Resource Allocation using Adaptive Characterization of Online, Data-Intensive Workloads
Resource Allocation using Adaptive Characterization of Online, Data-Intensive Workloads
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发表时间：
2017
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通讯作者：
Jaimie Kelley
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作者：
Jaimie Kelley
云资源提供商在功率上限下最大化利用率与满足工作负载服务水平协议（SLA）之间进行平衡。随着工作负载使用的数据量增加，云中计算能力的压力也在增加。即使分配的资源满足交互式工作负载对低延迟的需求，交互式工作负载利用分配的资源处理的数据也可能不足以实现应答质量的标准。增加分配给特定工作负载的资源以满足其回答质量标准会降低云提供商在交互式工作负载上的整体利润。但是，如果工作负载的回答质量标准没有得到满足，交互式工作负载可能会寻求另一个位置。云实例可以按分钟购买，并且存在多个放置机会。正因为如此，云计算提供商需要将客户的利益放在首位，否则就会失去收入。为了最好地服务于自己和客户的利益，云提供商需要反映资源使用情况、回答质量和服务水平的数据。如果云提供商知道每个调度的工作负载所使用的功率量，它可以更好地满足其功率上限要求而不会受到惩罚。如果云提供商知道当前延迟和计划工作负载的响应质量，它可以决定何时重新分配资源。然而，这是困难的，因为任何在线数据收集都会带来管理费用。虽然云提供商通常
Cloud resource providers balance maximizing utilization under a power cap with meeting workload Service Level Agreements (SLA). As the amount of data used by workloads increases, so do the pressures on compute capacity in the cloud. Even if the resources assigned meet an interactive workload’s need for low latency, the data that interactive workload processes with allocated resources may not be sufficient to achieve a standard of answer quality. Increasing the resources allocated to a specific workload to meet its answer quality standard reduces the overall profit a cloud provider can make on interactive workloads. However, if a workload’s answer quality standard is not met, the interactive workload may seek another placement. Cloud instances can be purchased by the minute, and multiple opportunities for placement exist. Because of this, cloud providers need to put their clients’ interests first or lose revenue. To best serve their own and their clients interest, cloud providers need data which reflects resource usage, answer quality, and service level. If a cloud provider knows the amount of power used by each workload scheduled, it can better fulfill its power cap requirements without penalty. If a cloud provider knows the current latency and answer quality of scheduled workloads, it can decide when to reallocate resources. However, this is difficult because any collection of data online imposes overheads. While cloud providers generally