The IT infrastructure library (ITIL) provides clarity to IT service management and its processes. ICO 20000 further describes the responsibilities of service providers operating in ITIL conformant environment. However, neither one specifically addresses external service providers' challenges that arise in a cost conserving operational model when service provider is supporting multiple customers using shared resources. This paper provides a summary of a required extension to the service provider domain for an IT service management (ITSM) solution with ITIL conformant core.

The need for service differentiation in Internet services has motivated interest in controlling multi-tier web applications. This paper describes a tier-to-tier (T2T) management architecture that supports decentralized actuator management in multi-tier systems, and a testbed implementation of this architecture using commercial software products. Based on testbed experiments and analytic models, we gain insight into the value of coordinated exploitation of actuators on multiple tiers, especially considerations for control efficiency and control granularity. For control efficiency, we show that more effective utilization of tiers can be achieved by using actuators on the bottleneck tier rather than only using actuators on the entry tier. For granularity of control (the ability to achieve a wide range of service level objectives) we show that a fine granularity of control can be achieved through a coordinated, cross-tier exploitation of coarse grained actuators (e.g., multiprogramming level), an approach that can greatly reduce controllerinduced variability.

In this paper we present a hybrid performance model for modeling differentiated service of multi-tier web applications with per-tier concurrency limits, cross-tier interactions, as well as a work-conserving resource allocation model. The service dependencies between multiple tiers are captured first using a layered queueing model. We then show how to model per-tier concurrency limits and service differentiation between multiple classes while maintaining work conservation at each tier. We use a function approximation approach combined with a coupled processor model. Our model is calibrated from an actual multitier J2EE testbed, and we show the ability of the model to accurately model common performance metrics. Our proposed (layered) model shows 78% improvement in root mean square error over a single-tier machine repair model as well as a tandem queue model. We also demonstrate one application of the model for model-based resource allocation.

A key concern in utility computing is managing capacity so that application service providers (ASPs) and computing utilities (CUs) operate in a cost effective way. To this end, we propose a framework for applying inventory control to capacity management for utility computing. The framework consists of: conceptual foundations (e.g., establishing connections between concepts in utility computing and those in inventory control); problem formulations (e.g., what factors should be considered and how they affect computational complexity); and quality of service (QoS) forecasting, which is predicting the future effect on QoS of ASP and CU actions taken in the current period (a critical consideration in searching the space of possible solutions).

Utility computing provides a pay-as-you-go approach to information systems in which application providers (e.g., web sites) can better manage their costs by adding capacity in response to increased demands and shedding capacity when it is no longer needed. This paper addresses application providers who use clusters of servers. Our work develops a framework to determine the number of servers that minimizes the sum of quality-of-service (QoS) costs resulting from service level penalties and server holding costs for the server cluster. The server characteristics considered are service rate, failure rates, repair rates, and costs. The contributions of this paper are: 1) a model for the performance and availability of an e-Commerce system that is consistent with data from a multisystem testbed with an e-Commerce workload; 2) a business-oriented cost model for resource allocation for application providers; 3) a closed form approximation for the optimal allocation of servers for an application provider based on the performance model in 1) and the cost model in 2); and 4) a simple criteria for utility owners and server manufacturers to make tradeoffs between server characteristics.

Load balancing is widely used in computing systems as a way to optimize performance by reducing bottleneck utilizations, such as adjusting the size of buffer pools to balance resource demands in a database management system. Load balancing is generally approached as a constrained optimization problem in which only the benefits of load balancing are considered. However, the costs of control are important as well. Herein, we study the value of including in controller design the trade-off between the cost of transient imbalances in resource utilizations and the cost of changing resource allocations. An example of the latter are actions such as resizing buffer pools that can reduce throughputs. This is because requests for data in pools whose memory is reduced immediately have longer access times whereas requests for data in pools whose memory is increased must fill this memory with data from disk before accessed times are reduced. We frame our study of control costs in terms of the widely used linear quadratic regulator (LQR). We develop a cost model that allows us to specify the LQR Q and R matrices based on the impact on system performance of changing resource allocations and transient load imbalances. Our studies of a DB2 universal database server using benchmarks for online transaction processing and decision support workloads show that incorporating our cost model into the MIMO LQR controller results in a 14% improvement in performance beyond that achieved by dynamically allocating the size of buffers without properly considering the cost of control.

Optimizing configuration parameters for achieving service level objectives is time-consuming and skills-intensive. This paper proposes a generic approach to automating this task. By generic, we mean that the approach is relatively independent of the target system for which the optimization is done. Our approach uses online adjustment of configuration parameters to discover the system's performance characteristics. Doing so creates two challenges: (1) handling interdependencies between configuration parameters and (2) minimizing the deleterious effects on production workload while the optimization is underway. Our approach addresses (1) by including in the architecture a rule-based component that handles interdependencies between configuration parameters. For (2), we use a feedback mechanism for online optimization that searches the parameter space in a way that generally avoids poor performance at intermediate steps. Our studies of a DB2 Universal Database Server under an e-commerce workload indicate that our approach is effective in practice.

Quantitative models are needed for a variety of management tasks, including identification of critical variables to use for health monitoring, anticipating service-level violations by using predictive models, and ongoing optimization of configurations. Unfortunately, constructing quantitative models requires specialized skills that are in short supply. Even worse, rapid changes in provider configurations and the evolution of business demands mean that quantitative models must be updated on an ongoing basis. This paper describes an architecture and algorithms for online discovery of quantitative models without prior knowledge of the managed elements. The architecture makes use of an element schema that describes managed elements using the Common Information Model (CIM). Algorithms are presented for selecting a subset of the element metrics to use as explanatory variables in a quantitative model and for constructing the quantitative model itself. We further describe a prototype system based onthis architecture that incorporates these algorithms. We apply the prototype to online estimation of response times for DB2 Universal Database under a TPC-W workload. Of the approximately 500 metrics available from the DB2 performance monitor, our system chooses three to construct a model that explains 72 percent of the variability of response time.