Client-based Logging: A New Paradigm of Distributed Transaction Management

The proliferation of inexpensive workstations and networks has created a new era in distributed computing. At the same time, non-traditional applications such as computer-aided design (CAD), computer-aided software engineering (CASE), geographic-information systems (GIS), and office-information systems (OIS) have placed increased demands for high-performance transaction processing on database systems. The combination of these factors gives rise to significant challenges in the design of modern database systems. In this thesis, we propose novel techniques whose aim is to improve the performance and scalability of these new database systems. These techniques exploit client resources through client-based transaction management. Client-based transaction management is realized by providing logging facilities locally even when data is shared in a global environment. This thesis presents several recovery algorithms which utilize client disks for storing recovery related information (i.e., log records). Our algorithms work with both coarse and fine-granularity locking and they do not require the merging of client logs at any time. Moreover, our algorithms support fine-granularity locking with multiple clients permitted to concurrently update different portions of the same database page. The database state is recovered correctly when there is a complex crash as well as when the updates performed by different clients on a page are not present on the disk version of the page, even though some of the updating transactions have committed. This thesis also presents the implementation of the proposed algorithms in a memory-mapped storage manager as well as a detailed performance study of these algorithms using the OO1 database benchmark. The performance results show that client-based logging is superior to traditional server-based logging. This is because client-based logging is an effective way to reduce dependencies on server CPU and disk resources and, thus, prevents the server from becoming a performance bottleneck as quickly when the number of clients accessing the database increases.

Towards Eco-Dharma: The Contribution of Gandhian Thought to Ecological Ethics in India

Ecological concern prompts poor and indigenous people of India to consider how a society can ensure both protection of nature and their rightful claim for a just and sustainable future. Previous discussions defended the environment while ignoring the struggles of the poor for sustenance and their religious traditions and ethical values. Mohandas Karamchand Gandhi addressed similar socio-ecological concerns by adopting and adapting traditional religious and ethical notions to develop strategies for constructive, engaged resistance. The dissertation research and analysis verifies the continued relevance of the Gandhian understanding of dharma (ethics) in contemporary India as a basis for developing eco-dharma (eco-ethics) to link closely development, ecology, and religious values. The method of this study is interpretive, analytical, and critical. Françoise Houtart’s social analytical method is used to make visible and to suggest how to overcome social tensions from the perspective of marginalized and exploited peoples in India. The Indian government's development initiatives create a nexus between the eco-crisis and economic injustice, and communities’ responses. The Chipko movement seeks to protect the Himalayan forests from commercial logging. The Narmada Bachao Andolan strives to preserve the Narmada River and its forests and communities, where dam construction causes displacement. The use of Gandhian approaches by these movements provides a framework for integrating ecological concerns with people's struggles for survival. For Gandhi, dharma is a harmony of satya (truth), ahimsa (nonviolence), and sarvodaya (welfare of all). Eco-dharma is an integral, communitarian, and ecologically sensitive ethical paradigm. The study demonstrates that the Gandhian notion of dharma, implemented through nonviolent satyagraha (firmness in promoting truth), can direct community action that promotes responsible economic structures and the well-being of the biotic community and the environment. Eco-dharma calls for solidarity, constructive resistance, and ecologically and economically viable communities. The dissertation recommends that for a sustainable future, India must combine indigenous, appropriate, and small- or medium-scale industries as an alternative model of development in order to help reduce systemic poverty while enhancing ecological well-being.

An Online Distributed Algorithm for Inferring Policy Routing Configurations

We present an online distributed algorithm, the Causation Logging Algorithm (CLA), in which Autonomous Systems (ASes) in the Internet individually report route oscillations/flaps they experience to a central Internet Routing Registry (IRR). The IRR aggregates these reports and may observe what we call causation chains where each node on the chain caused a route flap at the next node along the chain. A chain may also have a causation cycle. The type of an observed causation chain/cycle allows the IRR to infer the underlying policy routing configuration (i.e., the system of economic relationships and constraints on route/path preferences). Our algorithm is based on a formal policy routing model that captures the propagation dynamics of route flaps under arbitrary changes in topology or path preferences. We derive invariant properties of causation chains/cycles for ASes which conform to economic relationships based on the popular Gao-Rexford model. The Gao-Rexford model is known to be safe in the sense that the system always converges to a stable set of paths under static conditions. Our CLA algorithm recovers the type/property of an observed causation chain of an underlying system and determines whether it conforms to the safe economic Gao-Rexford model. Causes for nonconformity can be diagnosed by comparing the properties of the causation chains with those predicted from different variants of the Gao-Rexford model.