Mobile cloud computing (MCC) has increasingly been adopted in healthcare industry by healthcare professionals (HCPs) which has resulted in the growth of medical software applications for these platforms. There are different applications which help HCPs with many important tasks. Mobile cloud computing has helped HCPs in better decision making and improved patient care. MCC enables users to acquire the benefit of cloud computing services to meet the healthcare demands. However, the restrictions posed by network bandwidth and mobile device capacity has brought challenges with respect to energy consumption and latency delays. In this paper we propose dynamic energy consumption mobile cloud computing model (DEMCCM) which addresses the energy consumption issue by healthcare mobile devices using dynamic cloudlets.
Healthcare professionals (HCPs) use applications which are deployed on Mobile cloud computing (MCC) platform for better decision making and patient care. MCC enables users to take the advantage of cloud computing services. It is the combination of mobile computing, networking and cloud computing. Even though MCC has been adopted widely it faces the issue of energy been wasted by mobile devices when it cannot establish connection with wireless network and it keeps searching for it. In this paper we use dynamic energy consumption mobile cloud computing model (DEMCCM) which addresses energy consumption issue by healthcare mobile devices by using dynamic cloudlets which optimizes the usage of cloud infrastructure services. DEMCCM uses dynamic programming model to enable cloudlets within changing environments to assist MCC. The applications running on these mobile platforms rely on the speed and connectivity of the wireless communication. Also, instability in the wireless network shortens the battery life of these mobile devices.
DEMCCM offers a unique technique to avoid energy wastage by HCPs mobile devices when the networking environment is unstable. This model basically focuses on the communication between HCPs devices and cloud servers. The cloudlets choose the cloud server for better service performance based on the dynamic programming. The cloud server chosen by the dynamic model cloudlets is the optimal solution provided by the model which is expected to avoid energy wastage of the HPCs mobile devices. The main contribution of this paper is the dynamic energy consumption implementation for cloudlets to improve the performance and save energy. Our model is an extension of work done by (Keke Gai, Meikang Qiu, Hui Zhao, LixinTao, Ziliang Zong et al.,2015) [1].
Green Technology is the usage of computers and its resources to be echo friendly. The model implemented in this paper enables green computing by reducing the energy consumption. It focuses on cloudlet techniques such as virtualization and dynamic programming. By applying DEMCCM we aim to reduce the energy consumption on mass HCPs devices without weakening the cloud service performance.
Mobile Cloud Computing integrates cloud computing with mobile devices to make them resourceful in terms of memory, storage and computation power. Although cloud is useful for computation, traditional offloading techniques cannot be used for HCPs mobile devices directly because these techniques are generally energy-unaware and face wireless network bandwidth issues [2]. The below table shows the issues between cloud and mobile cloud computing services.
When a local execution on a mobile device consumes more resource, computation offloading can serve as a major performance booster by transferring resource intensive computational tasks to an external platform like a cluster, or a grid, or a cloud. The decision of computational offloading is an intricate process and the decision itself is influenced by different factors. The entities that influence the computation offloading decisions are HCPs preference, connection, smart phone and cloud service.
A HCP may prefer to enable computation offloading. If the HCP’s objective is to protect their patient’s data and are not certain about the integrity of the offloaded data, then they can disable computation offloading.
Network Connection can affect the decision of computation offloading. Modern mobile devices used by HCPs can communicate through different networking interfaces such as Wi-Fi, 3G / 4G and each of these interfaces can have their own limitations. Wi-Fi technology, for instance, can provide higher bandwidth and shorter delays. Cellular connections like 3G / 4G provide lower bandwidth and suffer from higher delays. It consumes more amount of energy for data transmission. If both of these connections are available to a user, then they can prefer to use Wi-Fi connection. However, HCPs needs to be mobile themselves and Wi-Fi connection is not feasible in all the places. Thus forcing them to switch to cellular network connection 3G / 4G, for which they are charged based on the bandwidth usage. Hence, from connection point of view, the decision to enable or disable computation offloading can be influenced by network bandwidth, delay and cost.
Smartphone plays a significant role in computation offloading decision. They have achieved great development in terms of hardware resources. Today’s smart phones are equipped with high performance processors, memory, sensors and storage. For instance, the Apple Iphone X features a 14.73 cm display and runs on iOS v11.0.1 operating system. HCPs who use such powerful smartphones are less likely to opt for mobile cloud support as compared to HCPs who have low performance smartphones that runs out of resources quickly.
Application Model should also be considered while opting for computation offloading. Each mobile cloud application is different in terms of both their design and object
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