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Multi-objective Swarm Intelligence schedulers for online scientific Clouds

机译：在线科学云的多目标群智能调度程序

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摘要

Cloud Computing is a promising paradigm for parallel computing. However, as Cloud-based services become more dynamic, resource provisioning in Clouds becomes more challenging. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. In a Cloud, an appropriate number of Virtual Machines (VM) is created and allocated in physical resources for executing jobs. This work focuses on the Infrastructure as a Service (IaaS) model where custom VMs are launched in appropriate hosts available in a Cloud to execute scientific experiments coming from multiple users. Finding optimal solutions to allocate VMs to physical resources is an NP-complete problem, and therefore many heuristics have been developed. In this work, we describe and evaluate two Cloud schedulers based on Swarm Intelligence (SI) techniques, particularly Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. We also perform a sensitivity analysis by varying the specific-parameter values of each algorithm to evaluate the impact on the performance of the two objective metrics. The intra-Cloud network traffic is also measured. Simulated experiments performed using CloudSim and job data from real scientific problems show that the use of SI-based techniques succeeds in balancing the studied metrics compared to Genetic Algorithms.
机译：云计算是并行计算的一个有希望的范例。但是，随着基于云的服务变得更加动态，云中的资源配置变得越来越具有挑战性。该范式具有几乎无限的资源前景，似乎非常适合解决资源贪婪的科学计算问题。在云中，将创建适当数量的虚拟机（VM），并将其分配在物理资源中以执行作业。这项工作着重于基础架构即服务（IaaS）模型，其中在云中可用的适当主机中启动自定义VM，以执行来自多个用户的科学实验。寻找最佳解决方案以将VM分配给物理资源是一个NP完全问题，因此已经开发了许多启发式方法。在这项工作中，我们描述和评估了基于群智能（SI）技术的两个Cloud Scheduler，特别是蚁群优化（ACO）和粒子群优化（PSO）。要研究的主要性能指标是按云计算的服务用户数以及在线（非批处理）调度方案中已创建的VM总数。我们还通过更改每种算法的比参数值来执行敏感性分析，以评估对两个客观指标性能的影响。还测量了云内网络流量。使用CloudSim和来自实际科学问题的工作数据进行的模拟实验表明，与遗传算法相比，基于SI的技术的使用成功地平衡了所研究的指标。

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