Cloud computing plays a significant role in Information Technology(IT)industry to deliver scalable resources as a service.One of the most important factor to increase the performance of the cloud server is maximizing t...Cloud computing plays a significant role in Information Technology(IT)industry to deliver scalable resources as a service.One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling.The main advantage of this scheduling is to max-imize the performance and minimize the time loss.Various researchers examined numerous scheduling methods to achieve Quality of Service(QoS)and to reduce execution time.However,it had disadvantages in terms of low throughput and high response time.Hence,this study aimed to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the Virtual Machines(VMs).For this purpose,the research proposed novel Particle Swarm Optimization-Bandwidth Aware divisible Task(PSO-BATS)scheduling with Multi-Layered Regression Host Employment(MLRHE)to sort out the issues of task scheduling and ease the scheduling operation by load balancing.The proposed efficient sche-duling provides benefits to both cloud users and servers.The performance evalua-tion is undertaken with respect to cost,Performance Improvement Rate(PIR)and makespan which revealed the efficiency of the proposed method.Additionally,comparative analysis is undertaken which confirmed the performance of the intro-duced system than conventional system for scheduling tasks with highflexibility.展开更多
With the growing popularity of 3G-powered devices, there are growing demands on energy-efficient data trans- mission strategies for various embedded systems. Different from the past work in energy-efficient real-time ...With the growing popularity of 3G-powered devices, there are growing demands on energy-efficient data trans- mission strategies for various embedded systems. Different from the past work in energy-efficient real-time task scheduling, we explore strategies to maximize the amount of data transmitted by a 3G module under a given battery capacity. In particular, we present algorithms under different workload configurations with and without timing constraint considerations. Experiments were then conducted to verify the validity of the strategies and develop insights in energy-efficient data transmission.展开更多
文摘Cloud computing plays a significant role in Information Technology(IT)industry to deliver scalable resources as a service.One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling.The main advantage of this scheduling is to max-imize the performance and minimize the time loss.Various researchers examined numerous scheduling methods to achieve Quality of Service(QoS)and to reduce execution time.However,it had disadvantages in terms of low throughput and high response time.Hence,this study aimed to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the Virtual Machines(VMs).For this purpose,the research proposed novel Particle Swarm Optimization-Bandwidth Aware divisible Task(PSO-BATS)scheduling with Multi-Layered Regression Host Employment(MLRHE)to sort out the issues of task scheduling and ease the scheduling operation by load balancing.The proposed efficient sche-duling provides benefits to both cloud users and servers.The performance evalua-tion is undertaken with respect to cost,Performance Improvement Rate(PIR)and makespan which revealed the efficiency of the proposed method.Additionally,comparative analysis is undertaken which confirmed the performance of the intro-duced system than conventional system for scheduling tasks with highflexibility.
基金supported by the Excellent Research Projects of "National Taiwan University" under Grant No. 99R80304
文摘With the growing popularity of 3G-powered devices, there are growing demands on energy-efficient data trans- mission strategies for various embedded systems. Different from the past work in energy-efficient real-time task scheduling, we explore strategies to maximize the amount of data transmitted by a 3G module under a given battery capacity. In particular, we present algorithms under different workload configurations with and without timing constraint considerations. Experiments were then conducted to verify the validity of the strategies and develop insights in energy-efficient data transmission.
