Cloud-based serverless computing systems, either public or privately provisioned, aim to provide the illusion of infinite resources and abstract users from details of the allocation decisions. With the goal of providing a low cost and a high QoS, the serverless computing paradigm offers opportunities that can be harnessed to attain the goals. Specifically, our strategy in this dissertation is to avoid redundant computing, in cases where independent task requests are similar to each other and for tasks that are pointless to process. We explore two main approaches to (A) reuse part of computation needed to process the services and (B) proactively pruning tasks with a low chance of success to improve the overall QoS of the system. For the first approach, we propose a mechanism to identify various types of "mergeable" tasks, which can benefit from computational reuse if they are executed together as a group. To evaluate the task merging configurations extensively, we quantify the resource-saving magnitude and then leveraging the experimental data to create a resource-saving predictor. We investigate multiple tasks merging approaches that suit different workload scenarios to determine when it is appropriate to aggregate tasks and how to allocate them so that the QoS of other tasks is minimally affected. For the second approach, we developed the mechanisms to skip tasks whose chance of completing on time is not worth pursuing by drop or defer them. We determined the minimum chance of success thresholds for tasks to pass to get scheduled and executed. We dynamically adjust such thresholds based on multiple characteristics of the arriving workload and the system's conditions. We employed approximate computing to reduce the pruning mechanism's computational overheads and ensure that the mechanism can be used practically.

中文翻译：

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具有成本效益和QoS效率的无服务器云计算

公共或私有配置的基于云的无服务器计算系统旨在提供无限资源的幻觉，并从分配决策的细节中抽象出用户。以提供低成本和高QoS为目标，无服务器计算范例提供了可以利用以实现目标的机会。具体而言，本文的策略是在独立任务请求彼此相似且处理无意义的任务时避免冗余计算。我们探索两种主要方法，以（A）重用处理服务所需的部分计算，以及（B）主动修剪任务的可能性很小，以成功改善系统的整体QoS。对于第一种方法，我们提出了一种机制来识别各种类型的“可合并”任务，如果将它们作为一组一起执行，则可以从计算重用中受益。为了广泛评估任务合并配置，我们量化了资源节省量，然后利用实验数据创建了资源节省预测器。我们研究了适用于不同工作负载场景的多种任务合并方法，以确定何时适合聚合任务以及如何分配任务，以使对其他任务的QoS影响最小。对于第二种方法，我们开发了一种机制来跳过任务，这些任务如果不能按时完成或推迟就不应该按时完成任务。我们确定了通过任务以计划和执行的成功机会阈值的最小机会。我们会根据到达的工作负载和系统的多个特征动态调整此类阈值 的条件。我们采用近似计算来减少修剪机制的计算开销，并确保该机制可以实际使用。