A novel quality-based computation offloading framework for edge cloud-supported internet of things

A comprehensive review on internet of things task offloading in multi-access edge computing

“…Over 40 % of the literature surveyed in this survey focused on task allocation as the primary problem-solving goal [ 73 , 74 , 77 , 79 , 81 , [84] , [85] , [86] , 88 , [138] , [139] , [140] , 143 , 145 , [90] , [91] , [147] , 94 , [96] , [97] , [98] , 100 , 104 , 106 , [108] , [109] , [110] , 125 , 130 , 131 , 137 ]. The goal of resource allocation is addressed in approximately 10 % of the reviewed literature [ 99 , 103 , 105 , 111 , 116 , 121 ]. In addition, 48 % of the research work focuses on both task allocation and resource allocation issues [ 76 , 80 , 83 , 87 , 89 , 141 , 142 , 144 , 146 , 92 , 95 , 101 , 102 , 107 , [112] , [113] , [114] , [115] , [117] , [118] , [119] , [120] , [122] , [123] , [124] , [12...…”

“…In the research on some task offloading mechanisms, full consideration has been given to using the characteristics of the 5G network to optimize the transmission process of task data [ 86 , 87 , 141 , 94 , 99 , 105 , 124 , 125 , 149 , 158 , 159 ]. In addition, there are also a small number of research attempts to explore the advanced features of future networks based on 6G to build IoT task offloading mechanisms in MEC [ 111 ].…”

“…Device-to-edge-cloud (D2EC) task offloading in a MEC network involves offloading a task from a user's device to a cloud infrastructure that is located at the network's edge. This approach combines the benefits of both edge computing and cloud computing to optimize task execution [ 81 , 85 , [139] , [140] , [141] , [142] , 90 , 92 , 95 , 96 , 98 , 99 , 101 , [108] , [109] , [110] , [111] , [112] , 115 , 119 , 121 , 122 , 124 , 125 , 129 , 131 , 133 , 134 , 136 , 149 , 156 , 168 ].…”

“…In computational offloading within a MEC network, multiple servers as task offloading destinations refer to the scenario where a task from a user's device is offloaded and executed on more than one edge server or cloudlet. This approach involves parallel execution of the task, with different servers sharing the computational load [ 74 , 77 , 81 , [85] , [86] , [87] , [138] , [139] , [140] , [141] , [142] , 144 , [90] , [91] , [92] , [147] , [94] , [95] , [96] , [97] , [98] , [99] , 101 , 102 , 105 , [107] , [108] , [109] ], [ 111 , 112 , 114 , 115 , [117] , [118] , [119] , [121] , [122] , [123] , [124] , [125] , [126] , [127] , [128] , [129] , [131] , [132] , [133] , [134] , 136 , 160 , 161 , 167 , 169 ].…”

“…This study lacks attention to the availability status of computing resources. A work [ 111 ] further proposed research on task offloading optimization for sixth-generation (6G) communication technology and introduced collective reinforcement learning methods for resource allocation and network optimization.…”