Despite the widespread use of server virtualization technologies in cloud data centers, system administrators experience multiple challenges in configuring the hypervisor's scheduler parameters to optimize its performance. Manually tuning the scheduler's parameters is a common practice, however, this approach is not effective particularly when dealing with dynamically changing workload and resource utilizations on the host machines. This problem becomes even harder if cloud resources are overbooked while hosting both latency-sensitive and batched applications. To address these issues, this paper presents iTune, which is a framework for engineering the performance of a hypervisor intelligently via autonomous scheduler configurations. Concretely, iTune optimizes the Xen hypervisor's scheduler configuration parameters autonomously through a three phase process comprising: (1) Discoverer, which monitors and saves the resource usage history of the host machines and groups set of related host machine workloads, (2) Optimizer, where optimum Xen scheduler configuration parameters for each workload cluster are explored by employing a simulated annealing machine learning algorithm, and (3) Observer, where iTune monitors the resource usage of host machines online, classifies them into one of the categories found in the Discoverer phase, and loads the optimum scheduler parameters determined in the Optimizer phase. Experimental results validate our claims.

Cloud service providers (CSPs) often overbook their resources with user applications despite having to maintain service-level agreements with their customers. Overbooking is attractive to CSPs because it helps to reduce power consumption in the data center by packing more user jobs in less number of resources while improving their profits. Overbooking becomes feasible because user applications tend to overestimate their resource requirements utilizing only a fraction of the allocated resources. Arbitrary resource overbooking ratios, however, may be detrimental to soft real-time applications, such as airline reservations or Netflix video streaming, which are increasingly hosted in the cloud. The changing dynamics of the cloud preclude an offline determination of overbooking ratios. To address these concerns, this paper presents iOverbook, which uses a machine learning approach to make systematic and online determination of overbooking ratios such that the quality of service needs of soft real-time systems can be met while still benefiting from overbooking. Specifically, iOverbook utilizes historic data of tasks and host machines in the cloud to extract their resource usage patterns and predict future resource usage along with the expected mean performance of host machines. To evaluate our approach, we have used a large usage trace made available by Google of one of its production data centers. In the context of the traces, our experiments show that iOverbook can help CSPs improve their resource utilization by an average of 12.5% and save 32% power in the data center.

Power and performance tradeoffs are critical and challenging issues faced by cloud service providers (CSPs) while managing their data centers. On the one hand, CSPs strive to reduce power consumption of their data centers to not only decrease their energy costs but to also reduce adverse impact on the environment. On the other hand, CSPs must deliver performance expected by the applications hosted in their cloud in accordance with predefined Service Level Agreements (SLAs). Not doing so will lead to loss of customers and thereby major revenue losses for the CSPs. Addressing these dual set of challenges is hard for the CSPs because power management and performance assurance are conflicting objectives, particularly in the context of multi-tenant cloud systems where multiple virtual machines (VMs) may be hosted on a single physical server. The problem becomes even harder when real-time applications are hosted in these VMs. To address these challenges and make appropriate tradeoffs, we present iPlace, which is an intelligent and tunable power- and performance-aware VM placement middleware. The placement strategy is based on a two-level artificial neural network which predicts (1) CPU usage at the first level, and (2) power consumption and performance of a host machine at the second level that uses the predicted CPU usage. The efficacy of iPlace is evaluated in the context of a VM consolidation algorithm that is applied to running virtual machines and host machines in a private cloud.

There is a growing trend towards migrating applications and services to the cloud. This trend has led to the emergence of different cloud service providers (CSPs), in turn leading to different cost models offered by these CSPs to lease their resources, variabilities in the granularity and specification of resources provided, and heterogeneous APIs offered by the CSPs to the users to program resource requests and deployment for their cloud-hosted services. These challenges make it hard for customers of the cloud to seamlessly transition their services to the cloud or migrate between different CSPs. To address these challenges, this paper presents a solution based on model-driven engineering (MDE). Specifically, we describe the design of the domain-specific modeling languages in our MDE framework and the associated generative mechanisms that address the challenges related to estimating performance and cost to host the services in the cloud, automated deployment and resource management.