Adaptive Trigger Mechanism Of Dynamic Policy For Resource Allocation

With the rapid incensement of Internet users, the size of high-speed networks is becoming bigger and bigger. An endless stream of network services, such as multimedia services, video conferencing, Internet radio, and so on, comes one after another. A surprising number of real-time information transfers to various places through the global networks at different speeds. The main idea of Policy-Based Network Management (PBNM) is to manage the internet resources and channel capacity by a high-level abstraction strategy, using management based on services rather than device-based. Network administrator creates a high-level strategy (rather than the process) to determine the desired status of the network. These strategies are specially processed, combined with the current network status, changing to dynamic configuration data, and then distributed to the network device to guide their behavior.Using this high-level abstract model, we can simplify the management of large networks to ensure the consistency and integrity of behavior of network equipments. The main functions of PBNM framework is for the network QOS guarantees, security and network IP address configuration and so on, and it could very well optimize the management and allocation of resources and improve the compatibility and scalability. PBNM is the best solution of network management.Channel resources are most scarce resources in a wireless network. In order of the better QoS and the most effective use of broadband, channel allocation methods are also constantly improving. In the current network, the switch was mainly caused by the wireless link state changes. Under the mobile environments, two key performance indicators are closely related with the study of this paper: CDP (Call Dropping Probability) and CBP (Call Blocking Probability). Call Dropping Probability is probability of call dropping when this mobile call switch to another cell and can't get the bandwidth resources in the new target cell and was forced to drop. Call blocking probability of new call connection request being refused to connect to the network due to the overload of current network. A system indicator: Resources Utilization (RU) describes the ratio of the occupancy of bandwidth to the total bandwidth of the wireless network. In order to take full advantage of the system, this indicator should be maximized.In order to overcome the shortcomings of the static strategies and to effectively manage the resource, we bring forward a new concept of dynamic strategy based on traditional static strategy. Dynamic strategy generating is based on forecast information which comes from mobility prediction. The value of conditions and actions in dynamic policies can be modified according to the current network status. It can be dynamically adjusted under a certain rules, thus a number of static policies with the similar structures and functions can be compressed into a few simple dynamic strategy. The traditional PBNM model based on static policy facilitate the management of entire network, but in order to adapt the dynamic resource management based on prediction under the mobile environment, this paper presents a dynamic policy framework in order to generate dynamic strategy parameters very quickly. Network management with dynamic policy is of good dynamic adaptability. When the network resources are relatively idle, they can provide more resources for services, otherwise, when the network resources are relatively tight, they can compress broadband of some low-level business to a certain extent so that the network can accept more calls.A wireless network on the one hand ensures the QoS needs of mobile users, on the other hand accept more users as possible, which lead directly to the dynamic complexity in network resource allocation, requiring the network management to have a sense of network status and to provide guaranteed end-user services efficiently. Dynamic strategy and dynamic strategy model can automatically generate the required dynamic strategy based on mobile environment changes and network conditions. However, the prediction information is not totally veracious, the inaccurate factors of which are mainly in two aspects: one is the predicted switching time. As the arrival time of handoff call has a certain margin of error compared to the prediction information. If the system reserves the resource as soon as the prediction information reaches, before the handoff call actually reaches, the network channel will result in waste of resources. The second is switching target cell address. Most of the forecast message contains one or more of the target cell address into which handoff calls could switch. Dynamic strategy applies the implementation in the system when the network conditions may not achieve the desired condition, or has changed, making the dynamic strategies are not well adapted to the new network conditions. So the advantages of dynamic strategy can not be performed very well.Therefore, this paper brings forward an adaptive trigger mechanism for the dynamic strategy .It can dynamically set the threshold value to trigger the reservation actions to adapt to the current situation and to avoid unnecessary resource reservation. This article defines multi-user roles in the systems, takes the probability of switching into consideration, puts forward the reservation divestment strategy according to the priority level of users and ensures guaranteed QoS for all calls. This trigger mechanism can effectively reduce the CDP and CBP, improve resource utilization, and guarantee a better QoS for the high priority handoff calls.