Juan C. Rubio

An updated performance comparison of virtual machines and Linux containers

Cloud computing makes extensive use of virtual machines because they permit workloads to be isolated from one another and for the resource usage to be somewhat controlled. In this paper, we explore the performance of traditional virtual machine (VM) deployments, and contrast them with the use of Linux containers. We use KVM as a representative hypervisor and Docker as a container manager. Our results show that containers result in equal or better performance than VMs in almost all cases. Both VMs and containers require tuning to support I/Ointensive applications. We also discuss the implications of our performance results for future cloud architectures.

System power management support in the IBM POWER6 microprocessor

The IBM POWER6™ microprocessor chip supports advanced, dynamic power management solutions for managing not, just the chip but the entire server. The design facilitates a programmable power management solution for greater flexibility and integration into system- and data-center-wide management solutions. The design of the POWER6 microprocessor provides real-time access to detailed and accurate information on power, temperature, and performance. Together, the sensing, actuation, and management support available in the POWER6 processor, known as the EnergyScale™ architecture, enables higher performance, greater energy efficiency, and new power management capabilities such as power and thermal capping and power savings with explicit performance control. This paper provides an overview of the innovative design of the POWER6 processor that enables these advanced, dynamic system power management solutions.

Architecting for power management: The IBM® POWER7™ approach

The POWER7 processor is the newest member of the IBM POWER® family of server processors. With greater than 4X the peak performance and the same power budget as the previous generation POWER6®, POWER7 will deliver impressive energy-efficiency boosts. The improved peak energy-efficiency is accompanied by a wide array of new features in the processor and system designs that advance IBMs EnergyScale™ dynamic power management methodology. This paper provides an overview of these new features, which include better sensing, more advanced power controls, improved scalability for power management, and features to address the diverse needs of the full range of POWER servers from blades to supercomputers. We also highlight three challenges that need attention from a range of systems design and research teams: (i) power management in highly virtualized environments, (ii) power (in)efficiency of systems software and applications, and (iii) memory power costs, especially for servers with large memory footprints.

TAPO: Thermal-aware power optimization techniques for servers and data centers

A large portion of the power consumption of data centers can be attributed to cooling. In dynamic thermal management mechanisms for data centers and servers, thermal setpoints are typically chosen statically and conservatively, which leaves significant room for improvement in the form of improved energy efficiency. In this paper, we propose two hierarchical thermal-aware power optimization techniques that are complementary to each other and achieve (i) lower overall system power with no performance penalty or (ii) higher performance within the same power budget.

Thermal response to DVFS: analysis with an Intel Pentium M

Increasing power density in computing systems from laptops to servers has spurred interest in dynamic thermal management. Based on the success of dynamic voltage and frequency scaling (DVFS) in managing power and energy, DVFS may be a viable option for thermal management, as well. However, publicly available data on the thermal effects of DVFS are very limited. In this work, we characterize the thermal response of Intel Pentium M system to DVFS, identifying the response timescale and influence of factors beyond voltage and frequency on processor temperature.

Application-Aware Power Management

This paper presents our approach for application-aware power management. We combine continuous monitoring of critical workload indicators, online power and performance model usage and timely p-state control to realize application-aware power management. We present two new power management solutions enabled by our methodology: PerformanceMaximizer (PM) finds the best possible performance under specified power constraints and PowerSave (PS) saves energy while keeping performance above specified requirements. We evaluate both using the SPEC-CPU2000 suite on a Pentium M platform discussing implications of workload characteristics and benefits of being workload-aware

Energyscale for IBM POWER6 microprocessor-based systems

With increasing processor speed and density, denser system packaging, and other technology advances, system power and heat have become important design considerations. The introduction of new technology including denser circuits, improved lithography, and higher clock speeds means that power consumption and heat generation, which are already significant problems with older systems, are significantly greater with IBM POWER6™ processor-based designs, including both standalone servers and those implemented as blades for the IBM BladeCenter® product line. In response, IBM has developed the EnergyScale™ architecture, a system-level power management implementation for POWER6 processor-based machines. The EnergyScale architecture uses the basic power control facilities of the POWER6 chip, together with additional board-level hardware, firmware, and systems software, to provide a complete power and thermal management solution. The EnergyScale architecture is performance aware, taking into account the characteristics of the executing workload to ensure that it meets the goals specified by the user while reducing power consumption. This paper introduces the EnergyScale architecture and describes its implementation in two representative platform designs: an eight-way, rack-mounted machine and a server blade. The primary focus of this paper is on the algorithms and the firmware structure used in the EnergyScale architecture, although it also provides the system design considerations needed to support performance-aware power management. In addition, it describes the extensions and modifications to power management that are necessary to span the range of POWER6 processor-based system designs.

Adaptive energy-management features of the IBM POWER 7 chip

The IBM POWER7® processor implements several new adaptive power-management techniques that, in concert with the EnergyScalei firmware, allow it to proactively take advantage of variations in workload, environmental conditions, and overall system utilization to meet customer-directed power and performance goals. These features build on the support and the capabilities provided by its predecessor, i.e., the IBM POWER6™ processor. Among these are per-core frequency scaling with available autonomous frequency controls, per-chip automated voltage slewing, power-consumption estimation, soft power capping, and hardware instrumentation assist.

Power management solutions for computer systems and datacenters

The growing power and cooling requirements of high-density computing systems pose significant challenges for the design and operation of computers and their facilities. The rising operating expenses for datacenters demand the implementation of energy-efficient technologies and the best power management solutions. This tutorial addresses power management and cooling solutions from the individual computer system level to the datacenter. The audience will learn about the fundamental nature of the problems, approaches to developing solutions, available commercial solutions, and current research directions.

Power, Performance, and Thermal Management for High-Performance Systems

In future high-performance systems it will be essential to balance often-conflicting objectives of performance, power, energy, and temperature under variable workload and environmental conditions. In this work, we describe a goal-driven approach that conveys multiple expectations to managers that dynamically tune operating states to best meet those demands. We show the benefit of a concise goal specification for complex objectives and the feasibility of managing multiple constraints while maintaining high performance and safe operation. We evaluate key features of our approach with a prototype implementation on a Pentium M platform with Red Hat Enterprise 4 that controls voltage and frequency scaling to achieve the desired performance, power and temperature goals.