We present Ripple, an architecture and a programming model for a broad set of data analytics. Ripple builds on the ideas of iterated MapReduce and adds two innovations. First it has a richer programming model, including more ideas from the Bulk Synchronous Parallel (BSP) model of computation and others. By doing so, Ripple creates a flexible and higher-level platform that is easier for both application programmers and platform implementors. Second, Ripple is based on a limited interface for key/value storage making it portable among many different key/value store implementations. By building on these two ideas Ripple improves the scope, performance, and openness of the data analytics platform. We evaluate Ripple using three representative, and non-trivial, data analysis scenarios requiring iterative computation. Using these examples, we show how Ripple achieves clear performance advantages over iterated MapReduce.

This paper presents a scheduling technique for multi-job MapReduce workloads that is able to dynamically build performance models of the executing workloads, and then use these models for scheduling purposes. This ability is leveraged to adaptively manage workload performance while observing and taking advantage of the particulars of the execution environment of modern data analytics applications, such as hardware heterogeneity and distributed storage. The technique targets a highly dynamic environment in which new jobs can be submitted at any time, and in which MapReduce workloads share physical resources with other workloads. Thus the actual amount of resources available for applications can vary over time. Beyond the formulation of the problem and the description of the algorithm and technique, a working prototype (called Adaptive Scheduler) has been implemented. Using the prototype and medium-sized clusters (of the order of tens of nodes), the following aspects have been studied separately: the scheduler's ability to meet high-level performance goals guided only by user-defined completion time goals; the scheduler's ability to favor data-locality in the scheduling algorithm; and the scheduler's ability to deal with hardware heterogeneity, which introduces hardware affinity and relative performance characterization for those applications that can benefit from executing on specialized processors.

To reduce the cost of infrastructure and electrical energy, enterprise datacenters consolidate workloads on the same physical hardware. Often, these workloads comprise both transactional and long-running analytic computations. Such consolidation brings new performance management challenges due to the intrinsically different nature of a heterogeneous set of mixed workloads, ranging from scientific simulations to multitier transactional applications. The fact that such different workloads have different natures imposes the need for new scheduling mechanisms to manage collocated heterogeneous sets of applications, such as running a web application and a batch job on the same physical server, with differentiated performance goals. In this paper, we present a technique that enables existing middleware to fairly manage mixed workloads: long running jobs and transactional applications. Our technique permits collocation of the workload types on the same physical hardware, and leverages virtualization control mechanisms to perform online system reconfiguration. In our experiments, including simulations as well as a prototype system built on top of state-of-the-art commercial middleware, we demonstrate that our technique maximizes mixed workload performance while providing service differentiation based on high-level performance goals.

When managing dynamic virtual environments, a consideration that is often overlooked is that of licences. This consideration is particularly important when the environment contains expensive enterprise software with complex licensing terms: if the management system does not understand those terms, it is very likely that too many licences will be used, placing the administrator in an awkward position with the software vendors. We describe a licence management technique integrated with an existing virtual machine management system, and demonstrate its ability to adhere to a variety of licence rules. We also describe two sets of licence rules in sufficient detail to motivate the design and implementation of the system. We evaluate our technique through simulation.

An autonomic manager for enterprise server hardware management, called AMP, is described. AMP is designed to handle multiple aspects of hardware management and to work in conjunction with other management components, in particular application managers, in a way that reduces energy waste, protects server health, and preserves a high degree of autonomy both for itself and for the managers with which it works. AMP interacts with other managers in two ways: (1) exchange of nominal control over individual servers; and (2) provision of a synthetic cost function giving AMP's assessment of relative desirability of using different servers. The high-level architecture of AMP is discussed, with particular focus on the way it effects a natural decomposition of the combined hardware-and-application management problem, and on initial versions of the algorithms it uses to manage server power states and determine the cost function. AMP's viability in practice is demonstrated via prototype implementation in which it operates on real servers in collaboration with a state-of-the-art application manager. The overall system behavior is investigated via simulation.

Systems management techniques that allocate resources to running entities, such as processes and virtual machines (VMs), often require estimates of the resources required by each of these resource consumers. For example, many proposed virtual machine placement algorithms attempt to allocate VMs to physical hosts in such a way as to minimize the number of physical hosts that are occupied, while ensuring that each VM receives the CPU required to do its task adequately. The common practice is to assume that the CPU requirement is equal to the current CPU utilization, or to use a prediction of it over an appropriate time horizon. In this paper, we demonstrate that, when multiple VMs or processes co-reside on a physical host, the measured CPU utilization may provide a poor estimate of the actual requirement. We derive a simple, much more accurate alternative estimate of CPU demand, implement it, and demonstrate its superiority experimentally. Furthermore, we demonstrate that using our demand estimation framework in conjunction with dynamic resource allocation in a virtualized environment greatly improves the effectiveness of dynamic placement, resulting in one-shot convergence to optimal placement and significant improvements in the overall performance of the individual VMs.

MapReduce is a data-driven programming model proposed by Google in 2004 which is especially well suited for distributed data analytics applications. We consider the management of MapReduce applications in an environment where multiple applications share the same physical resources. Such sharing is in line with recent trends in data center management which aim to consolidate workloads in order to achieve cost and energy savings. In a shared environment, it is necessary to predict and manage the performance of workloads given a set of performance goals defined for them. In this paper, we address this problem by introducing a new task scheduler for a MapReduce framework that allows performance-driven management of MapReduce tasks. The proposed task scheduler dynamically predicts the performance of concurrent MapReduce jobs and adjusts the resource allocation for the jobs. It allows applications to meet their performance objectives without over-provisioning of physical resources.

In this paper we address the problem of managing heterogeneous workloads in a virtualized data center. We consider two different workloads: transactional applications and long-running jobs. We present a technique that permits collocation of these workload types on the same physical hardware. Our technique dynamically modifies workload placement by leveraging control mechanisms such as suspension and migration, and strives to optimally trade off resource allocation among these workloads in spite of their differing characteristics and performance objectives. Our approach builds upon our previous work on dynamically placing transactional workloads. This paper extends our framework with the capability to manage long-running workloads. We achieve this goal by using utility functions, which permit us to compare the performance of various workloads, and which are used to drive allocation decisions. We demonstrate that our technique maximizes heterogeneous workload performance while providing service differentiation based on high-level performance goals.

We study the problem of dynamic resource allocation to clustered Web applications. We extend application server middleware with the ability to automatically decide the size of application clusters and their placement on physical machines. Unlike existing solutions, which focus on maximizing resource utilization and may unfairly treat some applications, the approach introduced in this paper considers the satisfaction of each application with a particular resource allocation and attempts to at least equally satisfy all applications. We model satisfaction using utility functions, mapping CPU resource allocation to the performance of an application relative to its objective. The demonstrated online placement technique aims at equalizing the utility value across all applications while also satisfying operational constraints, preventing the over-allocation of memory, and minimizing the number of placement changes. We have implemented our technique in a leading commercial middleware product. Using this real-life testbed and a simulation we demonstrate the benefit of the utility-driven technique as compared to other state-of-the-art techniques.

With the continued growth of computing power and reduction in physical size of enterprise servers, the need for actively managing electrical power usage in large datacenters is becoming ever more pressing. By far the greatest savings in electrical power can be effected by dynamically consolidating workload onto the minimum number of servers needed at a given time and powering off the remainder. However, simple schemes for achieving this goal fail to cope with the complexities of realistic usage scenarios. In this paper we present a combined power-and performance-management system that builds on a state-of-the-art performance manager to achieve significant power savings without unacceptable loss of performance. In our system, the degree to which performance may be traded off against power is itself adjustable using a small number of easily-understood parameters, permitting administrators in different facilities to select the optimal tradeoff for their needs. We characterize the power saved, the effects of the tradeoff between power and performance, and the changes in behavior as the tradeoff parameters are adjusted, both in simulation and in a sample deployment of the real system.

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 real-system experiments and simulation. We also present some of the significant challenges to wider usage of virtual servers in autonomic datacenter management.