Energy efficiency with an application container

Efficient use of energy is an issue that the information technology (IT) world gives prominence to both in academia and industry. Cloud computing and the Internet of things, today's most popular subjects, make the efficient use of resources and energy even more important. Every year, millions of smart devices connected to the Internet increase the demand for data center capacity to provide service to those devices. This increases energy consumption in the IT sector. Thus, more efficient use of energy in these systems is of critical importance. The increase in the migration to cloud computing makes fast and efficient infrastructure-as-a-service and platform-as-a-service services provided by Internet service providers important. On the other hand, virtual machines have been used for a long time as an alternative to physical servers. The application containerization concept is shaping the virtualization world by offering faster deployment, reduced resource consumption, easier manageability, and reduced energy consumption, as demonstrated in this study. Our study shows that this new concept is more energy-efficient than virtualization technologies that are currently being used

Unifying hardware and software benchmarking: a resource-agnostic model

Lilja (2005) states that “In the field of computer science and engineering there is surprisingly little agreement on how to measure something as fun- damental as the performance of a computer system.”. The field lacks of the most fundamental element for sharing measures and results: an appropriate metric to express performance. Since the introduction of laptops and mobile devices, there has been a strong research focus towards the energy efficiency of hardware. Many papers, both from academia and industrial research labs, focus on methods and ideas to lower power consumption in order to lengthen the battery life of portable device components. Much less effort has been spent on defining the responsibility of software in the overall computational system energy consumption. Some attempts have been made to describe the energy behaviour of software, but none of them abstract from the physical machine where the measurements were taken. In our opinion this is a strong drawback because results can not be generalized. In this work we attempt to bridge the gap between characterization and prediction, of both hardware and software, of performance and energy, in a single unified model. We propose a model designed to be as simple as possible, generic enough to be abstract from the specific resource being described or predicted (applying to both time, memory and energy), but also concrete and practical, allowing useful and precise performance and energy predictions. The model applies to the broadest set of resource possible. We focus mainly on time and memory (hence bridging hardware benchmarking and classical algorithms time complexity), and energy consumption. To ensure a wide applicability of the model in real world scenario, the model is completely black-box, it does not require any information about the source code of the program, and only relies on external metrics, like completion time, energy consumption, or performance counters. Extending the benchmarking model, we define the notion of experimental computational complexity, as the characterization of how the resource usage changes as the input size grows. Finally, we define a high-level energy model capable of characterizing the power consumption of computers and clusters, in terms of the usage of resources as defined by our benchmarking model. We tested our model in four experiments: Expressiveness: we show the close relationship between energy and clas- sical theoretical complexity, also showing that our experimental com- putational complexity is expressive enough to capture interesting be- haviour of programs simply analysing their resource usage. Performance prediction we use the large database of performance mea- sures available on the CPU SPEC website to train our model and predict the performance of the CPU SPEC suite on randomly selected computers. Energy profiling: we tested our model to characterize and predict the power usage of a cluster running OpenFOAM, changing the number of active nodes and cores. Scheduling: on heterogeneous systems applying our performance pre- diction model to features of programs extracted at runtime, we predict the device where is most convenient to execute the programs, in an heterogeneous system

Energy-efficient storage in virtual machine environments

Current trends in increasing storage capacity and virtualization of resources combined with the need for energy efficiency put a challenging task in front of system designers. Previous studies have suggested many approaches to reduce hard disk energy dissipation in native OS environments; however, those mechanisms do not perform well in virtual machine environments because a virtual machine (VM) and the virtual machine monitor (VMM) that runs it have different semantic contexts. This paper explores the disk I/O activities between VMM and VMs using trace driven simulation to understand the I/O behavior of the VM system. Subsequently, this paper proposes three mechanisms to address the isolation between VMM and VMs, and increase the burstiness of hard disk accesses to increase energy efficiency of a hard disk. Compared to standard shutdown mechanisms, with eight VMs the proposed mechanisms reduce disk spin-ups, increase the disk sleep time, and reduce energy consumption by 14.8 % with only 0.5 % increase in execution time. We implemented the proposed mechanisms in Xen and validated our simulation results