Location-based services and applications have the advantage of both user mobility and cloud resources in the MCC atmosphere. But these applications are characterized by high energy requirement. In this section we discuss some energy-efficient location-based appli- cations in the MCC environment [31]. Recent location sensing technologies make use of GPS, Wi-Fi, and GSM. It is observed that GPS offers continuous service for 9 h, while Wi-Fi and GSM provide 40 and 60 h, respectively. However, GPS is preferred over the others because of its accuracy in spite of its high energy utilization. In this section, we first dis- cuss the business model of MCC and the related commercial products of location-based application (LBA), and then some location sensing approaches used in the MCC environ- ment. LBA is one of the most emblematic applications in the software as a service (SaaS) layer of the MCC architecture. It gains a user’s present location and presents a varied range of user-position-related services such as social network, health care, mobile commerce, and so on. Certain energy-efficient, location-based services in the MCC environment are discussed in Ma et al. [31].
EnTracked (energy-efficient robust position tracking for mobile devices) [31,32] uses an accelerometer to detect the position. It locates mobile devices in a robust and energy- efficient way. An EnTracked server sends the request for location determination to the EnTracked client. The EnTracked client uses GPS to determine the location and sends the response to the server, which forwards the response to the requesting application. It was further extended as EnTrackedT [33] (energy-efficient trajectory tracking for mobile
devices), which performs trajectory tracking rather than position tracking as in EnTracked. As a substitute of the discussed method, the CAPS (cell-ID aided positioning system) [34], which is based on the steadiness of traversed routes and consistent cell-ID shifting spots, is used. CAPS employs cell-ID sequence matching to determine user location based on the history of cell-ID and GPS positions [34]. It keeps a record of the user’s route, that is, the visited cell-IDs, for future usage. EnLoc (energy-efficient localization for mobile phones) [35] is explored for the purpose of recording actual mobility of individuals with the help of the logical mobility tree (LMT), as shown in Figures 5.25 and 5.26. The vertices of the LMT are also referred to as uncertainty points [31]. The basic idea is to sample the activity at a few uncertainty points and allow EnLoc [35] to forecast the remaining. The scheme mentioned earlier highly relies on, and limited by, the spatiotemporal consistency in user mobility [31]. It cannot handle users’ deviation from habits. Thus, EnLoc [35] uses mobility of large populations as an indicator of the individual mobility [31].
EnLoc [35] generates a probability map for a given area from the statistical behavior of large populations [30,34]. Then, an individual’s mobility in that area can be predicted [31]. For example, think about a person close to traffic junction of street X: since the person had never been to street X previously, it is tricky to foresee how he or she will act at the junction. Nevertheless, if nearly everyone is taking a right turn to Street Y, the person’s
Home
College
Tennis court
Grocery
Bus Cafe Tailor Home
Clothes
Shopping mall Cafe
Library Gym
FIGURE 5.25
Spatial logical mobility tree (LMT).
Home College Gym Cafe Shopping Grocery 9.15 p.m. 9.00 p.m. Library 8.00 a.m. 6.00 p.m. 6.10 p.m. 7.15 p.m. 10.00 a.m. 1.30 p.m. 12.00 a.m. Bus stand 9.00 p.m. 7.30 p.m. 9.00 a.m. 6.30 p.m. 8.30 a.m. FIGURE 5.26 Spatiotemporal LMT.
progress can be determined accordingly. A comparative analysis of the discussed methods is presented in Table 5.14.
All the procedures discussed until now are associated with sensing, were organized in real-world situations, and have proven to be energy efficient [31–35].
5.10 Conclusion
MCC is a popular technology that efficiently reduces the energy consumption of mobile devices to develop green mobile networks. In this chapter, we discussed green mobile computing, green cloud computing, and green mobile cloud computing. The architec- ture of energy-efficient resource management for mobile devices and the issues and requirements of energy saving on mobile cloud computing were studied. The use of MCC to reduce energy consumption of mobile devices was discussed. Graphical repre- sentations showed that computation offloading to the mobile cloud is useful when a large amount of computation is to be performed as compared to the amount of communication needed. The architecture of ad hoc cloud for the provisioning of seamless service was discussed along with its functions. A few green location management applications for mobile cloud environment were also described. It was observed that the employment of green MCC can result in efficient green mobile networks and that this technology can be applied to the femtocell networks. The use of MCC in a femtocell-based network can reduce the energy consumption of each femtocell present therein, which can result in a greener femtocell-based mobile network.
Questions
1. What is green mobile computing? Explain its components.
2. Discuss the energy efficiency of femtocell-based network over only macrocell-based network.
TABLE 5.14
Comparative Analyses of Location Management Strategies
Comparison
Parameters EnTracked [32] EnTrackedT [33] CAPS [34] EnLoc [35]
Target Position tracking Trajectory tracking Trajectory tracking Position tracking
Sensors GPS, accelerometer GPS, compass,
accelerometer GSM with GPS GPS, compass, Wi-Fi, accelerometer
Scheme Dynamically
determines the current position of the user by tracking pedestrian users carrying GPS- enabled devices Dynamic calculation with reduced energy-sensitive sensors and determines the entire route traversed by the user Matches cell ID sequence with previous sequences to determine the entire route of the user
Uses logical mobility tree to determine the location of the user
3. Define green mobile cloud computing.
4. What is femtocell? How does femtocell help make a mobile network “green”? 5. Explain the MCC-based working model of femtocell.
6. Discuss the green handover algorithm. Also compare handover latency with data size and velocity for green protocol.
7. What are the issues and requirements for green MCC?
8. How do location-based services and applications benefit within the MCC environment when it becomes a “green” network?
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6
Resource Allocation in Mobile Cloud Computing
ABSTRACT Rapid resource allocation and release is a challenging era of mobile cloud computing. This chapter discusses the various resource allocation schemes of mobile cloud computing including energy aware resource management. Different task schedul- ing methods are also presented. Challenges to be faced in the field of resource allocation are explored.
6.1 Introduction
The emergence of two different but important fields, mobile computing and cloud comput- ing, has given birth to a new concept of mobile cloud computing (MCC). Mobile devices have the limitation of storage and processing power. In MCC, a mobile device is aug- mented by offloading its task and data into a resourceful cloud. A cloud is a rich collection of resources such as memory, storage, processing power, network, server, database, and applications. A cloud user employs these resources in a “pay as you use” or “elastic” man- ner. When the user sends a service request to the cloud, the cloud provider allocates the desired resource to the user. So, it is very important for the cloud provider to use a sound resource-allocation strategy to maintain the quality of service (QoS) of the cloud.
Several resource-allocation methods [1–3], strategies, algorithms, and middleware have already been developed. In this chapter, various frameworks and issues regarding resource allocation for MCC will be discussed.