Automated tools for the development of microservice compositions for hybrid scientific computations

Abstract

In recent years, a significant amount of research is focused on the development of tools for creating composite web-services for solving both business and scientific complex problems. This study discusses tools for building compositions or ensembles of microservices (depending on the method of integration) developed based on the HPCSOMAS framework. These tools are oriented on the application in a package of applied microservices for solving computationally complex problems of structural analysis and parametric synthesis of controlled dynamic systems in a heterogeneous high-performance computing environment. In particular, binary dynamic systems are studied using the Boolean constraint method for both their qualitative analysis and synthesis of laws to control these systems. Creating and executing composite services is carried out on a semantic peer-to-peer network of agents. The HPCSOMAS framework supports two modes of these processes, both the static creation and application of a composite service based on the procedural formulation of the problem and dynamic, based on the declarative formulation. In the first case, agents deployed on the network perform hierarchical control over the execution of the composition of microservices, in the second case, decentralized asynchronous management of the ensemble of microservices. Both operating modes are automated, and the validity of the resulting composite service is checked based on a logical approach. The tools are aimed both at a professional programmer and the end-user, a specialist in the subject domain. The HPCSOMAS framework supports the execution of composite microservices in a hybrid computing infrastructure, which includes both cloud and on-premises resources.

1. Introduction

The complexity of the modern heterogeneous distributed computing environment (HDCE) prevents the widespread use of its capabilities by specialists in the subject domain. Therefore, the demanded trend is the development of specialized approaches that free the end-user from the need to study the technical details for the creation of distributed service-oriented applications. As a result, in recent years, a considerable number of research efforts focus on the development of frameworks for creating composite web services for solving both business and scientific complex problems [ 1 ]. One of the advantages of service-oriented computing is the ability to combine several services to get more meaningful and complex ones [ 2, 3 ]. A composite web service can be created either proactively or reactively [ 4 ]. In the first case, components of the composite web service are assembled in advance, before runtime. In the second case, a composite web service is created dynamically at the request of the user. This request is executed in the ever-changing HDCE, so users need more adaptive and personalized services [ 5 ]. The most general standards and research approaches developed over the last years for web service composition are described in [ 6 ]. New challenges concerning this problem are mentioned in this publication also, in particular, challenges of automated RESTful services composition and its orientation to the subject domain.

Our research is focused on the development of service-oriented applications based on HPCATAMP

technology. These applications are designed for solving computationally complex problems of the subject domain of structural analysis and parametric synthesis of controlled dynamic systems in

HDCE [7]. In particular, binary dynamic systems (BDS) are studied using the Boolean constraint

method [ 8 ] for both their qualitative analysis and synthesis of laws to control these systems. Currently, there is an intensive development and complication of controlled dynamic systems. Despite a large number of existing methods [ 10 ], the problem of synthesis of control laws for various classes of these systems remains relevant.

The above HPCATAMP technology is implemented based on the HPCSOMAS platform [10], the

basic version of which is periodically modified based on experience in solving problems from the above domains, for example, [ 11-13 ]. In this study, we describe the multi-agent tools of the cloud version of this HPCSOMAS-MSС platform (version HPCSOMAS 3.2) for creating composite services in the form of an ensemble of microservices implemented based on the RESTful architectural style.

Compositions and ensembles of microservices are used when performing computations in the package of applied microservices (AMP [7]). As an illustrative example, we describe the application of composite services of the AMP for solving problems of qualitative analysis of BDS.

2. Related work

In review papers [3, 15-20], various issues of the development of composite services are considered.

As noted in [ 3 ], most approaches are based on the composition of the workflow (WF) or methods of artificial intelligence (AI), in particular, AI-planning. The paper [ 3 ] mainly addresses the issues of comparing dynamic composition based on these two approaches. In general, the WF approach is used in the situation when a process model is already defined in the user request, so the search for atomic services may be automated. AI-planning methods are used when the user request does not have a process model but has many constraints and user preferences. In this case, a process model is automatically generated. In [ 15 ], a consolidated structure of the analysis of models, languages, methods, platforms, and tools for the composition of services is presented. Several representative systems (in particular, [21-23]) that automate the composition of services were analyzed in [ 15 ] based on criteria of application, notation (visual or textual), paradigm (Flow-based, Rule-based, Querybased), composition constructs, and the target user. Most systems are based on the Flow paradigm, focused on business processes. The target users of the analyzed systems are mainly professional programmers, and only three of the twelve systems are aimed at the end user-programmer, an expert in the subject area. In [ 15 ], there is a need for the development of compositional tools that can be mastered not only by professionals. The necessity of extending the composition of services and developing composition methods to ensure the integration of cloud resources with heterogeneous highperformance environments is substantiated. Despite the variety of tools based on the WF approach presented in later publications [24–26], some questions remain relevant, in particular, the target user qualification, decentralized control, adaptive execution models, high-level composition tools, faulttolerance. An alternative to the WF approach is the approach based on AI-planning [ 3 ]. In [ 16 ], the advantages and disadvantages of AI methods are considered. In particular, it is noted that AI-planning is suitable for dynamically creating web services with incomplete information, but the main problem in planning is scalability. Also, this planning is unsuitable for web services composition based on choreography, which implies decentralized control, parallel workflows, and unexpected circumstances. Issues related to the compatibility and the provision of portability for the efficient composition of web services in heterogeneous and decentralized environments also remain open. In [ 17, 18 ], AI-planning is performed based on decentralized multi-agent control. However, after the construction of the plan, its execution is centralized. In [19], the development of a service composition system based on the declarative description and usage of the peer-to-peer (P2P) paradigm that provides the execution of the resulting composite services based on decentralized control is presented.

In [20], a review of existing methods, approaches, and standards for web services discovery and

composition development were performed. As noted in this publication, the verification of composition validity is unused in most of the considered approaches for composition development, so the composition specifications in the form of mathematical formalisms are desirable for such verification.

Given the above advantages and disadvantages of existing approaches, we use a hybrid approach, providing the developed tools with the ability to carry out the microservices composition in both static and dynamic ways. This approach is based on the multiagent-based P2P network and microserviceoriented technology. Under the composite service, we mean the composition of microservices in static mode and the ensemble of microservices in dynamic. Composite services based on the proposed approach are designed to solve the above scientific problems and are focused on the use in a hybrid computing infrastructure, which includes both on-premises and cloud resources.

3. Creating composite service based on HPCSOMAC-MSC

Depending on the particular computing problems of the subject domain, as well as the requirements and preferences of the user, the HPCSOMAS-MSC platform provides two modes for creating a composite service, static and dynamic (figure 1). In the first case, based on the procedural formulation of the problem, a composite service is fully prepared, and only then it is launched. In the second case, for the non-procedural problem statement (NPS), the ensemble of microservices is composed dynamically in the process of computing. The dynamic composition of web services is a promising approach [ 8 ]. This approach is in demand when changes to the runtime requirements are frequent, and when a complete set of services cannot be specified during development [ 6 ]. The dynamic composition provides automating problems associated with the assembly of microservices. However, it limits the freedom of users in the process of this assembly [ 6 ]. The static one is suitable in the case of the functional requirements for the composite service remain unchanged for a long time [ 6 ]. When solving problems in the subject domain mentioned in the introduction, both of these cases may occur.

Therefore, the HPCSPMAS-MSC framework supports both the two modes, static and dynamic

creation and execution of composite services. The static mode is provided by the AMP manager-agent (AMPMA) and computational microservices agents (CMA). For dynamic one, the AMPMA initializes distributed computational agents (DCA). All agents are created during the AMP development based on

HPCSOMAS-MSC programming or automating tools. Figure 1. The HPCSOMAS-MSC architecture and AMP components.

3.1. Static composition of the microservices

The static composition is performed in several stages in the procedural formulation of the problem (figure 2):

The user sends a request to the AMPMA, in which the sequence of microservices for solving the problem (microservice composition scheme) is given. The description of AMP microservices functionality is provided by the AMPMA. Additionally, the user specifies nonfunctional requirements.

Based on the semantic description of microservices stored in the knowledge base (KB) in the form of fragments of the relations In  P  M and Out  M  P of microservices (M) with parameters (P), the AMPMA performs information planning, which is a verification of the admissibility of the microservice composition scheme. In the scheme {M1(; A1), M 2 ( A2; B2 ),...,M n1( An1; Bn1), M n ( An ; )}, microservices, the AMPMA adds two microservices where n is the number

of M1 and

M n for, correspondently,

sending input data (the set Ai ) and receiving output data (the set Bi ).

The search for the microservices required by the scheme is performed on the AMPMAs

installed on the HDCE nodes.

The validity of the microservices composition is checked according to the following condition. A composition scheme is admissible if, for any input parameter of any microservice in this scheme, there is exactly one previous microservice with the same output parameter. The following Boolean equation corresponds to this condition of the composition admissibility [27]: tn2 np1(stp  y)  0 , (1) where   qAp  y  1, if ( Ap  0)  ((q  Ap )(Bq'  0))  0, if Ap  0. 

(lr11 lgr1 (zr  z g )  lr1 zr ), if ( Ap  0)  ((q  Ap )(Bq'  0))

In equation (1), the matrix S is a matrix of the dimension n  n of Boolean variables sij . The S

describes the execution scheme of the composite microservice. The element sij  1 if the microservice M j  M ( j  1, n ) is in the i-th string of the matrix S. Matrices A and B are Boolean matrices of the dimension n  m . Elements of these matrices are formed as follows: aij  1 ( bij  1) if the parameter Pj is the input (output) one for the microservice M j . Let Ai , Bi and Ai' , Bi' ( i  1, n ) are correspondently the strings and columns of matrices A and B. The m is the number of parameters of the execution scheme S. The values of strings and columns are zero if unit values are absent in these matrices elements. The z is the array of the length l consisting of elements of the matrix S. The array z is formed as follows:

z  {sij : i  1,t 1, j  Bq' } .

Microservices have the following types:

   

Atomic, performing one specific computational function,

Simple, consisting of an atomic microservices sequence,

Flow Pattern, used by the computational microservices agent (CMA) to control the flow of tasks,

Composite, consisting of a composition of the above microservices and embedded composite

microservices.

The microservice composition scheme describes a plan for conducting a computational experiment to solve a specific problem of a subject domain. This scheme is built based on a computational model of a subject domain and organizes the logic of execution of the microservice composition using task flow control templates. As templates, ones similar to basic control flow patterns and advanced branching and synchronization patterns [28] are used. A hierarchical control system is used to interpret the composite service. CMAs that run Flow Pattern microservices distribute tasks on the computational layers during execution. For example, for the parallel split template, the parallel subtasks are executed at this layer. Double numbering (<layer number>.<New layer number>) allows the dynamic creation of a new layer between two existing layers. Control by layer subtasks execution is carried out by the agent creating this layer. After the layer subtasks are completed, the agent returns control higher in the hierarchy. 3.2. Dynamic creating the microservices ensemble

Based on the dynamic approach, the microservice ensemble is assembled by a group of Distributed

Computational Agent (DCA) deployed in a P2P semantic network. When registering a microservice on DCA, the description of the microservice interface is recorded in the local agent KB. All DCAs have the same behavioral model based on the agent’s knowledge and reactions to external and internal events. The dynamic assembly of microservices is performed as follows (figure 3):  The user performs an NPS in the AMPMA interface.  The AMPMA checks the admissibility and redundancy of the NPS. At the first stage, by a distributed logical inference method developed by the authors based on the “direct wave” method, the possibility of solving the problem on a distributed on HDCE nodes KB is checked. In the case of solvability, an active DCA group is formed. Agents of this group control the launch of microservices from the ensemble of microservices corresponding to this group. The set of ensemble microservices are redundant if some input parameters of the NPS do not affect the achievement of the goal. In the second stage, such parameters are excluded from the NPS using the distributed backward-wave method applied to the set of output parameters. The implementation of the distributed logical inference method based on the discrete-event finite-state model FSMwVW (Finite State Machine with Variables and Works)  of an agent behavior is presented in [29]. The modified model FFSMwVW is described in [30].

A cooperative solution of the problem is performed by agents based on decentralized asynchronous control (on input data readiness) by direct P2P interactions defined by fragments of the In and Out relations specified in the local DCA KB. Figure 3. The dynamic composition.

4. Illustrative example

In an illustrative example, composite services are used in the developed by authors AMP BCMQABDS for qualitative analysis of BDS based on the Boolean constraint method (BCM, [ 8 ]). The computations are performed in a hybrid infrastructure, resources of which are given in table 1. The resource integration in this infrastructure is carried out by the installation of AMPMAs on this resource. The AMPMA must be configured after the installation. This configuration is set in the MSCDT [31] agent editor (figure 3). In figure 3, the configuring the AMPMA on the VDS resource is shown. The AMPMA has neighboring (connected agents) AMPMAs, the set of CMAs that can be seen in the scrolling list (Compute services list in figure 4). AMPMA with the microservice for building the Boolean model, verifying its feasibility (sequential SAT and 2QBF solvers), and post-processing the results can be installed on on-premises computers. Parallel solvers are installed on on-premises high-performance clusters. Additional resources are allocated using Cloud Resources Agent (CRA) from the public cloud (VDS for the example).

As an example of applying a composite service created based on a dynamic approach, we consider the

problem of searching for attractors and their basins in an autonomous asynchronous BDS.

In [1], we consider a synchronous autonomous BDS, the vector-matrix equation of which has the form

xt  F (xt1) ,

Taking into account (4), equation (3) can be rewritten as:

Φ1(x0,x1)  in1(xi1  Fi (x0 ))  0 .

Φk (x0,x1,...,xk )  tk1Φ1(xt1,xt )  0 . where x is the state vector of the dimension n ( x  Bn , B  {0,1} ), t T  {1,2,...,k} is the discrete time, and the F(x) is the vector-function of logic algebra called the transition function ( F : Bn  Bn ).

Let us define the trajectory x (t, x0 ) of (1) for each initial state x0  Bn as the finite sequence of states

x0 , x1,...,xk from the set B n . The state xi is a successor of the state xi-1, and xi-1 is a predecessor of the state xi. In an autonomous BDS, each state has only one successor, and the number of predecessors of this state can vary from zero to 2n-1. A cycle of the length k is a closed trajectory (x0 = xk) in which all other states are pairwise distinct. An equilibrium state is the cycle of the length k=1. A cycle is called an isolated if predecessors are absent for it.

The non-isolated cycle is called an attractor. Let X* be the attractor. The region of attraction (basin) of an attractor of the radius k is the set of all states, from which the set X* is reachable in i  k time steps.

Equation (2) is equivalent to a single Boolean equation of the form Φk (x0,x1,...,xk )  t1 in1 (xit  Fi (xt1))  0 , k where xit and Fi are i-th components of vectors xt and F;  denotes modulo-2 addition. For one-step transition (k = 1), equation (3) takes the form (2) (3) (4)

Problem statement. It is required to find attractors and their basins for the given BDS.

The Boolean models of BDS dynamical properties required for solving this problem are given in table

2. These models were constructed based on the BCM. In figure 4, the fragment of the computation model for solving the above problem is given. Table 2. Required Boolean models. Problem

In table 2, the equation G* (xk )  0 determines a set of goal states [8].

For the dynamic approach, the following NPS is formulated using the web-interface of the AMPMA:

T1 = ( A10  {L, k}; B10  {A _ IP, ABAS,Y _ BAS}) .

The meanings of input parameters A10 and output parameters B10 are given in figure 5. As a result of the forming ensemble stage, the microservices ensemble is assembled from the following microservices: {1, 2, 5, 6, 8, 9, 12, 13}.

We use the NPS T1 for searching basins of attractors for the given BDS of the dimension n=11 using the example of gene regulator network from [34], dynamic of which is described by the following equations: x1t  x1t1,x2t  x2t1  x8t1  x1t1  x2t1  x1t1  x8t1,x3t  x3t1  x8t1  x1t1  x3t1  x3t1  x8t1,x4t  x2t1, x5t  x4t1  x5t1  x6t1  x8t1  x5t1  x6t1  x8t1  x1t01  x4t1  x5t1  x8t1  x1t01 x4t1  x5t1  x6t1  x1t01   x5t1  x6t1  x8t1  x1t11  x4t1  x5t1  x8t1  x1t11  x4t1  x5t1  x6t1  x1t11  x5t1  x8t1  x1t01  x1t11   x5t1  x6t1  x1t01  x1t11  x4t1  x5t1  x1t01  x1t11  x4t1  x6t1  x8t1  x1t11  x6t1  x8t1  x1t01  x1t11   x4t1  x8t1  x1t01  x1t11  x4t1  x6t1  x1t01  x1t11  x4t1  x6t1  x8t1  x1t01, x6t  x5t1  x6t1  x1t01  x3t1  x6t1  x1t01  x3t1  x5t1  x6t1  x3t1  x5t1  x1t01, x7t  x4t1  x6t1  x7t1  x8t1  x6t1  x7t1  x8t1  x1t01  x4t1  x7t1  x8t1  x1t01  x4t1  x6t1  x8t1  x1t01  (5)  x4t1  x6t1  x8t1  x1t01, x8t  x5t1  x6t1  x7t1  x8t1  x5t1  x7t1  x8t1  x9t1  x6t1  x7t1  x8t1  x9t1  x5t1  x6t1  x8t1  x9t1   x5t1  x7t1  x8t1  x1t01  x6t1  x7t1  x8t1  x1t01  x5t1  x6t1  x8t1  x1t01  x7t1  x8t1  x9t1  x1t01   x5t1  x8t1  x9t1  x1t01  x6t1  x8t1  x9t1  x1t01  x5t1  x6t1  x7t1  x1t01  x5t1  x7t1  x9t1  x1t01   x6t1  x7t1  x9t1  x1t01  x5t1  x6t1  x9t1  x1t01  x5t1  x6t1  x7t1  x9t1, x9t  x6t1  x8t1,x1t0  x8t1  x9t1,x1t1  x8t1  x1t01  x9t1  x1t01  x8t1  x9t1.

In (5), the indexes of the components xi0 of the Boolean state vector x in the time t=0 corresponds the following order of the gene regulator network [34, 35]: Cln3; SBF; MBF; Cln1,2; Sic1; Cln5,6; CDh1;

Clb1,2; Mcm1; Cdc20; Swi5. The sign “ ” denotes logical conjunction.

As a result, seven equilibrium states were found, including an isolated fixed point and six attractors (table 3). The presented results are consistent with those obtained in [34, 35]. The decimal code of the attractor state is calculated by the formulain1ai 2i1 .

For example, the decimal code of the attractor (01010000000) (table 3, no. 1) is calculated as 0*1+1*2+0*4+1*8+0*16+0*32+0*64+0*128+0*256+0*512+0*1024+0*2048=10. Attractor basins (ABAS) of the maximum radius k (RAB) are used for the visualization (figure 6-11). For this, the NPS T2= ( A20= {ABAS, Y_BAS};B2O = {PDAB}) is formulated. Using the resulting parameter PDAB (plotter data) of the NPS T2 that stores in the user folder on the AMPMA resource, the developed by authors graphical microservice (Grapher) of the BCM-QABDS visualizes online a basin for the attractor no.1 from table 3 (figure 6).

The Grapher interface allows the choice of visualization options (figure 6). For example, the first two options set the horizontal increasing radius of the basin tree and the absence of marks (the decimal attractor state code) at the graph vertices. Other basins of attractors (table 3) with labeled vertices of the graph are shown in figures 7-10. In the case of a large basin size, its structure is visualized using a radial tree (set by Graph type in the interface). Attractor number 7 from table 3 is shown in the form of a radial tree (Fig. 11). The attractor is marked in red. The service Grapher was developed using the static microservice composition because requirements to its functionality are unchanged. Solving complex problems is carried out by formulating NPSs in the AMPMA interface with the subsequent creation and execution of the corresponding ensembles of microservices. In the presented example, NPSs T1 and T2 were used for finding the equilibrium states of a given BDS, determining whether they are attractors, finding attractor basins, and the post-processing of results for the visualization.

BCM-based approach to solving problems of qualitative analysis of BDS functioning on a finite time interval is achieved by constructing Boolean models of dynamic properties, checking their satisfiability by SAT or 2QBF solvers, and post-processing solutions. Unlike other SAT-oriented approaches, Boolean models contain constraints describing both dynamics of BDS and the specification of the dynamical property. BCM is a declarative method that provides the possibility of data parallelism by splitting the Boolean model and distributed solving the obtained independent subtasks. Thus, this approach allows us to significantly increase the dimension of the BDS state vector and the period of its functioning when solving the problems of the qualitative analysis of BDS in the HDCE.

Mathematical editors, converters, simplifiers, and generators are required for building Boolean models. Boolean model verification programs are sequential and parallel solvers that have different system requirements. The integration of the above heterogeneous software in the form of microservices is carried out based on presented HPCSOMAS-MSC tools for the development and execution of microservice compositions. The microservices are implemented based on RESTful style. This approach provides independence, replicability, the autonomy of used software, and its interaction through the lightweight message transfer mechanism.

Agents of the semantic P2P network perform decentralized asynchronous control of the microservice ensemble execution. This approach provides adaptability, reliability, and scalability of computations. The openness of this agent network allows the allocation of additional resources in the case of its lack or failure.

HPCSOMAS-MSC tools for microservice composition development provide for creation microservices and agents both the class library and configurable ready-made patterns. Also, the system MSCDT for automation of these processes is provided. Thus, these tools are oriented on both professional programmers and end-users, specialists in a subject domain.

5. Conclusion

Based on the HPCSOMAS-MSC framework, tools were developed to automate the construction and execution of composite services in the form of a composition of microservices and an ensemble of microservices based on respectively static or dynamic approaches. Composite service is performed in the first case based on hierarchical control, in the second - based on decentralized asynchronous control according to data readiness. The absence of a central control node increases the reliability of the calculations. Using a P2P agent network allows agents to be mobilized if it is necessary to attract additional resources due to their shortage or failure. These situations are handled in the behavioral model of agents [30]. The developed tools were tested on several problems from the previously mentioned research domain (for example, [ 11, 13, 14, 30 ]). As the development of research, the possibility of adapting behavioral models of agents oriented to different HPCSOMAS-MSC functioning modes is considered to provide a hybrid functioning, for example, launching an ensemble of microservices on a computational layer in the hierarchy of microservices composition.

Acknowledgments

The present investigation was supported by Russian Foundation of Basic Research, projects no. 18-0700596. The authors would like to thank the Irkutsk Supercomputer Center of SB RAS for providing access to cluster computational resources.

semantic web services composition in an ebxml context Computer Science [Online].

Available: http://arxiv.org/abs/1103.0632v1. [19] Benatallah B, Dumas M, Sheng Q Z and Ngu A H H 2002 Declarative composition and peer-topeer provisioning of dynamic Web services Proc. 18th Int. Conf. on Data Engineering, San Jose, CA, USA, 2002 pp 297-308 [20] Shah Tejas R, Patel S V A Survey on issues and challenges of Web service development, composition, discovery VNSGU journal of science and technology 5(1), 134 - 153 [21] Casati F, Ilnicki S, Jin L, Krishnamoorthy V and Shan M 2000 Adaptive and dynamic service composition in eFlow Proc. of 12th Int. Conf. CAiSE 2000, Stockholm, Sweden, June 5–9, 2000 Proceedings CAISE 13–31 [22] Pautasso C 2005 JOpera: An agile environment for web service composition with visual unit testing and refactoring. Proc. of IEEE Symposium on Visual Languages and Human-Centric Computing pp 311–313 [23] Hull D, Wolstencroft K, Stevens R et al. 2006 Taverna: a tool for building and running workflows of services Nucleic Acids Research 34 729–732 [24] Deelman E, Vahi K, Rynge M, Juve G, Mayani R and Ferreira da Silva R 2016 Pegasus in the cloud: science automation through workflow technologies IEEE Internet Computing 20(1) 70-76 [25] Talia D. Workflow systems for science: concepts and tools. ISRN Software Engineering. Vol.

2013, Article ID 404525, 15 pages. http://dx.doi.org/10.1155/2013/404525 [26] Wolstencroft K., Haines R., Fellows D., Williams A., Withers D., Owen S., Goble C. (2013).

The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Research, 41(Web Server issue), W557–W561. http://doi.org/10.1093/nar/gkt328 [27] Oparin G A , Novopashin A P 2008 Boolean models and planning methods for parallel abstract programs Autom Remote Control 69 1423–1432 [28] van der Aalst W, ter Hofstede A, Kiepuszewsk B et al. 2003 Workflow patterns Distributed and

Parallel Databases 14 5–51 [29] Oparin G A, Bogdanova V G, Pashinin A A and Gorsky S A 2018 Distributed solvers of applied problems based on microservices and agent networks Proc. of the 41st Int. Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, May, 2018 pp 1415-1420 [30] Bychkov I, Oparin G, Bogdanova V and Pashinin A 2019 Intellectual technology for computation control in the package of applied microservices Proc. of the 1st International Workshop on Information, Computation, and Control Systems for Distributed Environments, Irkutsk, Russia, July 8-9, 2019 pp 15-28 [31] Oparin G, Bogdanova V and Pashinin A 2019 Automation of microservices creation for qualitative analysis of binary dynamic systems Proc. of the 1st International Workshop on Information, Computation, and Control Systems for Distributed Environments, Irkutsk, Russia, July 8-9, 2019 pp 88-98 [32] Irkutsk Supercomputer Center of SB RAS. [Online]. Available: http://hpc.icc.ru/. [33] First VDS. [Online]. Available: https://firstvds.ru/. [34] Li F, Long T, Lu Y, Ouyang Q and Tang C The yeast cell-cycle network is robustly designed

PNAS April 6, 2004 vol 101(14) pp 4781-4786 [35] Boolean network model of the control of the budding yeast cell cycle regulation. [Online].

Available: https://people.kth.se/~dubrova/BooleNet/budding_yeast.net

References

[1] Tan W , Zhou M 2013 Business and Scientific Workflows : A Web service-oriented approach , Wiley, IEEE Press

[2] Huhns M N , Sing M P 2005 Service-oriented computing: key concepts and principles , IEEE Internet Computing 9 ( 1 ) pp 75 - 81

[3] Rao J and Su X 2005 A Survey of Automated Web Service composition methods Semantic Web Services and Web Process Composition . SWSWPC, LNCS vol 33872004 ed J Cardoso and A Sheth (Berlin, Heidelberg: Springer) pp 43 - 54

[4] Maamar Z and Wives L K 2010 Social networks and Web services-based systems . [Online]. Available: https://www.igi -global.com/chapter/social-networks-web-services-based/41252.

[5] Seheon Song and Seok-Won Lee 2013 A goal-driven approach for adaptive service composition using planning Mathematical and Computer Modelling 58 261 - 273

[6] Sheng Q Z , Qiao X , Vasilakos A V , Szabo C , Bourne S and Xu X 2014 Web services composition: A decade's overview Information Sciences 280 218 - 238

[7] Oparin G A , Bogdanova V G , Pashinin A A and Gorsky S A 2019 Microservice-oriented approach to automation of distributed scientific computations Proc. of the 42st Int. Convention on Information and Communication Technology, Electronics and Microelectronics , MIPRO 2019 , Opatija, Croatia, May 2019 pp 253 - 258

[8] Oparin G , Bogdanova V and Pashinin A 2019 Qualitative analysis of autonomous synchronous binary dynamic systems MESA 10 ( 3 ) 407 - 419

[9] Nemirovskii A A 1993 Several NP -hard problems arising in robust stability analysis Mathematics of Control, Signals, and Systems 6 99 - 105

[10] Somov Ye I , Butyrin S , Oparin G A and Bogdanova V G 2016 Methods and software for computer-aided design of the spacecraft guidance , navigation and control systems MESA 7 ( 4 ) 613 - 624

[11] Bychkov I V , Oparin G A , Bogdanova V G , Pashinin A A and Gorsky S A 2017 Automation Development Framework of Scalable Scientific Web Applications Based on Subject Domain Knowledge Parallel Computing Technologies . PaCT 2017 . LNCS vol 10421 ed V Malyshkin (Cham: Springer)

[12] Oparin G , Feoktistov A , Bogdanova V and Sidorov I 2016 Automation of multi-agent control for complex dynamic systems in heterogeneous computational network AIP Conference Proceedings . ICNPAA-2016 , July 5-8 , La Rochelle , France vol 1798 ( 1 ) p 020117

[13] Bychkov I , Oparin G , Feoktistov A , Bogdanova V and Sidorov I 2017 The Service-Oriented Multiagent Approach to High-Performance Scientific Computing Numerical Analysis and Its Applications. NAA 2016 . LNCS vol 10187 ed I Dimov I et . al (Cham: Springer) pp 261 - 268

[14] Oparin G , Bogdanova V , Gorsky S and Pashinin A 2019 The synthesis of stabilizing feedback for binary dynamic systems: a logical approach MESA 10(3 ) 477 - 486

[15] Angel Lagares Lemos , Florian Daniel and Boualem Benatallah 2015 Web service composition: A survey of techniques and tools ACM Computing Surveys 48(3) article 33

[16] Rodríguez G , Soria Á and Campo M 2016 AI-based Web Service Composition: A Review IETE Technical Review vol 33 ( 4 ) pp 378 - 385

[17] Farnaghi M , Mansourian A 2018 Multi-Agent Planning for Automatic Geospatial Web Service Composition in Geoportals ISPRS Int . J. Geo-Inf 7 404

[18] Hioual O and Boufaida Z 2011 An agent based architecture (using planning) for dynamic and