This paper presents a heuristic approach to accelerate the reconfiguration of two-dimensional degradable VLSI arrays linked by 4-port switches in presence of faulty processing elements (PEs). In particular, we proposed a technique to preprocess the host array by 1) identifying fault-free PEs that cannot form the target array due to their proximity to faulty PEs, and 2) labeling these fault-free PEs as faults. The proposed preprocessing method minimizes the number of PEs that will be considered for reconfiguration, thus accelerating the reconfiguration process. Simulation results show that the runtime of two well-known algorithms are significantly reduced by employing the preprocessing technique. In addition, we demonstrate the scalability of the proposed technique by showing that the runtime reduction rate increases with increasing fault density.

The problem of reconfiguring two-dimensional VLSI arrays with faults is to find a maximum logical array without faults. The existing algorithms only consider faults associated with processing elements, and all switches and links are assumed to be fault-free. But switch faults may often occur in the network-on-chips with high density. In this paper, two novel approaches are proposed to tackle the reconfiguration problem of degradable VLSI arrays with switch faults. The first approach extends the well-known existing algorithm with simple pre-processing and row bypass scheme. The second one employs a novel row and column rerouting scheme to maximize the size of the logical array. Simulation results show that the proposed two approaches can effectively generate the logical arrays on the given host array with switch faults, and the second algorithm performs more favorably with the increasing number of the switch faults.

A significant fraction of network events (such as topology or route changes) and the resulting performance degradation stem from premeditated network management and operational tasks. This paper introduces a general class of Graceful Network State Migration (GNSM) problems, where the goal is to discover the optimal sequence of operations that progressively transition the network from its initial to a desired final state while minimizing the overall performance disruption. We investigate two specific GNSM problems: 1) Link Weight Reassignment Scheduling (LWRS) studies the optimal ordering of link weight updates to migrate from an existing to a new link weight assignment; and 2) Link Maintenance Scheduling (LMS) looks at how to schedule link deactivations and subsequent reactivations for maintenance purposes. LWRS and LMS are both combinatorial optimization problems. We use dynamic programming to find the optimal solutions when the problem size is small, and leverage ant colony optimization to get near-optimal solutions for large problem sizes. Our simulation study reveals that judiciously ordering network operations can achieve significant performance gains. Our GNSM solution framework is generic and applies to similar problems with different operational contexts, underlying network protocols or mechanisms, and performance metrics.

A significant fraction of network events (such as topology or route changes) and the resulting performance degradation stem from premeditated network management and operational tasks. This paper introduces a general class of graceful network operation (GNO) problems, where the goal is to discover the optimal sequence of operations that progressively transition the network from its initial to a desired final state while minimizing the overall performance disruption. We investigate two specific GNO problems: (a) link weight reassignment scheduling (LWRS) studies the optimal ordering of link weight updates to migrate from an existing to a new link weight assignment, and (b) link maintenance scheduling (LMS) looks at how to schedule link deactivations and subsequent reactivations for maintenance purposes. LWRS and LMS are both combinatorial optimization problems. We use dynamic programming to find the optimal solutions when the problem size is small, and leverage ant colony optimization to get near-optimal solutions for large problem sizes. Our simulation study reveals that judiciously ordering network operations can achieve significant performance gains. Our GNO solution framework is generic and applies to similar problems with different operational contexts, underlying network protocols or mechanisms, and performance metrics.