Due to rising energy costs, energy-efficient data centers have gained increasingly more attention in research and practice. Optimizations targeting energy efficiency are usually performed on an isolated level, either by producing more efficient hardware, by reducing the number of nodes simultaneously active in a data center, or by applying dynamic voltage and frequency scaling (DVFS). Energy consumption is, however, highly application dependent. We therefore argue that, for best energy efficiency, it is necessary to combine different measures both at the programming and at the runtime level. As there is a tradeoff between execution time and power consumption, we vary both independently to get insights on how they affect the total energy consumption. We choose frequency scaling for lowering the power consumption and heterogeneous processing units for reducing the execution time. While these options showed to be effective already in the literature, the lack of energy-efficient software in practice suggests missing incentives for energy-efficient programming. In fact, programming heterogeneous applications is a challenging task, due to different memory models of the underlying processors and the requirement of using different programming languages for the same tasks. We propose to use the actor model as a basis for efficient and simple programming, and extend it to run seamlessly on either a CPU or a GPU. In a second step, we automatically balance the load between the existing processing units. With heterogeneous actors we are able to save 40–80 % of energy in comparison to CPU-only applications, additionally increasing programmability. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The costs of current data centers are mostly driven by their energy consumption (specifically by the air conditioning, computing and networking infrastructure). Yet, current pricing models are usually static and rarely consider the facilities' energy consumption per user. The challenge is to provide a fair and predictable model to attribute the overall energy costs per virtual machine (VM). Current pay-as-you-go models of Cloud providers allow users to easily know how much their computing will cost. However, this model is not fully transparent as to where the costs come from (e.g., energy). In this paper we introduce EPAVE, a model for Energy-Proportional Accounting in VM-based Environments. EPAVE allows transparent, reproducible and predictive cost calculation for users and for Cloud providers. We show these characteristics of EPAVE by a number of use cases in heterogeneous data centers and discuss the applicability of EPAVE.

Power estimation of software processes provides critical indicators to drive scheduling or power capping heuristics. State-of-the-art solutions can perform coarse-grained power estimation in virtualized environments, typically treating virtual machines (VMs) as a black box. Yet, VM-based systems are nowadays commonly used to host multiple applications for cost savings and better use of energy by sharing common resources and assets.

Dramatic environmental and economic impact of the ever increasing power and energy consumption of modern computing devices in data centers is now a critical challenge. On one hand, designers use technology scaling as one of the methods to face the phenomenon called dark silicon (only segments of a chip function concurrently due to power restrictions). On the other hand, designers use extreme-scale systems such as teradevices to meet the performance needs of their applications which in turn increases the power consumption of the platform. In order to overcome these challenges, we need novel computing paradigms that address energy efficiency. One of the promising solutions is to incorporate parallel distributed methodologies at different abstraction levels. The FP7 project ParaDIME focuses on this objective to provide different distributed methodologies (software-hardware techniques) at different abstraction levels to attack the power-wall problem. In particular, the ParaDIME framework will utilize: circuit and architecture operation below safe voltage limits for drastic energy savings, specialized energy-aware computing accelerators, heterogeneous computing, energy-aware runtime, approximate computing and power-aware message passing. The major outcome of the project will be a processor architecture for a heterogeneous distributed system that utilizes future device characteristics for drastic energy savings. Wherever possible, ParaDIME will adopt multidisciplinary techniques, such as hardware support for message passing, runtime energy optimization utilizing new hardware energy performance counters, use of accelerators for error recovery from sub-safe voltage operation, and approximate computing through annotated code. Furthermore, we will establish and investigate the theoretical limits of energy savings at the device, circuit, architecture, runtime and programming model levels of the computing stack, as well as quantify the actual energy savings achieved by the ParaDIME approach for the complete computing stack with the real environment.

The power envelope has become a major issue for the design of computer systems. One way of reducing energy consumption is to downscale the voltage of microprocessors. However, this does not come without costs. By decreasing the voltage, the likelihood of failures increases drastically and without mechanisms for reliability, the systems would not operate any more. For reliability we need (1) error detection and (2) error recovery mechanisms. We provide in this paper a first study investigating the combination of different error detection mechanisms with transactional memory, with the objective to improve energy efficiency. According to our evaluation, using reliability schemes combined with transactional memory for error recovery reduces energy by 54% while providing a reliability level of 100%.