Data centers are often under-utilized due to over-provisioning as well as time-varying resource demands of typical enterprise applications. One approach to increase resource utilization is to consolidate applications in a shared infrastructure using virtualization. Meeting application-level quality of service (QoS) goals becomes a challenge in a consolidated environment as application resource needs differ. Furthermore, for multi-tier applications, the amount of resources needed to achieve their QoS goals might be different at each tier and may also depend on availability of resources in other tiers. In this paper, we develop an adaptive resource control system that dynamically adjusts the resource shares to individual tiers in order to meet application-level QoS goals while achieving high resource utilization in the data center. Our control system is developed using classical control theory, and we used a black-box system modeling approach to overcome the absence of first principle models for complex enterprise applications and systems. To evaluate our controllers, we built a testbed simulating a virtual data center using Xen virtual machines. We experimented with two multi-tier applications in this virtual data center: a twotier implementation of RUBiS, an online auction site, and a two-tier Java implementation of TPC-W. Our results indicate that the proposed control system is able to maintain high resource utilization and meets QoS goals in spite of varying resource demands from the applications.

Virtualization technology offers powerful resource management mechanisms, including performance-isolating resource schedulers, live migration, and suspend/resume. But how should networked virtual computing systems use these mechanisms? A grand challenge is to devise practical policies to drive these mechanisms in a self-managing or “autonomic” system, without relying on human operators. This paper explores architectural and algorithmic issues for resource management policy and orchestration in Shirako, a system for on-demand leasing of shared networked resources in federated clusters. Shirako enables a flexible factoring of resource management functions across the participants in a federated system, to accommodate a range of models of distributed virtual computing. We present extensions to Shirako to provision fine-grained virtual machine “slivers ” and drive virtual machine migration. We illustrate the interactions of provisioning and placement/migration policies, and their impact. 1

Virtual machine monitors are becoming popular tools for the deployment of database management systems and other enterprise software applications. In this paper, we consider a common resource consolidation scenario, in which several database management system instances, each running in a virtual machine, are sharing a common pool of physical computing resources. We address the problem of optimizing the performance of these database management systems by controlling the configurations of the virtual machines in which they run. These virtual machine configurations determine how the shared physical resources will be allocated to the different database instances. We introduce a virtualization design advisor that uses information about the anticipated workloads of each of the database systems to recommend workload-specific configurations offline. Furthermore, runtime information collected after the deployment of the recommended configurations can be used to refine the recommendation. To estimate the effect of a particular resource allocation on workload performance, we use the query optimizer in a new what-if mode. We have implemented our approach using both PostgreSQL and DB2, and we have experimentally evaluated its effectiveness using DSS and OLTP workloads.

Abstract — Today’s shared hosting platforms often employ virtualization to allow multiple enterprise applications with time-varying resource demands to share a common infrastructure in order to improve resource utilization. Meeting application-level quality of service (QoS) goals becomes a challenge in such an environment as enterprise applications often have a multi-tier architecture and complex interactions and dependencies among individual tiers. In addition, when the shared infrastructure becomes overloaded, appropriate resource control needs to be performed at these individual tiers in a coordinated fashion in order to provide differentiated services to co-hosted applications. In this paper, we present an adaptive multivariate controller that dynamically adjusts the resource shares to individual tiers of multiple applications in order to meet a specified level of service differentiation. The controller parameters are automatically tuned at runtime based on a quadratic cost function and a system model that is learned online using a recursive least-squares (RLS) method. To evaluate our controller design, we built a testbed hosting two instances of the RUBiS application, a multi-tier online auction web site, using Xen virtual machines. Our results indicate that our controller is able to meet given QoS differentiation targets between co-hosted applications while the total demand from these applications exceeds the capacities of the shared systems. I.

As a growing number of websites open up their APIs to external application developers (e.g., Facebook, Yahoo! Widgets, Google Gadgets), these websites are facing an intriguing scalability problem: while each user-generated application is by itself quite small (in terms of size and throughput requirements), there are many many such applications. Unfortunately, existing data-management solutions are not designed to handle this form of scalability in a cost-effective, manageable and/or flexible manner. For instance, large installations of commercial database systems such as Oracle, DB2 and SQL Server are usually very expensive and difficult to manage. At the other extreme, low-cost hosted datamanagement solutions such as Amazon’s SimpleDB do not support sophisticated data-manipulation primitives such as joins that are necessary for developing most Web applications. To address this issue, we explore a new point in the design space whereby we use commodity hardware and free software (MySQL) to scale to a large number of applications while still supporting full SQL functionality, transactional guarantees, high availability and Service Level Agreements (SLAs). We do so by exploiting the key property that each application is “small ” and can fit in a single machine (which can possibly be shared with other applications). Using this property, we design replication strategies, data migration techniques and load balancing operations that automate the tasks that would otherwise contribute to the operational and management complexity of dealing with a large number of applications. Our experiments based on the TPC-W benchmark suggest that the proposed system can scale to a large number of small applications. 1.

... (MMOGs) can include millions of concurrent players spread across the world. To keep these highly-interactive virtual environments online, a MMOG operator may need to provision tens of thousands of computing resources from various data centers. Faced with large resource demand variability, and with misfit resource renting policies, the current industry practice is to maintain for each game tens of self-owned data centers. In this work we investigate the dynamic resource provisioning from external data centers for MMOG operation. We introduce a novel MMOG workload model that represents the dynamics of both the player population and the player interactions. We evaluate several algorithms, including a novel neural network predictor, for predicting the resource demand. Using trace-based simulation, we evaluate the impact of the data center policies on the resource provisioning efficiency; we show that dynamic provisioning can be much more efficient than its static alternative.

Abstract—Server virtualization opens up a range of new possibilities for autonomic datacenter management, through the availability of new automation mechanisms that can be exploited to control and monitor tasks running within virtual machines. This offers not only new and more flexible control to the operator using a management console, but also more powerful and flexible autonomic control, through management software that maintains the system in a desired state in the face of changing workload and demand. This paper explores in particular the use of server virtualization technology in the autonomic management of data centers running a heterogeneous mix of workloads. We present a system that manages heterogeneous workloads to their performance goals and demonstrate its effectiveness via realsystem experiments and simulation. We also present some of the significant challenges to wider usage of virtual servers in autonomic datacenter management. I.

Resource virtualization is currently being employed at all levels of the IT infrastructure to improve provisioning and manageability, with the goal of reducing total cost of ownership. This means that database systems will increasingly be run in virtualized environments, inside virtual machines. This has many benefits, but it also introduces new tuning and physical design problems that are of interest to the database research community. In this paper, we discuss how virtualization can benefit database systems, and we present the tuning problems it introduces, which relate to setting the new “tuning knobs ” that control resource allocation to virtual machines in the virtualized environment. We present a formulation of the virtualization design problem, which focuses on setting resource allocation levels for different database workloads statically at deployment and configuration time. An important component of the solution to this problem is modeling the cost of a workload for a given resource allocation. We present an approach to this cost modeling that relies on using the query optimizer in a special virtualization-aware “what-if ” mode. We also discuss the next steps in solving this problem, and present some long-term research directions. 1.

Advances in server virtualization offer new mechanisms to provide resource management for shared server infrastructures. Resource sharing requires coordination across self-interested system participants (e.g., providers from different administrative domains or third-party brokering intermediaries). Assignments of the shared infrastructure must be fluid and adaptive to meet the dynamic demands of clients. This thesis addresses the hypothesis that a new, foundational layer for virtual computing is sufficiently powerful to support a diversity of resource management needs in a general and uniform manner. Incorporating resource management at a lower virtual computing layer provides the ability to dynamically share server infrastructure between multiple hosted software environments (e.g., grid computing middleware and job execution systems). Resource assignments within the virtual layer occur through a lease abstraction, and extensible policy modules define management functions. This research makes the following contributions: • Defines the foundation for resource management in a virtual computing layer. Defines protocols and extensible interfaces for formulating resource contracts

This paper introduces a self-configuring architecture for on-demand resource allocation to applications in a shared database cluster. We use a unified approach to load and fault management based on data replication and reactive replica provisioning. While data replication provides scaling and high availability, reactive provisioning dynamically allocates additional replicas to applications in response to peak loads or failure conditions, thus providing per application performance. We design an efficient method for data migration when joining a new replica to a running application that allows for the quick addition of replicas with minimal disruption of transaction processing. Furthermore, by augmenting the adaptation feedback loop with awareness of the delay introduced by the data migration process in our replicated system, we avoid oscillations in resource allocation. We investigate our transparent database provisioning mechanisms in the context of multitier dynamic content Web servers. We dynamically expand/contract the respective allocations within the database tier for two different applications, the TPC-W e-commerce benchmark and the RUBIS online auction benchmark. We demonstrate that our techniques provide quality of service under different load and failure scenarios. Categories and Subject Descriptors: H.2.4 [Database Management]: Systems—Query processing; H.2.7 [Database Management]: Database Administration—Logging and recovery