Centre for Information Science Research Annual Report, 1987-1991

Annual reports from various departments of the AN

Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

Not availabl

Routing and Wavelength Assignment for Multicast Communication in Optical Network-on-Chip

An Optical Network-on-Chip (ONoC) is an emerging chip-level optical interconnection technology to realise high-performance and power-efficient inter-core communication for many-core processors. Within the field, multicast communication is one of the most important inter-core communication forms. It is not only widely used in parallel computing applications in Chip Multi-Processors (CMPs), but also common in emerging areas such as neuromorphic computing. While many studies have been conducted on designing ONoC architectures and routing schemes to support multicast communication, most existing solutions adopt the methods that were initially proposed for electrical interconnects. These solutions can neither fully take advantage of optical communication nor address the special requirements of an ONoC. Moreover, most of them focus only on the optimisation of one multicast, which limits the practical applications because real systems often have to handle multiple multicasts requested from various applications. Hence, this thesis will address the design of a high-performance communication scheme for multiple multicasts by taking into account the unique characteristics and constraints of an ONoC. This thesis studies the problem from a network-level perspective. The design methodology is to optimally route all multicasts requested simultaneously from the applications in an ONoC, with the objective of efficiently utilising available wavelengths. The novelty is to adopt multicast-splitting strategies, where a multicast can be split into several sub-multicasts according to the distribution of multicast nodes, in order to reduce the conflicts of different multicasts. As routing and wavelength assignment problem is an NP-hard problem, heuristic approaches that use the multicast-splitting strategy are proposed in this thesis. Specifically, three routing and wavelength assignment schemes for multiple multicasts in an ONoC are proposed for different problem domains. Firstly, PRWAMM, a Path-based Routing and Wavelength Assignment for Multiple Multicasts in an ONoC, is proposed. Due to the low manufacture complexity requirement of an ONoC, e.g., no splitters, path-based routing is studied in PRWAMM. Two wavelength-assignment strategies for multiple multicasts under path-based routing are proposed. One is an intramulticast wavelength assignment, which assigns wavelength(s) for one multicast. The other is an inter-multicast wavelength assignment, which assigns wavelength(s) for different multicasts, according to the distributions of multicasts. Simulation results show that PRWAMM can reduce the average number of wavelengths by 15% compared to other path-based schemes. Secondly, RWADMM, a Routing and Wavelength Assignment scheme for Distribution-based Multiple Multicasts in a 2D ONoC, is proposed. Because path-based routing lacks flexibility, it cannot reduce the link conflicts effectively. Hence, RWADMM is designed, based on the distribution of different multicasts, which includes two algorithms. One is an optimal routing and wavelength assignment algorithm for special distributions of multicast nodes. The other is a heuristic routing and wavelength assignment algorithm for random distributions of multicast nodes. Simulation results show that RWADMM can reduce the number of wavelengths by 21.85% on average, compared to the state-of-the-art solutions in a 2D ONoC. Thirdly, CRRWAMM, a Cluster-based Routing and Reusable Wavelength Assignment scheme for Multiple Multicasts in a 3D ONoC, is proposed. Because of the different architectures with a 2D ONoC (e.g., the layout of nodes, optical routers), the methods designed for a 2D ONoC cannot be simply extended to a 3D ONoC. In CRRWAMM, the distribution of multicast nodes in a mesh-based 3D ONoC is analysed first. Then, routing theorems for special instances are derived. Based on the theorems, a general routing scheme, which includes a cluster-based routing method and a reusable wavelength assignment method, is proposed. Simulation results show that CRRWAMM can reduce the number of wavelengths by 33.2% on average, compared to other schemes in a 3D ONoC. Overall, the three routing and wavelength assignment schemes can achieve high-performance multicast communication for multiple multicasts of their problem domains in an ONoC. They all have the advantages of a low routing complexity, a low wavelength requirement, and good scalability, compared to their counterparts, respectively. These methods make an ONoC a flexible high-performance computing platform to execute various parallel applications with different multicast requirements. As future work, I will investigate the power consumption of various routing schemes for multicasts. Using a multicast-splitting strategy may increase power consumption since it needs different wavelengths to send packets to different destinations for one multicast, though the reduction of wavelengths used in the schemes can also potentially decrease overall power consumption. Therefore, how to achieve the best trade-off between the total number of wavelengths used and the number of sub-multicasts in order to reduce power consumption will be interesting future research

Distributed and Lightweight Meta-heuristic Optimization method for Complex Problems

The world is becoming more prominent and more complex every day. The resources are limited and efficiently use them is one of the most requirement. Finding an Efficient and optimal solution in complex problems needs to practical methods. During the last decades, several optimization approaches have been presented that they can apply to different optimization problems, and they can achieve different performance on various problems. Different parameters can have a significant effect on the results, such as the type of search spaces. Between the main categories of optimization methods (deterministic and stochastic methods), stochastic optimization methods work more efficient on big complex problems than deterministic methods. But in highly complex problems, stochastic optimization methods also have some issues, such as execution time, convergence to local optimum, incompatible with distributed systems, and dependence on the type of search spaces. Therefore this thesis presents a distributed and lightweight metaheuristic optimization method (MICGA) for complex problems focusing on four main tracks. 1) The primary goal is to improve the execution time by MICGA. 2) The proposed method increases the stability and reliability of the results by using the multi-population strategy in the second track. 3) MICGA is compatible with distributed systems. 4) Finally, MICGA is applied to the different type of optimization problems with other kinds of search spaces (continuous, discrete and order based optimization problems). MICGA has been compared with other efficient optimization approaches. The results show the proposed work has been achieved enough improvement on the main issues of the stochastic methods that are mentioned before.Maailmasta on päivä päivältä tulossa yhä monimutkaisempi. Resurssit ovat rajalliset, ja siksi niiden tehokas käyttö on erittäin tärkeää. Tehokkaan ja optimaalisen ratkaisun löytäminen monimutkaisiin ongelmiin vaatii tehokkaita käytännön menetelmiä. Viime vuosikymmenien aikana on ehdotettu useita optimointimenetelmiä, joilla jokaisella on vahvuutensa ja heikkoutensa suorituskyvyn ja tarkkuuden suhteen erityyppisten ongelmien ratkaisemisessa. Parametreilla, kuten hakuavaruuden tyypillä, voi olla merkittävä vaikutus tuloksiin. Optimointimenetelmien pääryhmistä (deterministiset ja stokastiset menetelmät) stokastinen optimointi toimii suurissa monimutkaisissa ongelmissa tehokkaammin kuin deterministinen optimointi. Erittäin monimutkaisissa ongelmissa stokastisilla optimointimenetelmillä on kuitenkin myös joitain ongelmia, kuten korkeat suoritusajat, päätyminen paikallisiin optimipisteisiin, yhteensopimattomuus hajautetun toteutuksen kanssa ja riippuvuus hakuavaruuden tyypistä. Tämä opinnäytetyö esittelee hajautetun ja kevyen metaheuristisen optimointimenetelmän (MICGA) monimutkaisille ongelmille keskittyen neljään päätavoitteeseen: 1) Ensisijaisena tavoitteena on pienentää suoritusaikaa MICGA:n avulla. 2) Lisäksi ehdotettu menetelmä lisää tulosten vakautta ja luotettavuutta käyttämällä monipopulaatiostrategiaa. 3) MICGA tukee hajautettua toteutusta. 4) Lopuksi MICGA-menetelmää sovelletaan erilaisiin optimointiongelmiin, jotka edustavat erityyppisiä hakuavaruuksia (jatkuvat, diskreetit ja järjestykseen perustuvat optimointiongelmat). Työssä MICGA-menetelmää verrataan muihin tehokkaisiin optimointimenetelmiin. Tulokset osoittavat, että ehdotetulla menetelmällä saavutetaan selkeitä parannuksia yllä mainittuihin stokastisten menetelmien pääongelmiin liittyen

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems