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| Issues | Contribution | Techniques | Environment | Metrics | Limitations |
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| BCMU [51] | Offloading to the cloud, experiences high wireless area network latencies | Architecture | Cloudlet | Amazon EC2 cloud, computer and mobile devices with WiFi | Execution time | Deployment and scheduling of application to cloudlet are not explored |
|
| QoS:MCC [52] | Issue of low QoS in cloud | Architecture for QOS | Fuzzy cognitive map and framework | Simulation-based on an example, using video conferencing | (i) Transmission rate
(ii) Packet lost
(iii) Cost | Most networking simulation tool needs to be used |
|
| CISMCA [53] | Energy consumption, bandwidth, and server load | Improved performance of mobile cloud | Integrated scheme/model and algorithms | Mobile cloud client, implemented on Android and Amazon EC2 instance and WIFI | (i) Resource utilization
(ii) Request completion ratio
(iii) Energy consumption ratio | The proposed work did not achieve low energy consumption when compared to cloud resource allocation for mobile applications (CRAM) |
|
| CQoEACP [54] | Improving the system performance of the mobile internet, to experience QoE | Quality of experience in MCC | Artificial neural network scheme | User-defined testbed; cloud, mobile Internet base station | (i) Throughput rate
(ii) Execution efficiency
(iii) Symbol error rate | The scheme is inclined to be more complex compared to other schemes |
|
| APMNOC [55] | Prediction of performance is difficult due to mobility, the instability of 3G/WiFi connections, and the complexity of virtualization | Models for the performance testing of 3G/Wifi were achieved | Analytical method and Markov reward approach and stochastic submodel | MATLAB and SHARPE software package | (i) Request rejection probability
(ii) Mean response delay | Some assumptions such as exponential interarrivals need to be considered to achieve high prediction of performance |
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