Brian J. Watson

1000 islands: an integrated approach to resource management for virtualized data centers

Recent advances in hardware and software virtualization offer unprecedented management capabilities for the mapping of virtual resources to physical resources. It is highly desirable to further create a “service hosting abstraction” that allows application owners to focus on service level objectives (SLOs) for their applications. This calls for a resource management solution that achieves the SLOs for many applications in response to changing data center conditions and hides the complexity from both application owners and data center operators. In this paper, we describe an automated capacity and workload management system that integrates multiple resource controllers at three different scopes and time scales. Simulation and experimental results confirm that such an integrated solution ensures efficient and effective use of data center resources while reducing service level violations for high priority applications.

Probabilistic performance modeling of virtualized resource allocation

Virtualization technologies enable organizations to dynamically flex their IT resources based on workload fluctuations and changing business needs. However, only through a formal understanding of the relationship between application performance and virtualized resource allocation can over-provisioning or over-loading of physical IT resources be avoided. In this paper, we examine the probabilistic relationships between virtualized CPU allocation, CPU contention, and application response time, to enable autonomic controllers to satisfy service level objectives (SLOs) while more effectively utilizing IT resources. We show that with only minimal knowledge of application and system behaviors, our methodology can model the probability distribution of response time with a mean absolute error of less than 6% when compared with the measured response time distribution. We then demonstrate the usefulness of a probabilistic approach with case studies. We apply basic laws of probability to our model to investigate whether and how CPU allocation and contention affect application response time, correcting for their effects on CPU utilization. We find mean absolute differences of 8-10% between the modeled response time distributions of certain allocation states, and a similar difference when we add CPU contention. This methodology is general, and should also be applicable to non-CPU virtualized resources and other performance modeling problems.

AppRAISE: application-level performance management in virtualized server environments

Managing application-level performance for multitier applications in virtualized server environments is challenging because the applications are distributed across multiple virtual machines, and workloads are dynamic in their intensity and transaction mix resulting in time-varying resource demands. In this paper, we present AppRAISE, a system that manages performance of multi-tier applications by dynamically resizing the virtual machines hosting the applications. We extend a traditional queuing model to represent application performance in virtualized server environments, where virtual machine capacity is dynamically tuned. Using this performance model, AppRAISE predicts the performance of the applications due to workload changes, and proactively resizes the virtual machines hosting the applications to meet performance thresholds. By integrating feedforward prediction and feedback reactive control, AppRAISE provides a robust and efficient performance management solution. We tested AppRAISE using Xen virtual machines and the RUBiS benchmark application. Our empirical results show that AppRAISE can effectively allocate CPU resources to application components of multiple applications to meet end-to-end mean response time targets in the presence of variable workloads, while maintaining reasonable trade-offs between application performance, resource efficiency, and transient behavior.

Creating a sustainable IT ecosystem: Enabling next-generation urban infrastructures

In this paper, we describe an integrated design and management approach to creating a sustainable IT ecosystem: a physical infrastructure where information technology has been seamlessly interwoven to improve environmental efficiency while achieving lower cost. Specifically, we describe five principles to achieve such integration: ecosystem-scale life-cycle design; scalable and configurable resource microgrids; pervasive sensing; knowledge discovery and visualization; and autonomous control. Application of the approach is demonstrated for the case study of an urban water infrastructure, and we find that the proposed approach could potentially enable reduction of life-cycle energy use by over 15%.

Sustainable IT Ecosystems: Enabling Next-Generation Cities

In this paper, we describe an integrated design and management approach for building next-generation cities. This approach leverages IT technology in both the design and operational phases to optimize sustainability over a broad set of metrics while lowering costs. We call this approach a Sustainable IT Ecosystem. Our approach is based on five principles: ecosystem-scale life-cycle design; scalable and configurable infrastructure building blocks; pervasive sensing; data analytics and visualization; and autonomous control. Application of the approach is demonstrated for two case studies: an urban water infrastructure and an urban power microgrid. We conclude by discussing future opportunities to co-design and integrate these independent infrastructures, gaining further efficiencies.Copyright