ITEC -  Lehrstuhl Betriebssysteme

SimuBoost

Full system simulation allows simulating an entire physical machine on top of a host operating system (OS) and thus provides a powerful foundation to study the runtime behavior and interaction of computer architecture, operating systems and applications. Since the entire execution environment in such a system is virtual, every operation carried out can be inspected easily.

A well-known limitation of full system simulation is the low execution speed offered by current simulators. Compared to hardware-assisted virtualization, functional simulation is orders of magnitude slower. In practice, this slowdown creates severe obstacles for comprehensive use of functional full system simulation:

  • Interactivity Scenarios that should capture interactivity with a human user or an external network device are not feasible. A single keystroke can quickly take from multiple seconds up to minutes until being fully processed, making human-user interactions cumbersome and unnatural. Network protocols such as TCP, in turn, react to the slowdown with throttling and timeouts.
  • Accuracy of Results Since the simulation considerably slows down the simulated applications and operating system, activities dependent on events external to the virtual machine such as I/O operations appear to complete faster – a phenomenon called time dilation. This distorts measurements and produces unrealistic execution behavior.
  • Coverage Evaluating a test scenario in full length can take considerable time, forcing researchers to reduce coverage.

Representative sampling can reduce the run-time overhead by limiting complex analyses to short time frames that are representative for the analyzed workload. However, an initial functional simulation to identify such intervals is still needed and the accuracy achievable with this technique also heavily depends on sufficient phase behavior in the workload, which is not always present. Moreover, in some scenarios (e.g., analysis of memory duplication) limiting the observation window is not an option. An acceleration technique to enable full-length analyses of long-running workloads is thus desirable.

ApproachSimuBoost strives to close the performance gap between virtualization and functional simulation through the use of scalable parallelization. The core idea is to run the workload in a virtual machine (VM), taking checkpoints in regular intervals. Due to the difference in execution speed between virtualization and simulation, the spans between subsequent checkpoints can then be simulated and analyzed simultaneously in one job per interval. By transferring jobs to multiple nodes, a parallelized and distributed simulation of the target workload can be achieved, thereby reducing the overall simulation time.

Key challenges in SimuBoost are:

  • Checkpointing SimuBoost has to create checkpoints in short intervals (<1s - 2s) to bootstrap parallel simulations. To achieve a high speedup, the downtime, that is the time the VM has to be paused for each checkpoint, must be as short as possible. We use incremental copy-on-write checkpointing with asynchronous scanning of page tables to minimize downtime. To reduce the amount of data that needs to be stored and transferred to remote nodes, SimuBoost employs multicast data distribution with various data reduction and compression techniques that allow SimuBoost to operate in regular Gigabit Ethernet networks.
  • Functional Continuity Full system simulators usually implement a deterministic execution model. Using hardware-assisted virtualization, however, introduces non-deterministic behavior as devices work asynchronously to the CPU. In consequence, non-deterministic events (e.g., interrupts) appear at different points in the virtualization and simulation stages. This leads to state deviation, where the continuity at interval boundaries in the simulation breaks. SimuBoost logs non-deterministic events in the virtualization and precisely replays them in the simulation, keeping both stages synchronized. This also allows interactive workloads to be faithfully replayed in the simulation.

We have a working prototype of SimuBoost. Our evaluation confirms previous research that has shown significant speedup potential for the partitioning and parallelization of simulation time. SimuBoost demonstrates for the first time that the concept can also be very effectively used to accelerate continuous functional full system simulation. For most workloads, we measure a remaining slowdown of simulation over hardware-assisted virtualization of less than 30%, irrespective of the degree of instrumentation (tracing memory reads and writes). Only benchmarks with considerable run-time overhead during the checkpointing and recording phase show higher remaining slowdowns (apache: 120%, postmark: 63% – 81%).

Contact: Dr.-Ing. Marc Rittinghaus

Publications
Author Title Source

Dr.-Ing. Marc Rittinghaus

Dissertation, Fakultät für Informatik, Institut für Technische Informatik (ITEC), Karlsruher Institut für Technologie (KIT)

Marc Rittinghaus

Poster session of 8th Eurosys Doctoral Workshop (EuroDW 2014), Amsterdam, Netherlands, April 13, 2014

Marc Rittinghaus, Konrad Miller, Marius Hillenbrand, and Frank Bellosa

11th International Workshop on Dynamic Analysis (WODA 2013), Houston, Texas, March 16, 2013

Talks
Speaker Title Conference

Marc Rittinghaus

GI-BS Fachgruppentreffen, Fujitsu Augsburg, October 2016

Marc Rittinghaus

GI-BS Fachgruppentreffen, TU Braunschweig, April 2013

Student Projects
Author Title Type Date Advisor
Marco Schlumpp Bachelorarbeit 16.10.2019

Prof. Dr. Frank Bellosa
Dr. Marc Rittinghaus

Janis Schötterl-Glausch Masterarbeit 30.11.2018

Prof. Dr. Frank Bellosa
Marc Rittinghaus

Benedikt Morbach Masterarbeit 13.09.2018

Prof. Dr. Frank Bellosa
Marc Rittinghaus

 

Jan Ruh Masterarbeit 15.07.2018

Prof. Dr. Frank Bellosa
Marc Rittinghaus

Johannes Werner Bachelorarbeit 12.03.2018

Prof. Dr. Frank Bellosa
Marc Rittinghaus

Michael Zangl Masterarbeit 12.11.2017

Prof. Dr. Frank Bellosa
Marc Rittinghaus

Andreas Pusch Masterarbeit 26.10.2017

Prof. Dr. Frank Bellosa
Marc Rittinghaus

Simon Veith Masterarbeit 31.01.2017

Prof. Dr. Frank Bellosa
Marc Rittinghaus

Janis Schoetterl-Glausch Bachelorarbeit 31.10.2016

Prof. Dr. Frank Bellosa
Marc Rittinghaus

Nico Böhr Bachelorarbeit 30.09.2015

Prof. Dr. Frank Bellosa, Marc Rittinghaus

Jan Ruh Bachelorarbeit 06.09.2015

Prof. Dr. Frank Bellosa, Marc Rittinghaus

Bastian Eicher Masterarbeit 04.09.2015

Prof. Dr. Frank Bellosa, Marc Rittinghaus

Nikolai Baudis Bachelorarbeit 02.11.2013

Prof. Dr. Frank Bellosa, Marc Rittinghaus