Jerry Rolia

Workload Analysis and Demand Prediction of Enterprise Data Center Applications

Advances in virtualization technology are enabling the creation of resource pools of servers that permit multiple application workloads to share each server in the pool. Understanding the nature of enterprise workloads is crucial to properly designing and provisioning current and future services in such pools. This paper considers issues of workload analysis, performance modeling, and capacity planning. Our goal is to automate the efficient use of resource pools when hosting large numbers of enterprise services. We use a trace based approach for capacity management that relies on i) the characterization of workload demand patterns, ii) the generation of synthetic workloads that predict future demands based on the patterns, and m) a workload placement recommendation service. The accuracy of capacity planning predictions depends on our ability to characterize workload demand patterns, to recognize trends for expected changes in future demands, and to reflect business forecasts for otherwise unexpected changes in future demands. A workload analysis demonstrates the busrtiness and repetitive nature of enterprise workloads. Workloads are automatically classified according to their periodic behavior. The similarity among repeated occurrences of patterns is evaluated. Synthetic workloads are generated from the patterns in a manner that maintains the periodic nature, burstiness, and trending behavior of the workloads. A case study involving six months of data for 139 enterprise applications is used to apply and evaluate the enterprise workload analysis and related capacity planning methods. The results show that when consolidating to 8 processor systems, we predicted future per-server required capacity to within one processor 95% of the time. The accuracy of predictions for required capacity suggests that such resource savings can be achieved with little risk.

Characterizing the Scalability of a Large Web-Based Shopping System

This article presents an analysis of five days of workload data from a large Web-based shopping system. The multitier environment of this Web-based shopping system includes Web servers, application servers, database servers, and an assortment of load-balancing and firewall appliances. We characterize user requests and sessions and determine their impact on system performance scalability. The purpose of our study is to assess scalability and support capacity planning exercises for the multitier system. We find that horizontal scalability is not always an adequate mechanism for supporting increased workloads and that personalization and robots can have a significant impact on system scalability.

Resource pool management: Reactive versus proactive or let's be friends

The consolidation of multiple workloads and servers enables the efficient use of server and power resources in shared resource pools. We employ a trace-based workload placement controller that uses historical information to periodically and proactively reassign workloads to servers subject to their quality of service objectives. A reactive migration controller is introduced that detects server overload and underload conditions. It initiates the migration of workloads when the demand for resources exceeds supply. Furthermore, it dynamically adds and removes servers to maintain a balance of supply and demand for capacity while minimizing power usage. A host load simulation environment is used to evaluate several different management policies for the controllers in a time effective manner. A case study involving three months of data for 138 SAP applications compares three integrated controller approaches with the use of each controller separately. The study considers trade-offs between: (i) required capacity and power usage, (ii) resource access quality of service for CPU and memory resources, and (iii) the number of migrations. Our study sheds light on the question of whether a reactive controller or proactive workload placement controller alone is adequate for resource pool management. The results show that the most tightly integrated controller approach offers the best results in terms of capacity and quality but requires more migrations per hour than the other strategies.

An integrated approach to resource pool management: Policies, efficiency and quality metrics

The consolidation of multiple servers and their workloads aims to minimize the number of servers needed thereby enabling the efficient use of server and power resources. At the same time, applications participating in consolidation scenarios often have specific quality of service requirements that need to be supported. To evaluate which workloads can be consolidated to which servers we employ a trace-based approach that determines a near optimal workload placement that provides specific qualities of service. However, the chosen workload placement is based on past demands that may not perfectly predict future demands. To further improve efficiency and application quality of service we apply the trace-based technique repeatedly, as a workload placement controller. We integrate the workload placement controller with a reactive controller that observes current behavior to i) migrate workloads off of overloaded servers and ii) free and shut down lightly-loaded servers. To evaluate the effectiveness of the approach, we developed a new host load emulation environment that simulates different management policies in a time effective manner. A case study involving three months of data for 138 SAP applications compares our integrated controller approach with the use of each controller separately. The study considers trade-offs between i) required capacity and power usage, ii) resource access quality of service for CPU and memory resources, and iii) the number of migrations. We consider two typical enterprise environments: blade and server based resource pool infrastructures. The results show that the integrated controller approach outperforms the use of either controller separately for the enterprise application workloads in our study. We show the influence of the blade and server pool infrastructures on the effectiveness of the management policies.

Capacity Management and Demand Prediction for Next Generation Data Centers

Advances in server, network, and storage virtualization are enabling the creation of resource pools of servers that permit multiple application workloads to share each server in the pool. This paper proposes and evaluates aspects of a capacity management process for automating the efficient use of such pools when hosting large numbers of services. We use a trace based approach to capacity management that relies on i) a definition for required capacity, ii) the characterization of workload demand patterns, iii) the generation of synthetic workloads that predict future demands based on the patterns, and iv) a workload placement recommendation service. A case study with 6 months of data representing the resource usage of 139 workloads in an enterprise data center demonstrates the effectiveness of the proposed capacity management process. Our results show that when consolidating to 8 processor systems, we predicted future per-server required capacity to within one processor 95% of the time. The approach enabled a 35% reduction in processor usage as compared to todays current best practice for workload placement.

A capacity management service for resource pools

Resource pools are computing environments that offer virtualized access to shared resources. When used effectively they can align the use of capacity with business needs (flexibility), lower infrastructure costs (via resource sharing), and lower operating costs (via automation). This paper describes the Quartermaster capacity manager service for managing such pools. It implements a trace-based technique that models workload (e.g., application) resource demands, their corresponding resource allocations, and resource access quality of service. The primary advantages of the technique are its accuracy, generality, support for resource access qualities of service, and optimizing search method. We pose general capacity management questions for resource pools and explain how the capacity manager helps to address them in an automated manner. A case study demonstrates and validates the method on empirical data from an enterprise application. We show that the technique exploits much of the resource savings to be achieved from resource sharing and is significantly more accurate at estimating per-server required capacity than a benchmark method used in practice to manage a resource pool. Finally, we explain how the problems relate to other practices regarding enterprise capacity management and software performance engineering.

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.

Capacity planning and power management to exploit sustainable energy

This paper describes an approach for designing a power management plan that matches the supply of power with the demand for power in data centers. Power may come from the grid, from local renewable sources, and possibly from energy storage subsystems. The supply of renewable power is often time-varying in a manner that depends on the source that provides the power, the location of power generators, and the weather conditions. The demand for power is mainly determined by the time-varying workloads hosted in the data center and the power management policies implemented by the data center. A case study demonstrates how our approach can be used to design a plan for realistic and complex data center workloads. The study considers a data centers deployment in two geographic locations with different supplies of power. Our approach offers greater precision than other planning methods that do not take into account time-varying power supply and demand and data center power management policies.

Automating Enterprise Application Placement in Resource Utilities

Enterprise applications implement business resource management systems, customer relationship management systems, and general systems for commerce. These applications rely on infrastructure that represents the vast majority of the world’s computing resources. Most of this infrastructure is lightly utilized and incurs high operations management costs. Server and storage consolidation are the current best practices for decreasing costs of ownership in such environments. However, capacity related decisions about which applications should be placed on a consolidated server are often made informally. This paper presents an approach for automating such exercises. We characterize the complex time varying demands of such applications and then assign them to a small number of servers such that their capacity requirements are satisfied. The approach can be repeated on an on-going basis to ensure the continued efficient use of resources. A case study using data from 41 data center servers is used to demonstrate the effectiveness of the technique.

Statistical service assurances for applications in utility grid environments

In this paper we introduce techniques that support advance resource reservation and admission control for applications acquiring information technology (IT) resources from resource utilities. These resource utilities offer programmatic access to resources for complex multi-tier applications. Such utilities may participate in grids. As a workload, we consider business applications which require resources continuously but that have resource demands that change regularly based on calendar patterns such as time of day and day of week. Applications acquire resources as needed to ensure a quality of service to their end users and they release resources when they are not needed to lower their infrastructure costs. We characterize the resource demands of such applications statistically using application demand profiles. The profiles are used to make resource reservations. An admission control system exploits the profiles to enable the overbooking of resources while offering statistical assurances regarding access to resources. Different assurance levels correspond to alternative classes of service. Policing techniques determine whether requests for resources conform to a reservation and therefore whether they must be serviced. We illustrate the feasibility of our approach with a case study that uses resource utilization information from 48 data center servers. Simulation experiments explore the sensitivity of the assurances to correlations between application resource demands, the precision of the demand profiles, and the effectiveness of the policing mechanisms.