Cloud Instance Management and Resource Prediction for Computation-as-a-Service Platforms
Cloud Instance Management and Resource Prediction for Computation-as-a-Service Platforms
复制标题
计算即服务平台的云实例管理和资源预测
DOI：
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发表时间：
2016
期刊：
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通讯作者：
Y. Andreopoulos
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作者：
Joseph Doyle;Vasileios Giotsas;M. A. Anam;Y. Andreopoulos
计算即服务（CaaS）产品在过去几年中获得了牵引力，因为它们有效地平衡了软件即服务的可伸缩性和基础设施即服务平台的定制可能性。为了有效运行，CaaS平台必须具有三个关键属性：(i)根据可用性和预定的完成时间（TTC）限制，将单个处理任务被动地分配给可用的云实例（计算单元），（ii）准确的资源预测，（iii）有效地控制服务工作负载的云实例数量，以便在及时完成工作负载和降低资源利用成本之间进行优化。在本文中，我们提出了三种分别满足这些属性的方法：(i)基于比例公平和TTC约束的服务速率分配机制，（ii）用于资源预测的卡尔曼滤波估计，以及（iii）用于控制服务工作负载的计算单元数量的加性增加乘减（AIMD）算法（以传输控制协议中的资源管理而闻名）。与基于响应性资源预测的方法相比，将我们的三个建议集成到单个CaaS平台中可以将Amazon EC2现货实例成本降低27%以上，并且与当前CaaS平台（Amazon Lambda和Autoscale）中的最新技术相比，可以将计费成本降低38%至60%。
Computation-as-a-Service (CaaS) offerings have gained traction in the last few years due to their effectiveness in balancing between the scalability of Software-as-a-Service and the customisation possibilities of Infrastructure-as-a-Service platforms. To function effectively, a CaaS platform must have three key properties: (i) reactive assignment of individual processing tasks to available cloud instances (compute units) according to availability and predetermined time-to-completion (TTC) constraints, (ii) accurate resource prediction, (iii) efficient control of the number of cloud instances servicing workloads, in order to optimize between completing workloads in a timely fashion and reducing resource utilization costs. In this paper, we propose three approaches that satisfy these properties (respectively): (i) a service rate allocation mechanism based on proportional fairness and TTC constraints, (ii) Kalman-filter estimates for resource prediction, and (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of compute units servicing workloads. The integration of our three proposals into a single CaaS platform is shown to provide for more than 27% reduction in Amazon EC2 spot instance cost against methods based on reactive resource prediction and 38% to 60% reduction of the billing cost against the current state-of-the-art in CaaS platforms (Amazon Lambda and Autoscale).