A NOVEL PREFETCHING METHOD FOR SCENE-BASED

MOBILE SOCIAL NETWORK SERVICE

作者 :Song Li, Wendong Wang, Yidong Cui, Kun Yu, Hao Wang報告者 :饒展榕

Outline

• Introduction• Background and related work• Prefetching method in scene-based mobile SNS• Experimental results• Conclusions

Introduction

• Traditional SNS is only based on online Internet, which means users need to log on through their personal computers (PCs).

• However, compared with PC, mobile phone is the more constant companion to people.

• With the advent of an increasingly stable mobile platform, mobile SNS has become one of the most important developing fields.

• In this paper author apply a novel prefetching method into a real prototype called scene life system proposed by us.

• Scene life is a mobile social network application based on scenes in the form of image in our case.

• In this pre fetching method, client prefetches images based on the relationship between the user and hislher friends and access history.

• Then, if the user does request one of the pre fetched images, it will already be in the client's cache.

Background and related work

• The Six Degree of Separation is the theoretical root of SNS.

• This theory refers to the idea that, if a person is one step away from each person he or she knows and two steps away from each person who is known by one of the people he or she knows, then everyone is an average of six "steps" away from any person on Earth .

• Scene life system is a scene-based mobile SNS system in wireless environment.

• The circle represents the people in the world, and the center represents one person.• Dashed lines in the circle are used to partition the world into six

"degrees".

Prefetching method in scene-basedmobile SNS• Typically, there is a pause after each image is loaded, while the user

sees the displayed image or browses other modules linked to it.

• This time could be used by the client to prefetch images that are likely to be accessed afterwards, thereby avoiding retrieval latency if and when those files are actually requested.

• Otherwise it will increase network traffic which is a notable waste in wireless environment, also a waste of limited resources of mobile phone.

• In our pre fetching method, the client is responsible for computing the likelihood that a particular image will be accessed next and sends the information to the server.

• The server receives requests from the client, disposes them and sends back requested data.

3.1 Architecture of the system with prefetching• On the client side, there are three parts, including the interface of

scene life, a prediction engine and a prefetch engine.

• On receiving a new scene request from the user, the interface of scene life passes on the name of requested image to the prediction engine.

• The prefetch engine uses the prediction information to decide whether or not to prefetch images.

• It could also make its decision based on a variety of other factors, such as the contents of the local cache (which might already contain the file), the current system load, and so on.

3.2 Prediction algorithm

• Relation:Author use a bivariate function R (ρ , θ) to represent the relationship between the user and friend.

• Lookahead window:it determines the number of images that should be taken into consideration in the algorithm.• Node: each node in the graph stands for an image file that has ever

been accessed by the user.• Arc: there is an arc from A to B if and only if at some point in time, B

was accessed within the lookahead window.• Weight: there is a weight on the arc from node A to B, denoted as w.

It can be represented as follow:

• Prefetch threshold: it determines whether an image file is a candidate for prefetching, denoted as δ.

Experimental results

Conclusions

• In this paper, a prefetching method is proposed for scene-based mobile social network service.

• Experiment results have shown that this proposed method can significantly reduce scene access times yielding a better QoS.

• In future work, we will try to improve the performance of the method by considering the resource of the client, such as power, CPU, memory, etc.