James Kok Konjaang obtained his HND in Marketing from Bolgatanga Polytechnic and BSc. in Management with Computing from Regent University College of Science and Technology, both in Ghana in 2006 and 2012 respectively. He obtained MSc. in Computer Science from University Putra Malaysia (UPM), in 2016. He is currently a lecturer at the ICT unit of the Bolgatanga Polytechnic. His research interests include Cloud Computing, Algorithms, Scientific workflow Scheduling and Grid Computing. Address: Seri Kembangan, Malaysia
Cloud computing, a multipurpose and highperformance internet-based computing, can model and trans... more Cloud computing, a multipurpose and highperformance internet-based computing, can model and transform a large range of application requirements into a set of workflow tasks. It allows users to represent their computational needs conveniently for data retrieval, reformatting, and analysis. However, workflow applications are big data applications and often take long hours to finish executing due to their nature and data size. In this paper, we study the cost optimised scheduling algorithms in cloud and proposed a novel task splitting algorithm named Cost Optimised Heuristic Algorithm (COHA) for the cloud scheduler to optimise the execution cost. In this algorithm, the large tasks are split into sub-tasks to reduce their execution time. The design purpose is to enable all tasks to adequately meet their deadlines. We have carefully tested the performance of the COHA with a list of workflow inputs. The simulation results have convincingly demonstrated that COHA can effectively perform VM allocation and deployment, and well handle randomly arrived tasks. It can efficiently reduce execution costs while also allowing all tasks to properly finish before their deadlines. Overall, the improvements in our algorithm have remarkably reduced the execution cost by 32.5% for Sipht, 3.9% for Montage, and 1.2% for CyberShake workflows when compared to the state of art work.
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.
The rise in demand for cloud resources (network, hardware and software) requires cost effective s... more The rise in demand for cloud resources (network, hardware and software) requires cost effective scientific workflow scheduling algorithm to reduce cost and balance load of all jobs evenly for a better system throughput. Getting multiple scientific workflows scheduled with a reduced makespan and cost in a dynamic cloud computing environment is an attractive research area which needs more attention. Scheduling multiple workflows with the standard Max-Min algorithm is a challenge because of the high priority given to task with maximum execution time first. To overcome this challenge, we proposed a new mechanism call Expanded Max-Min (Expa-Max-Min) algorithm to effectively give equal opportunity to both cloudlets with maximum and minimum execution time to be scheduled for a reduce cost and time. Expa-Max-Min algorithm first calculates the completion time of all the cloudlets in the cloudletList to find cloudlets with minimum and maximum execution time, then it sorts and queue the cloudlets in two queues based on their execution times. The algorithm first select a cloudlet from the cloudletList in the maximum execution time queue and assign it to a resource that produces minimum completion time, while executing cloudlets in the minimum execution time queue concurrently. The experimented results demonstrats that our proposed algorithm, Expa-Max-Min algorithm, is able to produce good quality solutions in terms of minimising average cost and makespan and able to balance loads than Max-Min and Min-Min algorithms.
—Effective scheduling algorithm to reduce total completion time and promote resource utilization ... more —Effective scheduling algorithm to reduce total completion time and promote resource utilization with load balancing in a grid computing environment is required. Scheduling tasks on heterogeneous machines distributed over a grid system proves to be an NP complete problem. Many algorithms have been developed to counter this problem by researchers. However, it is obvious that, task selection is a key challenge to these heuristics. For this reason, a substantial enhancement in the computational efficacy of the algorithm might be welcome. In this paper, a new batch mode scheduling algorithm (MinExt) is proposed. The intent is to reduce the total completion time (makespan), utilization of idle resources and load balance. To achieve this, the proposed algorithm made an initial task queue, we collects the Average Completion Time (Act) of all tasks, then for all tasks greater than Act is scheduled first and follow by the set of tasks less than or equal to the Act. Our simulation results indicate that the algorithm minimizes total completion time and utilizes the idle resources effectively with load balancing in comparing to other algorithms. I. INTRODUCTION This Grid computing system [1] are novel technology for building high-speed computing environment in which heterogeneous, homogeneous, distributed and dynamically resources integrated across the world through networks. A computational grid is a group of heterogeneous processors, and machines feast through several administrative fields with the intention of providing managers easy contact to these machines. It allows virtualization of dispersed computing and data machines such as processors, network bandwidth and storage volume to make a particular system [2]. Figure 1 depicts key steps in grid scheduling. Grid Task scheduling had turned into major research aims, seeing as direct influences for performance of grid applications. It's described as the course of choosing the best resource for a suitable task. Grid task scheduling is a joint module of computing that efficiently uses the idle time of machines [3, 4]. Allocation strategy [5] is done in two categories; immediate and batch mode heuristics. In immediate mode, task is represented on a resource as quickly as it reaches the scheduler. While in Batch mode heuristics, tasks are not allocated on the resources as they reach; instead, they are collected into a set that is inspected for allocation at prescribe periods called mapping events. This paper considers a static batch mode scheduling algorithm. The major contribution of this paperwork is to devise a new batch mode scheduling algorithm that is efficient for mapping independent tasks with the intensification of minimizing makespan, maximizing average resource utilization rate and loads balanced. The concept of the algorithm relies on Average completion time. A condition for mapping tasks to their paramount resources is considered, this enhances the efficiency of grid computing system environment. The rest of this paper is as follows: Section 2 presents the related works along with several well-known scheduling policies. In Section 3, a new batch mode scheduling algorithm (MinExt) is proposed. Section 4 describes experimental setup, results and discussion. Finally, a Section 5 presents conclusions while Section 6 gave references. e-ISSN : 0975-4024
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, and Round Robin algorithms.
Money, the medium of exchange has seen significant transformation over centuries. In the last few... more Money, the medium of exchange has seen significant transformation over centuries. In the last few hundred years, money has been regulated by banks. Such regulation of the flow of money in itself has become a money making venture becoming the main stay of several kinds of institutions including banking institutions. The paper focuses on the emerging trend of the use of mobile devices to facilitate the payment of goods and services. This is essentially, using a mobile phone to perform some of the duties of traditional banks. This fast paced substitution of the mobile phone with traditional banking is sometimes viewed as a threat to the existence of banks in Ghana. The paper finds, through the use of empirical data and interviews, that although, banks may lose minimally in the short term, mobile money is actually more complementary and will only fast track the achievement of the goal of a cashless economy.
Cloud computing is a new archetype that provides dynamic computing services to cloud users throug... more Cloud computing is a new archetype that provides dynamic computing services to cloud users through the support of datacenters that employs the services of datacenter brokers which discover resources and assign them Virtually. The focus of this research is to efficiently optimize resource allocation in the cloud by exploiting the Max-Min scheduling algorithm and enhancing it to increase efficiency in terms of completion time (makespan). This is key to enhancing the performance of cloud scheduling and narrowing the performance gap between cloud service providers and cloud resources consumers/users. The current Max-Min algorithm selects tasks with maximum execution time on a faster available machine or resource that is capable of giving minimum completion time. The concern of this algorithm is to give priority to tasks with maximum execution time first before assigning those with the minimum execution time for the purpose of minimizing makespan. The drawback of this algorithm is that, the execution of tasks with maximum execution time first may increase the makespan, and leads to a delay in executing tasks with minimum execution time if the number of tasks with maximum execution time exceeds that of tasks with minimum execution time, hence the need to improve it to mitigate the delay in executing tasks with minimum execution time. CloudSim is used to compare the effectiveness of the improved Max-Min algorithm with the traditional one. The experimented results show that the improved algorithm is efficient and can produce better makespan than Max-Min and DataAware.
— Sales management is a key function which helps small and medium size enterprises (SMEs) in moni... more — Sales management is a key function which helps small and medium size enterprises (SMEs) in monitoring and tracking stock and coordinating transaction processing. The efficiency of sales management dependends on effective tools and facilities, especially mordern information and communication technologies. Despite this, majority of businesses in developing countries, especially those in remote areas do not take full advantage of these technologies due to challenges related to the design of these technologies. This paper presents the design and development of a tailor-made computerized sales management system for SMEs in Northern Ghana.The object-oriented methodology is employed with UML, VB.NET and Microsoft Access Database for the design and development of the system which is flexible and tailor-made for SMEs in the region.
Cloud computing, a multipurpose and highperformance internet-based computing, can model and trans... more Cloud computing, a multipurpose and highperformance internet-based computing, can model and transform a large range of application requirements into a set of workflow tasks. It allows users to represent their computational needs conveniently for data retrieval, reformatting, and analysis. However, workflow applications are big data applications and often take long hours to finish executing due to their nature and data size. In this paper, we study the cost optimised scheduling algorithms in cloud and proposed a novel task splitting algorithm named Cost Optimised Heuristic Algorithm (COHA) for the cloud scheduler to optimise the execution cost. In this algorithm, the large tasks are split into sub-tasks to reduce their execution time. The design purpose is to enable all tasks to adequately meet their deadlines. We have carefully tested the performance of the COHA with a list of workflow inputs. The simulation results have convincingly demonstrated that COHA can effectively perform VM allocation and deployment, and well handle randomly arrived tasks. It can efficiently reduce execution costs while also allowing all tasks to properly finish before their deadlines. Overall, the improvements in our algorithm have remarkably reduced the execution cost by 32.5% for Sipht, 3.9% for Montage, and 1.2% for CyberShake workflows when compared to the state of art work.
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.
The rise in demand for cloud resources (network, hardware and software) requires cost effective s... more The rise in demand for cloud resources (network, hardware and software) requires cost effective scientific workflow scheduling algorithm to reduce cost and balance load of all jobs evenly for a better system throughput. Getting multiple scientific workflows scheduled with a reduced makespan and cost in a dynamic cloud computing environment is an attractive research area which needs more attention. Scheduling multiple workflows with the standard Max-Min algorithm is a challenge because of the high priority given to task with maximum execution time first. To overcome this challenge, we proposed a new mechanism call Expanded Max-Min (Expa-Max-Min) algorithm to effectively give equal opportunity to both cloudlets with maximum and minimum execution time to be scheduled for a reduce cost and time. Expa-Max-Min algorithm first calculates the completion time of all the cloudlets in the cloudletList to find cloudlets with minimum and maximum execution time, then it sorts and queue the cloudlets in two queues based on their execution times. The algorithm first select a cloudlet from the cloudletList in the maximum execution time queue and assign it to a resource that produces minimum completion time, while executing cloudlets in the minimum execution time queue concurrently. The experimented results demonstrats that our proposed algorithm, Expa-Max-Min algorithm, is able to produce good quality solutions in terms of minimising average cost and makespan and able to balance loads than Max-Min and Min-Min algorithms.
—Effective scheduling algorithm to reduce total completion time and promote resource utilization ... more —Effective scheduling algorithm to reduce total completion time and promote resource utilization with load balancing in a grid computing environment is required. Scheduling tasks on heterogeneous machines distributed over a grid system proves to be an NP complete problem. Many algorithms have been developed to counter this problem by researchers. However, it is obvious that, task selection is a key challenge to these heuristics. For this reason, a substantial enhancement in the computational efficacy of the algorithm might be welcome. In this paper, a new batch mode scheduling algorithm (MinExt) is proposed. The intent is to reduce the total completion time (makespan), utilization of idle resources and load balance. To achieve this, the proposed algorithm made an initial task queue, we collects the Average Completion Time (Act) of all tasks, then for all tasks greater than Act is scheduled first and follow by the set of tasks less than or equal to the Act. Our simulation results indicate that the algorithm minimizes total completion time and utilizes the idle resources effectively with load balancing in comparing to other algorithms. I. INTRODUCTION This Grid computing system [1] are novel technology for building high-speed computing environment in which heterogeneous, homogeneous, distributed and dynamically resources integrated across the world through networks. A computational grid is a group of heterogeneous processors, and machines feast through several administrative fields with the intention of providing managers easy contact to these machines. It allows virtualization of dispersed computing and data machines such as processors, network bandwidth and storage volume to make a particular system [2]. Figure 1 depicts key steps in grid scheduling. Grid Task scheduling had turned into major research aims, seeing as direct influences for performance of grid applications. It's described as the course of choosing the best resource for a suitable task. Grid task scheduling is a joint module of computing that efficiently uses the idle time of machines [3, 4]. Allocation strategy [5] is done in two categories; immediate and batch mode heuristics. In immediate mode, task is represented on a resource as quickly as it reaches the scheduler. While in Batch mode heuristics, tasks are not allocated on the resources as they reach; instead, they are collected into a set that is inspected for allocation at prescribe periods called mapping events. This paper considers a static batch mode scheduling algorithm. The major contribution of this paperwork is to devise a new batch mode scheduling algorithm that is efficient for mapping independent tasks with the intensification of minimizing makespan, maximizing average resource utilization rate and loads balanced. The concept of the algorithm relies on Average completion time. A condition for mapping tasks to their paramount resources is considered, this enhances the efficiency of grid computing system environment. The rest of this paper is as follows: Section 2 presents the related works along with several well-known scheduling policies. In Section 3, a new batch mode scheduling algorithm (MinExt) is proposed. Section 4 describes experimental setup, results and discussion. Finally, a Section 5 presents conclusions while Section 6 gave references. e-ISSN : 0975-4024
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, and Round Robin algorithms.
Money, the medium of exchange has seen significant transformation over centuries. In the last few... more Money, the medium of exchange has seen significant transformation over centuries. In the last few hundred years, money has been regulated by banks. Such regulation of the flow of money in itself has become a money making venture becoming the main stay of several kinds of institutions including banking institutions. The paper focuses on the emerging trend of the use of mobile devices to facilitate the payment of goods and services. This is essentially, using a mobile phone to perform some of the duties of traditional banks. This fast paced substitution of the mobile phone with traditional banking is sometimes viewed as a threat to the existence of banks in Ghana. The paper finds, through the use of empirical data and interviews, that although, banks may lose minimally in the short term, mobile money is actually more complementary and will only fast track the achievement of the goal of a cashless economy.
Cloud computing is a new archetype that provides dynamic computing services to cloud users throug... more Cloud computing is a new archetype that provides dynamic computing services to cloud users through the support of datacenters that employs the services of datacenter brokers which discover resources and assign them Virtually. The focus of this research is to efficiently optimize resource allocation in the cloud by exploiting the Max-Min scheduling algorithm and enhancing it to increase efficiency in terms of completion time (makespan). This is key to enhancing the performance of cloud scheduling and narrowing the performance gap between cloud service providers and cloud resources consumers/users. The current Max-Min algorithm selects tasks with maximum execution time on a faster available machine or resource that is capable of giving minimum completion time. The concern of this algorithm is to give priority to tasks with maximum execution time first before assigning those with the minimum execution time for the purpose of minimizing makespan. The drawback of this algorithm is that, the execution of tasks with maximum execution time first may increase the makespan, and leads to a delay in executing tasks with minimum execution time if the number of tasks with maximum execution time exceeds that of tasks with minimum execution time, hence the need to improve it to mitigate the delay in executing tasks with minimum execution time. CloudSim is used to compare the effectiveness of the improved Max-Min algorithm with the traditional one. The experimented results show that the improved algorithm is efficient and can produce better makespan than Max-Min and DataAware.
— Sales management is a key function which helps small and medium size enterprises (SMEs) in moni... more — Sales management is a key function which helps small and medium size enterprises (SMEs) in monitoring and tracking stock and coordinating transaction processing. The efficiency of sales management dependends on effective tools and facilities, especially mordern information and communication technologies. Despite this, majority of businesses in developing countries, especially those in remote areas do not take full advantage of these technologies due to challenges related to the design of these technologies. This paper presents the design and development of a tailor-made computerized sales management system for SMEs in Northern Ghana.The object-oriented methodology is employed with UML, VB.NET and Microsoft Access Database for the design and development of the system which is flexible and tailor-made for SMEs in the region.
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