2017 6th ICT International Student Project Conference (ICT-ISPC)
Recently, cloud computing have been witnessing high deployment rate of large scale scientific and... more Recently, cloud computing have been witnessing high deployment rate of large scale scientific and business applications, this is due to the on-demand provisioning of shared pool of computational resources like networks, storage, and servers, it offers. Each of these applications is made up of various tasks whose execution determine the overall performance of the application. Task scheduling problem on cloud is an NP-hard problem, and thus task scheduling constitute one of the crucial aspects of resources management system in cloud computing, which ensures the attainment of the general user Quality of Service (QoS) performance in terms of response time, total execution time(makespan), throughput among others. In addition, appropriate task scheduling is effective in reducing the operational cost of cloud service providers in terms of energy consumption and resource utilization. This paper focuses on task scheduling problem using a novel Chaotic Symbiotic Organisms Search (CSOS) algorithm to minimize makespan and cost. The main idea is to prevent the premature convergence of SOS at early stages of optimization process by implementing a chaotic map, which enlarges the search space and provides diversity. The performance of the proposed CSOS algorithm is evaluated by extensive simulation using CloudSim toolkit simulation framework and compared with SOS and PSO. Simulation results reveal significant improvement in performance by the proposed CSOS in reducing cost and makespan, in task scheduling.
Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015)
League Championship Algorithm (LCA) is a sports-inspired population based algorithmic framework f... more League Championship Algorithm (LCA) is a sports-inspired population based algorithmic framework for global optimization over a continuous search space first proposed by Ali Husseinzadeh Kashan in the year 2009. A general characteristic between all population based optimization algorithms similar to the LCA is that, it tries to progress a population of achievable solutions to potential areas of the search space when seeking the optimization. In this paper, we proposed a job scheduling algorithm based on an enhanced LCA optimization technique for the infrastructure as a service (IaaS) cloud. Three other established algorithms i.e. First Come First Served (FCFS), Last Job First (LJF) and Best Effort First (BEF) were used to evaluate the performance of the proposed algorithm. All four algorithms assumed to be non-preemptive. The parameters used for this experiment are the average response time, average completion time and the makespan time. The results obtained shows that, LCA scheduling algorithm perform moderately better than the other algorithms as the number of virtual machines increases.
Fog computing is a new paradigm of computing that extends cloud-computing operations to the edges... more Fog computing is a new paradigm of computing that extends cloud-computing operations to the edges of the network. The fog-computing services provide location sensitivity, reduced latency, geographical accessibility, wireless connectivity, and enhanced improved data streaming. However, this computing paradigm is not an alternative for cloud computing and it comes with numerous security and privacy challenges. This paper provides a systematic literature review on the security challenges in fog-computing system. It reviews several architectures that are vital to support the security of fog environment and then created a taxonomy based on the different security techniques used. These include machine learning, cryptographic techniques, computational intelligence, and other techniques that differentiate this paper from the previous reviews in this area of research. Nonetheless, most of the proposed techniques used to solve security issues in fog computing could not completely addressed the security challenges due to the limitation of the various techniques. This review is intended to guide experts and novice researchers to identify certain areas of security challenges in fog computing for future improvements.
Nature-inspired algorithms take inspiration from living things and imitate their behaviours to ac... more Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm.
In cloud computing, resources are dynamically provisioned and delivered to users in a transparent... more In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 % in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment.
Recently, cloud computing has begun to experience tremendous growth because government agencies a... more Recently, cloud computing has begun to experience tremendous growth because government agencies and private organisations are migrating to the cloud environment. Hence, having a task scheduling strategy that is efficient is paramount for effectively improving the prospects of cloud computing. Typically, a certain number of tasks are scheduled to use diverse resources (virtual machines) to minimise the makespan and achieve the optimum utilisation of the system by reducing the response time within the cloud environment. The task scheduling problem is NP-complete; as such, obtaining a precise solution is difficult, particularly for large-scale tasks. Therefore, in this paper, we propose a metaheuristic enhanced discrete symbiotic organism search (eDSOS) algorithm for optimal task scheduling in the cloud computing setting. Our proposed algorithm is an extension of the standard symbiotic organism search (SOS), a nature-inspired algorithm that has been implemented to solve various numeric...
Neural Computing and Applications (ISI & Scopus indexed, IF = 4.213, Q1), 2019, 2019
Nature-inspired algorithms take inspiration from living things and imitate their behaviours to ac... more Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a compr...
Abstract In Cloud Computing model, users are charged according to the usage of resources and desi... more Abstract In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms cannot effectively obtain global optimum solutions. In this paper, a chaotic symbiotic organisms search (CMSOS) algorithm is proposed to solve multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. Chaotic optimization strategy is employed to generate initial ecosystem (population), and random sequence based components of the phases of SOS are replaced with chaotic sequence to ensure diversity among organisms for global convergence. In addition, chaotic local search strategy is applied to Pareto Fronts generated by SOS algorithms to avoid entrapment in local optima. The performance of the proposed CMSOS algorithm is evaluated on CloudSim simulator toolkit, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, the performance of the proposed CMSOS algorithm was found to be competitive with the existing with the existing multi-objective task scheduling optimization algorithms. The CMSOS algorithm obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) with no computational overhead. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery.
Journal of Network and Computer Applications (ISI & Scopus indexed, IF = 3.991), 2019
In Cloud Computing model, users are charged according to the usage of resources and desired Quali... more In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms cannot effectively obtain global optimum solutions. In this paper, a chaotic symbiotic organisms search (CMSOS) algorithm is proposed to solve multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. Chaotic optimization strategy is employed to generate initial ecosystem (population), and random sequence based components of the phases of SOS are replaced with chaotic sequence to ensure diversity among organisms for global convergence. In addition, chaotic local search strategy is applied to Pareto Fronts generated by SOS algorithms to avoid entrapment in local optima. The pe...
TCP/IP is a dynamic window based protocol that is used over the Internet. Its widely use is mainl... more TCP/IP is a dynamic window based protocol that is used over the Internet. Its widely use is mainly based on its high dynamic nature of adaptability to any kind of network capacity. Since the development of TCP as an internet standard, special attention has ...
Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. O... more Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.
2017 6th ICT International Student Project Conference (ICT-ISPC)
Recently, cloud computing have been witnessing high deployment rate of large scale scientific and... more Recently, cloud computing have been witnessing high deployment rate of large scale scientific and business applications, this is due to the on-demand provisioning of shared pool of computational resources like networks, storage, and servers, it offers. Each of these applications is made up of various tasks whose execution determine the overall performance of the application. Task scheduling problem on cloud is an NP-hard problem, and thus task scheduling constitute one of the crucial aspects of resources management system in cloud computing, which ensures the attainment of the general user Quality of Service (QoS) performance in terms of response time, total execution time(makespan), throughput among others. In addition, appropriate task scheduling is effective in reducing the operational cost of cloud service providers in terms of energy consumption and resource utilization. This paper focuses on task scheduling problem using a novel Chaotic Symbiotic Organisms Search (CSOS) algorithm to minimize makespan and cost. The main idea is to prevent the premature convergence of SOS at early stages of optimization process by implementing a chaotic map, which enlarges the search space and provides diversity. The performance of the proposed CSOS algorithm is evaluated by extensive simulation using CloudSim toolkit simulation framework and compared with SOS and PSO. Simulation results reveal significant improvement in performance by the proposed CSOS in reducing cost and makespan, in task scheduling.
Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015)
League Championship Algorithm (LCA) is a sports-inspired population based algorithmic framework f... more League Championship Algorithm (LCA) is a sports-inspired population based algorithmic framework for global optimization over a continuous search space first proposed by Ali Husseinzadeh Kashan in the year 2009. A general characteristic between all population based optimization algorithms similar to the LCA is that, it tries to progress a population of achievable solutions to potential areas of the search space when seeking the optimization. In this paper, we proposed a job scheduling algorithm based on an enhanced LCA optimization technique for the infrastructure as a service (IaaS) cloud. Three other established algorithms i.e. First Come First Served (FCFS), Last Job First (LJF) and Best Effort First (BEF) were used to evaluate the performance of the proposed algorithm. All four algorithms assumed to be non-preemptive. The parameters used for this experiment are the average response time, average completion time and the makespan time. The results obtained shows that, LCA scheduling algorithm perform moderately better than the other algorithms as the number of virtual machines increases.
Fog computing is a new paradigm of computing that extends cloud-computing operations to the edges... more Fog computing is a new paradigm of computing that extends cloud-computing operations to the edges of the network. The fog-computing services provide location sensitivity, reduced latency, geographical accessibility, wireless connectivity, and enhanced improved data streaming. However, this computing paradigm is not an alternative for cloud computing and it comes with numerous security and privacy challenges. This paper provides a systematic literature review on the security challenges in fog-computing system. It reviews several architectures that are vital to support the security of fog environment and then created a taxonomy based on the different security techniques used. These include machine learning, cryptographic techniques, computational intelligence, and other techniques that differentiate this paper from the previous reviews in this area of research. Nonetheless, most of the proposed techniques used to solve security issues in fog computing could not completely addressed the security challenges due to the limitation of the various techniques. This review is intended to guide experts and novice researchers to identify certain areas of security challenges in fog computing for future improvements.
Nature-inspired algorithms take inspiration from living things and imitate their behaviours to ac... more Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm.
In cloud computing, resources are dynamically provisioned and delivered to users in a transparent... more In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 % in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment.
Recently, cloud computing has begun to experience tremendous growth because government agencies a... more Recently, cloud computing has begun to experience tremendous growth because government agencies and private organisations are migrating to the cloud environment. Hence, having a task scheduling strategy that is efficient is paramount for effectively improving the prospects of cloud computing. Typically, a certain number of tasks are scheduled to use diverse resources (virtual machines) to minimise the makespan and achieve the optimum utilisation of the system by reducing the response time within the cloud environment. The task scheduling problem is NP-complete; as such, obtaining a precise solution is difficult, particularly for large-scale tasks. Therefore, in this paper, we propose a metaheuristic enhanced discrete symbiotic organism search (eDSOS) algorithm for optimal task scheduling in the cloud computing setting. Our proposed algorithm is an extension of the standard symbiotic organism search (SOS), a nature-inspired algorithm that has been implemented to solve various numeric...
Neural Computing and Applications (ISI & Scopus indexed, IF = 4.213, Q1), 2019, 2019
Nature-inspired algorithms take inspiration from living things and imitate their behaviours to ac... more Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a compr...
Abstract In Cloud Computing model, users are charged according to the usage of resources and desi... more Abstract In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms cannot effectively obtain global optimum solutions. In this paper, a chaotic symbiotic organisms search (CMSOS) algorithm is proposed to solve multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. Chaotic optimization strategy is employed to generate initial ecosystem (population), and random sequence based components of the phases of SOS are replaced with chaotic sequence to ensure diversity among organisms for global convergence. In addition, chaotic local search strategy is applied to Pareto Fronts generated by SOS algorithms to avoid entrapment in local optima. The performance of the proposed CMSOS algorithm is evaluated on CloudSim simulator toolkit, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, the performance of the proposed CMSOS algorithm was found to be competitive with the existing with the existing multi-objective task scheduling optimization algorithms. The CMSOS algorithm obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) with no computational overhead. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery.
Journal of Network and Computer Applications (ISI & Scopus indexed, IF = 3.991), 2019
In Cloud Computing model, users are charged according to the usage of resources and desired Quali... more In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms cannot effectively obtain global optimum solutions. In this paper, a chaotic symbiotic organisms search (CMSOS) algorithm is proposed to solve multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. Chaotic optimization strategy is employed to generate initial ecosystem (population), and random sequence based components of the phases of SOS are replaced with chaotic sequence to ensure diversity among organisms for global convergence. In addition, chaotic local search strategy is applied to Pareto Fronts generated by SOS algorithms to avoid entrapment in local optima. The pe...
TCP/IP is a dynamic window based protocol that is used over the Internet. Its widely use is mainl... more TCP/IP is a dynamic window based protocol that is used over the Internet. Its widely use is mainly based on its high dynamic nature of adaptability to any kind of network capacity. Since the development of TCP as an internet standard, special attention has ...
Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. O... more Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.
In cloud computing, resources are dynamically provisioned and delivered to users in a transparent... more In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 % in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment.
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Papers by Mohammed Abdullahi
automatically on-demand. Task execution failure is no longer accidental but a common characteristic of
cloud computing environment. In recent times, a number of intelligent scheduling techniques have been
used to address task scheduling issues in cloud without much attention to fault tolerance. In this research
article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique
for fault tolerance awareness to address cloud task execution which would reflect on the current available
resources and reduce the untimely failure of autonomous tasks. Experimental results show that our
proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate.
It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic
algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 %
in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the
experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to
improve the overall performance of the cloud environment.