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| Issues | Contribution | Techniques | Implementation | Metrics | Limitations |
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| ThinkAir [17] | (i) Increase in complex mobile application
(ii) Low energy and computational power | (i) Virtualizations
(ii) Parallelization of VM clone | (i) Framework
(ii) Virtualization | CloudPlatform | Energy and time consumption | Implementation/support for parallelizable application is required |
|
| PMAOC [28] | (i) Battery lifetime
(ii) Lack of systematic mechanism to evaluate the effect of offloading an app on the cloud. | (i) Mathematical model
(ii) Dynamic algorithm and free sequencing protocol (FSP) | Model, algorithm, and protocol (FSP) | An Amazon EC2 Windows cloud instance | (i) Workload size
(ii) Network type computation cost
(iii) Signal strength
(iv) Call graph structure | More time and complexity can be experienced due to encryption/decryption |
|
| PIEMCO [29] | Low battery power and low computation power for mobile devices | Analysis and architecture for mobile cloud offloading | Analysis and architecture | Not mentioned | Not mentioned | No detailed experiments were implemented in this work |
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| DECM [30] | Restrictions of wireless bandwidth and device capacity have caused energy waste and latency delay | Energy waste was minimized | Model | User-developed simulator called DECM-Sim | (i) Energy consumption
(ii) Time constraint
(iii) Service quality | Applicability of DECM in multiple industries with different service requirements have not been investigated |
|
| MOCA [31] | Low battery and computation power of mobile devices | Architecture of the in-network cloud offloading | Architecture | AlterNet mobile testbed, Openstack cloud platform with a laptop with WIFI | Not mentioned | No standardized performance metric was considered to evaluate the performance of the MOCA |
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| DREAM [32] | To handle complicated resource interference issue due to the resource sharing in dynamic mobile cloud | Resource and task allocation architecture and algorithm | Architecture and an algorithm | Phone, WIFI and virtual cloud server on the Windows azure cloud system | (i) Average energy consumption
(ii) Average delay | Security issues are not looked into in this architecture |
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| MobiByte [33] | Traditional mobile development model does not support offloading of the task to the cloud | Application development model | Context-aware model | Prototype application using android platform and virtual public cloud and smart phone | (i) Computation time
(ii) Energy consumption
(iii) Memory utilization | Communication aspects of the applications offloading not considered |
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| PERDAM [34] | To tackle low computation, battery power and issue of remote display access | Evaluation of performance of remote display access in MCC | Performance evaluation | Experimental testbed: Android phone, amazon computer server WIFI, and LTE | (i) Bandwidth utilization transfer time
(ii) Power consumption | No model for energy consumption of remote access on mobile devices in a specific context was considered |
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| E2MC3 [35] | The issue of energy cost during offloading of the task to the cloud | Analysis of power consumption in some handheld mobile devices | Analysis | Mobile as a thin client, 3G, and Wi-Fi | (i) Energy usage(ii) Throughput | An offloading decision-making model not considered |
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| EERAM [36] | Task scheduling to minimize energy and monetary cost | Task scheduling | Mathematical model | Server machine and mobile phone | (i) Energy
(ii) Delay | The scheduler model does not handle network congestion and task priority |
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| EOPME [37] | Task scheduling in MCC environment | Task scheduling | Mathematical model | Server machine and mobile phone | Energy consumed | Task priority not considered |
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