It is well known that the cameras for surveillance are widely installed for public security,especially in the metropolitan zones.For example,it is reported that Shenzhen in China has equipped 2×10⁵ cameras in 2007^[1],and Chongqing in China has launched the plan of installing 5×10⁵ cameras since 2011^[2].Therefore,the surveillance videos become an important tool and data source in the field of public security.

However,“how to manage and utilize surveillance data” is a challenge issue for a large surveillance network system.One of the critical reasons is that data sources are widely spread and hard to be predict.Usually,surveillance monitors do not transmit the video data to the manager side.They send the data back when triggered. However,few research so far focus on understanding and exploring how to deploy the sufficient equipment so as to fully exploit the surveillance data.

The paper proposes a large surveillance data system,which is able to manage enormous monitors and computing nodes efficiently.We make a theoretical analysis on the equilibrium demand of the system,introduce a new queueing network model to characterize the dynamic video stream uploading behaviors from surveillance network,and derive the server capacity to optimize its realistic processing. To the best of our knowledge,this paper makes the first attempt to address the issue on how to quantify the dynamic incoming data flows.

Based on the quantitative model,we can optimize configuration of computing nodes to achieve the best application performance at a reduced cost.Furthermore,we will utilize history data records to manipulate the execution environment of computing nodes in the network.We formulate and deal with two optimization problems,which are the minimal server deployment and the dynamic routing for a satisfied QoS(quality of service). We propose an algorithm that can dynamically redirect the bursty requests flow to the sufficient nodes to avoid the overflow of the queueing and keep the system responding in time.Experimental results show that it can dramatically improve the overall performance of our system.

Queueing network is a theory widely applied to many fields,such as processing control,communication,etc.Samari et al^[3] describes an analytic model for performance studies of distributed computer networks.The model factors each node of a network into processing and channel components, and models each one separately using M/D/r or M/M/l queues.Recently,queueing network is utilized for enhancing the QoS of video application on the internet.Wu et al^[4] leverages Jackson queueing network^[5] with infinite-server queues to model the channel churns,though it has an impractical assumption that server resource are unlimited.Kumar et al^[6] proposed a simple stochastic fluid model that seeks to expose the fundamental characteristics and limitations of P2P streaming systems.Wu et al^[7] put forward a Jackson queueing network model to derive the demand for server capacity in VOD application.Jagannathan et al^[8] designed scheduling policies that are robust to busty traffic on a simple queueing network consisting of two links only.Yang et al^[9] used Jackson's network theorem to characterize the performance of mobile application servers among different cloud computing environments.Unlike previous research,to the best of our knowledge,our study is the first one to investigate and optimize the surveillance video network by using a queueing network model,which is more challenging than live streaming providing.

As more distributed systems are widely adopted for various applications,recent works trend to investigate work patterns and resource utilization in data centers.These investigations of real workload have revealed a vast of amount of wastage in data centers.Logothetis et al^[10] addresses the need for “stateful dataflow” programs that can rapidly sift through huge,evolving data sets and presents a generalized architecture for continuous bulk processing that raises the level of abstraction for building incremental applications.Resource provision is another related topic in this field.Researchers mainly focused on predicating sufficient resource for the quality of service.Most of them are applied in cloud platform or grid networks^[11-12].

The overall system is a huge project,which is still under construction.The workload trace we archived for evaluation is from part of the system, which are 10 nodes under a level-2 node for 30 d.

The system parameters are listed as following.Each level-1 node is connected with several hundred monitors by high-speed fiber channel.The video rate is around 4 Mbit/s.Each time slice of the uploading video is set to 10 s. Thus its size is 4 Mbit/s×10 s=5 Mbyte.The configuration of servers in each node is Intel Xeon Processor E5540,4 Gbyte DDR memory,7 200 r/min SAS hard disk.It can handle average 2 Mbyte video data per second based on experiments.Thus we could have the average service rate μ=P/(r₀T₀)=2/5=0.4. The number of servers in level-1 for each node are listed Table 1.

Table 1 Summary of nodes

node id servers node id servers1 15 6 152 20 7 203 18 8 204 20 9 185 18 10 18

The external arrivals are summed up and made an average value Λ(times/s)for each hour in our evaluation for now.The joining rate of queues in level-1 nodes and level-2 nodes are α₁=0.95 and α₂=0.05 for normal state.After applying the optimization algorithm,to manipulate the joining flow in the next interval we will change their value for each level-1 nodes based on its statistical result of Λ in current interval.As shown above,the guideline of average sojourn time in the queue W_{s 0} is set to the time slice of the uploading video,which is equal to 10 s,to keep this system running smooth.Whenever it does not meet this standard within the time interval,we will record it as a “unsatisfied time interval”.The “unsatisfied rate” will be the ratio of “unsatisfied time interval” to the time elapsed for stating the health of the system,which is the lower the better.Fig 5 shows that the improvement of the “unsatisfied rate” of all level-1 nodes after applying the optimization.Those data are collected in 30 days for 720 intervals.We can infer that it would get a better result when the iterative interval is shorter.

Fig.5 Optimization vs raw of unsatisfied rate in level-1 nodes

The external arrivals are summed up and made an average value Λ(unit:times/s)for each hour in our evaluation for now.The joining rate of queues in level-1 nodes and level-2 nodes are α₁=0.95 and α₂=0.05 for normal state.After applying the optimization algorithm,we will change their value for each level-1 node based on its statistical result of Λ in current interval in order to manipulate the joining flow in the next interval.

Although the number of servers in a certain node is not easy to increase instantly,we can also use the data trace to estimate its optimized number at a reduced cost.The current server number of the level-2 node is 60,while its unsatisfied rate is reduced to 9.7×10^-3 under optimization.Based on the historical data collected,we can infer the relationship of server number and unsatisfied rate.Fig 6 plots its relationship varying the number of servers from 40 to 100 before and after the optimization.It can both help us to select an appropriate number of servers,and reveal the efficiency of our optimization.

Fig.6 Optimization vs raw of unsatisfied rate varying on the number of servers in level-2 node

Fig7 reveals that the optimization can further change the distribution of the arrival rate in level-2 node.The level-2 node is more powerful than level-1 nodes in case of emergency,while it may be a relative low utilization rate averagely for a long time.The prediction optimization helps the level-1 nodes to export overloaded flows to the level-2 node.Fig 7 shows that the arrival rate will trend to distribute more equally in lower value zones,and the high arrival rate occurs much less.It proves that the optimization can greatly increase the utilization of the level-2 node and decrease the impact of bursty traffic.

Fig.7 Arrival rate distribution on level-2 node before/after optimization

5 Conclusions

In this paper,we propose a profound design of a large distributed surveillance network system that can be widely applied.We make a theoretical analysis on the equilibrium demand of the system,introduce a new queueing network model to characterize the dynamic video stream uploading behaviors from surveillance network,and derive the required server capacity to support the smooth processing.We then formulate two optimization problems for our system,including minimal server deployment,and dynamic routing for a satisfied QoS.We propose a practical algorithm,which can dynamically redirect the bursty requests flow to the sufficient nodes to avoid the overflow of the queueing and keep the system response in time.With this algorithm and utilizing instantaneous network statistics,the service provider periodically derives the incoming workload in the next interval,makes the decision for its routing strategies,and communicates with its higher layer for overall management.

We verify our analysis and evaluate our algorithm design in a realistic environment.The results show that it can archive a good performance under reduced equipments.

Since the system is still under construction,the future work may come from many fields.For example,we may consider the optimization of the price cost of utility and the distribution of computing servers.Furthermore,it is an important issue for us to archive more data logs from system for further data mining.It would both help the system working more efficient and discovering more interest information for research or for the benefit of civil affairs.