In a traditional Mobile Cloud Computing (MCC), a stream of data produced by mobile users (MUs) is uploaded to the remote cloud for additional processing throughout the Internet. Though, due to long WAN distance it cau...In a traditional Mobile Cloud Computing (MCC), a stream of data produced by mobile users (MUs) is uploaded to the remote cloud for additional processing throughout the Internet. Though, due to long WAN distance it causes high End to End latency. With the intention of minimize the average response time and key constrained Service Delay (network and cloudlet Delay) for mobile users (MUs), offload their workloads to the geographically distributed cloudlets network, we propose the Multi-layer Latency Aware Workload Assignment Strategy (MLAWAS) to allocate MUs workloads into optimal cloudlets, Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with two other existing strategies.展开更多
In this paper, we are investigating the power consumption of mobile device while performing offloading system. The offloading system is way in which mobile application can be divided into local and remote execution in...In this paper, we are investigating the power consumption of mobile device while performing offloading system. The offloading system is way in which mobile application can be divided into local and remote execution in order to alleviate the CPU energy consumption. However, existing offloading systems do not consider data transfer communication energy while performing mobile offloading system. They have just focused on mobile CPU energy consumption. In this paper, we are investigating the energy consumption mobile CPU and communication energy collaboratively while performing mobile offloading for complex application. To cope up with the above problem, we have proposed Energy Efficient Task Scheduler (EETS) algorithm, whose aim is to determine optimal tasks execution in offloading system in order to minimize mobile CPU and communication energy. Simulation results show that EETS outperforms as compared to baseline approaches.展开更多
文摘In a traditional Mobile Cloud Computing (MCC), a stream of data produced by mobile users (MUs) is uploaded to the remote cloud for additional processing throughout the Internet. Though, due to long WAN distance it causes high End to End latency. With the intention of minimize the average response time and key constrained Service Delay (network and cloudlet Delay) for mobile users (MUs), offload their workloads to the geographically distributed cloudlets network, we propose the Multi-layer Latency Aware Workload Assignment Strategy (MLAWAS) to allocate MUs workloads into optimal cloudlets, Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with two other existing strategies.
文摘In this paper, we are investigating the power consumption of mobile device while performing offloading system. The offloading system is way in which mobile application can be divided into local and remote execution in order to alleviate the CPU energy consumption. However, existing offloading systems do not consider data transfer communication energy while performing mobile offloading system. They have just focused on mobile CPU energy consumption. In this paper, we are investigating the energy consumption mobile CPU and communication energy collaboratively while performing mobile offloading for complex application. To cope up with the above problem, we have proposed Energy Efficient Task Scheduler (EETS) algorithm, whose aim is to determine optimal tasks execution in offloading system in order to minimize mobile CPU and communication energy. Simulation results show that EETS outperforms as compared to baseline approaches.
