A search-based identification of variable microservices for enterprise SaaS

doi:10.1007/s11704-022-1390-4

Front. Comput. Sci.

2023, Vol. 17

Issue (3) : 173208 https://doi.org/10.1007/s11704-022-1390-4

RESEARCH ARTICLE

A search-based identification of variable microservices for enterprise SaaS

Sedigheh KHOSHNEVIS(

)

Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran 1311537541, Iran

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Abstract

Recently, SaaS applications are developed as a composition of microservices that serve diverse tenants having similar but different requirements, and hence, can be developed as variability-intensive microservices. Manual identification of these microservices is difficult, time-consuming, and costly, since, they have to satisfy a set of quality metrics for several SaaS architecture configurations at the same time. In this paper, we tackle the multi-objective optimization problem of identifying variable microservices aiming optimal granularity (new metric proposed), commonality, and data convergence, with a search-based approach employing the MOEA/D algorithm. We empirically and experimentally evaluated the proposed method following the Goal-Question-Metric approach. The results show that the method is promising in identifying fully consistent, highly reusable, variable microservices with an acceptable multi-tenancy degree. Moreover, the identified microservices, although not structurally very similar to those identified by the expert architects, provide design quality measures (granularity, etc.) close to (and even better than) the experts.

Keywords SaaS cloud computing microservice search-based software engineering MOEA/D

Corresponding Author(s): Sedigheh KHOSHNEVIS

Just Accepted Date: 06 May 2022 Issue Date: 07 November 2022

Cite this article:

Sedigheh KHOSHNEVIS. A search-based identification of variable microservices for enterprise SaaS[J]. Front. Comput. Sci., 2023, 17(3): 173208.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-022-1390-4
https://academic.hep.com.cn/fcs/EN/Y2023/V17/I3/173208

Fig.1 A BPFM diagram for the sample BOMP SaaS

Number of tenants and instances of running SaaS	Available	Not available
Multi-tenant SaaS	$p (a) = t (a) T$ (1)	$p (a) = c (a) C$ (3)
Multi-tenant SaaS	t(a) = number of tenants using some functionality involving activity a,T = total number of tenants	$p (a) = c (a) C$ (3)
Single-tenant SaaS	$p (a) = i (a) I$ (2)	c(a) = number of possible potential configurations including activity a,C = number of all possible configurations derivable from the BPFM
Single-tenant SaaS	i(a) = number of instances using a functionality involving activity a,I = total number of instances

Tab.1 Weights assigned to BPFM activities in SWC

Fig.2 Phases of SAMIM and their inputs and outputs

Fig.3 Steps inside each phase of SAMIM

Fig.4 Encoding the solution structure (chromosome) filled with data regarding the sample BOMP SaaS

Tab.2 Inconsistencies due to the participation of mandatory services in (a) excludes and (b) includes relationships between microservices ms1 and ms2

Question	Metriccode	Metric name	Formula	Description
Q1	M1	ScalarSimilarity (ScS)	$S c S = ∑ i = 1 n 1 ? \| x ? m ? x ? e x ? e \| n$	n: No. of obj. functions $x ? m$ : value (method) $x ? e$ : average value (experts)
Q1	M2	StructuralSimilarity (StS)	$S S D (m, n) = \| S O m ∩ S O n \| \| S O m ∪ S O n \|$ $S C (m, S n) = M a x (S S D (m, S n j))$ $? j = 1, 2, …, k$ $S t S (S i, S j) = ∑ n = 1 p S C (S i n, S j) \| E \|$	SO_i: operations in microservice iSSD(m,n): similarity b/w two singlemicroservices m and nSC(m,S_n): similarity b/w singlemicroservice m and a set of microservices S_nE: edges of the bipartite graph that is formed between microservice set S_i and microservice set S_j (each edge carries anSSD value)
Q2	M3	ConsistencyDegree (CD)	$C D = c m$	c: No. of consistent VMA modelsm: total VMA models
	M4	Multi-tenancyDegree (MTD)	$M T D = n s s$	ns: No. of non-mandatory microservicess: Total no. of microservices
	M5	In-spaceReusabilityDegree (ISRD)	$I S R D = ∑ i = 1 s C i C s$	C_i: No. of config.s that microservice i is used in.C: total no. of config.ss: total no. of microservices

Tab.3 The metrics used for the evaluation

Tab.4 Details of experimental cases

Tab.5 Values of objective functions provided by expert subjects and SAMIM

Tab.6 Measuring scalar similarity by comparing the average values of objective functions provided by the expert subjects and by SAMIM

Fig.5 Comparing values of business entity convergence (V_CONV, related to Obj_conv) obtained from the participants and SAMIM

Fig.6 Comparing values of granularity (V_GRAN, related to Obj_gran) obtained from the participants and SAMIM

Fig.7 Comparing values of commonality degree (V_COMM, related to Obj_comm) obtained from the participants and SAMIM

Tab.7 Average values of structural similarity between results provided by the expert subjects and by SAMIM (StS)

Tab.8 Values of multi-tenancy degree provided by the expert subjects and by SAMIM

Tab.9 Values of in-space reusability degree for the six experimental cases

Tab.10 A summary of studies in microservice identification

Fig.8 Summary of results in terms of measures of five metrics

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