computation. Mobile battery is below the threshold Θ: Access to the wireless network requires considerable amount of energy battery power, so as the user battery drops below a certain level. Feasibility to using network becomes low if the communication is quite high. Feasibility of exploiting resources through network becomes appreciable if communication cost is low. Mobile battery is between threshold and ɣ Θ: If mobile battery in a certain range, exploitation of cloud resources though network seem quite attractive. Figure 410: Effect of UE Battery on offloading cost Effect of user preference parameter on Performance . If we In this scenario, we repeated the previous experiment with different user preference parameter increase from 0.5 to 0.8, user more preference to the energy saving over response time. As shown in the graphs, with = 0.8, the offloading cost changes according to the phase of battery in which we are operating. In this scenario, as mobile battery is below a i) Mobile battery is the threshold Θ: certain threshold so most of the processing is performed inside the cloud. Due to sensitivity of the user to battery = 0.8, the offloading cost is more than the scenario = 0.5. In this scenario, as battery battery is above a ii) Mobile battery is above threshold ɣ: certain threshold so device is insensitive to processing done in mobile or cloud. The offloading cost is lesser for = 0.8 as compared to = 0.5. This scenario shows the same result as i) iii) Mobile battery is between threshold and ɣ Θ: due to over sensitivity to mobile battery. Figure 411: Effect of UE Battery on offloading cost
computation. Mobile battery is below the threshold Θ: Access to the wireless network requires considerable amount of energy battery power, so as the user battery drops below a certain level. Feasibility to using network becomes low if the communication is quite high. Feasibility of exploiting resources through network becomes appreciable if communication cost is low. Mobile battery is between threshold ɣ and Θ: If mobile battery in a certain range, exploitation of cloud resources though network seem quite attractive. Figure 410: Effect of UE Battery on offloading cost Effect of user preference parameter on Performance In this scenario, we repeated the previous experiment with different user preference parameter . If we increase from 0.5 to 0.8, user more preference to the energy saving over response time. As shown in the graphs, with = 0.8, the offloading cost changes according to the phase of battery in which we are operating. i) Mobile battery is the threshold Θ: In this scenario, as mobile battery is below a certain threshold so most of the processing is performed inside the cloud. Due to sensitivity of the user to battery = 0.8, the offloading cost is more than the scenario = 0.5. ii) Mobile battery is above threshold ɣ: In this scenario, as battery battery is above a certain threshold so device is insensitive to processing done in mobile or cloud. The offloading cost is lesser for = 0.8 as compared to = 0.5. iii) Mobile battery is between threshold ɣ and Θ: This scenario shows the same result as i) due to over sensitivity to mobile battery. Figure 411: Effect of UE Battery on offloading cost