A generalized software framework for accurate and efficient management of performance goals

A number of techniques have been proposed to provide runtime performance guarantees while minimizing power consumption. One drawback of existing approaches is that they work only on a fixed set of components (or actuators) that must be specified at design time. If new components become available, these management systems must be redesigned and reimplemented. In this paper, we propose PTRADE, a novel performance management framework that is general with respect to the components it manages. PTRADE can be deployed to work on a new system with different components without redesign and reimplementation. PTRADE's generality is demonstrated through the management of performance goals for a variety of benchmarks on two different Linux/x86 systems and a simulated 128-core system, each with different components governing power and performance tradeoffs. Our experimental results show that PTRADE provides generality while meeting performance goals with low error and close to optimal power consumption.United States. Defense Advanced Research Projects Agency. The Ubiquitous High Performance Computing Progra

PERFUME: Power and performance guarantee with fuzzy MIMO control in virtualized servers

Abstract—It is important but challenging to assure the per-formance of multi-tier Internet applications with the power consumption cap of virtualized server clusters mainly due to system complexity of shared infrastructure and dynamic and bursty nature of workloads. This paper presents PERFUME, a system that simultaneously guarantees power and performance targets with flexible tradeoffs while assuring control accuracy and system stability. Based on the proposed fuzzy MIMO control technique, it accurately controls both the throughput and percentile-based response time of multi-tier applications due to its novel fuzzy modeling that integrates strengths of fuzzy logic, MIMO control and artificial neural network. It is self-adaptive to highly dynamic and bursty workloads due to online learning of control model parameters using a computationally efficient weighted recursive least-squares method. We implement PERFUME in a testbed of virtualized blade servers hosting two multi-tier RUBiS applications. Experimental results demonstrate its control accuracy, system stability, flexibility in selecting trade-offs between conflicting targets and robustness against highly dynamic variation and burstiness in workloads. It outperforms a representative utility based approach in providing guarantee of the system throughput, percentile-based response time and power budget in the face of highly dynamic and bursty workloads. I

Data Center Sprinting: Enabling Computational Sprinting at the Data Center Level

Abstract-Microprocessors may need to keep most of their cores off in the era of dark silicon due to thermal constraints. Recent studies have proposed Computational Sprinting, which allows a chip to temporarily exceed its power and thermal limits by turning on all its cores for a short time period, such that its computing performance is boosted for bursty computation demands. However, conducting sprinting in a data center faces new challenges due to power and thermal constraints at the data center level, which are exacerbated by recently proposed power infrastructure under-provisioning and reliance on renewable energy, as well as the increasing server density. In this paper, we propose Data Center Sprinting, a methodology that enables a data center to temporarily boost its computing performance by turning on more cores in the era of dark silicon, in order to handle occasional workload bursts. We demonstrate the feasibility of this approach by analyzing the tripping characteristics of data center circuit breakers and the discharging characteristics of energy storage devices, in order to realize safe sprinting without causing undesired server overheating or shutdown. We evaluate a prototype of Data Center Sprinting on a hardware testbed and in datacenter-level simulations. The experimental results show that our solution can improve the average computing performance of a data center by a factor of 1.62 to 2.45 for 5 to 30 minutes

Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads