Measuring the energy consumption of software components is a major building block for generating models that allow for energy-aware scheduling, accounting and budgeting. Current measurement techniques focus on coarse-grained measurements of application or system events. However, fine grain adjustments in particular in the operating-system kernel and in application-level servers require power profiles at the level of a single software function. Until recently, this appeared to be impossible due to the lacking fine grain resolution and high costs of measurement equipment.In this paper we report on our experience in using the Running Average Power Limit (RAPL) energy sensors available in recent Intel CPUs for measuring energy consumption of short code paths. We investigate the granularity at which RAPL measurements can be performed and discuss practical obstacles that occur when performing these measurements on complex modern CPUs. Furthermore, we demonstrate how to use the RAPL infrastructure to characterize the energy costs for decoding video slices.
In modern commodity operating systems, core functionality is usually designed assuming that the underlying processor hardware always functions correctly. Shrinking hardware feature sizes break this assumption. Existing approaches to cope with these issues either use hardware functionality that is not available in commercial-off-the-shelf (COTS) systems or poses additional requirements on the software development side, making reuse of existing software hard, if not impossible.In this paper we present Romain, a framework that provides transparent redundant multithreading 1 as an operating system service for hardware error detection and recovery. When applied to a standard benchmark suite, Romain requires a maximum runtime overhead of 30 % for triplemodular redundancy (while in many cases remaining below 5 %). Furthermore, our approach minimizes the complexity added to the operating system for the sake of replication.
Software-implemented fault tolerance (SIFT) mechanisms allow to tolerate transient hardware faults in commercial offthe-shelf (COTS) systems without using specialized resilient hardware. Unfortunately, existing SIFT methods at both the compiler and the operating system levels are often restricted to single-threaded applications and hence do not apply to multithreaded software on modern multicore platforms.We present RomainMT , an operating system service that provides replication for unmodified multithreaded applications. Replicating these programs is challenging, because scheduling-induced non-determinism may cause replicated threads to execute different valid code paths. This complicates the distinction between valid behavior and the effects of hardware errors.RomainMT solves these problems by transparently making multithreaded execution deterministic. We present two alternative mechanisms that differ in the assumptions made about the respective applications and investigate their performance implications. Our evaluation using the SPLASH2 benchmark suite shows that the overhead for triple-modular redundancy (TMR) is 24% for applications with two application threads and 65% for four application threads.
Network energy is a significant, although not the largest, cost factor in medium to large scale server installations. On the other hand, most server installations work with redundant link and infrastructure layouts to reduce the risk of network outages. Introducing eBond, an energy-aware bonding network device, we exploit possible heterogeneities in these redundant layouts to adapt network device energy consumption to dynamic server bandwidth demands. Replaying the trace of a realistic scenario in a simulation of eBond with fine grain energy profiles measured at two network cards we achieve energy savings up to 75 % for the server-side network interconnect.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.