In recent years, computation offloading, through which applications on a mobile device can offload their computations onto more resource-rich clouds, has emerged as a promising technique to reduce battery consumption as well as augment the devices' limited computation and memory capabilities. In order for computation offloading to be energy-efficient, an accurate estimate of battery consumption is required to decide between local processing and computation offloading. In this paper, we propose a novel technique for estimating battery consumption without requiring detailed information about the mobile application's internal structure or its execution behavior. In our approach, the relationship is derived between variables that affect battery consumption (i.e., the input to the application, the transmitted data, and resource status) and the actual consumed energy from the application's past run history. We evaluated the performance of the proposed technique using two different types of mobile applications over different wireless network environments such as 3G, Wi-Fi, and LTE. The experimental results show that our technique can provide tolerable estimation accuracy and thus make correct decisions between local processing and computation offloading.

During the investment process for enhancing the level of IT security, organizations typically rely on two kinds of security countermeasures, i.e., proactive security countermeasures (PSCs) and reactive security countermeasures (RSCs). The PSCs are known to prevent security incidents before their occurrence, while the RSCs identify security incidents and recover the damaged hardware and software during or after their occurrence. Some researchers studied the effect of the integration of PSCs and RSCs, and showed that the integration can control unwanted incidents better than a single type of security countermeasure. However, the studies were made mostly in a qualitative manner, not in a quantitative manner. In this paper, we focus on deriving a quantitative model that analyzes the influence of different conditions on the efficiency of the integrated security countermeasures. Using the proposed model, we analyze for the first time how vulnerability and the potential exploits resulting from such vulnerability can affect the efficiency of the integrated security countermeasures; furthermore, we analytically verify that as the efficiency of PSCs increases, the burden of RSCs decreases, and vice versa. Also, we describe how to select possibly optimal configurations of the integrated security countermeasures.