As more and more service providers choose Cloud platforms, a resource provider needs to provision resources and supporting runtime environments (REs) for heterogeneous workloads in different scenarios. Previous work fails to resolve this issue in several ways: (1) it fails to pay attention to diverse RE requirements, and does not enable creating coordinated REs on demand; (2) few work investigates coordinated resource provisioning for heterogeneous workloads. In this paper, our contributions are three-fold: (1) we present an RE agreement that expresses diverse RE requirements, and build an innovative system PhoenixCloud that enables a resource provider to create REs on demand according to RE agreements; (2) we propose two coordinated resource provisioning solutions for heterogeneous workloads in two typical Cloud scenarios: first, a large organization operates a private Cloud for two heterogeneous workloads; second, a large organization or two service providers running heterogeneous workloads revert to a public Cloud; and (3) A comprehensive evaluation has been performed in experiments. For typical workload traces of parallel batch jobs and Web services, our experiments show that: a) In the first Cloud scenario, when the throughput is almost same like that of a dedicated cluster system, our solution decreases the configuration size of cluster by about 40%; b) in the second scenario, our solution decreases not only the total resource consumption, but also the peak resource consumption maximally to 31% with respect to that of EC2 + RightScale solution.
翻译:由于越来越多的服务提供者选择云平台,资源提供者需要在不同情况下为不同工作量提供资源和支持运行时间环境(RES),从而在不同情况下提供不同工作量。以前的工作未能以几种方式解决这一问题:(1) 未能关注不同的可再生能源要求,无法根据需求创建协调的可再生能源;(2) 很少有工作调查为不同工作量协调提供资源的情况。在本文件中,我们的贡献有三重:(1) 提出一项表明不同可再生能源要求的可再生能源协议,并建立一个创新系统FenixCloud,使资源提供者能够根据可再生能源协议的需求创建可再生能源;(2) 我们提出两种典型的云情景下不同工作量的两种协调资源提供解决方案:(1) 大型组织没有关注不同的可再生能源要求,无法根据需求创建协调一致的可再生能源;(2) 很少有工作调查调查为不同工作量的协调资源提供情况;(2) 很少调查为不同工作量的协调提供协调一致的资源;(2) 在试验中,我们的贡献有三重:(1) 对平行批量工作和网络服务的典型工作量痕迹,我们的实验表明:a) 在第一个云层假设中,资源通过量几乎与专门集群系统一样,我们的解决办法只是将组合的总配置规模降低到大约40 %的资源比例;b) 在方案中,我们的资源比例上,我们的资源比例也只有大约31个方案。