A General Distributed Scalable Peer to Peer Scheduler for Mixed Tasks in Grids

Abstract. We consider non-preemptively scheduling a bag of independent mixed tasks in computational grids. We construct a novel Generalized Distributed Scheduler (GDS) for tasks with different priorities and deadlines. Tasks are ranked based upon priority and deadline and scheduled. Tasks are shuffled to earlier points to pack the schedule and create fault tolerance. Dispatching is based upon task-resource matching and accounts for computation as well as communication capacities. Simulation results demonstrate that with respect to the number of high-priority tasks meeting deadlines, GDS outperforms prior approaches by over 40% without degrading schedulability of other tasks. Indeed, with respect to the total number of schedulable tasks meeting deadlines, GDS outperforms them by 4%. The complexity of GDS is O(n 2 m) where n is the number of tasks and m the number of machines. GDS successfully schedules tasks with hard deadlines in a mix of soft and firm tasks, without a knowledge of a complete state of the grid. This way it helps open the grid and makes it amenable for commercialization

Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid

Metaheuristic algorithms have been used to solve scheduling problems in grid computing.However, stand-alone metaheuristic algorithms do not always show good performance in every problem instance. This study proposes a high level hybrid approach between ant colony system and genetic algorithm for job scheduling in grid computing.The proposed approach is based on a high level hybridization.The proposed hybrid approach is evaluated using the static benchmark problems known as ETC matrix.Experimental results show that the proposed hybridization between the two algorithms outperforms the stand-alone algorithms in terms of best and average makespan values

Scheduling jobs in computational grid using hybrid ACS and GA approach

Metaheuristics algorithms show very good performance in solving various job scheduling problems in computational grid systems.However, due to the complexity and heterogeneous nature of resources in grid computing, stand-alone algorithm is not capable to find a good quality solution in reasonable time.This study proposes a hybrid algorithm, specifically ant colony system and genetic algorithm to solve the job scheduling problem.The high level hybridization algorithm will keep the identity of each algorithm in performing the scheduling task.The study focuses on static grid computing environment and the metrics for optimization are the makespan and flowtime.Experiment results show that the proposed algorithm outperforms other stand-alone algorithms such as ant system, genetic algorithms, and ant colony system for makespan.However, for flowtime, ant system and genetic algorithm perform better

Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms will enhance ACS in terms of exploration mechanism and solution refinement by implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment, performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan

Uma abordagem de seleção de recursos consciente de consumo de energia baseada em topologia de rede, tamanho de arquivos e potência de equipamentos

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2015.Recentes avanços na área da Computação de Alto Desempenho (HPC) tem gerado uma grande variedade de possibilidades para pesquisas na área. Arquiteturas paralelas e distribuídas modernas apresentam um aumento considerável em sua capacidade de processamento. Entretanto, esse crescimento de desempenho é acompanhado por um aumento de consumo de energia. Neste cenário, a comunidade científica tem estudado técnicas voltadas à redução de consumo de energia em tais plataformas. Arquiteturas de alto desempenho são amplamente utilizadas em ambientes empresarial e acadêmico quando há a necessidade de grande poder computacional. Recentemente, infraestruturas legadas têm sido adaptadas ao modelo de nuvem computacional, o qual fornece recursos sob demanda e permite a usuários contratar serviços de infraestrutura, plataforma e software. Neste trabalho propomos uma abordagem genérica de alocação de recursos energeticamente eficiente que melhora a eficiência energética de ambientes de alto desempenho heterogêneos selecionando recursos menos custosos. A abordagem proposta considera o custo para transferência de dados, assim como o estado e eficiência energética dos nodos computacionais. Após realizados diversos experimentos em um ambiente simulado de nuvem, concluiu-se que, em alguns casos, a abordagem proposta reduz consideravelmente o consumo de energia em comparação com abordagens existentes na literatura.Abstract : Recent advances in High Performance Computing (HPC) have led to a wide range of new possibilities for research. In this context, modern parallel and distributed architectures have presented a steady increase in their processing capabilities. However, such growth is usually followed by an increase in energy consumption. Because of that, the research community has been focusing on techniques to reduce energy consumption on such platforms. HPC architectures are now widely used in business and academic environments when high computing power is crucial. Recently, legacy structures have been adapted to the cloud computing model, which provides resources on demand such as infrastructure, software or platform. In this work we propose a generic energy-efficient scheduling approach that improves the energy efficiency of high performance heterogeneous environments by selecting the least costly resources. The proposed approach takes into consideration the cost of data transfers as well as the state and energy efficiency of computing nodes. After carrying out several experiments in a cloud simulated environment we concluded that, in some cases, the proposed approach achieves considerably better energy efficiency than other existing approaches in the literature

Um mecanismo para distribuição de carga em ambientes virtuais de computação maciçamente paralela

Orientador: Marco Aurelio Amaral HenriquesDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoMestrad

Resource-Efficient Wireless Systems for Emerging Wireless Networks

As the wireless medium has become the primary source of communication and Internet connectivity, and as devices and wireless technologies become more sophisticated and capable, there has been a surge in the capacity demands and complexity of applications that run over these wireless devices. To sustain the volume and QoE guarantees of the data generated, the opportunity and need to rethink wireless network design across all the layers of the protocol stack has firmly emerged as a solution to enable the timely and reliable delivery of data, while handling the inherent challenges of a crowded wireless medium, such as congestion, interference, and hidden terminals. The research work presented in this dissertation builds efficient solutions and protocols with a theoretical foundation to address the challenges that arise in rethinking wireless network design. Example challenges include managing the overhead associated with complex systems. My work particularly focuses on the opportunities and challenges of sophisticated technology and systems in emerging wireless networks. I target the main thrusts in the evolution of wireless networks that create significant opportunity to achieve higher theoretical capacity, and have direct implications on our day-to-day wireless interactions: from enabling multifold increase in capacity in wireless physical links, to developing medium access techniques to exploit the high speed links, and making the applications more bandwidth efficient. I build deployable, and resource-aware wireless systems that exploit higher bandwidths by leveraging and advancing diverse research areas such as theory, analysis, protocol design, and wireless networking. Specifically, I identify the erroneous assumptions and fundamental limitations of existing solutions in capturing the true and complex interactions between wireless devices and protocols. I use these insights to guide practical and efficient protocol design, followed by thorough analysis and evaluation in testbed implementations via prototypes and measurements. I show that my proposed solutions achieve significant performance gains, at minimum cost to overhead