Distributed data storage is a building block for many distributed systems such as mobile file systems, web service replication systems, enterprise file systems, etc. New distributed data storage systems are frequently built as new environment, requirements or workloads emerge. The goal of this dissertation is to develop the science of distributed storage systems by making it easier to build new systems. In order to achieve this goal, it proposes a new policy architecture, PADS, that is based on two key ideas: first, by providing a set of common mechanisms in an underlying layer, new systems can be implemented by defining policies that orchestrate these mechanisms; second, policy can be separated into routing and blocking policy, each addresses different parts of the system design. Routing policy specifies how data flow among nodes in order to meet performance, availability, and resource usage goals, whereas blocking policy specifies when it is safe to access data in order to meet consistency and durability goals. This dissertation presents a PADS prototype that defines a set of distributed storage mechanisms that are sufficiently flexible and general to support a large range of systems, a small policy API that is easy to use and captures the right abstractions for distributed storage, and a declarative language for specifying policy that enables quick, concise implementations of complex systems. We demonstrate that PADS is able to significantly reduce development effort by constructing a dozen significant distributed storage systems spanning a large portion of the design space over the prototype. We find that each system required only a couple of weeks of implementation effort and required a few dozen lines of policy code. / text

This thesis presents a comparison between a basic shortest path routing policy of the Destination-Sequence Distance Vector (DSDV) protocol and two power-aware policy variations of it. In the two modified versions, the routes are selected based on the energy available on the nodes and not only the shortest path distance to the destination. Simulations are conducted for a given situation of nodes and the energy efficiency of the three aforementioned policies are evaluated for example scenarios. First, a brief overview of the theory behind the study is presented. It consists of an description of ad hoc networking, DSDV, and our energy-aware modifications to DSDV. After the fundamental theory, the method is presented. It consists of a description of how the simulated scenarios relates to a real-world scenario and the simplifications made in the model. We present an overview of the model used for simulation and the operation of the program. This section ends with an explanation of the three simulated policies: shortest path, simple weighted and doubled weighted. When the theory behind the thesis are completed, the simulations are conducted. The results are examined and a summary of their meaning is discussed. It is explained how the assumptions effect the reliability of the study and an estimation of the accuracy of the results are presented. We find that the power-aware policy variations (simple weighted and double weighted) both achieve better network lifetime than the basic shortest path policy, at the cost of slightly longer per-packet paths. These results are encouraging and show that very simple modifications to DSDV can achieve significant gains in the network lifetime, helping users get the most out of their networks. Future investigation could try to optimize these gains.

The objective of the research is to study optimal routing policy (ORP) problems and to develop an optimal adaptive routing algorithm practical for large-scale Stochastic Time-Dependent (STD) real-life networks, where a traveler could revise the route choice based upon en route information. The routing problems studied can be viewed as counterparts of shortest path problems in deterministic networks. A routing policy is defined as a decision rule that specifies what node to take next at each decision node based on realized link travel times and the current time. The existing routing policy algorithm is for explorative purpose and can only be applied to hypothetical simplified network. In this research, important changes have been made to make it practical in a large-scale real-life network. Important changes in the new algorithm include piece-wise linear travel time representation, turn-based, label-correcting, criterion of stochastic links, and dynamic blocked links. Complete dependency perfect online information (CDPI) variant is then studied in a real-life network (Pioneer Valley, Massachusetts). Link travel times are modeled as random variables with time-dependent distributions which are obtained by running Dynamic Traffic Assignment (DTA) using data provided by Pioneer Valley Planning Commission (PVPC). A comprehensive explanation of the changes by comparing the two algorithms and an in-depth discussion of the parameters that affects the runtime of the new algorithm is given. Computational tests on the runtime changing with different parameters are then carried out and the summary of its effectiveness are presented. To further and fully understand the applicability and efficiency, this algorithm is then tested in another large-scale network, Stockholm in Sweden, and in small random networks. This research is also a good starting point to investigate strategic route choice models and strategic route choice behavior in a real-life network. The major tasks are to acquire data, generate time-adaptive routing policies, and estimate the runtime of the algorithm by changing the parameters in two large-scale real-life networks, and to test the algorithm in small random networks. The research contributes to the knowledge base of ORP problems in stochastic time-dependent (STD) networks by developing an algorithm practical for large-scale networks that considers complete time-wise and link-wise stochastic dependency.

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