Jesus P.  2012.  Robust Distributed Data Aggregation. Abstractpaulo_cesar_de_oliveira_jesus.pdf

Distributed aggregation algorithms are an important building block of modern large scale systems, as it allows the determination of meaningful system-wide properties (e.g., network size, total storage capacity, average load, or majorities) which are required to direct the execution of distributed applications. In the last decade, several algorithms have been proposed to address the distributed computation of aggregation functions (e.g., COUNT, SUM, AVERAGE, and MAX/MIN), exhibiting different properties in terms of accuracy, speed and communication tradeoffs. However, existing approaches exhibit many issues when challenged in faulty and dynamic environments, lacking in terms of fault-tolerance and support to churn.
This study details a novel distributed aggregation approach, named Flow Updating, which is fault-tolerant and able to operate on dynamics networks. The algorithm is based on manipulating flows (inspired by the concept from graph theory), that are updated using idempotent messages, providing it with unique robustness capabilities. Experimental results showed that Flow Updating outperforms previous averaging algorithms in terms of time and message complexity, and unlike them it self adapts to churn and changes of the initial input values without requiring any periodic restart, supporting node crashes and high levels of message loss.
In addition to this main contribution, others can also be found in this research work, namely: a definition of the aggregation problem is proposed; existing distributed aggregation algorithm are surveyed and classified into a comprehensive taxonomy; a novel algorithm is introduced, based on Flow Updating, to estimate the Cumulative Distribution Function (CDF) of a global system attribute.
It is expected that this work will constitute a relevant contribution to the area of distributed computing, in particular to the robust distributed computation of aggregation functions in dynamic networks.

Almeida PS.  1998.  Control of object sharing in programming languages. Abstractpsa-phd-thesis.pdf

Current data abstraction mechanisms are not adequate to control sharing of state in the general case involving objects in linked structures. They only prevent the direct access to the state variables of single objects, as opposed to considering the state reachable by an object and the inter-object references, neglecting the fact that an object is not, in general, self-contained. The pervading possibility of sharing is a source of errors and an obstacle both to reasoning about programs and to language implementation techniques.
This thesis presents balloon types, a general extension to programming languages which makes the ability to share state a first class property of a data type, resolving a long-standing flaw in existing data abstraction mechanisms. Balloon types provide the balloon invariant, which expresses a strong form of encapsulation of state: it is guaranteed that no state reachable (directly or transitively) by an object of a balloon type is referenced by any `external' object.
The mechanism is syntactically very simple, relying on a non-trivial static analysis to perform checking. The static analysis is presented as an abstract interpretation based on a denotational semantics of a simple imperative first-order language with constructs for creating and manipulating objects.
Balloon types are applicable in a wide range of areas such as program transformation, memory management and distributed systems. They are the key to obtaining self-contained composite objects, truly opaque data abstractions and value types---important concepts for the development of large scale, provably correct programs.