International Journal of Modern Research in Engineering & Management (IJMREM) ||Volume|| 1||Issue|| 10 ||Pages|| 37-45 || November 2018|| ISSN: 2581-4540
A Machine Learning based Network Sharing System Design with MPTCP Yu-Ching Lin National Center for high-Performance Computing National Applied Research Laboratories Hsinchu City, Taiwan, R.O.C
-------------------------------------------------------ABSTRACT------------------------------------------------The information and communication technologies (ICT) integrate different types of wireless communication to provide IT-enabled services and applications. The great majority end devices are equipped with multiple network interfaces such as Wi-Fi and 4G. Our goal is to integrate the available network interfaces and technologies to enhance seamless communication efficiency and increase resources utilization. We proposed a heterogeneous network management algorithm based on machine learning methods which includes roaming and sharing functions. The roaming function provides the multiple network resources in physical and media access control layers. The sharing function supports multiple network resources allocation and the service handover process based on the Multi-Path TCP protocol. The simulation result also shows that the proposed scheme can increase the network bandwidth utilization effectively. The sharing system could be used in home, mobile and vehicular environments to realize ubiquitous social sharing networks.
KEYWORDS: heterogeneous network; bandwidth sharing; multipath TCP; machine learning. ------------------------------------------------------------------------------------------------------------------------------------------Date of Submission: Date, 13 November 2018 Date of Accepted: 21 November 2018 ------------------------------------------------------------------------------------------------------------------------------------------I. INTRODUCTION The rapid progress of wireless communication in recent years extends the information and communication services from digital homes to mobility life. It also extends the developments of all types of handheld mobile devices to offer real time streaming services in heterogeneous network environments. In such potentially scenarios, the mobile devices might have the capability to gather information of the surrounding access networks to make the accurate network selection and optimized handover decision [1, 2]. In order to achieve ubiquitous roaming in short, medium and long-range heterogeneous networks, one of the important issues is to achieve seamless handover in different heterogeneous networks effectively and rapidly [3, 4]. It is important to integrate the channel estimation information to estimate the change of adaptive signal-tracking decay in addition to designing algorithm so as to improve the estimation handover correctness and reduce the system computation load [5, 6]. If a network is aware of state of possible end to end paths, such state can be used to intelligently assign a suitable path to each flow depending upon the requirement of the flow. Besides, to keep the services connections and manage the services qualities according to the network states is another important issue [7, 8]. The proposed method provides bandwidth resources of all available networks for multi-users sharing at the same time, and achieves a well load-balance state of network interfaces. Mobile devices are able to leverage diverse heterogeneous network paths by Multipath Transmission Control Protocol (MPTCP). To optimize the utilization of the heterogeneous networks, the system design with an intelligent network scheduler based on Multipath TCP protocol [9, 10]. Multipath TCP is a major protocol extension to TCP that supports the transmission of a single data stream across different network interfaces and IP addresses. The major change to TCP protocol is one connection always bound to one four-tuple. Multipath TCP can be also seen as a set of TCP connections, called sub-flows, which are grouped together and managed by the two endpoints of the Multipath TCP connection. Multipath TCP not only increases robustness during network connection failure, but also potentially achieves higher data transmission throughput. The performance of Multipath TCP will be influenced by many factors. The main concern is the design of the workflow scheduler. The scheduler is responsible for the distribution of data flow over multiple paths and scheduling decisions might influence the system performance and load balance, especially when paths are heterogeneous. Therefore, the scheduler can have a significant impact on the performance of Multipath TCP. The system can be implemented on the Linux kernel-based platform such as Android and embedded Linux OS. Linux kernel Multipath TCP implementation is available for real world experiments [11].
www.ijmrem.com
IJMREM
Page 37
A Machine Learning based Network Sharing‌ The performance of the handover prediction and resources management is also analyzed in this paper. The system integrating with heterogeneous network technologies can serve as a home entertainment gateway, a vehicle on-board unit, or a portable network device. The rest of this paper is organized as follows. After introduction, the system architecture is described in section II. Section III describes the proposed scheme, including the methodology and system design. The performance analysis is described in section IV. Section V is the conclusion and future works.
II. SYSTEM DESCRIPTION The developed system integrates technologies of heterogeneous networks handover and Multipath bandwidth allocation management. The system can equip several heterogeneous communication interfaces. Some are for the purpose of local user connections, such as Wi-Fi or Bluetooth; some are for the purpose of Internet access connections, such as Ethernet, Wi-Fi or cellular networks. There is an inner network controller to manage the data flows of local networks and an outer network controller to manage the data flows of Internet access networks. The developed system equipped with heterogeneous network interfaces can provide with two functions: Roaming and Sharing. The Roaming function will search and choose the wireless network with best resources for users according to the surrounding environment. This is especially helpful when the system is used for roaming between the homogeneous or heterogeneous networks [12, 13]. The Sharing function will allocate the available resources to all users when the system is connected with multiple networks. The Resource Manager of the Sharing function will assign the service connections by Multipath TCP scheduler. The basics idea of Multipath TCP can be considered as the following simple use case. When a user use a portable device equipping Wi-Fi and 4G interfaces to connect to a server. To start a Multipath TCP connection, the portable device initiates a three-way handshake with the server over the 4G interface. This handshake includes specific options to negotiate the utilization of Multipath TCP. Once the TCP connection has been established, it becomes the first sub-flow of the Multipath TCP connection. If the user want to use the Wi-Fi interface at the same time, the portable device starts another three-way handshake over this interface and indicates that TCP connection is the second sub-flow of the Multipath TCP connection. Once the sub-flows have been established, data can be transmitted over anyone of them. The number of sub-flows associated to one Multipath TCP connection can vary over time. Besides, Multipath TCP uses two levels of sequence numbers. The regular TCP sequence number ensures that the data sent over one sub-flow are in sequence. The Multipath TCP sequence number ensures the data received over sub-flows having different delays [14, 15]. The system can communicate with another gateway or server as a client, or share its network resources for users as a server. It can be applied for many scenarios in our life to realize ubiquitous services sharing and roaming for various network environments. For example, when people stay at home, the system can be started up with the Sharing function and acted as a home entertainment gateway. The system can dynamically allocate wired and wireless network bandwidth for all users connected to the system. When people are outside the building, the system can use the Roaming function and act as an intelligent mobile internet device. The system can choose one or more wireless networks with available resources to satisfy user traffic demand. If people carry the system into a car or a bus, the system will also become a vehicle on-board unit and share the mobile network resources for users on the vehicle with the Sharing function. For the Roaming function, the handover technology is implemented to detect the parameters of channel condition in each network interfaces, including frequency responses and power gain, then decide the networks handover. When the handover between network interfaces occurs, the communication link of data and physical interfaces are adjusted adaptively based on the handover decision. The result of handover decision may also change the routing state between the inner network users and the outer network interfaces. Therefore, the Sharing function is implemented to manage the network resources allocation and dynamic routing at the Network layer [16, 17]. The communication link of network and physical layer are adjusted adaptively based on the handover decision and application services. The quality of different types of application services will also be guaranteed.
III. SYSTEM DESIGN Heterogeneous Networks Management: We proposed a heterogeneous network management algorithm to maintain the qualities of access networks and services with Multipath TCP scheduler. The proposed algorithm shown in Fig. 1 cooperates with Roaming function and Sharing function according to the situational application. When the system starts up, the system will detect the available network resources and the status of environment. The system decides to use Roaming or Sharing function according to this information or by user commands.
www.ijmrem.com
IJMREM
Page 38
A Machine Learning based Network Sharing… The algorithm of the heterogeneous network management can be divided into three processes, and the details of each process are described as follows: Startup RSS, SNR…
Environment Detection
I/O, GPS… HO Initialization
Sharing Process
Handover Control Process
Roaming Process
User Link Detection
Network Analyzer
Search New Link
Network Capability Setup
Service Requirement Setup
Resource Manager
>1
On-Line Service Analyzer
Vertical HO Make before Break
Service Layer Establish Service Migration
Multipath Setup
HO Preparation =1
Network Interface?
Resource Release
Horizontal HO Physical Connection
Network Layer Establish HO Commitment
HO: Handover
HO Completion
Figure 1. The sharing system design. Handover Control Process: The Network Analyzer function monitors the status of networks and predicts the power gain or signal-to-noise ratio variation of each network. When the system discovers the signal qualities of networks occurring significant variation, the Network Analyzer function notifies the Roaming and Sharing processes for the handover initialization. The handover process includes channel estimation and handover controller. In the power tracking mode of channel estimation, the received signal strength power predictor is triggered to predict the power variation. The power prediction scheme is to predict the signal strength decay in the handover process. The handover controller makes the handover decision based on the real-time channel estimation results. We use least mean square method to establish a mathematical recursive model, and integrate channel estimation information to estimate the signal strength decay as the basis of performance analysis of heterogeneous networks. The signal strength decay estimated value is rectified by referring to the previous iteration of channel theoretical signal model and recursive parameter change estimated by the channel. The signal strength decay estimated value could be similar to the actual environment. The following equation is an exemplary mathematic recursive model established for the theoretic recursive computation.
G (t + t ) = G (t ) + 1 * (G Theory (t + t ) − G (t ))
where Ĝ is the signal strength decay estimated value, G Theory is the signal decay theoretic value, t is the current time, Δt is the time difference, and μ1 is a first group recursive parameter. Based on the signal strength decay tracking value obtained by power gain prediction and the current wireless channel state information, the network handover controller analyzes the heterogeneous network performance to predict the handover necessity in the future. The On-line Service Analyzer function receives the information of available network resources sent from Search New Link module and examines the characteristics of user services sent from the user/service management module. It decides whether to execute vertical or horizontal handover and reallocates resources to on-line services based on their priorities. If the handover process occurs, the Service Layer Establish function will setup a new service thread for this service. The contents of the old service thread will be migrated to the new service thread, and the settings or parameters of the service will be recorded in a control module buffer. The new service thread uses the primitives which are established by the vertical handover and Network Link Establish modules to communicate with the new network attachment point and continues the old service process. The new service thread will connect to the other communication party via the primitives which establishes new network layer and link layer connections using heterogeneous network interfaces. After the new service thread is established, the old service thread connected to the original network attachment point breaks. The service maybe suffering a temporary pause during vertical handover, but this scheme will make the service continuously. The Service Migration function will change the connection of service according to handover type. The application layer service will use the new service thread to continue its service.
www.ijmrem.com
IJMREM
Page 39
A Machine Learning based Network Sharing‌ If the old service thread has not broken, the service will not need to setup the new service threads to carry out the service migration processes. The connection of service will be changed to the new access point or base station at the same network interface. The Service Migration function also decides the network layer handover of services relying on the Sharing Process. Finally, the system will release the old service process and the old network resource to complete the handover control process. The services will be continued by applying the handover control process and Multipath TCP sub-flows at network and application layers even if the physical communication link is changed when roaming between the heterogeneous networks. Roaming Process: The Roaming Function is responsible for choosing the most appropriate network interface according to the surrounding environment. When the Roaming Function is enabled, the system analyzes the qualities of used networks. The Search New Link function checks the network information sent from the Network Analyzer function to see if necessary, to search the new network link for the handover preparation. If there exists another network interface which uses different wireless technology and has better quality, the system could execute the vertical handover to make a new wireless physical link before the old physical link break. Next, the Network Layer Establish function will reserve the old IP and routing table information to keep the connections of used services continuously on original communication links, until new IP and routing table of new communication link has been setup. Then the Service Layer Establish function of Handover Control Process is applied to execute the seamless service handover at Application Framework Layer. If there is only single wireless network interface, the system will execute the horizontal handover to make the network interface connecting to the network access point with better signal strength or bandwidth according to the information sent from Search New Link function. Next, the Network Layer Establish function will change the IP and routing table information according to the new network access point for the change of communication links, and then applies the Services Handover function of Handover Control Process to execute the service handover at Application Layer. Sharing Process: The Sharing Function of the system which uses Resource Management function to control the network layer handover is used to allocate the available network resources for users.When the Sharing Function is enabled, the system analyzes the qualities of used networks. The User Link Detection function detects the user connections and the active network interface statuses sent from Network Analyzer function to see if any network resource varied. If there exist any network interface being removed from the system or added to it, the Network Capability Setup function will analyze the QoS parameters sent from the User Link Detection function, and classifies the connected networks into several groups of different qualities. If there exist any user joining to the sharing network or leaving from it, the Service Requirement Setup function will update the users’ information which includes user preference and the characteristics of online services. Then Service Requirement Setup function classifies services into several groups of different QoS requirement according to the service characteristics. As either of Network Capability Setup and Service Requirement Setup function is enabled, the Resource Management function will be triggered to rearrange connections between the network interfaces and services according to the information provided by Network Capability Setup function and Service Requirement Setup function. The loading ratio of each service group of each network interface will be derived. Then how many numbers of services will be assigned to each network interface could be decided. Finally, the Multipath Setup function setups the network routing tables with user services and network interface configured by Resource Management function. The results of the resource management will be sent to the Service Migration function to execute the Handover Control Process. Adaptive Resources Allocation: We design the dynamic multipath routing algorithm based on the Linux kernel module to provide bandwidth resources of any kind of heterogeneous network interfaces for multi-users at the same time. In order to achieve multiple routing path and load balance, we apply the Linux Multipath TCP, iptables, load balancing and multipath routing modules. This method involves not only distributing the routing path of user services, but also managing the network resources dynamically according to the variation of communication environments and the requirements of services, in order to keep high bandwidth utilization of each network interface and achieve a well load-balance state [18, 19].To realize the purpose of load balancing and services quality guarantee, we classified the user services into several groups according to the different characters of services, and classified the network channels into several groups according to the different quality of networks. The different policy of connection distribution is applied between these service groups and network groups. The resource classification algorithm are described as follows and the workflow module is shown in Fig. 2.
www.ijmrem.com
IJMREM
Page 40
A Machine Learning based Network Sharing… Initial Polling Network interval up or down Yes Local/Access Network Setup
Local User Detection
Table 1 6
User ID check Yes Load User Service list
SG(2)
Access Resource Detection
No
Load Default Service list
Service Group Classification according to QoS Requirement
SG(1)
No
….
Network Interface Classification according to QoS Condition ….
NIC(1)
SG(m)
NIC(2)
NIC(n)
2
1
Figure 2. Resources Classification workflow Resources Detection: When the system starts up, the system drives the available communication interfaces and monitors the parameters of the networks quality variations. This information is recorded and update into a Resource Table. The system allows multi-user connecting with different local networks, such as Wi-Fi or Ethernet. After the system started up, the system detects the states of the user devices which are connected to it and monitors the connection variations periodically. This information is recorded and update into a User/Service Table. Networks and Services Classification: The network classification module analyzes the network parameters which include signal strength, mobility, and the available bandwidth of each network interface, and then classifies the network channels into different types of qualities. This information is also recorded in the Resource Table. The network classification module analyzes the state parameters of each application service, which includes bandwidth requirements, service types, and using frequency. All services are divided into some different groups according to the bandwidth requirements and service types. All information is also recorded in the User/Service Table. Services Relationship Computation: The Networks Priority module setups a QoS requirement weight X m (1* , 2* ,..., k* ) for each group of services according to the characteristic parameters, such as service types,
bandwidth requirements, and using frequency. The QoS ability weight Yn (ˆ1 , ˆ 2 ,..., ˆ k ) is set up for each network interface according to the characteristic parameters, such as signal strength and bandwidth capacity. The value of weight can be set in interval [0, 1] on the basis of service and network quantities. The production of the QoS requirement weight and QoS ability weight is applied for relativity computation. The results of relativity computation represent the priority relationship between network interfaces and user services. Each services group has an own priority value for network interfaces, which is shown as Fig. 3. The service will be assigned to which network interface just according to this priority value, Pmxn. 1
2
Service QoS requirement weight setup Network QoS condition weight setup ˆ1 , ˆ 2 ,..., ˆ k ) X m (1* , 2* ,..., k* ) Y1 (ˆ1 , ˆ 2 ,..., ˆ k ) …. …. Yn (
X 1 (1* , 2* ,..., k* )
Priority Calculator
Pmn
LRmn
P1n ( SG1 ) X 1 Y1 X 1 Yn = X m1 (1* , 2* ,..., k* ) Y1n (ˆ1 , ˆ 2 ,..., ˆ k ) = = X m Y1 X m Yn mn P1n ( SGm ) m1
Load Ration Calculato P1n ( SG1 ) Pn ( SG1 ) P1 ( SG1 ) Sum( P Sum( P LR1n ( SG1 ) ( SG )) Sum( P1n ( SG1 )) 1 n 1 1 n ( SG1 )) = = = P1 ( SGm ) Pn ( SGm ) LR1n ( SGm ) P1n ( SGm ) m1 Sum( P1n ( SGm )) mn Sum( P1n ( SGm )) m1 Sum( P1n ( SGm )) 3
Figure 3. Services Relationship Computation
www.ijmrem.com
IJMREM
Page 41
A Machine Learning based Network Sharing… Each service group is setup the different service loading ratios with different network interfaces according to the network priority values. The loading ratios LRmxn(SGi), are the quantity ratios of all services in each service group assigned to network interfaces. According to the results of loading ratios LRmxn, the actual quantity of services in the group assigned to network interfaces can be setup as Service Loading Number LNmxn(SGi), where LN1n ( SGi ) = N ( SGi ) * LR1n ( SGi )
. The system will adjust the service loading numbers to the load balance state according to the relativities of services and network interfaces when the network interface is over loading. Services Link Allocation: The Service Link Allocation function is responsible for managing service connections and service qualities. This function configures the network routing tables with user services and network channels according to the characteristics of services and networks. The routing tables of services and network interfaces are setup according to the service loading numbers, and consider the conditions of services using frequencies, user quantities, and routing allocation results of each service group. The high using frequency services are allocated with a higher priority to the network interface with a higher loading ratio. The system will also adjust the service link states to load balance state according to the link allocation results of other service groups. The purpose of this step is ensuring the qualities of services and the network interfaces loading balance, and the workflow module can be implemented as shown in Fig. 4. 3 i : index of m m : service group number
i=1 Load Number Calculator
LN1n ( SGi ) = N ( SGi ) * LR1n ( SGi )
No
Fine Load Number adjustment of SGi Table 2
i=1
i= i+1
Yes Information Setup Yes
i<m 5
No 4
Figure 4. Services Load-balance Management Dynamic Multipath Routing Setup: The routing table will be setup with routing information of each user according to the decision made by Service Links Allocation function, and the system will execute network services based on the routing information. The system will adjust the routing information according the executing state actually. When a network interface is over loading, the part of services on the interface will be allocated to another network interface according to the relativity parameters to ensure the load balance of the network interface. The system will also monitor the user habits and network states to maintain the routing information periodically. The user habits include the connection of services occupied how much bandwidth and executed how many times during a unit time. The purpose of this step is to fit the requirement of users and realize the dynamically resource management mechanism, which is shown as Fig. 5. 4 i : index of m j : index of l (i) i=1 l (i): service number of service group i m : service group number Load Service content list of SGi j= 1
Service frequency classification
6 Fine Service link adjustment of SGi
Table 3
5 No
Information Setup
i=i+1 j=j+1
Service link allocation i=1 Yes
No
j < l(i)
Yes
Yes i <m No Routing Table Setup
6
Figure 5. Dynamic Multipath Routing Setup
www.ijmrem.com
IJMREM
Page 42
A Machine Learning based Network Sharingâ&#x20AC;Ś Machine Learning Approach: Machine learning techniques have been extensively applied for performance analysis and troubleshooting [20,21]. The design of network sharing system can be proposed to use machine learning methods to model the relationship between the resources allocation to application requirements. Such a model can be used to predict the resource need of an application to meet its performance target. During the training process, a machine learning model gradually tunes its internal network by utilizing the training data set. The accuracy of any model is contingent upon selection of a proper training data set and is evaluated using a different testing data set. The training starts with a boot-strapping phase which requires system administrators to identify the best-case and worst-case resource allocation considered feasible across each resource dimension. The input parameter set is then chosen by first including these boundary allocation values and selecting additional values obtained by equally dividing the range between the lowest and highest values across each resource allocation. This input parameter set and the corresponding output values are chosen as the initial training data set. After this initial training, the modeling accuracy with the initial training data set is measured by predicting for the testing data set. If satisfactory accuracy is achieved, the training process concludes. Otherwise, additional bandwidth allocation values are computed by preferentially varying highly correlated input parameters by further subdividing the allocation range with the goal of populating the training set with allocation values that represent the output parameter range more uniformly. The sharing system introduces machine learning to classify the traffic and assigns an appropriate path to each of the flow based on their performance requirement. The idea is to provide constrained resources of a network like bandwidth and low latency paths according to their priority and class. A machine learning trainer and machine learning classifier are integrated into bandwidth sharing controller. The machine learning trainer is used to train the classifier. The trained machine learning classifier classifies the packet into one of the predefined set of classes. Based on the class identified and policy, the controller decides the path to be used for the specific application. The machine learning classifier module implements the decision tree classifier. Support Vector Machine (SVM) is the best classifier for classifying the network traffic. By using correlation-based filters the number of parameters to be considered for classification is reduced. The performance analysis of machine learning algorithm has been conducted on Naive Bayes, Naive Bayes Kernel Estimation, Bayesian Network, SVM, K-Nearest Neighbors and Neural Network. Among which SVM has achieved the highest overall accuracy while classifying. SVM classifier achieves more than 98.0% average accuracy on all traces with 5,000 training flows, which amount to only 2.5% of the size of the testing sets. But without port number their classification accuracy went to 56 - 70%. The machine learning method makes the resources allocation of the sharing system more intelligent.
IV. PERFORMANCE ANALYSIS Heterogeneous networks resources management is the key technology of the sharing system. We evaluate the quality of each service and the bandwidth utilization of each network interface of this system. The bandwidth utilization results of heterogeneous network sharing system is shown as Table I. The system equips with three Wi-Fi network interfaces and four 4G LTE (Long Term Evolution) network cards. One of the Wi-Fi network interface is used as the local network interface communicating with client users, and others are for Internet access. The maximum bandwidth of Wi-Fi interfaces are about 55Mb/s, and the maximum bandwidth of 4G interfaces are about 24Mb/s. The experiment applies four users connecting to the heterogeneous networks resources management function, and each user applying different network services from Internet on his computer or mobile device. These services are analyzed by the Service Analyzer of the sharing system and classified into different service type such as S1, S2, and S3. These services mapping on which network interface are decided by the Resource Management Module. The bandwidth usage indicates that the data flow rate of each network interface when four users applying network services at the same time. Bandwidth utilization indicates that the usage ratio of the available maximum bandwidth of each network interface. Fig. 6 shows the performance of the sharing system applying with the resource management algorithm. The system applies with the proposed intelligent resource management function will increase the bandwidth utilization of each network interface, and the system will have a better load balance state. Prioritizing the flow and assigning appropriate path is like the one-to-one mapping of the highest priority to the lowest latency path, the second highest priority to the second lowest latency path. This assignment sometimes makes the application to suffers more jitter when two or more low latency paths share a set of common edges. As link cost calculation is updated once in t seconds is used to compute K shortest paths. Any new flow that arrives within t seconds takes the next shortest path. Based on the required bandwidth of the flow and classification of machine learning, the path is assigned after t seconds by observing first 50 packets of the flow.
www.ijmrem.com
IJMREM
Page 43
A Machine Learning based Network Sharing… The results of experiment prove that the heterogeneous network sharing system can share bandwidth resources of all network interfaces to the network services of users at the same time. When the service numbers increase, the bandwidth utilization of each network interface is close to equal, which means that the heterogeneous networks resources management function is provided with a well load-balance function. TABLE I.
BANDWIDTH UTILIZATION OF THE SHARING SYSTEM
Figure 6. Performance of the Sharing System with Resource Management The heterogeneous networks resources management algorithm makes the network services with different bandwidth requirement can be distributed into the proper connection of the network interfaces to guarantees the quality of services.
V. CONCLUSION In this paper, we proposed a machine learning bases heterogeneous network sharing system with Multipath TCP protocol which provides the intelligent network resources and user services management to guarantee the communication quality. In order to achieve ubiquitous computing, the proposed algorithm supports the dynamically heterogeneous networks handover and intelligent resource management between heterogeneous network interfaces. The proposed scheme also applies machine learning concept to provide all resources of heterogeneous network interfaces of the system for multi-users sharing, and achieve an intelligent load-balance state of the system. The performance analysis also proves that the system can provide the better usage than the usual network sharing systems. The system will be developed with different kinds of platforms to provide more customize services and realize ubiquitous social sharing networks in the future.
REFERENCES [1]
[2] [3]
Akhila S, Jayanthi K Murthy, Arathi R Shankar and Suthikshn Kumar, “An Overview on Decision Techniques for Vertical Handoffs across Wireless Heterogeneous Networks”, International Journal of Scientific & Engineering Research, IJSER Volume 3, Issue 1, January 2012. F. Giust, L. Cominardi and C. J. Bernardos, “Vertical handoffs in fourth-generation multinetwork environments”, IEEE Commun. Mag., vol. 53, no. 1, pp. 142-149, 2015. A. Ahmed, L. M. Boulahis and D. Gaiti, “Enabling vertical handover decisions in heterogeneous wireless networks: A state-of-the-art and a classification“, IEEE Commun. Surveys Tuts., vol. 16, no. 2, pp. 776-809, 2014.
www.ijmrem.com
IJMREM
Page 44
A Machine Learning based Network Sharing… [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]
[14]
[15] [16]
[17] [18]
[19]
[20]
[21]
A. Miyim, M. Ismail, R. Nordin, and M. Taha, “Mitigating vertical handover prediction in 4G wireless networks“, Journal of Asian Scientific Research, vol. 2, no. 11, pp. 686-697, November 2012. Li Bin and Liu Shengmei, “Vertical Handoff Algorithm Based on Mobility Prediction“, Communication and Network, vol. 39, no. 1, pp. 93-95, 2013. J. Park and Y. Lim, “A handover prediction model and its application to link layer triggers for fast handover“, Wireless Personal Communication, vol. 52, pp. 501-516, 2010. Shin-Hun Kang and Jae-Hyun Kim, “QoS-Aware Path Selection for Multi-Homed Mobile Terminals in Heterogeneous Wireless Networks”, Proc. of the IEEE CCNC 2010, Jan, 2010. X. Cai and F. Liu, “Network Selection for Group Handover in Multi-Access Networks“, In: Proc. of the International Conference of Communications, ICC 2008, Beijing, P. R. China, pp.2164-2168. A. Ford, C. Raiciu, M. Handley, and O. Bonaventure, “TCP Extensions for Multipath Operation with Multiple Addresses”, draft-ietf-mptcp-multiaddressed-09. IETF Internet draft, June 2012. S. Pokhrel, M. Panda and H. Vu, “Analytical Modeling of Multipath TCP Over Last-Mile Wireless”, IEEE/ACM Transactions on Networking, January 2017. MultiPath TCP - Linux Kernel Implementation Project. http://multipath-tcp.org. D. Fooladivanda and C. Rosenberg, “Joint resource allocation and user association for heterogeneous wireless cellular networks”, Proc. of IEEE trans. on Wireless Comm., Oct. 2012. X. Wang, M. Chen, Z. Han, D. Wu, and T. Kwon, "TOSS: Traffic offloading by social network service-based opportunistic sharing in mobile social networks," in Proc. of IEEE INFOCOM 2014, pp. 2346-2354. S. Ferlin-Oliveira, T. Dreibholz, and O. Alay, “Tackling the challenge of bufferbloat in multi-path transport over heterogeneous wireless networks”, in Quality of Service (IWQoS), 2014 IEEE 22nd International Symposium of. IEEE, 2014, pp. 123–128. D. Zhou, W. Song, P. Wang, and W. Zhuang, “Multipath TCP for user cooperation in LTE networks”, Network, IEEE, vol. 29, no. 1, pp. 18–24, 2015. Yi-ting Luo, Bing Qi, Yuan-yuan Chen and Liang-rui Tang, “Strengthen-Gray Relative Analysis access selection algorithm based on load balance in heterogeneous wireless networks”, in Proc. of IEEE Natural Computation (ICNC), 2012. Tosh, D.K. and Sengupta, S., “Heterogeneous access network(s) selection in multi-interface radio devices”, Proc. Of IEEE Pervasive Computing and Communication Workshops 2015. G. Attiya and Y. Hamam, “Two Phase Algorithm for Load Balancing in Heterogeneous Distributed Systems“, In 12th Euromicro Conference on Parallel, Distributed and Network-Based Processing, pages 434–439, 2004. Akbari, H., Berenbrink, P., Elsasser, R. and Kaaser, D., “Discrete Load Balancing in Heterogeneous Networks with a Focus on Second-Order Diffusion”, Proc. Of IEEE Distributed Computing Systems (ICDCS), 2015. Yann Jacob, Ludovic Denoyer, and Patrick Gallinari, “Learning latent representations of nodes for classifying in heterogeneous social networks”, Proc. of 7th ACM international conference on Web search and data mining (WSDM) 2014. Jonghwan, C., Han, D., Kim, J. and Kim, C.-K., “Machine learning based path management for mobile devices over MPTCP”, Proc. Of IEEE Big Data and Smart Computing (BigComp) 2017.
www.ijmrem.com
IJMREM
Page 45