The fundamental problems with cloud computing environment are resource allocation and cloudlets s... more The fundamental problems with cloud computing environment are resource allocation and cloudlets scheduling. When scheduling cloudlets in cloud environment, different cloudlets needs to be executed simultaneously by the available resources in order to meet consumers’ expectations and to achieve better performances by minimizing makespan and balancing load effectively. To achieve this, we proposed a new noble mechanism called Modified Max-Min (MMax-Min) algorithm, inspired from Max-Min algorithm. The proposed algorithm finds a cloudlet with maximum completion time and minimum completion time and assigns either of the cloudlets for execution according to the specifications for the purpose of boosting up cloud scheduling processes and increasing throughput. From the results of the simulation using CloudSim, it shows that our proposed approach is able to produce good quality solutions, producing good values of makespan and balancing load effectively as compared to the standard Max-Min, a...
The fundamental problems with cloud computing environment are resource allocation and cloudlets s... more The fundamental problems with cloud computing environment are resource allocation and cloudlets scheduling. When scheduling cloudlets in cloud environment, different cloudlets needs to be executed simultaneously by the available resources in order to meet consumers’ expectations and to achieve better performances by minimizing makespan and balancing load effectively. To achieve this, we proposed a new noble mechanism called Modified Max-Min (MMax-Min) algorithm, inspired from Max-Min algorithm. The proposed algorithm finds a cloudlet with maximum completion time and minimum completion time and assigns either of the cloudlets for execution according to the specifications for the purpose of boosting up cloud scheduling processes and increasing throughput. From the results of the simulation using CloudSim, it shows that our proposed approach is able to produce good quality solutions, producing good values of makespan and balancing load effectively as compared to the standard Max-Min, a...
Cloud computing involves a large number of shared virtual servers that are accessible from both p... more Cloud computing involves a large number of shared virtual servers that are accessible from both public and private networks. It has provided scalable and multitenant computing approaches for Infrastructure as a Service, Software as a Service, and Platform as a Service to cloud users on pay-per-use bases. Over the past decades, researchers from different domains such as astronomy, physics, earth science, and bioinformatics have used scientific workflow applications to model many real-world problems in both paralleled and distributed computing environments. However, achieving efficient workflow scheduling is challenging. This is due to the large size of the task set that each workflow application generates. The complex dependencies between these workflows make it difficult to find an optimal solution to workflow scheduling problems within polynomial time. This paper analyzed workflows scheduling problems in cloud and grid computing environment through providing a comprehensive survey based on the state-of-the-art meta-heuristic algorithms. We analyzed the literature from four perspectives, including (i) existing meta-heuristics, (ii) scheduling efficiency, system performance, and execution budget, (iii) scheduling environment and (iv) quality of service performance metrics. Also, we have presented the research gaps and provided future directions for future investigation.
Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling effic... more Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling efficiency. This has attracted the interest of many researchers, who devoted their time and resources to improve the performance of scheduling in cloud computing. However, scientific workflows are big data applications, hence the executions are expensive and time consuming. In order to address this issue, we have extended our previous work "Cost Optimised Heuristic Algorithm (COHA)" and presented a novel workflow scheduling algorithm named Multi-Objective Workflow Optimization Strategy (MOWOS) to jointly reduce execution cost and execution makespan. MOWOS employs tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. Moreover, two new algorithms called MaxVM selection and MinVM selection are presented in MOWOS for task allocations. The design purpose of MOWOS is to enable all tasks to successfully meet their deadlines at a reduced time and budget. We have carefully tested the performance of MOWOS with a list of workflow inputs. The simulation results have demonstrated that MOWOS can effectively perform VM allocation and deployment, and well handle incoming streaming tasks with a random arriving rate. The performance of the proposed algorithm increases significantly in large and extra-large workflow tasks than in small and medium workflow tasks when compared to the state-of-art work. It can greatly reduce cost by 8%, minimize makespan by 10% and improve resource utilization by 53%, while also allowing all tasks to meet their deadlines.
Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling effic... more Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling efficiency. This has attracted the interest of many researchers, who devoted their time and resources to improve the performance of scheduling in cloud computing. However, scientific workflows are big data applications, hence the executions are expensive and time consuming. In order to address this issue, we have extended our previous work ”Cost Optimised Heuristic Algorithm (COHA)” and presented a novel workflow scheduling algorithm named Multi-Objective Workflow Optimization Strategy (MOWOS) to jointly reduce execution cost and execution makespan. MOWOS employs tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. Moreover, two new algorithms called MaxVM selection and MinVM selection are presented in MOWOS for task allocations. The design purpose of MOWOS is to enable all tasks to successfully meet their deadlines at a reduced time and budget. We ...
The fundamental problems with cloud computing environment are resource allocation and cloudlets s... more The fundamental problems with cloud computing environment are resource allocation and cloudlets scheduling. When scheduling cloudlets in cloud environment, different cloudlets needs to be executed simultaneously by the available resources in order to meet consumers’ expectations and to achieve better performances by minimizing makespan and balancing load effectively. To achieve this, we proposed a new noble mechanism called Modified Max-Min (MMax-Min) algorithm, inspired from Max-Min algorithm. The proposed algorithm finds a cloudlet with maximum completion time and minimum completion time and assigns either of the cloudlets for execution according to the specifications for the purpose of boosting up cloud scheduling processes and increasing throughput. From the results of the simulation using CloudSim, it shows that our proposed approach is able to produce good quality solutions, producing good values of makespan and balancing load effectively as compared to the standard Max-Min, a...
The fundamental problems with cloud computing environment are resource allocation and cloudlets s... more The fundamental problems with cloud computing environment are resource allocation and cloudlets scheduling. When scheduling cloudlets in cloud environment, different cloudlets needs to be executed simultaneously by the available resources in order to meet consumers’ expectations and to achieve better performances by minimizing makespan and balancing load effectively. To achieve this, we proposed a new noble mechanism called Modified Max-Min (MMax-Min) algorithm, inspired from Max-Min algorithm. The proposed algorithm finds a cloudlet with maximum completion time and minimum completion time and assigns either of the cloudlets for execution according to the specifications for the purpose of boosting up cloud scheduling processes and increasing throughput. From the results of the simulation using CloudSim, it shows that our proposed approach is able to produce good quality solutions, producing good values of makespan and balancing load effectively as compared to the standard Max-Min, a...
Cloud computing involves a large number of shared virtual servers that are accessible from both p... more Cloud computing involves a large number of shared virtual servers that are accessible from both public and private networks. It has provided scalable and multitenant computing approaches for Infrastructure as a Service, Software as a Service, and Platform as a Service to cloud users on pay-per-use bases. Over the past decades, researchers from different domains such as astronomy, physics, earth science, and bioinformatics have used scientific workflow applications to model many real-world problems in both paralleled and distributed computing environments. However, achieving efficient workflow scheduling is challenging. This is due to the large size of the task set that each workflow application generates. The complex dependencies between these workflows make it difficult to find an optimal solution to workflow scheduling problems within polynomial time. This paper analyzed workflows scheduling problems in cloud and grid computing environment through providing a comprehensive survey based on the state-of-the-art meta-heuristic algorithms. We analyzed the literature from four perspectives, including (i) existing meta-heuristics, (ii) scheduling efficiency, system performance, and execution budget, (iii) scheduling environment and (iv) quality of service performance metrics. Also, we have presented the research gaps and provided future directions for future investigation.
Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling effic... more Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling efficiency. This has attracted the interest of many researchers, who devoted their time and resources to improve the performance of scheduling in cloud computing. However, scientific workflows are big data applications, hence the executions are expensive and time consuming. In order to address this issue, we have extended our previous work "Cost Optimised Heuristic Algorithm (COHA)" and presented a novel workflow scheduling algorithm named Multi-Objective Workflow Optimization Strategy (MOWOS) to jointly reduce execution cost and execution makespan. MOWOS employs tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. Moreover, two new algorithms called MaxVM selection and MinVM selection are presented in MOWOS for task allocations. The design purpose of MOWOS is to enable all tasks to successfully meet their deadlines at a reduced time and budget. We have carefully tested the performance of MOWOS with a list of workflow inputs. The simulation results have demonstrated that MOWOS can effectively perform VM allocation and deployment, and well handle incoming streaming tasks with a random arriving rate. The performance of the proposed algorithm increases significantly in large and extra-large workflow tasks than in small and medium workflow tasks when compared to the state-of-art work. It can greatly reduce cost by 8%, minimize makespan by 10% and improve resource utilization by 53%, while also allowing all tasks to meet their deadlines.
Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling effic... more Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling efficiency. This has attracted the interest of many researchers, who devoted their time and resources to improve the performance of scheduling in cloud computing. However, scientific workflows are big data applications, hence the executions are expensive and time consuming. In order to address this issue, we have extended our previous work ”Cost Optimised Heuristic Algorithm (COHA)” and presented a novel workflow scheduling algorithm named Multi-Objective Workflow Optimization Strategy (MOWOS) to jointly reduce execution cost and execution makespan. MOWOS employs tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. Moreover, two new algorithms called MaxVM selection and MinVM selection are presented in MOWOS for task allocations. The design purpose of MOWOS is to enable all tasks to successfully meet their deadlines at a reduced time and budget. We ...
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Papers by James Kok Konjaang