We present the design and modeling of an intelligent modular switching architecture, designated MSA, for packet transport over next-generation ultra-dense wavelength division multiplexed (WDM) networks. The proposed architecture reduces the electronic processing overhead in sending the packets from source to destination. This is achieved by sending routing/forwarding information on a dedicated control channel. The control packets are processed electronically by a control processor at each intermediate node. The processing of control packets ensures that the data packets propagate from source to destination in photonic form (that is, without any processing by intermediate nodes). Due to the flexible nature of the proposed architecture it can be applied to support opaque optical networks to all-optical networks and can be used to transport IP/MPLS and ATM traffic. The performance evaluation of the proposed switching architecture shows that it drastically reduces the electronic processing overhead. Saving in the processing overhead can leverage bandwidth utilization without increasing the hardware gear cost. Furthermore, a large saving in terms of electrical-to-optical (E/O) and optical-to-electrical (O/E) converters cost per node can also be achieved.

This paper presents a resource-constrained scheduling algorithm based on tabu search for high-level behavioral synthesis of functional pipelines. The proposed algorithm, designated as TLS, integrates list scheduling with tabu search. List scheduling algorithms have been widely used in high-level synthesis systems, because they are very fast, easy to implement and the low computational complexity of these algorithms make them applicable to large data flow graphs. In list scheduling, the quality of the solution is very sensitive to the priority assigned to the operations. The distinct feature of our technique is that we use tabu search to find suitable priorities. Tabu search is a metaheuristic that can be superimposed on existing procedures to guide these procedures towards searching in more desirable neighborhoods and to prevent them from becoming trapped at locally optimal solutions. The proposed algorithm is conceptually very simple, easy to implement and is very effective in conquering the intractable nature of the resource-constrained scheduling problem. The proposed technique can handle multicycle operations, structural pipelined operations, chained operations and functional pipelining. We demonstrate the effectiveness of our technique by comparing against the existing scheduling techniques for functional pipelining on a number of benchmark examples reported in the literature.

The m-way graph partitioning problem (GPP) is an intractable combinatorial optimization problem with many important applications in the design automation of VLSI circuits and in the mapping problem for distributed computing systems. In this paper, we introduce a technique based on a problem-space genetic algorithm (PSGA) for the GPP to reduce the weighted cut-size while keeping the size of each subset balanced. The proposed PSGA based approach integrates a problem-specific simple and fast heuristic with a genetic algorithm to search a large solution space efficiently and effectively to find the best possible solution in an acceptable CPU time. Experimental study shows that our technique produces better results with respect to both the quality of the solution and the computational time over the previous work. The PSGA is a simple, versatile and a generic optimization technique which can also be applied to other combinatorial optimization problems.

Abstract The task assignment problem is one of assigning tasks of a parallel program among the processors of a distributed computing system in order to reduce the job turnaround time and to increase the throughput of the system. Since the task assignment problem is known to be NP‐complete except in a few special situations, satisfactory suboptimal solutions obtainable in a reasonable amount of computation time are generally sought. In the paper we introduce a technique based on the problem‐space genetic algorithm (PSGA) for the static task assignment problem in both homogeneous and heterogeneous distributed computing systems to reduce the task turnaround time and to increase the throughput of the system by properly balancing the load and reducing the interprocessor communication time among processors. The PSGA based approach combines the power of genetic algorithms, a global search method, with a simple and fast problem‐specific heuristic to search a large solution space efficiently and effectively to find the best possible solution in an acceptable CPU time. Experimental results on test examples from the literature show considerable improvements in both the assignment cost and the CPU times over the previous work. The proposed scheme is also applied to a digital signal processing (DSP) system consisting of 119 tasks to illustrate its balancing properties and computational advantage on a large system. The proposed scheme offers 12–30% improvement in the assignment cost as compared to the previous best known results for the DSP example.

This paper presents a new approach to datapath synthesis based on a problem-space genetic algorithm (PSGA). The proposed technique performs concurrent scheduling and allocation of functional units, registers, and multiplexers with the objective of finding both a schedule and an allocation which minimizes the cost function of the hardware resources and the total time of execution. The problem-space genetic algorithm based datapath synthesis system (PSGA-Synth) combines a standard genetic algorithm with a known heuristic to search the large design space in an intelligent manner. PSGA-Synth handles multicycle functional units, structural pipelining, conditional code and loops, and provides a mechanism to specify lower and upper bounds on the number of control steps. The PSGA-Synth was tested on a set of problems selected from the literature, as well as larger problems created by us, with promising results. PSGA-Synth not only finds the best known results for all the test problems examined in a relatively small amount of CPU time, but also has the ability to efficiently handle large problems.>