Energy-efficient service-oriented architecture for mobile cloud handover
© The Author(s). 2017
Received: 20 July 2016
Accepted: 28 March 2017
Published: 18 April 2017
Mobile cloud computing uses features to deliver outsourcing data to remotely available mobile devices. However, the flexible nature of the mobile device is a critical challenge for the mobile cloud computing environment. The mobile phone significantly degrades the data transfer performance when initiating the handover process. Thus, an energy-efficient handover process could improve the quality of service (QoS). Here, we introduce a secure energy-efficient and quality-of-service architecture (EEQoSA) for the handover process in the mobile cloud computing environment. The proposed architecture involves four layers: application, the Internet protocol multimedia subsystem (IPMS), communication, and media with connectivity layers.
These four layers collectively handle the energy-efficiency, security and QoS parameters. Existing service-oriented architectures designed for mobile cloud computing are based on the symmetric encryption cryptography to support different media services. However, this approach easily allows an adversary to expose the symmetric key and gain access to private data. Thus, our proposed architecture uses the secure and strong authentication (SSA) process at the IPMS layer by protecting the media services from unauthorized users, as the IPMS is the central layer that could be the entry point for an adversary. Furthermore, to extend the mobile lifetime during the handover process, an energy detection (ED) model is deployed at the communication layer to detect the energy level of the mobile device prior to the handover initialization process. The media with the connectivity layer supports the secure handover process using a priority enforcement module that allows only legitimate users to complete the re-registration process after initiating the handover. Finally, the architecture is tested using the CloudSim simulation environment and validated by a comparison with other known service-oriented architectures.
Mobile cloud computing is an evolving platform in which data storage and data handling are performed outside of the mobile device . Mobile cloud applications transport the data through the mobile device into the cloud servers, attracting many mobile subscribers . Mobile cloud computing is promoted as a promising platform to support different media services [3, 4]. Additionally, the use of smartphones has increased the importance of mobile cloud computing considerably, as the data stored on the mobile cloud servers is easily accessible anytime and anywhere except network and services are unattainable. Today, the usage of mobile subscribers continues to increase due to the provision of new exciting features, such as GPS navigation, MP3, MP4, multimedia messaging services, dispatching emergency responders, Bluetooth, built-in projector personal digital assistant functions, streaming video, camcorders, memory card readers, and instant messaging [5, 6]. These additional smartphone features have been utilized to facilitate the acquisition of outsourcing data (e.g., contractual-execution for the exchange of agreed services) from mobile cloud servers. However, mobility is one of the key challenges for mobile cellular phones [7, 8]. This issue is apparent when a mobile device initiates the handover process, which leads to a reconnection process and in turn to additional energy being consumed. In addition, the data-exchange performance may degrade or be hacked by an attacker because the attacker can install its malicious node into the entry point of the access point (AP) or base station (BS). Thus, the mobility management process should be properly addressed prior to accessing the mobile cloud computing environment [9, 10]. Several mobility management approaches have been proposed to handle the handover process in cloud computing [11–13]. However, these existing approaches still use the traditional methods to obtain the cloud services from the servers. As a result, excess energy consumption and quality of service (QoS) degradation occur.
Handover management and interference mitigation problems for the mobile cloud computing environment were examined in . Thus, a low-complexity management approach is introduced to combine the cloud radio access network with small cells. Fast mobility Internet Protocol version-6 (FMIPv6) was introduced to handle the handover process in mobile cloud computing . The protocol reduced the handover latency and packet loss using buffering and tunneling procedures. As a result, these mechanisms work well in the ad hoc wireless network but could suffer in the mobile cloud computing environment [16–18]. A service-aware location updated paradigm was introduced to identify the frequency and location of mobile devices without using the periodic registration update (PRU) . A service-oriented architecture was introduced for the heterogeneous cloud network to handle the handover management process, which combines the features of cloud computing and the heterogeneous small cell network. However, the architecture particularly focuses on the cloud computing extenuation management process. A robust architecture that can improve the handover process in the mobile cloud computing environment is urgently needed . Furthermore, adversaries attempt to attack sensitive data during the handover process and slow down the services [21, 22]. As a result, the privacy, integrity and authentication of the legitimate users are compromised . Secure packet authentication was introduced to restrict the access of adversaries to mobile cloud computing during the handover process. However, such authentication provides minimal user-privacy support . The architecture attempts to reduce the handover computational cost but not the energy consumption. Existing proposed mechanisms for the handover process in mobile cloud computing focus on reducing the computational cost, flow of the media contents and network connectivity. Hence, the architecture must properly support the energy-efficient handover process while maintaining secure communications and improving the QoS parameters in mobile cloud computing.
Thus, we introduce a state-of-art, service-oriented architecture comprised of four layers: application, the Internet protocol multimedia subsystem (IPMS), communication and media with connectivity. These four layers collectively help the mobile device access the cloud computing resources efficiently. Furthermore, this paper presents a priority enforcement module, which allows only legitimate users to complete the re-registration process after initiating the handoff and thus enables reliable data access and content delivery. A substantial amount of energy and bandwidth are saved through the fast and seamless handoff process. Furthermore, this paper outlines the secure and strong authentication (SSA) to secure the services available on the IPMS layer (e.g., web, videoconference, video-on-demand, Internet, fax, email, telephone, and voice over IP service). In addition, the SSA helps store the key on different mobile clouds, making it difficult for the attacker to break the key. Finally, this paper discusses the energy detection model that calculates the energy consumption of the mobile device when initiating the handoff (as this model provides the updated energy status of the mobile phone). The remainder of this paper is organized as follows: Energy-efficient, service-oriented architecture section presents the energy-efficient, service-oriented architecture designed for the handover process (including the strong secure authentication and priority-based module), Experimental setup and simulation results section presents the simulation results and analysis, and the paper is concluded in Conclusion section.
Energy-efficient, service-oriented architecture
Media with connectivity layer
This layer constitutes the home subscriber server (HSS) that interconnects with the cloud computing servers as an enterprise server. The HSS also links to the IPMS layer to successfully maintain data communication. The HSS involves the subscription-related information (SRI) server, location update (LU) server and role manager server. The mobile cloud user profiles are stored in the SRI, and the LU stores the mobile cloud user’s current location. Prior to transferring the cloud data to a legitimate mobile cloud user, encryption is performed for secure data communication. The attribute-based model is used to support the HSS encryption at the application layer.
Internet protocol multimedia subsystem (IPMS) layer
The IPMS layer offers utility services, such as web-browsing, video-on-demand, videoconferencing, fax, email, Internet, and voice over IP (VoIP) service. The IPMS involves the registration process, which helps obtain updated location information from the mobile cloud user. The IPMS uses a call session control function (CSCF) to bind a public user identity to the IP address of a mobile cloud user.
The session initiation protocol proxy is the entry point to be used to connect with the IPMS layer. The P-CSCF can be used with either a foreign network or home network. The P-CSCF is comprised of the session frame controller (SFC), which is used to establish the user network interface. Hence, the features of the SFC help protect the IPMS. The P-CSCF is assigned to the IPMS prior to registration and is not altered during the entire process. The P-CSCF also accepts the encrypted signal and declines the unencrypted signal to help protect the communication. Furthermore, the P-CSCF consists includes a policy decision function, which helps maintain the QoS of the media resources. The policy decision function fully organizes the bandwidth utilization.
The S-CSCF controls the session and is fixed in the home network. It maintains the registration process and sets the timer by involving two significant features. First, the S-CSCF provides the interface used to download the profile of mobile cloud device and makes the implication. Second, it supports the trail of the signaling messages and monitors all of the traffic for the locally registered mobile cloud devices. The S-CSCF has decision capability regarding the handover and directs and manages the policy of the network operation.
The I-CSCF acts as an alternate SIP. It is responsible for sharing the identity with the domain name system (DNS). The I-CSCF also includes two components: the profile record (PR) and the name controlling pointer (NCP). Both of these components are used to determine the available remote cloud server, which facilitates the registration process for the SIP packets. Furthermore, the PR has the additional task of stipulating the data in a DNS, which traces an appropriate port number and hostname of the particular service when a mobile cloud device initiates the handover process. The I-CSCF also forwards the SIP request to the S-CSCF to refresh the exiting registration process and informs the network of the updated status of the mobile cloud device. Therefore, the mobile cloud device is able to complete the re-registration process efficiently.
This layer routes the data and synchronizes the media and IPMS layers. It consists of the media gateway controller function (MGCF), media resource function controller (MRFC) and breakout gateway control function (BGCF). The BGCF acts as the SIP proxy, which is responsible for processing the request to route the data from the S-CSCF whenever it determines that a session cannot be established using a DNS. Furthermore, the BGCF contains the routing features based on the telephone records.
The MGCF is considered as the SIP endpoint and manages the call exchange between the bearer-independent call control (BICC) and SIP. The MRFC is the signaling component that infers information looming from the S-CSCF with an application server (AS) to manage the MRFP. During this process, determination of the energy consumption is of high significance to route and synchronize the data. Furthermore, the location update server initiates the re-registration process, which is comprised of two levels: periodic re-registration (PRR) and re-registration for change capabilities (RRCC). The PRR and RRCC levels involve the messaging process to complete the re-registration. Hence, the energy for the re-registration process should be calculated after completion of the handover process to help determine the remaining power of the mobile device.
Energy Consumption for the Re-Registration Process
Notations used in this work and their descriptions
Energy of the mobile device before the first handover process
Number of mobile nodes accessing the same BS or AP for registration
Packet size sent by the sender
Packet size sent by the receiver
Number of mobile devices in the range of APs/BS that want to initiate handover
A x , B x
Location information of the AP/BS
A i , B i
Location information of the nodes
Range of the APs/BS
Energy of the mobile device after the first handover process
Number of mobile devices in the range of APs/BS that want to initiate the re-registration process
Energy consumption of the mobile cloud server and a mobile device
Wait time for the re-registration process
Media with connectivity layer (MCL)
Rapid, Seamless handover procedure
A mobile cloud device can change its attachment from its respective home domain. This could lead to re-attachment with another domain and the possibility of several handovers during the process. The handover process affects the QoS parameters including the end-to-end delay and packet loss. Handing this situation to the mobile cloud computing environment, we introduce the fast, seamless handover mobile IPv6 (FSHIPv6) to support the mobility management. The FSHIPv6 includes the mobility management utilities to reduce the unexpected signaling load within the intra domain when several mobile cloud users initiate the handover processes. As a result, the packet-drop and latency are greatly increased. In our approach, the handover process involves two states: periodic re-registration (PRR) and re-registration for change capabilities (RCC). In the PR, the mobile cloud device remains attached with same AP (AP)/BS to keep sending the data until it becomes attached with either another AP or BS. The timer is kept as active and ON during both steps. In the RCC, the mobile cloud user uses the utility features, and the attachment process is completed with another AP/BS.
The PR aims to identify whether the mobile cloud user is still registered with the home network. In this state, the home network begins the re-registration process because the registration timer has timed out. The RCC aims to intimate the change in the location of the mobile cloud user to the home network. During the re-registration process, the timer triggers the RCC, whereas the PR controls the changing parameters. The registration timer is required for both the RCC and PR to efficiently initiate the new session. In our proposed fast, seamless handover, the IPMS identifies the current registration status of the mobile cloud device. The process is also supported with the priority enforcement module (PEM) that reduces the traffic load when the handover is in progress, as this feature assigns the priority to each device based on the nature of the traffic. Furthermore, the IPMS also refreshes the registration timer during the session establishment process and cloud server-access. As a result, the time consumed for the PR can be reduced in our approach.
Experimental setup and simulation results
The performance of our proposed secure energy-efficient and quality-of-service architecture (SEEQoSA) is confirmed through the CloudSim simulation environment. The CloudSim simulator is installed on the Ubuntu Linux operating system. All the experiments are performed on a laptop with an Intel Pentium Dual-Core E6500 Wolfdale Dual-Core 2.93 GHz and 5 GB of RAM. The computing machine uses the 64-bit version of Windows 8.
Showing the simulation parameters and its description
Intel Pentium Dual-Core E6500 Wolfdale Dual-Core 2.93 GHz
Windows 08 + Ubuntu Linux
1400 m × 1800 m
Mobile cloud devices
Maximum number of handover
Racks at application layer
Racks at IPMS layer
128 hosts in each rack
16 processors in each rack
Virtual disk space
Malicious Detection Probability
Reliable Data Delivery
Latency in the presence of a Malicious Node
Malicious detection probability
Reliable data delivery
Latency in the presence of a malicious node
This study introduced the SEEQoSA to achieve an efficient handover process in mobile cloud computing. The proposed paradigm consists of four layers: application, IPMS, communication and media with connectivity. The application layer serves as the enterprise server to control the operations of the other three layers. The IPMS provides different services, such as web, videoconferencing, and video-on-demand. The communication layer handles the faster re-registration process to avoid unexpected delays and data loss. Furthermore, the communication layer involves an energy-efficient detection model to determine the energy of each node when initiating the handoff process. The media with connectivity layer consists of the priority-based module, which allows only legitimate users to complete the re-registration process after initiating the handover and reduces the occurrence of extended delays during the handover. The architecture is implemented using C++, and the code is converted to the object tool command language (OTCL) run on the CloudSim platform. The results confirm the validity of our proposed architecture and comply with the QoS and energy-efficiency parameters. The architecture aims to facilitate energy-efficient and QoS-supported handoff processes. The simulation results validate that the SEEQoSA achieves a 5.5–12.8% higher malicious node detection probability with an 8.2–42.2% lower bandwidth consumption compared to other known approaches. The SEEQoSA consumes 0.67–7.87% less energy with 12–16 handover processes over 5,000 rounds. Furthermore, it has also a 0.7–1.4% higher data delivery rate compared to other service-oriented architectures.
The results confirm that the SEEQoSA is a more suitable choice for mobile phones when initiating the handover process in a cloud computing environment. In the future, we will determine possible malicious attacks on the SEEQoSA and will propose appropriate solutions.
We are also thankful to anonymous reviewers for their valuable feedback and comments for improving the quality of the manuscript.
This research work is part of QBH. dissertation work. The work has been primarily conducted by QBH under the supervision of JPD. Extensive discussions about the algorithms and techniques presented in this paper were carried between the two authors over the past year. Both authors read and approved the final manuscript.
About the Authors
Qassim Bani Hani is pursuing towards his Ph.D., Department of Computer Science and Engineering University of Bridgeport, Bridgeport, at the CT. Qassim’s interests are in Cloud computing, Cloud computing mobility, and Cloud localization. He has authored and coauthored several technical refereed papers in various conferences, and journal articles. He is IEEE member.
Julius Dichter is an Associate Professor in the department of Computer Science and Engineering at the University of Bridgeport in Connecticut. He received his M.S. degree from the University of New Haven and the Ph.D. from the University of Connecticut in the area of parallel computing optimization. He has authored and coauthored several technical refereed and non-refereed papers in various conferences, journal articles, and book chapters in research and pedagogical techniques. His research interests include parallel and distributed system performance, security of the cloud computing, algorithms and object-oriented systems. Dr. Dichter is a member of IEEE, ACM, and ISCA.
The authors declare that they have no competing interests.
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