A Novel Load Balancing Optimization Algorithm Based on Peer-to-Peer

A Novel Load Balancing Optimization Algorithm Based on Peer-to-Peer

Technology in Streaming Media

Wanneng Shu, Li Cheng

College of Computer Science, South-Central University for Nationalities, Wuhan 430074,

China

[email protected]

Abstract

Streaming media technology has brought a great revolution in human production, life style and the ways of work. How to alleviate effectively workload of media streaming server and assign reasonably resources among different media segments becomes one of primary research. We proposed a new load balancing scheduling algorithm based on Peer-to-Peer technology in streaming media. Simulation results show that the proposed algorithm can assign media streaming servers’ resources effectively and effectively increase the utilization percentage of the system resources.

Keywords

:Streaming Media, Load Balancing, Peer-To-Peer, Dynamic Weight

1. Introduction

With the development of the Internet, computer and streaming media technology, the application and the research of streaming media services are developed rapidly. It has brought a great revolution in human production life style and the ways of work [1, 2].It is the outcome results from computer technology, network communication, multimedia technology and video compress etc. Streaming media is video or audio content sent in compressed form over the Internet and played immediately. Media is usually streamed from prerecorded files but can also be distributed as part of a live broadcast feed. In a live broadcast, the video signal is converted into a compressed digital signal and transmitted from a Web server as multicast, sending a single file to multiple users at the same time. How to alleviate effectively workload of media streaming server and assign reasonably resources among different media segments becomes one of primary research.

P2P (Peer-to-Peer, P2P) technology appears to provide a better technical support and run programs for the realization of Internet-based streaming media applications [3]. Object distribution based on P2P technology and positioning model programs makes any object of any one node can access the Internet on the other nodes [4]. By all nodes in the logical structure of the dynamic tree node can independently enter or leave the tree logical structure, and have the ability to independently bear certain storage load and the computational load, which can effectively avoid a small number of computer overload operation other parts of the computer long-term idle happen. P2P technology allows each network node to join the current application full access to the server streaming system to provide services at the same time, provide certain services to other nodes in the network [5]. Figure 1 is the streaming media System architecture based on P2P technology.

In recent years, the streaming media business has developed rapidly, notable features in the video business is the high-traffic, high bandwidth, high resource consumption, so the natural load balancing also became the core of the network system design focus to consider the problem[6,7]. Streaming technologies are becoming increasingly important with the growth of the Internet because most users do not have fast enough access to download large multimedia files quickly [8]. With streaming, the client browser or plug-in can start displaying the data before the entire file has been transmitted. Load balancing is based on existing network infrastructure to take full advantage of the bandwidth of network devices and servers for the purpose to increase the network throughput, to strengthen the data processing capability, increase network flexibility and availability to address the network supply and business demand. The contradiction between a cheap, effective and transparent method.

Figure 1.

Streaming media System architecture based on P2P technology

The remaining of this paper is organized as follows: The traditional load balancing scheduling algorithm is introduced in Section2.The proposed algorithm is presented in Section 3. In Section 4, describes simulation and analysis of results, followed by the conclusions in Section 5.

2. Load balancing scheduling algorithm

Streaming media technologies have improved significantly since the 1990s, when delivery was typically uneven. However, the quality of streamed content is still dependent upon the user's connection speed. The role of load balancing is a reasonable allocation of resources to alleviate these resource bottlenecks, improve the resource utilization of the media server [9, 10]. Figure2. is the load balancer physical structure. Load balancing to build above the existing network structure, it provides a cheap and effective and transparent method extends the bandwidth of network devices and servers to increase throughput, enhance network data processing capability, increase network flexibility and availability[11]. The purpose of load balancing scheduling algorithm is to assign user requests assigned to the appropriate server in order to achieve a reasonable and efficient use of system resources, ensure quality of service, and give full play to the purpose of system performance [12]. The traditional load balancing algorithm is generally including the following:

Round robin (RR), also known as the circular method. Balanced scheduler client requests to sequence in turn assigned to each service node in the cluster [13]. The algorithm assumes that all server processing performance are the same, regardless of the server's current number of connections and speed of response. The algorithm is simple, suitable for each node in the cluster system configuration are the same situation, but not for the inconsistent performance of processing.

Weighted round robin (WRR) is the improvement of the round robin algorithm. The balanced scheduler load distribution of different processing capability of the service node, this algorithm can be rough to ensure that the processing capability of the service node is able to handle more client access [14]. The balanced scheduler by giving the node service processing capability empowers the value of the service node and adjusts the weights. Round-robin algorithm can be regarded as a special case of the weighted round-robin algorithm, namely the weights of each node is the same. Using the weighted round-robin will still load balanced during operation, because of the large number of requests may be directed to a service node. The weighted round robin scheduling algorithm is an improved method of WRR; it can solve the performance different between server performances, server processing performance with the corresponding weights. Rotation weighted round robin scheduling is allocated by way of the right level of value and round robin scheduling server. Weights server to receive the connection weights server than the right child low boil server to handle more users and weights exposed the servers to handle the same number of users.

Weighted least active connections (WLCS) is the number of active connections currently connected to the service node to get the ranking of the service node, combined with the service node hardware performance data to different service nodes in the system services structure, "the number of active connections" mainly refers to the collection of active TCP connections[15].

Locality-based least connections (LLC) scheduling algorithm is based on the locality of the least connection (destination IP address for the load balancing for cache cluster system [16]. The algorithm based on the request destination IP address to identify the destination IP service node, if available and not overloaded, then the service request sent to this node; service node does not exist, or the state of overloading and the server is half of the working state, at least connected to the principle of playing to elect the available servers, and this request is sent to the server.

Destination the hashing scheduling (DHS) algorithm is also the destination IP address load balancing, but it is a static mapping algorithm, a hash function will be a target IP address is mapped to a server [17]. First destination address hashing scheduling algorithm based on the request destination IP address as a hash key from a statically assigned hash table to find the corresponding server, the server is available and not overloaded, sends a request to the server otherwise, returns null.

Traditional load balancing the advantages of server selection algorithm are versatile, relatively easy, can play a role in load balancing to a certain extent. But the drawback is obvious. For example, the rotation algorithm when processing service is not the same length of service is running for some time, server clusters can not maintain a balanced operating status. The algorithm of the minimum number of links can keep the balance of the server cluster in the number of connections, but the various service teams of server resources consumption is different, the minimum number of links can not make the system to achieve load balancing.

3. Novel P2P load balancing algorithm

In this section, we proposed a Novel Load Balancing Scheduling Algorithm (NLBSA). In the following design algorithm model, we do not consider a peer to join and leave; not account for download; transmission rate is relatively stable.

Definition 1. Assume that

S



{ , ,..., }

s s

1 2

s

n _{represents a P2P streaming media system. System}

I/O requests is Poisson distribution 1

( ) n i i     



, the whole system can be seen as

n

queue queuing system

M M

/

/1

.

Based on current usage of each P2P node to optimize the load in order to achieve the full purpose of the resources of the P2P nodes. Parameters calculated according to the node running all aspects of the dynamic weights, adaptive load balancing algorithm based on the size of the dynamic weight load balancing network traffic. The purpose of dynamic weight is necessary to correctly reflect the status of

the node load to predict the node may load changes. Periodically from each node to collect the following parameters: Usage rate of CPU is

cpu s

( )%

i _{,Current Network Flow is}

t s

( )

i _{,Access rate of}

the Disk I/O is

io s

( )%

i _{,Response Time is}

rt s

( )

i _{,Processing amount is}

pr s

( )

i _.

Definition 2. The dynamic weight of node

s

_i can be described as follows:

1 2 3 4 5

( )

i

( )%

i

( )

i

( )%

i

( )

i

( )

i

Load s







cpu s







t s







io s







rt s







pr s

₍₁₎

In which,





i



1

_{.The purpose of dynamic weight is necessary to correctly reflect the status of}

the node load to predict the node may load changes. For different types of system applications, the importance of each parameter is different. For the convenience of the system is running in the proportion of the various parameters for different applications to make proper adjustments, we set a constant coefficient for each parameter



i_,

Server node every certain period of time to load information collection, the server

S

i_{,Can get the}

counter value

C

i1_and

C

i2_{the calculation time}

T

1 _and

T

2 _{the server}

s

i_{in the time}

T

2



T

1 _interval

receive new connections

N

i



|

C

i2



C

i1

|

_{. According to the node connections prediction task}

R

i _will

give the service node to increase the load

INC R

(

i

)

_{, and gives the following formula:}

Definition 3.

The increased load weight

INC R

(

i

)

_{between the time}

T

1 _and

T

2_.

2 1

( )

(

)

|

|

i i i i

Load s

INC R

C

C





(2)

_

( )

_i

( )

_i

(

_i

)

New Load s



Load s



INC R

(3) P2P streaming systems, server processing capacity of each node is set to

Max Load

_

,which the

dynamic load of each server node for the： 1

( , )

n k

Load i k





.

Definition 4. The overall system load balancing performance index



1

(

_

_

( ))

n

i j

Max Load

New Load s











(4) The smaller



the said the better load balancing performance of the system as a whole.

4. Experimental testing and results analysis

In this section, the goals of this evaluation are to measure the performance of our proposed algorithm. We use the simulation tool which is called .Net Framework 1.1 under Microsoft Visual Studio .Net 2003 environment. We will design the NLBSA algorithm, WRR and WLCS. Fig.3 shows the average response delay of the three algorithms increased with the program number changes, as described in Fig.4 is the average completion time of three algorithms increase with the program number changes, Fig.5 shows the three algorithms load balancing increase with the program number

changes. Fig.6 shows the number of link programs increase with the time. Fig.7 shows the load balancing degree increase with the time change.

We can see from Figs.3-4, average response delay and the average completion time of the NLBSA algorithm is the smallest, and with the gradual increase in the number of programs, show that the algorithm has good scalability. We can see from Figs.5-7, the performance of NLBSA is the most stable, while the WLCS algorithm and WRR algorithm is highly volatile, that stability of the NLBSA algorithm is better. From the above experiments, we can conclude that a good solution to the problem of load balancing, with good scalability. Access users continue to increase, the average rate of the server upload will no longer increase a threshold is reached, and it can solve network bandwidth of the data source. A valid connection can be established between the Peer, the formation of a stable streaming media distribution network, the playback of Internet video streaming.

0 50 100 150 50 100 150 200 250 300 350

The number of programs

A v er age r es po ns e d el ay / m s NLBSA WRR WLCS

Figure 3. Compare three algorithms with average response delay

0 50 100 150 150 200 250 300 350 400 450 500 550

The number of programs

A v er age c om pl et e t im e / s NLBSA WRR WLCS

0 50 100 150 0 5 10 15 20 25

The number of programs

Load b ala nc ing de gr e e NLBSA WRR WLCS

Figure 5. Compare three algorithms with load balancing degree

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0 50 100 150 200 250 300 350 400 450 500 Time /ms T h e num ber of l ink prog ra m s NLBSA WRR WLCS

Figure 6. Compare three algorithms with number of link programs

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0 2 4 6 8 10 12 14 16 18 Time /ms Load b al anc ing d egr ee NLBSA WRR WLCS

5. Conclusions

Streaming media is transmitted by a server application and received and displayed in real-time by a client application called a media player. In this paper, cluster technology, streaming media technology, load-balanced scheduling algorithm theory and technology, streaming media services cluster load balancing algorithm has made a comprehensive and in-depth analysis. We proposed a new load balancing algorithm based on Peer-to-Peer technology in streaming media, called NLBSA algorithm. Simulation results show that the proposed algorithm can assign media streaming servers’ resources effectively and effectively increase the utilization percentage of the system resources.

6. Acknowledgments

This research work was supported by National Natural Science Foundation of China (Grant No. 60975050 and 60902053).The Project was supported by the Special Fund for Basic Scientific Research of Central Colleges, South-Central University for Nationalities (Grant No. CZY11005 and CZQ12005), the Natural Science Foundation of South-Central University for Nationalities (Grant No. YZY10004). The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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