Cloud computing has emerged as the most favorable computing platform for researchers and industry... more Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in the Cloud computing. Swarm intelligence-based meta-heuristic algorithms are considered more suitable for Cloud scheduling and load balancing. The optimization procedure of swarm intelligence-based meta-heuristics consists of two major components that are the local and global search. These algorithms find the best position through the local and global search. To achieve an optimized mapping strategy for tasks to the resources, a balance between local and global search plays an effective role. The inertia weight is an important control attribute to effectively adjust the local and global search process. There are many inertia weight strategies; however, the existing approaches still require fine-tuning to achieve optimum scheduling. The selection of a suitable inertia weight strategy is also...
In most of the cases, the efficient mapping of tasks [4] to the Virtual Machines (VMs) lead to th... more In most of the cases, the efficient mapping of tasks [4] to the Virtual Machines (VMs) lead to the user satisfaction level, minimizing the task execution time, and improvement in terms of energy consumption. The task Scheduling approaches are classified into either static or dynamic type. In the case of static task scheduling, the tasks to VM mapping are planned and the scheduling is performed according to the planned mapping. However, for the dynamic scheduling approaches, the scheduling primarily relies on the run-time dynamics of the scheduled tasks according to the user requirements thus may require VM migration [5–7]. In the case of the static scheduling, VM migration is avoided and ultimately results in reducing the overhead and task execution time. Moreover, the static task scheduling approaches produce improved turnaround time and optimum Quality of Service (QoS) due to the availability of the computing resources for task execution [1]. With all its benefits, the static sche...
Recently, Cloud computing has emerged as one of the widely used platforms to provide compute, sto... more Recently, Cloud computing has emerged as one of the widely used platforms to provide compute, storage and analytics services to end-users and organizations on a pay-as-you-use basis, with high agility, availability, scalability, and resiliency. This enables individuals and organizations to have access to a large pool of high processing resources without the need for establishing a high-performance computing (HPC) platform. From the past few years, task scheduling in Cloud computing is reckoned as eminent recourse for researchers. However, task scheduling is considered an NP-hard problem. In this research work, we investigate and empirically compare some of the most prominent state-of-the-art scheduling heuristics in terms of Makespan, Average resource utilization (ARUR), Throughput, and Energy consumption. The comparison is then extended by evaluating the approaches in terms of individual VM level load imbalance. After extensive simulation, the comparative analysis has revealed that Task Aware Scheduling Algorithm (TASA) and Proactive Simulation-based Scheduling and Load Balancing (PSSLB) outperformed as compared to the rest of the approaches and seems to be optimal choice keeping in view the trade-of between the complexities involved and the performance achieved concerning Makespan, Throughput, resource utilization, and Energy consumption.
International Journal of Computer Applications, Feb 17, 2020
Effective software project management plays an important role during requirements collection and ... more Effective software project management plays an important role during requirements collection and implementation for any software system. In Global Software Development (GSD), its significance increase more as stakeholders are far away across the globe. In GSD, challenges such as language differences and time zone differences cause significant barrier during requirements collection and thus need of effective project management increase more and more to handle challenges of GSD. This study address possible solutions and practices for effective global software project management. Through Systematic Literature Review (SLR), 25 practices are identified. These practices will help software vendors to better manage software projects in GSD.
Cloud computing has emerged as the most favorable computing platform for researchers and industry... more Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in the Cloud computing. Swarm intelligence-based meta-heuristic algorithms are considered more suitable for Cloud scheduling and load balancing. The optimization procedure of swarm intelligence-based meta-heuristics consists of two major components that are the local and global search. These algorithms find the best position through the local and global search. To achieve an optimized mapping strategy for tasks to the resources, a balance between local and global search plays an effective role. The inertia weight is an important control attribute to effectively adjust the local and global search process. There are many inertia weight strategies; however, the existing approaches still require fine-tuning to achieve optimum scheduling. The selection of a suitable inertia weight strategy is also...
In most of the cases, the efficient mapping of tasks [4] to the Virtual Machines (VMs) lead to th... more In most of the cases, the efficient mapping of tasks [4] to the Virtual Machines (VMs) lead to the user satisfaction level, minimizing the task execution time, and improvement in terms of energy consumption. The task Scheduling approaches are classified into either static or dynamic type. In the case of static task scheduling, the tasks to VM mapping are planned and the scheduling is performed according to the planned mapping. However, for the dynamic scheduling approaches, the scheduling primarily relies on the run-time dynamics of the scheduled tasks according to the user requirements thus may require VM migration [5–7]. In the case of the static scheduling, VM migration is avoided and ultimately results in reducing the overhead and task execution time. Moreover, the static task scheduling approaches produce improved turnaround time and optimum Quality of Service (QoS) due to the availability of the computing resources for task execution [1]. With all its benefits, the static sche...
Recently, Cloud computing has emerged as one of the widely used platforms to provide compute, sto... more Recently, Cloud computing has emerged as one of the widely used platforms to provide compute, storage and analytics services to end-users and organizations on a pay-as-you-use basis, with high agility, availability, scalability, and resiliency. This enables individuals and organizations to have access to a large pool of high processing resources without the need for establishing a high-performance computing (HPC) platform. From the past few years, task scheduling in Cloud computing is reckoned as eminent recourse for researchers. However, task scheduling is considered an NP-hard problem. In this research work, we investigate and empirically compare some of the most prominent state-of-the-art scheduling heuristics in terms of Makespan, Average resource utilization (ARUR), Throughput, and Energy consumption. The comparison is then extended by evaluating the approaches in terms of individual VM level load imbalance. After extensive simulation, the comparative analysis has revealed that Task Aware Scheduling Algorithm (TASA) and Proactive Simulation-based Scheduling and Load Balancing (PSSLB) outperformed as compared to the rest of the approaches and seems to be optimal choice keeping in view the trade-of between the complexities involved and the performance achieved concerning Makespan, Throughput, resource utilization, and Energy consumption.
International Journal of Computer Applications, Feb 17, 2020
Effective software project management plays an important role during requirements collection and ... more Effective software project management plays an important role during requirements collection and implementation for any software system. In Global Software Development (GSD), its significance increase more as stakeholders are far away across the globe. In GSD, challenges such as language differences and time zone differences cause significant barrier during requirements collection and thus need of effective project management increase more and more to handle challenges of GSD. This study address possible solutions and practices for effective global software project management. Through Systematic Literature Review (SLR), 25 practices are identified. These practices will help software vendors to better manage software projects in GSD.
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