Globa Larysa prof, Dr.; Skulysh Mariia, PhD; Sulima Svitlana National Technical University of Ukraine “Kyiv Polytechnic Institute” Institute of Telecommunication Systems Information and Telecommunication Networks Department Method for resource allocation of virtualized network functions in hybrid environment Globa Larysa prof, Dr.; Skulysh Mariia, PhD; Sulima Svitlana

Urgency Growth of amount of services Traffic growth Cisco forecasts 24.3 exabytes per month of mobile data traffic by 2019

Mobile Core Network Typical LTE/EPC Network Partial virtualization of Mobile Core Network

Hybrid data center environment = dedicated hardware functional blocks + network functions as software components Hybrid virtualization of Mobile Core Network

The objective of the study is to improve the efficiency of service provisioning in the mobile networks through optimal resource allocation in hybrid data center environment The paper proposes an approach to model and investigate the dynamic allocation of network functions on a communication operator’s network

Method description Given: To find: graph of nodes requests intensity from network nodes cost of placing network functions on network nodes latency between network nodes cost of using a unit of resources probabilistic characteristics of service on the resources allowed value of request processing time To find: amount of resource of type i that should be allocated to the network function of type j location of network functions

SN = (N,L) cost(n), cost(n1,n2) cost(i,n) Lt L(n1,n2) a, b, c cni Parameters and variables of the optimization problem for resource allocation of virtualized network functions in hybrid environment (1/2) SN = (N,L) SN is a substrate (physical) network, consisting of nodes N and links L cost(n), cost(n1,n2) costs for physical node n and physical link (n1,n2)  L cost(i,n) costs for one unit of i  R at node n  N Lt the maximum latency for TAP (traffic aggregation point) t  T L(n1,n2) the network latency of a link (n1,n2)  L a, b, c weight factors in objective function a - weight factor of basic cost b – weight factor of cost per occupied resource unit c - weight factor of cost per occupied capacity unit cni capacity of resource i  R of physical node n  N c(n1,n2) bandwidth capacity of physical link (n1,n2)  L virtn binary parameter which indicates whether the node n is virtual physnj binary parameter which indicates whether the node n is dedicated hardware of function of type j

requests per second of network function j of TAP t stj,i Parameters and variables of the optimization problem for resource allocation of virtualized network functions in hybrid environment (2/2) μtj requests per second of network function j of TAP t stj,i processing time of a request on the resource type i of network function j of TAP t by one unit of resource Mtj demanded requests per second value for network function j of TAP t μcnj requests per second of dedicated hardware functional module n of type j dt(j1,j2) bandwidth demand for link (j1,j2) dtj,i variable for the amount of resource type i that is allocated for network function j of TAP t xnt,j variable for the embedding of network function j related to TAP t at the physical node n f(n1,n2)t,(j1,j2) variable for the embedding of link between j1 and j2 for TAP t at the physical link (n1,n2)

the objective function (1) is formulated as a linear combination (with weight factors a, b, c) of three cost terms: basic cost cost(n) that occurs if any network function is placed on a physical substrate node n  N, cost per occupied resource unit cost(i,n) on a physical substrate node n cost per occupied capacity unit cost(n1,n2) on a physical link (n1,n2)  L

Equation (2) guarantees, that for every TAP/service chain only one network function of each type is placed; This is achieved as the sum of variables xnt,j across all nodes equals to one Equation (3) guarantees that the resource allocation is performed on physical nodes that are chosen to host the respective network functions; This is guaranteed as when boolean variable xnt,j=1 then the variable dtj,i for the amount of resource type i that is allocated for network function j of TAP t differs from zero

Equations (4), (5) and (6) represent the resource constraints of the physical nodes and links, i.e. to guarantee that the amount of used resources on the node does not exceed the amount of available resources; Note: The link between two network functions is mapped on a path in the physical network; Thus, its bandwidth requirement affects not only the switching resources at the physical nodes where the network functions are placed but also the switching resources of the intermediate nodes which lie on the path ((4))

Equation (7) represents the flow conservation conditions for all paths (related to links) in the physical network (entering flow into the node equals the node leaving flow) Equation (8) guarantee that the result variables for the network function placement and the path mapping are binary

To take into account network function performance in the model the requests per second constraints were added in expression (9) to guarantee that the total service rate of network function j of TAP t denoted as μtj exceed the demanded value Service rate can be a constant value for the case of dedicated hardware function (physnj=1) that equals μcnj or a changeable value for the case of virtual network function (virtn=1) that depends on the amount of resources allocated to the function dtj,i and processing time of a request by one unit of resources stj,i To limit the connections latency the expression (10) is added to limit the total delay over the entire path

The main idea is - to define network functions placement method based on hybrid networks that contain both physical devices offering services and virtualized services The suggested approach take into account the fact that the performance of service component depends on the amount of the used resources

Dynamic resource allocation algorithm On the first step the possibility of allocation of the required amount of resources on the node network function j for TAP t is deployed on at the moment is checked If resources are insufficient then the second step is performed to check if the required amount of resources on the nodes where the functional blocks j are deployed for other TAPs is available If resources on such nodes are insufficient the third step is to check the other nodes If on the node other than the current (steps 2 and 3) the required amount of resources appears to be free then the migration of network function on such a node takes place

Dynamic resource allocation algorithm Step 1 Step 2 Step 3

Network request life cycle – activity diagram

Evaluation MATLAB code fragment >>ObjectiveFunction=@fitness; >>nvars=108;% Number of variables >>LB=zeros(1,108);% Lower bound >>UB=ones(1,90);% Upper bound >>IntCon=ones(1,90);% Integer variables >>ConstraintFunction=@constraint; >>[x,fval]=ga(ObjectiveFunction,nvars,[],[],[],[],LB,UB,ConstraintFunction,IntCon,options) A simple modeling example of the system with ten nodes, three network functional blocks and two types of resources showed that compared with the fixed resource allocation the proposed method can perform 3 times better X3

Conclusions The paper deals with the new problems of the dynamic provision of resources in virtualized mobile communication systems The approach to virtualization of mobile network is considered Method for allocation and management of the dynamic network functions' resources based on determination of optimal amount of resources allocated to network functions on communication operators' network is suggested The method can be used for the management of network functions deployment in hybrid hardware environment in order to minimize operator costs and improve quality of experience

Conclusions In further research the proposed model and method will be extended and implemented in a complex method for the dynamic resource allocation under varying workload conditions and will be used for the architecture of the management subsystem of mobile network operator development Future work will be connected with the evaluation of efficiency of resources (processing, storage and bandwidth resources) usage

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