Heterogeneous computing (HC) systems are essential parts of modern-day computing architectures such as cloud, cluster, grid, and edge computing. Many algorithms exist within the classical environment for mapping computational tasks to the HC system’s nodes, but this problem is not well explored in the quantum area. In this work, the practicality, accuracy, and computation time of quantum mapping algorithms are compared against eleven classical mapping algorithms. The classical algorithms used for comparison include A-star (A*), Genetic Algorithm (GA), Simulated Annealing (SA), Genetic Simulated Annealing (GSA), Opportunistic Load Balancing (OLB), Minimum Completion Time (MCT), Minimum Execution Time (MET), Tabu, Min-min, Maxmin, and Duplex. These algorithms are benchmarked using several different test cases to account for varying system parameters and task characteristics. This study reveals that a quantum mapping algorithm is feasible and can produce results similar to classical algorithms.
We propose a different reflection-based technique, named efficient neighbor channel reservation, where a contending burst is reflected from a suitable neighbor node and then resumes its original path. Our proposed scheme does not use any extra hardware and addresses several limitations of other schemes including: (a) eliminating the use of bulky fiber delay lines, (b) avoiding complexity required with burst segmentation, (c) preventing resource wastage that occurs with prereservation schemes, and (d) preventing loop formation inherent in most deflection routing schemes.
In this paper, we discuss the Passive Optical Network deployment on an arbitrary grid with guaranteed tolerance towards p-1 equipment failure. We show that this problem in general is NP-hard. We propose an algorithm, which guarantees a solution of 4-approximation to the optimal deployment, and further argue that this is the best lower bound achievable in our case. We do comparative studied with randomized layouts, were our proposed algorithm saves 45% - 55% deployment cost (fiber, equipment, etc.) on average.
Smart Dust particles, are small smart materials used for generating weather maps. We investigate question of the optimal number of Smart Dust particles necessary for generating precise, computationally feasible and cost effective 3-D weather maps. We also give an optimal matching algorithm for the generalized scenario, when there are N Smart Dust particles and M ground receivers.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.