Consolidating applications of conflicting service level objectives (SLOs) to share virtualized resources in cloud datacenters requires efficient resource management to ensure overall high Quality-of-Service (QoS). Applications of different performance targets often exhibit different resource demands. Thus, it is not trivial to translate individual application SLOs to corresponding resource shares in a shared virtualized environment to meet performance targets. In this paper, we present CtrlCloud, a performance-aware resource controlling system, that adaptively allocates resources, with a resource-share controller and an allocation optimization model. The controller automatically adapts resource demands based on performance deviations, while the optimization model resolves conflicts in resource demands from multiple co-located applications based on their ongoing performance achieved. We implement a proof-of-concept prototype of CtrlCloud in Python on top of Xen hypervisor. Our experimental results indicate that CtrlCloud can optimize allocations of CPU resources across multiple applications to maintain the 95th percentile latency within predefined SLO targets. CtrlCloud also provides QoS differentiation and yet fulfilling of CPU share demands from applications is maximized given resource availability. We further compare CtrlCloud against two other resource allocation methods commonly used in current clouds. CtrlCloud improves resource utilization by allocating resource shares optimal to 'actual needs' as it employs share-performance online modeling.

Under today's bursty web traffic, the fine-grained per-container control promises more efficient resource provisioning for web services and better resource utilization in cloud datacenters. In this paper, we present Two-stage Stochastic Programming Resource Allocator (2SPRA). It optimizes resource provisioning for containerized n-tier web services in accordance with fluctuations of incoming workload to accommodate predefined SLOs on response latency. In particular, 2SPRA is capable of minimizing resource over-provisioning by addressing dynamics of web traffic as workload uncertainty in a native stochastic optimization model. Using special-purpose OpenOpt optimization framework, we fully implement 2SPRA in Python and evaluate it against three other existing allocation schemes, in a Docker-based CoreOS Linux VMs on Amazon EC2. We generate workloads based on four real-world web traces of various traffic variations: AOL, WorldCup98, ClarkNet, and NASA. Our experimental results demonstrate that 2SPRA achieves the minimum resource over-provisioning outperforming other schemes. In particular, 2SPRA allocates only 6.16 percent more than application's actual demand on average and at most 7.75 percent in the worst case. It achieves 3x further reduction in total resources provisioned compared to other schemes delivering overall cost-savings of 53.6 percent on average and up to 66.8 percent. Furthermore, 2SPRA demonstrates consistency in its provisioning decisions and robust responsiveness against workload fluctuations.

Although most current cloud providers, such as Amazon Web Services (AWS) and Microsoft Azure offer different types of computing instances with different capacities, cloud users tend to hire a cluster of instances of particular type to ensure performance predictability for their applications. Nowadays, many large-scale applications including big data analytics applications feature workload patterns that have heterogeneous resource demands, for which, accounting for heterogeneity of virtual cloud instances to allocate would be highly advantageous to the application performance. However, performance predictability has been always an issue in such clusters of heterogeneous resources. In particular, to precisely decide on what instances from which types to enclose in a cluster, such that the desired performance is attained, remains an open question. To this end, we devise a resource allocation mechanism by formulating it as a Mixed-Integer programming model representing an optimization problem. Our resource allocation mechanism incorporates predictable average performance as a unified performance metric, which concerns two key performance-related issues: (a) Performance variation within same-type instances, and (b) Correlations of performance variabilities across different types. Our experimental results demonstrate that target performance is predictable and attainable for clusters of heterogeneous resources. Our mechanism constructs clusters whose performance is within 95 percent of the performance of optimal ones, hence deadlines are always met. By reoptimisation, our mechanism can react to performance mispredictions and support autoscaling for varying workloads. We experimentally verify our findings using clusters on Amazon EC2 with MapReduce workloads, and on a private cloud as well. We conduct comparison experiments with an existing recent resource allocation approach in literature.