Secret sharing and erasure coding-based approaches have been used in distributed storage systems to ensure the confidentiality, integrity, and availability of critical information. To achieve performance goals in data accesses, these data fragmentation approaches can be combined with dynamic replication. In this paper, we consider data partitioning (both secret sharing and erasure coding) and dynamic replication in data grids, in which security and data access performance are critical issues. More specifically, we investigate the problem of optimal allocation of sensitive data objects that are partitioned by using secret sharing scheme or erasure coding scheme and/or replicated. The grid topology we consider consists of two layers. In the upper layer, multiple clusters form a network topology that can be represented by a general graph. The topology within each cluster is represented by a tree graph. We decompose the share replica allocation problem into two subproblems: the Optimal Intercluster Resident Set Problem (OIRSP) that determines which clusters need share replicas and the Optimal Intracluster Share Allocation Problem (OISAP) that determines the number of share replicas needed in a cluster and their placements. We develop two heuristic algorithms for the two subproblems. Experimental studies show that the heuristic algorithms achieve good performance in reducing communication cost and are close to optimal solutions.

In distributed mobile systems, communication cost and disconnections are major concerns. In this paper, we address replica placement issues to achieve improved performance for systems supporting mobile transactions. We focus on handling correlated data objects and disconnections. Frequently, requests and/or transactions issued by mobile clients may access multiple data objects and should be considered together in terms of replica allocation. We discuss the replication cost model for correlated data objects and show that the problem of finding an optimal solution is NP. We further adjust the replication cost model for disconnections. A heuristic "expansion-shrinking" algorithm is developed to efficiently make replica placement decisions. The algorithm obtains near optimal solutions for the correlated data model and yields significant performance gains when disconnection is considered. Experimental studies show that the heuristic expansion-shrinking algorithm significantly outperforms the general frequency-based replication schemes.

Conventional admission control models incur some performance penalty. First, admission control computation can overload a server that is already heavily loaded. Also, in large-scale media systems with geographically distributed server clusters, performing admission control on each cluster can result in long response latency, if the client request is denied at one site and has to be forwarded to another site. Furthermore, in prefix caching, initial frames cached at the proxy are delivered to the client before the admission decisions are made. If the media server is heavily loaded and, finally, has to deny the client request, forwarding a large number of initial frames is a waste of critical network resources. In this paper, a novel distributed admission control model is presented. We make use of proxy servers to perform the admission control tasks. Each proxy hosts an agent to coordinate the effort. Agents reserve media server's disk bandwidth and make admission decisions autonomously based on the allocated disk bandwidth. We develop an effective game theoretic framework to achieve fairness in the bandwidth allocation among the agents. To improve the overall bandwidth utilization, we also consider an aggressive admission control policy where each agent may admit more requests than its allocated bandwidth allows. The distributed admission control approach provides the solution to the stated problems incurred in conventional admission control models. Experimental studies show that our algorithms significantly reduce the response latency and the media server load.

Summary form only given. We investigate a transaction based partial replication strategy for replicating database in the mobile environments. We propose a heuristic algorithm to select a set of data objects for replicating to and deallocating from the mobile node, based on the dynamically changing client access patterns. We conduct experiments to compare the performance of our algorithm with that of the full replication and frequency based partial replication algorithms. The results show that the transaction based partial replication algorithm always has a better performance than the full replication strategy. And, in most cases, it is better than frequency-based partial replication algorithms.

Summary form only given. Application level multicast approaches have attracted interests in both research and industrial communities. Among various approaches, constructing the overlay multicast tree based on mesh network is very promising for a large multicast group size. This approach is efficient in bandwidth utilization but suffer from high computation overhead. We present a new approach for constructing an overlay multicast tree in a large scale media streaming system using neural networks. The media server first constructs an overlay multicast tree for requests arriving in a relatively short time interval. Then the subsequent requests can join the multicast tree in a distributed way. To utilize the interface bandwidth efficiently, we formulate the original tree construction problem as a balanced multicast tree problem and propose a new SOFM-like neural network algorithm to obtain the solution. For the requests arriving at a later time, the proxy servers process the requests and join the multicast tree using a decentralized prediction based on a multilayered neural network. The tree is kept balanced in the tree joining process by having proxy servers predict the traffic load on its neighbors.

We present a novel distributed admission control model for multimedia delivery. Proxy servers form a large-scale distributed infrastructure and can be used to perform the admission control tasks. In our distributed admission control algorithm, an agent first characterizes its disk bandwidth requirement based on the distribution of requests arrived at the proxies. Then, it reserves the disk bandwidth from the server by maximizing its utility function. The server grants the bandwidth to the agents according to its capacity. Based on the granted bandwidth, each agent executes the admission control algorithm for its local requests. The distributed admission control model provides the admission control service close to the users and, hence, reduces the network load, media server load, and, in some cases, users' response latency.

Prefix caching has significant benefits in media delivery systems in terms of reduced startup latency and network traffic. However, with the conventional admission control model, prefix caching may incur some performance penalty. When the media server is heavily loaded and has to deny the client request finally, forwarding a large number of initial frames from proxy to clients can result in dramatic waste of critical network resource. An ideal solution to this problem is to let proxy servers perform the admission control on behalf of the media servers. In this paper, we present a distributed admission control policy. We consider an aggressive disk bandwidth allocation scheme where an agent may, with a certain probability, admit a new request when the current bandwidth used at the agent has reached its limit. We formulate the problem as a min-max optimization problem and provide a MINMAX algorithm to find the solutions. The effectiveness of our algorithm is demonstrated through simulation studies.

We discuss a multicriteria transaction (MCT) model to address problems encountered in large-scale e-commerce transaction processing systems. A MCT is a generalized transaction where users can specify their preferences in a request by specifying multiple criteria in the transaction. With each criteria specification, a set of alternate execution paths is included. Thus, a MCT is less likely to fail. Moreover, the MCT model allows easy extraction of criteria from a set of transactions and, hence, optimal decisions can be made to achieve better customer satisfaction. We discuss the design of a MCT processing system and compare the MCT model to conventional transaction processing approaches.