Under security threats, today's networks are being made to be intrusion tolerant. In a large scale, services are continuing (at a degraded level) while compromising and recovering are both progressing. One of the key problems in the restoration procedure regards to the resource allocation strategies, typically a minimized total cost concerning both service loss and resource expense. In this paper, we investigate the achievable minimal total cost and corresponding resource allocation strategy for different situations. The situations include nonlinear relationship between resource allocation rate and the restoration rate, and its variant when time factor is concerned. We present cost models and numerical results. The results show the impact from various system parameters on the critical conditions for a successful system restoration and the minimal cost. An important result of our study suggests that tight operational region exists under certain conditions.

For the last decade, a number of hierarchical reliable multicast schemes have been proposed in the literature due to their efficiency and scalability. They normally partition the group members into local groups and allocate one server for each local group to detect and recover packet loss. One common problem is how the server recovers the lost packet in the case that the server itself does not receive it. The usual solution is either to send requests to some upstream servers/source or to construct a tree-based hierarchy to organize the servers for recovery. However, these solutions may lead to long recovery latency. This paper presents a server relay recovery strategy (SRRS) for servers to achieve low recovery latency and low bandwidth usage for recovery. The basic idea is for each server to distributively compute and maintain a prioritized peer list that leads to minimal expected relay recovery latency. Our simulation results show that when the server distribution is not dense, SRRS outperforms SRM (scalable reliable multicast) and RMA (reliable multicast architecture) on both recovery latency and bandwidth usage in Internet-like topologies.

Sensor networks are resource constrained environments and not physically secured. Sensor nodes may be physically captured and reverse engineered by the attacker. The goal of our research is to design energy-efficient distributed authorization servers (DAS) resilient to logical attack (hacking) and physical attack. We propose to organize our DAS as a layered directed acyclic graph (DAG). Any request to a sensor must be routed through and authorized by every layer of the DAG. Similar concepts have been investigated by other researchers. Our research adds a structural component to the existing research. A common method for achieving longer life for a sensor network is to deploy redundant nodes and activate only a subset of nodes at one point of time. To the best of our knowledge, nobody addressed the problem of choosing the nodes to be activated. In This work we designed a set of algorithms to choose the set of nodes for forming the DAS between the command and control centers (CCCs) and sensors collecting data. Our algorithms maximize the time required to compromise the security as well reduces the energy requirement for authorization and communication.

Any reliable multicast protocol requires some recovery mechanism. A generic description of a recovery mechanism consists of a prioritized list of recovery servers/receivers (clients), hierarchically and/or geographically and/or randomly organized. Recovery requests are sent to the recovery clients on the list one-by-one until the recovery effort is successful. There are many recovery strategies available in literature fitting the generic description. We propose a polynomial time algorithm for choosing the recovery strategy with law recovery latency without sacrificing much bandwidth. We compared our method with two existing recovery methods, SRM (scalable reliable multicast) and RMA (reliable multicast architecture), by simulation and found that our method performs better. Although our theoretical analyses are based on a reliable network, our simulation results show that our strategy performs as well with the per link loss probability in a network up to 20% or more

A mobile computing system consists of mobile and stationary nodes. Checkpointing in mobile systems faces many new challenges, such as low wireless bandwidth, frequent disconnections and lack of stable storage at mobile nodes. Previously proposed checkpoint algorithms for mobile environments do not take into consideration the coordinating message overhead of the mobile participants. In this paper, we propose a new checkpoint algorithm for mobile distributed systems. Our algorithm is nonblocking and minimizes the number of checkpoint participants. We propose a new idea called proxy coordinator. By using proxy coordinator, mobile participants' coordinating message overhead is minimized to O(1), which can otherwise be as high as O(n) in other proposed algorithms.

In this paper, we consider the problem of multicasting large files over a reliable network. In a reliable network, errors are transient and any change in the multicast tree is a rare occurrence. Further, the loss of a packet is correlated in the sense that a packet lost at a link will result in a loss to all downstream recipients. We propose to partition the recipients into static subgroups during the construction of the multicast tree. Whenever a NACK is received, the source retransmits the packet to all members of the subgroup from which the NACK came. This recovery method eliminates the overhead of joining/leaving subgroups associated with dynamic recovery schemes. This scheme reduces the NACK implosion by allowing all NACK from a subgroup to be merged in one NACK. We proposed an objective function to judge the merits of static subgroup-based recovery schemes. Further, we proved that computing the optimal subgroups is NP-hard. We provide an heuristic for computing subgroups with low cost of recovery and study its performance by simulation.

Given the increasing importance of optimal sensor deployment for battlefield strategists, the converse problem of reacting to a particular deployment by an enemy is equally significant and not yet addressed in a quantifiable manner in the literature. We address this issue by modeling a two stage game in which the opponent deploys sensors to cover a sensor field and we attempt to maximally reduce his coverage at minimal cost. In this context, we introduce the concept of minimal sensor integrity which measures Me vulnerability of any sensor deployment. We find the best response by quantifying the merits of each response. While the problem of optimally deploying sensors subject to coverage constraints is NP-complete, in this paper we show that the best response (i.e. the maximum vulnerability) can be computed in polynomial time for sensors with arbitrary coverage capabilities deployed over points in any dimensional space. In the special case when sensor coverages form an interval graph (as in a linear grid), we describe a better O(Min(M/sup 2/, NM)) dynamic programming algorithm.

In this paper we propose a deflection routing based N/spl times/N ATM multicast switch. The switch consists of a copy network, a routing network and a novel mechanism to reduce memory requirements. We analyze the switch performance and show that the switch requires only O(log N) stages for low packet loss rates. Our theoretical results are backed by simulations of the switch. The switch is output queued and allows the delivery of multiple packets to the same destination during a time slot.

We propose an efficient low cost multicast ATM switch which is fair to all inputs. The switch consists of a novel copy network followed by a routing network which ensures sequencing. Both the copy and routing networks are based on deflection routing. The switch requires O(log N) stages and can be designed for any arbitrarily low level of packet loss. Switching elements in both the copying and routing networks have O(1) bit complexity, making the overall bit level hardware complexity of the network O(N log N). The latency of the switch is proportional to the number of stages O(log N). Unlike other existing copy networks, our copy network drops packets in a fair manner and hence can provide QoS support. The switch is output queued and allows the delivery of multiple packets to the same destination during a time slot.

We propose and analyze the architecture for a large-scale high-speed multicast switch called MSXmin. The hardware complexity of MSXmin is O(N log/sup 2/ N) which compares favorably with existing architectures. Further, the internal latency of the MSXmin is O(log/sup 2/ N) bits. While it is superior to the existing architectures in terms of the hardware complexity and the internal latency, it is comparable to other multicast switches in terms of the header overhead and translation table complexity. MSXmin is output buffered and based on the group knockout principle. Moreover, MSXmin is a dual-bit-controlled tree-based switch.

In parallel and distributed computing, the overall system performance is significantly influenced by how a task graph representing an application is mapped onto a specific multiprocessor topology. In this paper, algorithms with improved performance are proposed to map tree and linear task graphs onto shared memory multiprocessing architectures. Specifically, the task graphs are partitioned with our algorithms so that the load is balanced, processor utilisation is maximized, and the communication demand on the interconnection network is minimized in a shared memory multiprocessor. All the proposed algorithms are polynomial in time complexity. Bottleneck and processor minimization algorithms of this type are proposed. The bandwidth minimization algorithm has a complexity of O(n + p log q) (q/spl les/p/spl les/n), in contrast with the complexity of O(n log n) for the best known algorithm in the literature. Furthermore, we have identified cases where our algorithm will run in linear time on the average.>

We investigate the problem of constructing a prediction scheme for a given image that results in the minimum zero-order entropy of prediction errors. The problem is formulated as a combinatorial optimization problem. This allows the use of some well known techniques from combinatorial optimization in order to construct heuristic solutions. We describe a few heuristics and give preliminary implementation results. The techniques developed can also be generalized in a straight forward manner to composite source modeling where the data is modeled as an interleaved sequence emanating from k different sub-sources. Although the problems and proposed solutions are described in a strictly deterministic manner, they can also be formulated in a stochastic framework to yield solutions that are valid for a family of images emitted by the same source.>

The problem of partitioning task graphs in its general form is known to be NP-complete and it is extremely difficult to come up with simple but effective and fast heuristics too. In this paper, the tree task graphs are considered which arise from many important programming paradigms such as divide and conquer branch and bound, etc. The target architecture considered is shared memory architecture as it typifies a wide range of research prototypes and commercial products of multiprocessors. Optimal sequential and parallel algorithms for partitioning tree task graphs such that the bottleneck is minimized "with minimum number of processors are developed. The bandwidth minimization problem is NP-complete even for trees. Three effective, simple 2-pass heuristics and their parallel versions are given. The effectiveness and efficiency of those heuristics are validated through extensive simulations.