Noha Adly

Location management techniques for mobile systems

Abstract Locating users in mobile environment is an essential problem in PCS that becomes more challenging as the network size increases and the user population grows. In third generation mobile systems, the signaling traffic and processing overhead of location updates is expected to grow tremendously leading to poor performance. Therefore location management schemes should aim at reducing the cost of updates. Yet, the lookup delay should be kept minimum. This paper aims at classifying the various approaches used for location management of mobile users by grouping them into two main categories. The first category comprises techniques that focused on reducing the cost of looking up a user and adjusted the update process accordingly. These are classified into replication , caching and selective paging techniques. The second category consists of techniques that focused on reducing the cost of updates and maneuvered the update policy to reduce the lookup cost by informing the system with the maximum possible information about users mobility. This could be achieved by the use of statistic collection, estimation or prediction processes. We classify those schemes into three main classes: schemes based on forwarding pointers , learning-based schemes and prediction-based schemes. By investigating the technical significance of each class a new direction for future research is proposed which favors the second category of location techniques and emphasis the importance of adopting suitable learning and prediction techniques to optimize the overall location cost.

A profile-based hierarchical location management scheme for future PCS

To accommodate the growth in the user population and network size in mobile environments, hierarchical architectures of location databases have been proposed. Recent studies showed that the signaling traffic and processing overhead of location updates is expected to grow tremendously, leading to poor performance. The authors present a Profile based Hierarchical location management Scheme (PHS) that focuses on reducing the cost of updates while keeping the lookup cost low. Our proposal is motivated by the observation that users follow repetitive patterns that can be profiled and predicted with reasonable cost, allowing users to send updates occasionally and not on every move. Further, it deploys a simple prediction technique that reduces the uncertainty of a users location. We quantify the costs and benefits of PHS for several mobility patterns and show that it can result in up to 60% reduction in the update cost with minimal increase (7%) in lookup cost.

An Adaptive Synchronization Policy for Harvesting OAI-PMH Repositories

Metadata harvesting requires timely propagation of up-to-date information from thousands of Repositories over a wide area network. It is desirable to keep the data as fresh as possible while observing the overhead on the Harvester. An important dimension to be considered is that Repositories vary widely in their update patterns; they may experience different update rates at different times or unexpected changes to update patterns. In this paper, we define data Freshness metrics and propose an adaptive algorithm for the synchronization of the Harvester with the Repositories. The algorithm is based on meeting a desired level of Freshness while incurring the minimum overhead on the Harvester. We present a comparison between different policies for the synchronization within the framework devised. It is shown that the proposed policy outperform the other policies, especially for heterogeneous update patterns.

A new index structure for querying association rules

Association rules discovery is an important data mining technique which usually produces large number of rules. Subset and superset queries are common queries for association rules. We introduce a new index structure (SSST) for querying association rules, based on a unique set representation using a hierarchical structure. It supports both Subset and Superset queries. Further, it is scalable and adapts to different types of data. The performance of SSST is evaluated using real as well as synthetic datasets, spanning dense and sparse data. The experiments showed that the proposed structure outperforms other set indexing techniques significantly, especially for dense datasets. Also, it scales well with both the number of association rules and the query size.

HPP: A Reliable Causal Broadcast Protocol for Large-Scale Replication in Wide Area Networks

This paper describes a fast, reliable, scalable and efficient broadcast protocol called HPP (hierarchical propagation protocol) for weak-consistency replica management. It is based on organizing the nodes in a network into a logical hierarchy and maintaining a limited amount of state information at each node. It ensures that messages are not lost due to failures or partitions and minimizes redundancy. Furthermore, the protocol allows messages to be diffused while nodes are down provided the parent and child nodes of a failed node are alive. Moreover, the protocol allows nodes to be moved in the logical hierarchy and the network to be restructured dynamically in order to improve performance, while still ensuring that no messages are lost while the switch takes place and without disturbing normal operation. A performance study of the protocol in terms of availability and propagation delay indicates that the protocol reduces the delay by a factor of four compared to a protocol that does not diffuse messages past failed nodes.