云计算已成为各行业中十分重要的计算服务方式。传统的云计算研究主要侧重于云服务的定价方式、利润最大化、执行效率等服务质量,而绿色计算成为了近年来分布式计算的发展趋势。针对异构云环境中满足云用户计算成本约束的工作流任务集...云计算已成为各行业中十分重要的计算服务方式。传统的云计算研究主要侧重于云服务的定价方式、利润最大化、执行效率等服务质量,而绿色计算成为了近年来分布式计算的发展趋势。针对异构云环境中满足云用户计算成本约束的工作流任务集调度问题,提出了一种低时间复杂度、能量感知的预算等级调度(Energy-Aware Based on Budget Level Scheduling,EABL)算法。EABL算法包含并行任务集任务优先级的建立、任务预算成本的分配及最优执行虚拟机和能量高效频率的确定3个主要阶段,能在满足预算成本约束的同时最大限度地降低任务集执行过程中的能量消耗。采用真实世界的大规模工作流任务集对算法进行测试,结果表明,与著名的EA_HBCS和MECABP算法相比,EABL算法在充分利用预算成本的同时,有效地降低了工作流任务集在云数据中心计算过程中的能量消耗。展开更多
With computing systems undergone a fundamen- tal transformation from single-processor devices at the turn of the century to the ubiquitous and networked devices and the warehouse-scale computing via the cloud, the par...With computing systems undergone a fundamen- tal transformation from single-processor devices at the turn of the century to the ubiquitous and networked devices and the warehouse-scale computing via the cloud, the parallelism has become ubiquitous at many levels. At micro level, par- allelisms are being explored from the underlying circuits, to pipelining and instruction level parallelism on multi-cores or many cores on a chip as well as in a machine. From macro level, parallelisms are being promoted from multiple ma- chines on a rack, many racks in a data center, to the glob- ally shared infrastructure of the Internet. With the push of big data, we are entering a new era of parallel computing driven by novel and ground breaking research innovation on elas- tic parallelism and scalability. In this paper, we will give an overview of computing infrastructure for big data processing, focusing on architectural, storage and networking challenges of supporting big data paper. We will briefly discuss emerging computing infrastructure and technologies that are promising for improving data parallelism, task parallelism and encour- aging vertical and horizontal computation parallelism.展开更多
文摘云计算已成为各行业中十分重要的计算服务方式。传统的云计算研究主要侧重于云服务的定价方式、利润最大化、执行效率等服务质量,而绿色计算成为了近年来分布式计算的发展趋势。针对异构云环境中满足云用户计算成本约束的工作流任务集调度问题,提出了一种低时间复杂度、能量感知的预算等级调度(Energy-Aware Based on Budget Level Scheduling,EABL)算法。EABL算法包含并行任务集任务优先级的建立、任务预算成本的分配及最优执行虚拟机和能量高效频率的确定3个主要阶段,能在满足预算成本约束的同时最大限度地降低任务集执行过程中的能量消耗。采用真实世界的大规模工作流任务集对算法进行测试,结果表明,与著名的EA_HBCS和MECABP算法相比,EABL算法在充分利用预算成本的同时,有效地降低了工作流任务集在云数据中心计算过程中的能量消耗。
文摘With computing systems undergone a fundamen- tal transformation from single-processor devices at the turn of the century to the ubiquitous and networked devices and the warehouse-scale computing via the cloud, the parallelism has become ubiquitous at many levels. At micro level, par- allelisms are being explored from the underlying circuits, to pipelining and instruction level parallelism on multi-cores or many cores on a chip as well as in a machine. From macro level, parallelisms are being promoted from multiple ma- chines on a rack, many racks in a data center, to the glob- ally shared infrastructure of the Internet. With the push of big data, we are entering a new era of parallel computing driven by novel and ground breaking research innovation on elas- tic parallelism and scalability. In this paper, we will give an overview of computing infrastructure for big data processing, focusing on architectural, storage and networking challenges of supporting big data paper. We will briefly discuss emerging computing infrastructure and technologies that are promising for improving data parallelism, task parallelism and encour- aging vertical and horizontal computation parallelism.
