Node Availability for Distributed Systems considering processor and RAM utilization for Load Balancing

Node-Availability is a new metric that based on processor utilization, free RAM and number of processes queued at a node, compares different workload levels of the nodes participating in a distributed system. Dynamic scheduling and Load-Balancing in distributed systems can be achieved through the Node-Availability metric as decision criterion, even without previously knowing the execution time of the processes, nor other information about them such as process communication requirements. This paper also presents a case study which shows that the metric is feasible to implement in conjunction with a dynamic Load-Balancing algorithm, obtaining an acceptable performance

Node Availability for Distributed Systems considering processor and RAM utilization

Abstract Node-Availability is a new metric based on processor utilization and free RAM at a node. It compares different workload levels at two or more nodes participating in a distributed system, providing a decision criterion to be implemented in conjunction with a common load-balancing algorithm. Dynamic scheduling and Load-Balancing in distributed systems can achieved through the Node-Availability metric, even without previously knowing the execution time of the processes, nor other information about them such as process communication requirements. This paper also presents a case study which shows that the metric is feasible to implement in conjunction with a dynamic Load-Balancing algorithm, obtaining an acceptable performance

Estudo exploratório do uso de algoritmos genéticos no gerenciamento de tarefas e recursos em cloud computing

Monografia (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, Bacharelado em Engenharia de Computação, 2015.Com a notável expansão dos serviços em nuvem, a introdução de módulos de alocação mais eficientes se faz cada vez mais necessária. O gasto energético é a principal fonte de custo para as empresas provedoras, diretamente causado pela má utilização de recursos de hardware. A fim de prover maior flexibilidade na alocação de tarefas e um gerenciamento de recursos mais efetivo, analisaremos a viabilidade de se utilizar uma abordagem evolutiva para obter soluções com maior qualidade, assim como garantir maior lucro para a companhia provedora, satisfazendo ambos os lados da negociação. O uso de heurísticas evolutivas como otimizadores vem ganhando notoriedade no meio acadêmico, da mesma forma, deixaremos a nossa contribuição com a implementação e análise de um algoritmo genético básico funcionando como alocador, explicitando também as vantagens e desvantagens encontradas com o uso dessa abordagem. Veremos que os resultados encontrados são bastante animadores, entretanto, muito deve ser feito para que o módulo proposto possa ser aplicado em um sistema real. Uma ideia de trabalho futuro é combinar abordagens evolutivas com métodos de busca convencionais, assim como otimizar alguns parâmetros da própria implementação do algoritmo genético em busca de melhores resultados de otimização. _____________________________________________________________________________ ABSTRACTWith the undeniable ascension of cloud services, the introduction of efficient scheduling modules in this kind of system is more necessary than ever. Energy consumption is the main source of cost to the provider companies, partially caused by the poor administration of hardware resources. In order to offer greater flexibility to the task scheduling process and more effectiveness in resource management, we will analyze the feasibility of using an evolutionary approach to obtain solutions with greater quality, increasing the provider company’s profit, satisfying the interests of both sides. Evolutionary heuristics are gaining notoriety in the academic field as alternative solutions to optimization problems. Therefore, this work presents our contribution, which consists of the implementation and analysis of a basic genetic algorithm that works as a scheduler, also explaining the advantages and disadvantages encountered with the use of this approach. Later on, we show that the results of this research are quite encouraging, however, there is still much to be done as the main objective of almost every research is to apply the proposed method in a real system. A possible subject for a future work could be the hybridization of the genetic approach with a conventional search algorithm. Optimizing the genetic operators and some other implementation parameters are also in the plans. These actions shall improve the overall performance of the algorithm as well, consequently, returning better solutions and making the method more dependable

The Inter-cloud meta-scheduling

Inter-cloud is a recently emerging approach that expands cloud elasticity. By facilitating an adaptable setting, it purposes at the realization of a scalable resource provisioning that enables a diversity of cloud user requirements to be handled efficiently. This study’s contribution is in the inter-cloud performance optimization of job executions using metascheduling concepts. This includes the development of the inter-cloud meta-scheduling (ICMS) framework, the ICMS optimal schemes and the SimIC toolkit. The ICMS model is an architectural strategy for managing and scheduling user services in virtualized dynamically inter-linked clouds. This is achieved by the development of a model that includes a set of algorithms, namely the Service-Request, Service-Distribution, Service-Availability and Service-Allocation algorithms. These along with resource management optimal schemes offer the novel functionalities of the ICMS where the message exchanging implements the job distributions method, the VM deployment offers the VM management features and the local resource management system details the management of the local cloud schedulers. The generated system offers great flexibility by facilitating a lightweight resource management methodology while at the same time handling the heterogeneity of different clouds through advanced service level agreement coordination. Experimental results are productive as the proposed ICMS model achieves enhancement of the performance of service distribution for a variety of criteria such as service execution times, makespan, turnaround times, utilization levels and energy consumption rates for various inter-cloud entities, e.g. users, hosts and VMs. For example, ICMS optimizes the performance of a non-meta-brokering inter-cloud by 3%, while ICMS with full optimal schemes achieves 9% optimization for the same configurations. The whole experimental platform is implemented into the inter-cloud Simulation toolkit (SimIC) developed by the author, which is a discrete event simulation framework

DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS

Grid, an infrastructure for resource sharing, currently has shown its importance in many scientific applications requiring tremendously high computational power. Grid computing enables sharing, selection and aggregation of resources for solving complex and large-scale scientific problems. Grids computing, whose resources are distributed, heterogeneous and dynamic in nature, introduces a number of fascinating issues in resource management. Grid scheduling is the key issue in grid environment in which its system must meet the functional requirements of heterogeneous domains, which are sometimes conflicting in nature also, like user, application, and network. Moreover, the system must satisfy non-functional requirements like reliability, efficiency, performance, effective resource utilization, and scalability. Thus, overall aim of this research is to introduce new grid scheduling algorithms for resource allocation as well as for job scheduling for enabling a highly efficient and effective utilization of the resources in executing various applications. The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. The issue is not only to develop new algorithms, but also to evaluate them on an experimental computational grid, using synthetic and real workload traces, along with the other existing job scheduling algorithms. Experimental evaluation confirmed that the proposed grid scheduling algorithms possess a high degree of optimality in performance, efficiency and scalability

DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS

Grid, an infrastructure for resource sharing, currently has shown its importance in many scientific applications requiring tremendously high computational power. Grid computing enables sharing, selection and aggregation of resources for solving complex and large-scale scientific problems. Grids computing, whose resources are distributed, heterogeneous and dynamic in nature, introduces a number of fascinating issues in resource management. Grid scheduling is the key issue in grid environment in which its system must meet the functional requirements of heterogeneous domains, which are sometimes conflicting in nature also, like user, application, and network. Moreover, the system must satisfy non-functional requirements like reliability, efficiency, performance, effective resource utilization, and scalability. Thus, overall aim of this research is to introduce new grid scheduling algorithms for resource allocation as well as for job scheduling for enabling a highly efficient and effective utilization of the resources in executing various applications. The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical\ud analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. The issue is not only to develop new algorithms, but also to evaluate them on an experimental computational grid, using synthetic and real workload traces, along with the other existing job scheduling algorithms. Experimental evaluation confirmed that the proposed grid scheduling algorithms possess a high degree of optimality in performance, efficiency and scalability