it is necessary to act on them and, whenever possible, continue operation with minimal impact. This dissertation also deals with designing systems that can recover from faults. We look at techniques for tolerating faults in data and the state of the program. Particularly, we look at the problem where multiple servers have different data and program state and all of these need to be backed up to tolerate failures. Most current approaches to this problem involve some sort of replication. Other approaches based on erasure coding have high computational and communication overheads. We introduce the idea of fusible data structures to back up data. This approach relies on the inherent structure of the data to determine techniques for combining multiple such structures on different servers into a single backup data structure. We show that most commonly used data structures like arrays, lists, stacks, queues, and so on are fusible and present algorithms for this. This approach requires less space than replication without increasing the time complexities for any updates. In case of failures, data from the back up and other non-failed servers is required to recover. To maintain program state in case of failures, we assume that programs can be represented by deterministic finite state machines. Though this approach may not yet be practical for large programs it is very useful for small concurrent programs like sensor networks or finite state machines in hardware designs. We present the theory of fusion of state machines. Given a set of such machines, we present a polynomial time algorithm to compute another set of machines which can tolerate the required number of faults in the system.

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