This paper presents a quantitative study on the performance of 3G mobile data offloading through WiFi networks. We recruited 97 iPhone users from metropolitan areas and collected statistics on their WiFi connectivity during a two-and-a-half-week period in February 2010. Our trace-driven simulation using the acquired whole-day traces indicates that WiFi already offloads about 65% of the total mobile data traffic and saves 55% of battery power without using any delayed transmission. If data transfers can be delayed with some deadline until users enter a WiFi zone, substantial gains can be achieved only when the deadline is fairly larger than tens of minutes. With 100-s delays, the achievable gain is less than only 2%-3%, whereas with 1 h or longer deadlines, traffic and energy saving gains increase beyond 29% and 20%, respectively. These results are in contrast to the substantial gain (20%-33%) reported by the existing work even for 100-s delayed transmission using traces taken from transit buses or war-driving. In addition, a distribution model-based simulator and a theoretical framework that enable analytical studies of the average performance of offloading are proposed. These tools are useful for network providers to obtain a rough estimate on the average performance of offloading for a given WiFi deployment condition.

Mobile data offloading: how much can WiFi deliver?

http://wpage.unina.it/a.botta/pub/COMNET_WORKLOAD.pdf

A tool for the generation of realistic network workload for emerging networking scenarios

This is a quantitative study on the performance of 3G mobile data offloading through WiFi networks. We recruited about 100 iPhone users from a metropolitan area and collected statistics on their WiFi connectivity during about a two and half week period in February 2010. We find that a user is in WiFi coverage for 70% of the time on average and the distributions of WiFi connection and disconnection times have a strong heavy-tail tendency with means around 2 hours and 40 minutes, respectively. Using the acquired traces, we run trace-driven simulation to measure offloading efficiency under diverse conditions e.g. traffic types, deadlines and WiFi deployment scenarios. The results indicate that if users can tolerate a two hour delay in data transfer (e.g, video and image up-loads), the network can offload 70% of the total 3G data traffic on average. We also develop a theoretical framework that permits an analytical study of the average performance of offloading. This tool is useful for network providers to obtain a rough estimate on the average performance of offloading for a given inputWiFi deployment condition.

/pdf/mobile-data-offloading-how-much-can-wifi-deliver-mlq3m4r0pk.pdf

In this paper, we introduce a framework for designing energy efficient cloud computing services over non-bypass IP/WDM core networks. We investigate network related factors including the centralization versus distribution of clouds and the impact of demand, content popularity and access frequency on the clouds placement, and cloud capability factors including the number of servers, switches and routers and amount of storage required in each cloud. We study the optimization of three cloud services: cloud content delivery, storage as a service (StaaS), and virtual machines (VMS) placement for processing applications. First, we develop a mixed integer linear programming (MILP) model to optimize cloud content delivery services. Our results indicate that replicating content into multiple clouds based on content popularity yields 43% total saving in power consumption compared to power un-aware centralized content delivery. Based on the model insights, we develop an energy efficient cloud content delivery heuristic, DEER-CD, with comparable power efficiency to the MILP results. Second, we extend the content delivery model to optimize StaaS applications. The results show that migrating content according to its access frequency yields up to 48% network power savings compared to serving content from a single central location. Third, we optimize the placement of VMs to minimize the total power consumption. Our results show that slicing the VMs into smaller VMs and placing them in proximity to their users saves 25% of the total power compared to a single virtualized cloud scenario. We also develop a heuristic for real time VM placement (DEER-VM) that achieves comparable power savings.

Distributed Energy Efficient Clouds Over Core Networks

Background: Zipf’s law and Heaps’ law are observed in disparate complex systems. Of particular interests, these two laws often appear together. Many theoretical models and analyses are performed to understand their co-occurrence in real systems, but it still lacks a clear picture about their relation. Methodology/Principal Findings: We show that the Heaps’ law can be considered as a derivative phenomenon if the system obeys the Zipf’s law. Furthermore, we refine the known approximate solution of the Heaps’ exponent provided the Zipf’s exponent. We show that the approximate solution is indeed an asymptotic solution for infinite systems, while in the finite-size system the Heaps’ exponent is sensitive to the system size. Extensive empirical analysis on tens of disparate systems demonstrates that our refined results can better capture the relation between the Zipf’s and Heaps’ exponents. Conclusions/Significance: The present analysis provides a clear picture about the relation between the Zipf’s law and Heaps’ law without the help of any specific stochastic model, namely the Heaps’ law is indeed a derivative phenomenon from the Zipf’s law. The presented numerical method gives considerably better estimation of the Heaps’ exponent given the Zipf’s exponent and the system size. Our analysis provides some insights and implications of real complex systems. For example, one can naturally obtained a better explanation of the accelerated growth of scale-free networks.

/pdf/zipf-s-law-leads-to-heaps-law-analyzing-their-relation-in-23b3s1lbwg.pdf

Zipf's Law Leads to Heaps' Law: Analyzing Their Relation in Finite-Size Systems

This paper introduces a workload characterization study of the most popular short video sharing service of Web 2.0, YouTube. Based on a vast amount of data gathered in a five-month period, we analyzed characteristics of around 250,000 YouTube popular and regular videos. In particular, we collected lists of related videos for each video clip recursively and analyzed their statistical behavior. Understanding YouTube traffic and similar Web 2.0 video sharing sites is crucial to develop synthetic workload generators. Workload simulators are required for evaluating the methods addressing the problems of high bandwidth usage and scalability of Web 2.0 sites such as YouTube. The distribution models, in particular Zipf-like behavior of YouTube popular video files suggests proxy caching of YouTube popular videos can reduce network traffic and increase scalability of YouTube Web site. YouTube workload characteristics provided in this work enabled us to develop a workload generator to evaluate the effectiveness of this approach.

Workload generation for YouTube

In this paper we introduced a workload characterization study of the most popular video sharing service, YouTube, on the Web 2.0. For approximately a two-month period we collected the information of more than 17,000 video files and investigated the file attributes and popularity characteristics of YouTube. Since YouTube is considered as a huge video library, its access pattern has an important impact on the Internet traffic distribution. Comprehension of YouTube features and similar video sharing sites is critical to analyze network traffic and to develop novel user generated contents (UGC) services. This distribution models especially Zipf-like behavior of popular video files suggests caching may reduce network traffic and increase scalability of YouTube Web site.

Modeling of multimedia files on the Web 2.0

Due to the increase of the number of web pages consisting of multimedia embedded objects in recent years, finding a suitable model for the web pages including multimedia files has become an important issue for efficient catching and web page delivery time. Our characterization shows the existence of a few media objects embedded in the popular web pages that contain mostly image and text object. Also this characterization shows despite of existence of a few embedded media objects, web pages sizes are still small and the server can prefetch them efficiently in a proxy cache. Thus by prefetching the embedded objects of a web page containing a media object, we are able to reduce the delivery time for the initial portion of the multimedia object as well as the total down load time of the web page. We have developed a custom web server and a proxy cache to implement this idea. This paper shows the achieved improvement. Keywords web server, embedded objects, multimedia files, workload characterization, prefetching, network latency, heavytailed distribution.

A Prefetching Server for Reducing Startup Time of Embedded Multimedia

http://ieeexplore.ieee.org/iel5/4475928/4475929/04475961.pdf

Characterization of popular Web pages is essential to study issues such as proxy cache performance and inspection of effective resource management algorithms in Web. In this paper we used Web objects for such a characterization. A Web object is a Web page and a collection of files corresponding to the embedded objects which must be transferred to display the Web page. We collected data on the size and number of embedded objects to propose models for web objects. Due to the increase of the number of Web pages consisting of multimedia embedded objects in recent years, finding a suitable model for the Web objects including multimedia files has become an important issue. Characterization of multimedia files embedded in the Web objects is also valuable in improving their related Web page download time.

In this paper, we present a characterization of top 500 popular Web sites that fall into three different data sets: February 2006, February 2005, and February 2004. We have considered the popular Web pages for this study because they are more likely to be efficiently designed, and they have significant impact on network traffic.

This characterization shows the impacts of embedding multimedia files in the distribution models that we suggest for popular Web pages. This result can be used for development of workload generators that exhibit the properties of Web objects such as number of embedded objects and their sizes.

Mojgan Soraya

Papers

Workload generation for YouTube

Modeling of multimedia files on the Web 2.0

A Prefetching Server for Reducing Startup Time of Embedded Multimedia

A Prefetching Server for Reducing Startup Time of Embedded Multimedia

Workload characterization for the multimedia files embedded in the popular web pages