SimGrid VM: Virtual Machine Support for a Simulation Framework of Distributed Systems

Abstract

International audienceAs real systems become larger and more complex, the use of simulator frameworks grows in our research community. By leveraging them, users can focus on the major aspects of their algorithm, run in-siclo experiments (i.e., simulations), and thoroughly analyze results, even for a large-scale environment without facing the complexity of conducting in-vivo studies (i.e., on real testbeds). Since nowadays the virtual machine (VM) technology has become a fundamental building block of distributed computing environments, in particular in cloud infrastructures, our community needs a full-fledged simulation framework that enables us to investigate large-scale virtualized environments through accurate simulations. To be adopted, such a framework should provide easy-to-use APIs as well as accurate simulation results. In this paper, we present a highly-scalable and versatile simulation framework supporting VM environments. By leveraging SimGrid, a widely-used open-source simulation toolkit, our simulation framework allows users to launch hundreds of thousands of VMs on their simulation programs and control VMs in the same manner as in the real world (e.g., suspend/resume and migrate). Users can execute computation and communication tasks on physical machines (PMs) and VMs through the same SimGrid API, which will provide a seamless migration path to IaaS simulations for hundreds of SimGrid users. Moreover, SimGrid VM includes a live migration model implementing the precopy migration algorithm. This model correctly calculates the migration time as well as the migration traffic, taking account of resource contention caused by other computations and data exchanges within the whole system. This allows user to obtain accurate results of dynamic virtualized systems. We confirmed accuracy of both the VM and the live migration models by conducting several micro-benchmarks under various conditions. Finally, we conclude the article by presenting a first use-case of one consolidation algorithm dealing with a significant number of VMs/PMs. In addition to confirming the accuracy and scalability of our framework, this first scenario illustrates the main interest of SimGrid VM: investigating through in-siclo experiments pros/cons of new algorithms in order to limit expensive in-vivo experiments only to the most promising ones

Which is the best algorithm for virtual machine placement optimization?

“…Beside being used as is, CloudSim also serves as the basis for some specialized simulation environments, like CloudReports, ContainerCloudSim, or a simulation of scaling policies . A similarly well‐established simulator is SimGrid: As a simulation framework for distributed systems, it has existed for many years, but it has only recently been extended with the means necessary to model VM placement . DISSECT–CF is another alternative that has appeared recently and promises improved simulation speed but is still in an earlier phase of its development.…”

“…80 A similarly well-established simulator is SimGrid 81 : As a simulation framework for distributed systems, it has existed for many years, but it has only recently been extended with the means necessary to model VM placement. 82 DISSECT-CF is another alternative that has appeared recently and promises improved simulation speed 83 but is still in an earlier phase of its development. There are also simulators with special emphasis on the networking part of cloud computing, like GreenCloud.…”

Estimating Energy Consumption of Cloud, Fog, and Edge Computing Infrastructures

“…Yet, this simulator does not consider effect of packet length on energy consumption in its models. Finally, the SimGrid toolbox proposes accurate modeling of virtual machines [20], and energy consumption of servers [21]. But, the energy consumption of network components is not provided.…”

Putting the Next 500 VM Placement Algorithms to the Acid Test: The Infrastructure Provider Viewpoint

“…• Extensibility: after Grids, HPC and P2P systems, SimGrid has been extended with abstractions for virtualization technologies (i.e., Virtual Machines including a live migration model [21]) to allow users to investigate Cloud Computing challenges [27].…”

“…Such conditions are mandatory to perform simulations that are representative enough of CC production platforms [8], [9], [32]. Although several studies has been conducted on top of CloudSim [11], VMPlaceS has been designed on top of the latest extensions of SimGrid [21], a distributed system simulator that enables researchers to conduct cloud investigations. More precisely, we chose SimGrid because it integrates an accurate live VM migration model which is a key element when studying dynamic placement strategies [20].…”