The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Weisheng Hu - One of the best experts on this subject based on the ideXlab platform.
-
dimensioning of the store and transfer wdm network with limited node Storage under the sliding scheduled traffic model
IEEE\ OSA Journal of Optical Communications and Networking, 2017Co-Authors: Da Feng, Xiaojian Zhang, Weisheng HuAbstract:The so-called store-and-transfer WDM network (STWN) can store data in source Storage and provision lightpaths at an optimal time when wavelengths are clear of conflicts. Consequently, blocking of requests can be reduced and resource utilization of the network can be improved. In this work, we investigate dimensioning of STWN and propose a two-step method to jointly determine the number of wavelengths and Storage Size required to satisfy the demand, which is given as a load matrix with a deadline, blocking rate, and wavelength utilization. The method models the STWN as a TDM network and first obtains the required Storage Size by maximizing the number of fixed time slots in each period, then calculates the required number of wavelengths with the number of time slots. Numerical results show the following: high load between far apart source and destination nodes has significant impact on the required wavelengths and Storage. For instance, in a 24-node topology, 20% more wavelengths and Storage may be required to satisfy a biased load matrix than a randomly generated one. When calculating the required number of wavelengths, ourmethod outperforms traditional routing and wavelength assignment (RWA), because the TDM-based model supports fractional load. In the 24-node topology, 18% more wavelengths may be required byRWA than by our method. By installing amoderate amount of Storage,wavelength utilization can be effectively improved. With number of wavelengths and Storage Size equaling 29 and 274, in the 24-node topology, utilization can reach 0.9. We also find wavelength is equivalent with Storage regarding capacity of a STWN. For instance, with number of wavelengths and Storage Size equaling 114 and 131, in the 24-node topology, the blocking rate is the same as with number of wavelengths and Storage Size equaling 29 and 270.
-
dimensioning of store and transfer wdm network with limited Storage
Network Operations and Management Symposium, 2016Co-Authors: Da Feng, Xiaojian Zhang, Peng Wu, Junmin Wu, Weisheng HuAbstract:For large data transfers, the Store-and-Transfer WDM Network (STWN) provides Storage and lightpaths jointly. Constrained delays of the transfers restrict the number of waiting requests, thus the Storage Size can be limited. For dimensioning of STWN, we propose a method to determine the number of wavelengths and Storage Size required to satisfy the demand. The demand is given as a load matrix with a common deadline and blocking, then we express the load matrix as a base load multiplying an integer matrix consisting of mutually prime elements. The dimensioning process has two steps. 1) We model STWN as TDM network and analyse channels of the TDM system as M/G/1 subsystems. We calculate approximate performance of source-destination pairs and minimize the service rate provisioned for the demand with linear search to find the maximum allowed number of time-slots. 2) With the number of time-slots fixed, we optimize routing and wavelength and time-slot assignment to minimize the number of wavelengths required to accept the integer matrix. In step 1, the approximate performance calculated includes turnout, distribution of delay and Storage Size. Numerical results show how to drastically decrease the required resources.
Da Feng - One of the best experts on this subject based on the ideXlab platform.
-
dimensioning of the store and transfer wdm network with limited node Storage under the sliding scheduled traffic model
IEEE\ OSA Journal of Optical Communications and Networking, 2017Co-Authors: Da Feng, Xiaojian Zhang, Weisheng HuAbstract:The so-called store-and-transfer WDM network (STWN) can store data in source Storage and provision lightpaths at an optimal time when wavelengths are clear of conflicts. Consequently, blocking of requests can be reduced and resource utilization of the network can be improved. In this work, we investigate dimensioning of STWN and propose a two-step method to jointly determine the number of wavelengths and Storage Size required to satisfy the demand, which is given as a load matrix with a deadline, blocking rate, and wavelength utilization. The method models the STWN as a TDM network and first obtains the required Storage Size by maximizing the number of fixed time slots in each period, then calculates the required number of wavelengths with the number of time slots. Numerical results show the following: high load between far apart source and destination nodes has significant impact on the required wavelengths and Storage. For instance, in a 24-node topology, 20% more wavelengths and Storage may be required to satisfy a biased load matrix than a randomly generated one. When calculating the required number of wavelengths, ourmethod outperforms traditional routing and wavelength assignment (RWA), because the TDM-based model supports fractional load. In the 24-node topology, 18% more wavelengths may be required byRWA than by our method. By installing amoderate amount of Storage,wavelength utilization can be effectively improved. With number of wavelengths and Storage Size equaling 29 and 274, in the 24-node topology, utilization can reach 0.9. We also find wavelength is equivalent with Storage regarding capacity of a STWN. For instance, with number of wavelengths and Storage Size equaling 114 and 131, in the 24-node topology, the blocking rate is the same as with number of wavelengths and Storage Size equaling 29 and 270.
-
dimensioning of store and transfer wdm network with limited Storage
Network Operations and Management Symposium, 2016Co-Authors: Da Feng, Xiaojian Zhang, Peng Wu, Junmin Wu, Weisheng HuAbstract:For large data transfers, the Store-and-Transfer WDM Network (STWN) provides Storage and lightpaths jointly. Constrained delays of the transfers restrict the number of waiting requests, thus the Storage Size can be limited. For dimensioning of STWN, we propose a method to determine the number of wavelengths and Storage Size required to satisfy the demand. The demand is given as a load matrix with a common deadline and blocking, then we express the load matrix as a base load multiplying an integer matrix consisting of mutually prime elements. The dimensioning process has two steps. 1) We model STWN as TDM network and analyse channels of the TDM system as M/G/1 subsystems. We calculate approximate performance of source-destination pairs and minimize the service rate provisioned for the demand with linear search to find the maximum allowed number of time-slots. 2) With the number of time-slots fixed, we optimize routing and wavelength and time-slot assignment to minimize the number of wavelengths required to accept the integer matrix. In step 1, the approximate performance calculated includes turnout, distribution of delay and Storage Size. Numerical results show how to drastically decrease the required resources.
Xiaojian Zhang - One of the best experts on this subject based on the ideXlab platform.
-
dimensioning of the store and transfer wdm network with limited node Storage under the sliding scheduled traffic model
IEEE\ OSA Journal of Optical Communications and Networking, 2017Co-Authors: Da Feng, Xiaojian Zhang, Weisheng HuAbstract:The so-called store-and-transfer WDM network (STWN) can store data in source Storage and provision lightpaths at an optimal time when wavelengths are clear of conflicts. Consequently, blocking of requests can be reduced and resource utilization of the network can be improved. In this work, we investigate dimensioning of STWN and propose a two-step method to jointly determine the number of wavelengths and Storage Size required to satisfy the demand, which is given as a load matrix with a deadline, blocking rate, and wavelength utilization. The method models the STWN as a TDM network and first obtains the required Storage Size by maximizing the number of fixed time slots in each period, then calculates the required number of wavelengths with the number of time slots. Numerical results show the following: high load between far apart source and destination nodes has significant impact on the required wavelengths and Storage. For instance, in a 24-node topology, 20% more wavelengths and Storage may be required to satisfy a biased load matrix than a randomly generated one. When calculating the required number of wavelengths, ourmethod outperforms traditional routing and wavelength assignment (RWA), because the TDM-based model supports fractional load. In the 24-node topology, 18% more wavelengths may be required byRWA than by our method. By installing amoderate amount of Storage,wavelength utilization can be effectively improved. With number of wavelengths and Storage Size equaling 29 and 274, in the 24-node topology, utilization can reach 0.9. We also find wavelength is equivalent with Storage regarding capacity of a STWN. For instance, with number of wavelengths and Storage Size equaling 114 and 131, in the 24-node topology, the blocking rate is the same as with number of wavelengths and Storage Size equaling 29 and 270.
-
dimensioning of store and transfer wdm network with limited Storage
Network Operations and Management Symposium, 2016Co-Authors: Da Feng, Xiaojian Zhang, Peng Wu, Junmin Wu, Weisheng HuAbstract:For large data transfers, the Store-and-Transfer WDM Network (STWN) provides Storage and lightpaths jointly. Constrained delays of the transfers restrict the number of waiting requests, thus the Storage Size can be limited. For dimensioning of STWN, we propose a method to determine the number of wavelengths and Storage Size required to satisfy the demand. The demand is given as a load matrix with a common deadline and blocking, then we express the load matrix as a base load multiplying an integer matrix consisting of mutually prime elements. The dimensioning process has two steps. 1) We model STWN as TDM network and analyse channels of the TDM system as M/G/1 subsystems. We calculate approximate performance of source-destination pairs and minimize the service rate provisioned for the demand with linear search to find the maximum allowed number of time-slots. 2) With the number of time-slots fixed, we optimize routing and wavelength and time-slot assignment to minimize the number of wavelengths required to accept the integer matrix. In step 1, the approximate performance calculated includes turnout, distribution of delay and Storage Size. Numerical results show how to drastically decrease the required resources.
Wan Choi - One of the best experts on this subject based on the ideXlab platform.
-
optimal Storage allocation for wireless cloud caching systems with a limited sum Storage capacity
IEEE Transactions on Wireless Communications, 2016Co-Authors: Bi Hong, Wan ChoiAbstract:In wireless cloud Storage systems, the recovery failure probability depends on not only wireless channel conditions but also Storage Size of each distributed Storage node. For an efficient utilization of limited Storage capacity and the performance characterization of allocation strategies, we asymptotically analyze the recovery failure probability of a wireless cloud Storage system with a sum Storage capacity constraint for both high signal-to-noise ratio (SNR) regime and low SNR regime. Then, we find the optimal Storage allocation strategy across distributed Storage nodes in terms of the asymptotic recovery failure probability. Our analysis reveals that the maximal symmetric allocation is optimal for high SNR regime and the minimal allocation (with $\lfloor T\rfloor $ complete Storage nodes and an incomplete Storage node) is optimal for low SNR regime, where $T$ is the sum Storage capacity. Based on the numerical investigation, we also show that in intermediate SNR regime, a balance allocation between the minimal allocation and the maximal symmetric allocation would not be required if we select one between them according to SNR.
-
optimal Storage allocation for wireless cloud caching systems with a limited sum Storage capacity
arXiv: Information Theory, 2016Co-Authors: Bi Hong, Wan ChoiAbstract:In wireless cloud Storage systems, the recovery failure probability depends on not only wireless channel conditions but also Storage Size of each distributed Storage node. For an efficient utilization of limited Storage capacity and the performance characterization of allocation strategies, we asymptotically analyze the recovery failure probability of a wireless cloud Storage system with a sum Storage capacity constraint for both high SNR regime and low SNR regime. Then, we find the optimal Storage allocation strategy across distributed Storage nodes in terms of the asymptotic recovery failure probability. Our analysis reveals that the maximal symmetric allocation is optimal for high SNR regime and the minimal allocation (with $\lfloor T\rfloor$ complete Storage nodes and an incomplete Storage node) is optimal for low SNR regime, where $T$ is the sum Storage capacity. Based on the numerical investigation, we also show that in intermediate SNR regime, a balance allocation between the minimal allocation and the maximal symmetric allocation would not be required if we select one between them according to SNR.
Matteo Chiesa - One of the best experts on this subject based on the ideXlab platform.
-
systematic comprehensive techno economic assessment of solar cooling technologies using location specific climate data
Applied Energy, 2010Co-Authors: Marwan Mokhtar, Simon Brauniger, Sgouris Sgouridis, Afshin Afshari, Peter R Armstrong, Matteo ChiesaAbstract:A methodology for assessing solar cooling technologies is proposed. The method takes into account location specific boundary conditions such as the cooling demand time series, solar resource availability, climatic conditions, component cost and component performance characteristics. This methodology evaluates the techno-economic performance of the solar collector/chiller system. We demonstrate the method by systematic evaluation of 25 feasible combinations of solar energy collection and cooling technologies. The comparison includes solar thermal and solar electric cooling options and is extended to solar cooling through concentrated solar power plants. Solar cooling technologies are compared on an economic and overall system efficiency perspective. This analysis has implication for the importance of solar load fraction and Storage Size in the design of solar cooling systems. We also stress the importance of studying the relation between cooling demand and solar resource availability, it was found that overlooking this relation might lead to overestimations of the potential of a solar cooling system in the range of 22% to over 100% of the actual potential.
