Microservice-oriented Approach to Automation

of Distributed Scientific Computations

G.A. Oparin, V.G. Bogdanova, A.A. Pashinin, S.A. Gorsky

Matrosov Institute for System Dynamics and Control Theory of SB RAS, Irkutsk, Russia

bvg@icc.ru, gorsky@icc.ru

Abstract - We offer a microservice-oriented multi-agent

approach for solving computationally complex problems

arising in the course of scientific and applied research in

some subject areas. The computational experiments

specificity of a considered class of problems is stipulated by

the exponential increasing runtime due to increasing

dimension, multivariate calculations of the different input

data of the problem, as well as the variability of a

mathematical model and algorithm for solving the problem.

The use of microservices provides reusability, ease of

updating the components of a distributed application and its

cross-platform and allows operating with modularity

properties in new conditions of distributed computing when

inter-module communication is provided only through the

message passing mechanism. The designed software

platform for the offered approach automatizes both the

development of a distributed microservice application based

on an applied program package and the organization of

decentralized management of microservices composition.

New mechanisms for deploying and updating microservices

support the synchronization of cloud knowledge bases and

one installed on a user’s computer, providing an additional

opportunity using Dew computing paradigm that combines

the concept of Cloud computing with the capabilities of the

user's local computers. The practice of scientific

computational experiments has shown the effectiveness and

convenient in the usage of the offered approach.

Keywords - multi-agent control; Cloud computing;

microservices; knowledge base synchronization; microservices

deployment.

I. INTRODUCTION

In recent years, the questions of automating computational operations in the field of studying the dynamics and structural-parametric synthesis of control systems became very important in order to determine both the set of requirements for dynamic functioning indicators (for example, stability margin) and the set of required dynamic properties to which the system behavior should correspond [1]. Formation of the dynamic appearance of the control system and the associated complex of research projects carried out at the level of mathematical models, analytical and numerical methods of their studying. The process of investigating the control systems dynamics is a sequence of multivariate calculations — computational experiments. At each step of this sequence the structure and values of model parameters, methods and techniques vary, and planned calculations are performed. Future research direction is selected in dependence of experimental results estimation.

The primary objective of this research is to develop the infrastructure (instrumental and software environment) for automation of using the modern computing technology for qualitative analysis and parametric synthesis of dynamic control systems, in particular, binary dynamic systems (BDS), using tools and methods of knowledge bases and evidence-based programming. The development of models and methods for the study of the BDS dynamics is closely connected to the development of Boolean modeling technology, also related to our scientific interests.

The offered automation technology for high-performance scientific computing during the research in the mentioned above subject areas, in which algorithmic knowledge and ensemble programming play a fundamental role, here and after called HPCATAMP (High Performance Computing Automated Technology for implementation of Applied Microservices Package). Under the notion of ensemble programming, we mean the programming style in which an ensemble of ready-to-use (reusable) software event-driven components are integrated based on self-organizing mechanisms to solve a problem. The decentralized discrete-event calculation model is used. The primary concept in this technology is the concept of an application program package (APP). More accurately, the APP is a knowledge package representing one of the kinds of an intellectual system. The APP functional software is developed base on a modular principle. According to this principle, functional software is a set of basis, relatively simple, autonomous computational modules, the composition of which can be used to solve all the problems of the considered class. The microservice technology is appropriate for implementing APP based on the cloud computing paradigm. The distributed microservice model is represented by a set of small, loosely coupled, replaceable, interacting with the use of lightweight communication mechanisms autonomous microservices [2] that implement the functions of the component (modules) of the application. The applied program package implemented on the base of microservices will be called the applied microservice package (AMP).

The tendency of intensive development of research related to the creation and support of the functioning of resource-intensive distributed applications based on microservice architecture is observed in recent years. It actualizes both the improvement of existing approaches to the organization of interaction between microservices and the development of new ones. The majority of the

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research is related to the use of microservice technology in the field of IOT, microservice modeling is applied mainly in the business areas, while research related to scientific computing receives little attention. The complexity of difficult exhaustive problems with the properties of large-scale, openness, unpredictable dynamics, and component mobility determines the relevance of the development of microservice-oriented software for their solution. The proposed approach to automating the solution of such problems is subject-oriented, combining the advantages of microservice and multiagent technologies, in contrast to the existing ones. Correspondently with this approach the rights to perform microservices are delegated to intelligent agents functioning on the base of a discrete-event model and decentralized control of the process of solving an applied problem based on direct semantic interactions whose information support is the knowledge base and computational field [3, 4].

HPCATAMP technology is designed according to the offered microservice approach consists of models, methods, algorithms, software, and tools developed by the authors, and is oriented for research in the mentioned above subject areas. These tools are intended for creating and functioning control a microservices ensemble and automate the following steps of this process:

Microservices implementation on the base of the package modules.

Creating and configuring of the agents of a multiagent system (MAS) for managing computations.

Creating and configuring of the problem statement agents (PSA).

Deployment and testing of the microservices;

Update and synchronization user data.

The HPCSOMAS-MS tools considered in this paper are a development of early version HPCSOMAS 2.0 presented in [4], which has undergone significant changes, discussed in section III. For instance, the mechanisms for deploying and updating microservices that support synchronization of cloud- and knowledge bases installed on the user's computer are new in HPCSOMAS-MS. The synchronization ability provides an additional opportunity to operate using the currently developing Dew Computing paradigm, combining the concept of cloud computing with the capabilities of the on-premises computers [5].

II. RELATED WORKS

Microservices became a popular architectural style in software development [6]. The dynamism of the cloud computing environment, the complexity arising during the research in the subject areas of the difficult exhaustive problems having the properties outlined in Section I, leads to the intensive development of microservice-oriented software for their solution based on self-organization and the multi-agent approach [7, 8]. As an application of multi-agent technology in the implementation of the microservice architecture, there are works [9] and [10]. In [9], an automated lightweight «Microflow» approach is proposed for orchestrating semantically annotated

microservices using agent-based clients. An example of using the multi-agent system for decentralized self-adaptation of microservices based on Docker containers [10] is described in [11]. Authors recount various developments of self-adapting systems based both on a centralized approach [12] and on decentralized [13] or hierarchical [14] one. Distributed organization and decentralized management are indicated in [15, 16] among the main criteria for the quality and reliability of such software systems. In the same works, the disadvantages of using central node are associated with poor scalability, lack of reliability and data confidentiality issues. Multi-agent management of an ensemble of microservices based on direct interactions of agents provides better adaptability to dynamic environments and higher reactivity to external influences compared to indirect interactions [17, 18].

Taking into consideration the disadvantages and advantages of the above approaches, we use self-organizing MAS to organize decentralized management. Instead of using the program description of microservices choreography, as opposed to existing works, our approach uses a discrete-event model of the functioning of MAS agents, to which the rights to perform microservices are delegated. Informational support of direct semantic interaction of agents is a distributed knowledge base and computational field [19].

III. HPCATAMP TECHNOLOGY

A. HPCSOMAS-MS Architecture

HPCSOMAS-MS includes two new high-performance computing (HPC) automation subsystems based on the microservice approach. The first one is HPCMSBD (HPC Micro Services Building and Deployment) for building and deploying microservices. The second one is HPCMSMC (HPC Micro Services Multiagent Control) for managing the interaction of microservices (Fig. 1). With the help of HPCMSBD, the reactive CMA (Computational Microservice Agent) agents based on software modules and intelligent distributed control agents DSA (Distributed Solving Agent) are created in the form of microservices. The HPCMSMC subsystem automates the configuration of CMA- and DSA agents and supports their interaction during the problem solving based on a discrete-event model. HPCSOMAS-MS includes tools for updating microservices and synchronization of the agent’s knowledge bases (KB) and user’s databases (DB). As a KB, there is a computational model of the subject area, distributed the way that each agent has limited knowledge of both the capabilities of other system agents and the computational field (CF) topology as a whole. Interconnections of package modules and parameters are stored in the local KB of DSA agents.

A subject area (SA) is the fundamental feature of intellectual AMP. The SA is understood as a set of information about SA objects and the functional relationships between them, as well as the possibility of setting the problem in a non-procedural way: “calculate the values of objects from B0 set with the names b1, b2, ..., bn having the values of the objects from A0 set with the names a1, a2, …, ak ". Non-procedural problem formulation will be denoted here and after as

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);( 00 BAT . A non-procedural formulation is

transformed using planning tools into a problem solving scheme. On the base of the cloud computing paradigm, the planning of a problem solution scheme is carried out as a logical inference on a distributed computational SA model. The formalism in the form of a computational model [3] is used as the basis of the SA specification language.

B. Computational Model and Computational Field

The computational model is represented as a set of SA parameters and functional relationships between them. Each functional relation is implemented by a software module that calculates the values of the output parameters from the specified values of the input parameters. The CF is a set of networked logical computational nodes on which DSA agents are installed. The functionality of each agent is determined by the requirements for the inclusion of an agent-related module in the computational process of applied problem solving. A logical node is a physical computing resource, which can be: a set of processor cores and nodes of a computing cluster, a personal computer, a virtual machine, a mobile device. The zero value of the field is a trigger for the inclusion of the corresponding module in the process of solving an applied problem. The CF is discrete, the numeric value of the field in each node is calculated by the agent depending on CF state and in according with the rule of the field propagation over the network. The zero value of the field is a trigger for the inclusion of the corresponding module in the process of solving an applied problem.

C. Problem Statement Agent

For non-procedural problem statement on a distributed computational subject area model, the Problem Statement Agent (PSA) web-interface is provided. In the KB of PSA agent, the interconnections of modules and nodes of the CF are stored. In distributed and (or) cloud computing, the PSA agent is installed on dedicated computing nodes and is the entry point to the system. Another option (using Dew Computing technologies) allows installing the PSA agent with the minimum required functionality (DPSA agent) on the on-premises computer. Following the paper [5], we define our approach to the organization of calculations using AMP as the AMPiD category (Applied Microservices Package in Dew).

The user is provided with a PSA-interface for forming a problem solving request. For the non-procedural problem statement, the “Problem Solving” table is used. The user selects the SA model. As a result, a list of parameters appears, where the required parameters in the “Input” and “Output” columns should be marked. After the first step of the problem solving, two situations may arise. In the first case, an active group of agents will be formed (which will ensure that the output data values are determined by the given input data values at the stage of a joint solution of the problem), and the user will open a form for entering parameter values. In the second case, the user will receive a message that the problem cannot be solved. PSA- and DPSA-agents also support the operations with the following objects: the dictionary of SA parameters; microservices that implement the

functionality of applied subject modules; CF logical nodes associated with a specific computational resource; list of agents delegated to execute modules. Only the administrator and the developer of AMP have access rights to these objects. The screenshot of the PSA-agent for developers interface is shown in Fig. 2.

D. DSA Agents

A group of DSA agents performs decentralized control of solving the problem in AMP based on direct interactions. The mean to coordinate the behavior of agents in the formation of a group, and in the stage of joint actions is a way to control the behavior of agents “by the input data readiness” (event control). The developed by author discrete-event finite-automata model FSMwVW [3] is used as a behavioral model of the agent. At the stage of joint actions, having executed the module, the agent again goes into a waiting mode for input data or a signal about the completion of calculations. The agent includes a message decoder, a message handler, and a timer system (Fig. 3).

Figure 1. The scheme of interactions of HPCATAMP components

Figure 2. PSA web interface for developers

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Messages are stored in a queue and processed in the order they received. The DSA-agent type has the following basic modifiers: ordinary / control (consecutive / parallel). The control modifier is used for DSA agents with a predicate whose validation is performed before running the CMA agent. Parallel DSA agent is installed on the logical nodes of the CF, which corresponds to a multiprocessor computing resource.

E. Microservices Deployment

The architecture of the developed platform provides the resources for the automatic deployment of microservices and the installation of PSA-agents in automatic mode. The user needs to enter the values of required parameters in a web-interface of deployment service and select a set of microservices from a list. Installation of these microservices and additional software (if necessary) performs automatically with correspondent to given parameters.

The examination of the operability of microservices is performed correspondently testing described in [2]. The composition of the active group of agents, formed by the problem statement, is stored in the KB of the PSA-agent. In case of updating of the microservice, the active groups are automatically determined by the KB, in which this microservice is included, and the retesting caused by the update is automatically carried out only for these groups.

F. Synchronization

Under synchronization, we understand the process of keeping user space objects in the cloud resource and on an on-premises computer in an identical state. The synchronization is customizable: it is performed in automatic mode (by default), according to a timer signal, in command mode.

The synchronization service monitors the connection with the computers of d-users who have a DPSA-agent installed on on-premises computers. This information is entered with the user registration in his account – the Install field the «D» value is set. The value of this field is «C» for users working in the cloud. While internet connection lost, new data may appear in the cloud user space - the results of calculations made at this time. These updates are recorded in the table of updates. When an internet connection is restored, the cloud and local d-user space are synchronized using the update table (only objects specified in the synchronization service configuration settings). The configuration settings include lists of objects and synchronization modes. The results of the work are synchronized on a mandatory basis; the choice of the other objects is optional. By default, the automatic mode is used, in which the fixation of updates begins when the network connection to the Internet is lost, and synchronization is initiated when the connection is restored (Fig. 4).

User space is structured. Parameters, computational modules, computational experiment results, database, task templates are distributed in different folders. Information about significant results, including the date, the name of the result file, the name of the template, the solver, the time of the decision, is recorded in the user database. The

task template contains launch data - the task name, the actual parameters, set by entering a value or a file name. A user may restart task with this template.

IV. THE EXAMPLE OF AMP APPLICATION

Scientific studies, in which the proposed technology is used to build AMP, are carried out in several directions, listed in the introduction and described in [20, 21, 23]. The solution of computationally complex scientific and applied problems in the qualitative analysis of binary dynamic systems based on the Boolean constraints method [22] and parametric synthesis of feedback stabilizing connection for binary dynamic systems based on a logical approach [23] reduces to solving the systems of Boolean equations and requires creation of the Boolean models. Therefore, studies related to the development of Boolean modeling technology and the development of efficient parallel solvers of Boolean equations are carried out. Correspondently, multivariate calculations are required for the estimation of the model correctness, testing and

Figure 4. Syncronization cheme for Internet connection break

Figure 3. DSA agent architecture

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comparing the solvers, as well as evaluating the effectiveness of these solvers depending on the launch parameters. So we developed AMP based on HPCATAMP technology. This AMP includes a set of microservices intended to construct the Boolean model and solve the system of Boolean equations. These microservices are deployed correspondently there specifications (system and resource requirements). On the base of the proposed approach, a computational model of multivariate calculations is developed. As an illustrative example, we present a fragment of this model (fig. 5) for solving the problem of comparing two parallel MPI solvers HordeSat and developed by authors Hpcsat [24] and evaluating the efficiency of Hpcsat depending on the loading of slave processes (expressed by the number of subtasks [24]). The following briefly describes the parameters and functionality of microservices.

The VG2 microservice is designed to generate the next calculating variant (pair selection, BM model from the BML input list, starting with BMmin number to BMmax number with HBM step and solver from SL solver list with

HS step) and logical parameters (HRDHRP=1), for conditional launch of HordeSat and Hpcsat solvers correspondently by microservices SSHRD and SSHPC. The VG3 microservice is designed to generate the next calculating variant (pair selection, BM model from the BML input list and the slave process loading L changing from Lmin to Lmax with step HL). Variants generators VG2 and VG3 automatically change processor number p as

kp 2 from k=6 to k=9 (for this illustrative example).

The RS parameter is the result of the work of the solvers. In case of multivariate calculations, the RS result is processed by the PPHRD and PPHPC post processors (for HordeSat and Hpcsat, respectively). All RS of the variants are collected in an RA array, and then the GR graphic is plotted. The BEVAL estimates joined results RA and calculate scalability metrics [24]. The parameter CEP is a

composite parameter and includes simple, in particular, the type and object of estimation.

To compare solvers, let us add to the list of Boolean models (BML = {Pigeon11, Pigeon12, Pigeon13}) the problems of “pigeons and holes” of different dimensions. Solvers list SL = {HordeSat, Hpcsat} includes two solvers. To solve the problem of comparing solvers, the user needs to formulate the problem T = (A0 = {BML, SL, BMmin, BMmin, HBM, Smin, Smax, HS}; B0 = {GR}) (non-procedural formulation) using web-interface of PSA agent. As a result of logical inference on the computational model the active group of agent AG1 = {Ag1, Ag3, Ag4, Ag6, Ag7, Ag8, Ag9} is formed for solving the problem. Agents Ag3, Ag4, Ag5 is implemented as parallel. These agents are installed on nodes of a computational cluster. Fig. 6 shows the results of this problem solution. As results show, developed Hpcsat solver has an advantage in comparison with existing HordeSat.

Analogously, to solve the problem of loading dependency, the user needs to formulate the problem T = (A0 = {BML, BMmin, BMmin, HBM, LSmin, LSmax, HL, CEP}; B0 = {EVAL}). Corresponding agents group is AG2 = {Ag2, Ag5, Ag7, Ag8, Ag9}. Let the BML include testing UNSAT 3CNF [24], the dimension of which changes from 400 to 440 (variables number). Let L value increase in 4 times at each step (1, 4, 16, 64). Parameter CEP sets as CEP = {MARK|L|Hpcsat} to estimate the dependence of Hpcsat scalability from launch parameter L. The results of estimation are given in table 1. Metrics C1, C2 and C show contribution correspondently to a processors number, problem dimension and their integrated contribution [24] in the efficiency changes (the only parameter increasing when another launch parameter is fixed). These results allow concluding, that the increasing load of the slave processes helps to hold up decreasing of the efficiency for testing 3CNF (C1, C2 and C increase with increasing L). Agents Ag9 and Ag10 perform processing results and are less demanded on a resource. Such agents may be installed on the on-premises computer and, for example, to estimate received results while internet connection fails. The formed AG1 and AG2 group’s structure are stored in KB of HPCSOMAS-MS. Suppose that one of the microservices is update by a developer. The deployment service uses this information about the group structure for automated testing and

Figure 6. Comparison of solvers runtime.

Figure 5. Fragment of computational model

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launches tests only for groups, which include this microservice. Based on the above example, we can conclude that the user is given the opportunity to focus only on the problem formulation when conducting his research using describing above AMP developed on the base of offered technology.

V. CONCLUSION

The new technology and its basement software and tools are developed to automate scientific computations based on the proposed approach to the creation and operation of an applied microservice package, significantly reducing user time and efforts during computational experiments.

ACKNOWLEDGMENT

The present investigation was supported by Russian Foundation of Basic Research, projects no. 18-07-00596.

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TABLE I. METRICS

Loading C1 C2 C

1 -0,00956 -0,07615 -0,00911

4 -0,00371 -0,06357 -0,00768

16 -0,00154 -0,04019 -0,00489

64 0,000452 -0,02908 -0,00332

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