Current Perspective of Symbiotic Organisms Search Technique in Cloud Computing Environment: A Review

Zubair, Ajoze Abdulraheem; Razak, Shukor Abd; Ngadi, Md. Asri; Aliyu, Ahmed

doi:10.14569/ijacsa.2021.0120650

Cited by 4 publications

(6 citation statements)

References 28 publications

(37 reference statements)

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“…Metaheuristic algorithms have demonstrated exceptional effectiveness in delivering near-optimal scheduling solutions for complex large-size problems and, as such, have piqued the interest of various researchers [8,35]. Nevertheless, metaheuristic algorithms continue to suffer from being trapped in local optima, premature convergence, delayed convergence, and imbalance between the search methods [20][21][22]36].…”

Section: Metaheuristic Techniques Used In Cloud Task Schedulingmentioning

confidence: 99%

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A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

Zubair

Razak

Ngadi

et al. 2022

Sensors

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The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm’s mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61–20.08% and 1.92–25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO.

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Section: Metaheuristic Techniques Used In Cloud Task Schedulingmentioning

confidence: 99%

“…where A and B represent the two organisms selected randomly from the ecosystem. The mutual vector (R Mv ) and benefit factors (BF −1 and BF −2 ) are derived from the respective mathematical formulation in Equation (10) and Equations ( 4) and ( 5) as described in the work of [36].…”

Section: Mutualism Phasementioning

confidence: 99%

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

Zubair

Razak

Ngadi

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Metaheuristic algorithms have demonstrated exceptional effectiveness in delivering near-optimal scheduling solutions for a complex large size problem and as such have piqued the interest of various researchers [8,29]. Nevertheless, metaheuristic algorithms continue to suffer from being trapped in local optima, premature convergence, delayed convergence, and imbalance between the search methods [17][18][19]30].…”

Section: Metaheuristic Techniques Used In Cloud Task Schedulingmentioning

confidence: 99%

“…) and Benefit factors ( −1 −2 ) are derived from the respective mathematical formulation in Eq.10. and equations ( 4) and ( 5) as described in the work of [30] The new species _ ′ _ ′ are generated by moulding their structure from and BF (Benefit factors) corresponding to the best organism ( ) of the current population as shown in equations ( 13) and (14).…”

Section: Mutualism Phasementioning

confidence: 99%

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A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm for Ecosystem Sustainability

Zubair¹,

Razak²,

Ngadi³

2021

Preprint

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The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The modified SOS algorithm is developed to solve independent task scheduling problems. This paper proposes a modified symbiotic organisms search based scheduling algorithm for efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aimed to minimize the task execution time (Makespan), response, degree of imbalance and cost and improve the convergence speed for an optimal solution in an IaaS cloud. The performances of the proposed technique have been evaluated using a Cladism toolkit simulator, and the solutions are found to be better than the existing standard (SOS) technique and PSO.

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Design methodologies and engineering applications for ecosystem biomimicry: an interdisciplinary review spanning cyber, physical, and cyber-physical systems

Hinkelman

Yang

2023

Bioinspir. Biomim.

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Ecosystem biomimicry is a promising pathway for sustainable development. However, while typical form- and process-level biomimicry is prevalent, system-level ecosystem biomimicry remains a nascent practice in numerous engineering fields. This critical review takes an interdisciplinary approach to synthesize trends across case studies, evaluate design methodologies, and identify future opportunities when applying ecosystem biomimicry to engineering practices, including cyber systems (CS), physical systems (PS), and cyber-physical systems (CPS). After systematically sourcing publications from major databases, the papers were first analyzed at a meta level for their bibliographic context and for statistical correlations among categorical variables. Then, we investigated deeper into the engineering applications and design methodologies. Results indicate that CPS most frequently mimic organisms and ecosystems, while CS and PS frequently mimic populations-communities and molecules-tissues-organ systems, respectively (statistically highly significant). An indirect approach is most often used for mimicry at organizational levels from populations to ecosystems, while a direct approach frequently suits levels from molecules to organisms (highly significant). Dominant themes across engineering applications include symbiotic organism search algorithms for CS and ecological network analysis for CPS, while PS applications are highly diverse. For design methodologies, this work summarizes and details ten well-documented biomimetic process models among literature, which addresses an outdated concern for a lack of systematic methods for ecosystem biomimicry. In addition to the Biomimetics Standard ISO 18458, these methods include the Natural Step and Techno-Ecological Synergy framework, among others. Further, the analyses revealed future opportunities from less utilized design methods (e.g., interdisciplinary teams tackling indirect, ecosystem-level projects) to well-established engineering concepts ready for technological advancement (e.g., implementing membrane computing for physical applications). For future studies, this review provides a comprehensive reference for ecosystem biomimetic design practices and application opportunities across multiple engineering domains.

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Current Perspective of Symbiotic Organisms Search Technique in Cloud Computing Environment: A Review

Cited by 4 publications

References 28 publications

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm for Ecosystem Sustainability

Design methodologies and engineering applications for ecosystem biomimicry: an interdisciplinary review spanning cyber, physical, and cyber-physical systems

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