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

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

METICULOUS: An FPGA-based Main Memory Emulator for System Software Studies

Due to the scaling problem of the DRAM technology, non-volatile memory devices, which are based on different principle of operation than DRAM, are now being intensively developed to expand the main memory of computers. Disaggregated memory is also drawing attention as an emerging technology to scale up the main memory. Although system software studies need to discuss management mechanisms for the new main memory designs incorporating such emerging memory systems, there are no feasible memory emulation mechanisms that efficiently work for large-scale, privileged programs such as operating systems and hypervisors. In this paper, we propose an FPGA-based main memory emulator for system software studies on new main memory systems. It can emulate the main memory incorporating multiple memory regions with different performance characteristics. For the address region of each memory device, it emulates the latencies, bandwidths and bit-flip error rates of read/write operations, respectively. The emulator is implemented at the hardware module of an off-the-self FPGA System-on-Chip board. Any privileged/unprivileged software programs running on its powerful 64-bit CPU cores can access emulated main memory devices at a practical speed through the exactly same interface as normal DRAM main memory. We confirmed that the emulator transparently worked for CPU cores and successfully changed the performance of a memory region according to given emulation parameters; for example, the latencies measured by CPU cores were exactly proportional to the latencies inserted by the emulator, involving the minimum overhead of approximately 240 ns. As a preliminary use case, we confirmed that the emulator allows us to change the bandwidth limit and the inserted latency individually for unmodified software programs, making discussions on latency sensitivity much easier

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https://library.naist.jp/mylimedio/dllimedio/show.cgi?bookid=100052666&oldid=98489博士 (Doctor)工学 (Engineering)博第643号甲第643号博士（工学）奈良先端科学技術大学院大

Adding Virtual Machine Abstractions Into SimGrid, A First Step Toward the Simulation of Infrastructure-as-a-Service Concerns

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 provides easy-to-use APIs, close to the real ones and preferably fully compatible with those of an existing popular simulation framework. In this paper, we present the current implementation status of a highly-scalable and versatile simulation framework supporting VM environments, extending a widely-used, open-source frame- work, SimGrid. 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 thousands of SimGrid users. Preliminary validations showed that the resource sharing mechanism of the VM support worked correctly

Adding a Live Migration Model Into SimGrid, One More Step Toward the Simulation of Infrastructure-as-a-Service Concerns

International audienceVirtual machine (VM) placement problem has been active research area over the past decade. The research community needs an open simulation framework that can accurately and scalably simulate virtual machine operations including live migrations. However, existing cloud simulation frameworks cannot reproduce live migration behaviors correctly. A naive migration model, not considering memory update operations nor resource sharing contention, can drastically underestimate the duration of a live migration and the size of migration traffic. In this paper, we propose a simulation framework of virtualized distributed systems with the first class support of live migration operations. We developed a resource share calculation mechanism for VMs and a live migration model implementing the precopy migration algorithm of Qemu/KVM. We extended a widely-used simulation toolkits, SimGrid, which allows users to simulate large-scale distributed systems by using user friendly programming API. Through experiments, we confirmed that our simulation framework correctly reproduced live migration behaviors of the real world under various conditions. Through a first use case, we also confirmed that it is possible to conduct large scale simulations of complex virtualized workloads upon hundred thousands of VMs upon thousands of physical machines (PMs)

Adding a Live Migration Model Into SimGrid, One More Step Toward the Simulation of Infrastructure-as-a-Service Concerns

International audienceVirtual machine (VM) placement problem has been active research area over the past decade. The research community needs an open simulation framework that can accurately and scalably simulate virtual machine operations including live migrations. However, existing cloud simulation frameworks cannot reproduce live migration behaviors correctly. A naive migration model, not considering memory update operations nor resource sharing contention, can drastically underestimate the duration of a live migration and the size of migration traffic. In this paper, we propose a simulation framework of virtualized distributed systems with the first class support of live migration operations. We developed a resource share calculation mechanism for VMs and a live migration model implementing the precopy migration algorithm of Qemu/KVM. We extended a widely-used simulation toolkits, SimGrid, which allows users to simulate large-scale distributed systems by using user friendly programming API. Through experiments, we confirmed that our simulation framework correctly reproduced live migration behaviors of the real world under various conditions. Through a first use case, we also confirmed that it is possible to conduct large scale simulations of complex virtualized workloads upon hundred thousands of VMs upon thousands of physical machines (PMs)

Probability distribution of write failure in a memory cell array consisting of magnetic tunnel junction elements with distributed write error rates

Write failure (WF) is a major reliability issue for applications of magnetoresistive random access memory (MRAM), and much effort has been devoted to reducing the write error rate (WER), which is the probability of write failures of a memory cell. Recently, it was shown that the WER of MRAM obeys a skewed probability distribution even though the variation in material parameters obeys a normal distribution. However, little is known about the effect of WER distribution on WF in a memory cell array. Here, we study WF in a memory cell array consisting of magnetic tunnel junction elements with distributed WERs based on numerical simulations. We simulated Bernoulli trials of writing, assuming that the WER obeys a beta distribution. The results show that for typical writing patterns, WF in a memory cell array obeys a binomial distribution, with the mean of the WER as the probability of success. The statistical properties of WF in a memory cell array are not affected by the variance and skewness of the WER. The results provide a basic understanding of the statistical properties of WF in a memory cell array and will be useful for the development of computing systems that exploit erroneous memories

Ethereum Fraud Detection with Heterogeneous Graph Neural Networks

While transactions with cryptocurrencies such as Ethereum are becoming more prevalent, fraud and other criminal transactions are not uncommon. Graph analysis algorithms and machine learning techniques detect suspicious transactions that lead to phishing in large transaction networks. Many graph neural network (GNN) models have been proposed to apply deep learning techniques to graph structures. Although there is research on phishing detection using GNN models in the Ethereum transaction network, models that address the scale of the number of vertices and edges and the imbalance of labels have not yet been studied. In this paper, we compared the model performance of GNN models on the actual Ethereum transaction network dataset and phishing reported label data to exhaustively compare and verify which GNN models and hyperparameters produce the best accuracy. Specifically, we evaluated the model performance of representative homogeneous GNN models which consider single-type nodes and edges and heterogeneous GNN models which support different types of nodes and edges. We showed that heterogeneous models had better model performance than homogeneous models. In particular, the RGCN model achieved the best performance in the overall metrics.Comment: 8 pages, 5 figures, Accepted to KDD'22 Workshop on Mining and Learning with Graph