This document presents a hybrid approach to modeling Storage Attached Networks (SAN) using a combination of deterministic simulation and machine learning. A Software Package for Operation Simulation (SPOS) is developed to simulate SAN components and their interactions. A Set of Algorithms for Automated Parameter Settings (SAAPS) uses reinforcement learning to dynamically adjust SPOS parameters based on real SAN data, improving simulation accuracy even outside training ranges. This hybrid approach reduces development costs compared to detailed simulation alone or machine learning without a simulator framework.

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 9, Issue 11, November 2018, pp. 220–226, Article ID: IJCIET_09_11_022
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=11
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
HYBRID APPROACH TO DESIGN OF STORAGE
ATTACHED NETWORK SIMULATION
SYSTEMS
M.E. Karpov, K. Arzymatov, V.S. Belavin, A.A. Sapronov and A.E. Ustyuzhanin
National Research University Higher School of Economics, Moscow - 101000, Russia
A. V. Nevolin
YADRO, Moscow - 123022, Russia
ABSTRACT
Simulators of real-world IT systems are gaining popularity today. However, as it
often happens in the early stages of technological readiness, the same term can be
understood as different things - from visualisation systems to multi-level multi-agent
models. The critical feature of the simulation technology is the degree of trust, or
proximity of resemblance of their behaviour to the objects of simulation from the real
world. The article presents for the first time an overview of a hybrid approach to
modelling Storage attached networks (SAN), in which the parameters of an
approximate simulator are dynamically adjusted using machine learning methods, i.e.
reinforcement learning. Particular attention is paid to the analysis of the strengths
and weaknesses of the existing approaches of simulation and comparison the hybrid
approach presented in the article.
Keywords: simulator, system modelling, control optimisation, reinforcement
learning, storage attached network
Cite this Article: M.E. Karpov, K. Arzymatov, V.S. Belavin, A. V. Nevolin, A.A.
Sapronov and A.E. Ustyuzhanin, Hybrid Approach to Design of Storage Attached
Network Simulation Systems, International Journal of Civil Engineering and
Technology, 9(11), 2018, pp. 220–226.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=11
1. INTRODUCTION
The most common form of discrete simulation programs is modelling of sequence of discrete
events in which events of interest occur at discrete points in time. Discrete events include for
example user logon, errors, system failure, repair or replacement of components, etc. More
complex simulations require more development effort, but the principle remains the same. A

2. M.E. Karpov, K. Arzymatov, V.S. Belavin, A. V. Nevolin, A.A. Sapronov and A.E. Ustyuzhanin
http://www.iaeme.com/IJCIET/index.asp 221 editor@iaeme.com
chain of events is created, which is ordered by time in the transition from one event to
another, and in parallel statistics with changes in system parameters are collected.
The steps needed to build a correct discrete simulation algorithm:
 carefully analyse which components the system consists of, how these
components interact with each other under different loads and other external
conditions;
 list and classify events of interest;
 determine dependencies and causal relationships between events;
 determine the system statuses, methods of their description and transitions
between them;
 to estimate the ranges and distributions of the input parameters of the simulation;
 determine the observed values, set up the collection of statistical data on the
functioning of the simulation program.
An additional complication is the need to take into account the distribution of modern
systems operation in which the definition of “state” can be blurred between possible different
configurations of individual components.
Following these notes consider an example of already existing storage system's simulator,
namely, a software package of a CODES project [1, 2]. This project is developed by
researcher teams from Computer Science and Math department of Argonne National
Laboratory and Rensselaer Polytechnic Institute (US). The CODES simulator is based on the
technologies of the Rensselaer's Optimistic Simulation System (ROSS) which allows parallel
execution of event-driven system that can significantly decrease runtime of the simulation.
The main CODES use cases include large-scale storage systems, scientific distributed
applications, parallel and high performance computing systems with high-load input/output
operations and computational complexity. The C++ based ns3 framework [3] is also popular
among scientific researchers. The ASL simulation toolkit offers broad range of options for
defining own behavioural functions with subsequent deployment on parallel core
architectures [4]. The general approach to network-like structure simulation is the OMNeT++
framework [5]. Another work presents a simulation compliant with the fiber channel
technology often used in contemporary SAN architectures is developed as the SANSim tool
[6]. More simulation methods descriptions and studies dedicated to SAN system modelling
can be found in [7-11].
All of these works discuss the problem of storage system simulation from the
deterministic point of view, having component parameters defined at the beginning of the
simulation. The practical solutions for real-life storage systems should reflect a potential for
components qualities alteration under user’s load, degradation or even faults.
The alternative method for generating events of hidden states is to use machine learning
methods, notably generative adversarial networks (GANs) [12]. The GANs are quite popular
nowadays and have shown promising results in various fields of science and techniques [13-
16]. But the lack of huge training set can be a formidable challenge along with a poor
predictive capability beyond the training domain. This approach cannot be used in case of
small number of rare events.
The topic of simulation parameters optimization using reinforcement learning is generally
discussed in [17] and similar approach is discussed in application to specific range of Process
Network Synthesis simulation problems [18].

3. Hybrid Approach to Design of Storage Attached Network Simulation Systems
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An example of a Storage Area Network failure events simulation algorithm [19] is shown
at Fig. 1.
Figure 1 An example of a SAN failure events imitation algorithm
Any simulation program requires the setting of some number of input parameters.
According to the simulation results, it is often possible to obtain a very small error of the
observed values in comparison with the characteristics of the real system due to the
possibility of collecting a large amount of data. However, often the developers of the model
have to keep a balance between its accuracy and development costs [20]. In this context, the
best solution may be to use the following hybrid modelling approach.
2. SAN MODELLING
Below the hybrid approach to the modelling of storage system simulation is described. The
deterministic model of the SAN storage is created and includes most important functional
elements of the architecture. With the help of controlling agent, trained by reinforcement
learning, this model is adapted to significantly improve the quality of simulation process.
A software package for operation simulation (SPOS) of the SAN and a set of algorithms
for automated parameter settings (SAAPS) were developed to implement this idea. The SPOS
describes the structure of a SAN and the interaction of its components. The tasks to be solved
by the SPOS are the following:
 simulation of the storage components (storage controllers, disk chassis, PCI
Express, load balancer);
 modelling of SAN in the read/write/store mode;
 implementation of anomaly simulation in the final disc-based media, storage
controllers, connecting networks;
 imitation of synchronous/asynchronous interaction between client and storage.
Components shown in Fig. 2. have relationships as part of the simulation of the hardware
of the storage at the logical level.

4. M.E. Karpov, K. Arzymatov, V.S. Belavin, A. V. Nevolin, A.A. Sapronov and A.E. Ustyuzhanin
http://www.iaeme.com/IJCIET/index.asp 223 editor@iaeme.com
Figure 2 Logical representation of the hardware part of SAN as a set of interrelated hardware
components
SPOS is a program written in Go, programming language, based on functions imported
from the standard language library. The realization of the simulation is implemented on the
basis of a discrete-event model, where the system changes represent a chronological sequence
of events. The events occur at certain points in time and signify a change in the state of the
system.
The tasks of SPOS are the following:
 execution of external control actions scenarios;
 generation of time sequences of SAN states depending on external control actions
scenarios.
While running simulation at SPOS, SAN sends messages every discrete step of time as a
JSON object to SAAPS. This service uses time sequences of data with the current state of the
storage simulation to make decisions about such internal parameters of the simulator as:
 the effective bandwidth of network connections;
 network delay;
 computing resource of the storage controllers;
 read/write speed at disk drives.
Anomaly scenarios and degradation of the performance of SAN are also incorporated in
controlling functions of SAAPS:
 degradation of the network;
 network disruption;
 degradation of computing resources;
 breakage of the storage controller.
These scenarios are also induced by machine learning algorithms of SAAPS (neural
networks) trained on data from the real SAN of the corresponding configuration.
The usage of the combined scheme of the simulator and the neural network allows to
bypass the fundamental shortcomings of the latter, which do not allow to make realistic
predictions outside the training range. The possibility of extrapolation beyond the training

5. Hybrid Approach to Design of Storage Attached Network Simulation Systems
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sample is provided by the deterministic nature of the simulator, and the prediction accuracy
with limited detail of the latter by the neural network.
The main advantages of this approach are low requirement for detailed simulation and, as
a result, a significant reduction in the intellectual cost of its development. Compared to other
approaches, parameter adjustment does not require major changes within developing service.
Due to the use of methods of reinforcement learning [21], SAAPS allows you to configure
the desired parameters online. Within the framework of reinforcement learning the following
entities have to be defined:
 controlled object - the object under study;
 environment - the system that interacts with controlled object and which holds the
object;
 agent – a system that can control the environment;
 reward – a measure that determines the goodness of the controlled object state
change.
The goal of the reinforcement learning algorithm is to bring the controlled object to some
specific target state by means of finding optimal control strategy.
In our case the environment and controlled object (SAN) is described by the simulator
state and set of external (environmental) parameter, the target state is a degree of realism of
the simulation of SAN, reward is a distance between real SAN behaviour and simulated
behaviour. Agent’s functions are implemented by means of Q-learning algorithms and neural
networks. It should be noted that training of such hybrid model requires non-negligible
computational resources for numerous runs of different scenarios of SAN model behaviour.
The results of neural network training are shown in Fig. 3, which displays the average
value of the metric on the set of loads used in training with a confidence interval. The smaller
the value of the metric, the closer our system is to the reference one [22].
Figure 3 The average value of the metric on the set of loads used in training with a confidence
interval

6. M.E. Karpov, K. Arzymatov, V.S. Belavin, A. V. Nevolin, A.A. Sapronov and A.E. Ustyuzhanin
http://www.iaeme.com/IJCIET/index.asp 225 editor@iaeme.com
3. CONCLUSION
Today there is a wide choice of modelling software tools for SANs. Their common feature is
a detailed description of the structure of the simulated system and, thus, it is rather difficult to
customise to a specific instance, and it requires a significant investment of time and human
resources. In the approach, when all modelling is implemented solely by in-depth training
methods, it is necessary to use a lot of data to train such models.
A simulator with automatic parameter tuning developed by the authors of this article is a
synthesis of these two approaches, namely, forming the logical structure of the physical
processes of the data storage system and using reinforcement learning methods that allow
changing the parameters of the storage hardware components on the fly. Such a hybrid
provides greater ease of setup and high realism even outside of the training sample ranges.
ACKNOWLEDGMENTS
The research was carried out with the financial support of the Ministry of Science and Higher
Education of Russian Federation within the framework of the Federal Target Program
“Research and Development in Priority Areas of the Development of the Scientific and
Technological Complex of Russia for 2014-2020”. Unique identifier – RFMEFI58117X0023,
agreement 14.581.21.0023 on 03.10.2017.
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