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Cellular Network Performance Measurement

Cellular Network Performance Measurement. Class Presentation for CS 234 - Advanced Networks b y Pramit Choudary , Balaji Raao & Ravindra Bhanot (Group 18) Instructor: Professor Nalini Venkatasubramanian 05/10/2012. Papers considered.

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Cellular Network Performance Measurement

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  1. Cellular Network Performance Measurement Class Presentation for CS 234 - Advanced Networks by PramitChoudary, BalajiRaao & RavindraBhanot (Group 18) Instructor: Professor NaliniVenkatasubramanian 05/10/2012

  2. Papers considered • Paper 1: Understanding Traffic Dynamics in Cellular Data Networks by U. Paul, A. Subramanian, M. Buddhikot, S. Das, IEEE INFOCOM 2011, Shanghai, China • Paper 2: An Untold Story of Middleboxes in Cellular Networks, SIGCOMM 2011, Toronto, Ontario, Canada (NOTE: Please refer to the relevant papers listed above in place of ‘paper 1’ or ‘paper 2’ found in the presentation slides.)

  3. Background - Internet/Data Access? • Dial-up connection • Broadband (DSL, Cable Internet, Fiber Optics) • Wi-Fi (IEEE 802.11 standard) & WiMAX (IEEE 802.16 standard) • Mobile Broadband using 2.5G, 3G, 4G technologies Each claim to cater different data rates, ranges in operation, needs of end user/application, energy savings, etc using different protocol designs, business strategies, network deployments and many more.

  4. Background - Cellular Networks and interconnecting subsystems 4G: Fourth generation of cell phone mobile communications standard 3G: Third generation of cell phone mobile communications standard Femtocell: Small cellular base station designed for use in a home or small business IMS: IP Multimedia Subsystem, used to provide mobile and fixed multimedia services Image courtesy: radisys.com

  5. Background - Broadband Cellular Networks • E.g. HSPA- Mobile telephony protocols used in 3G cellular networks for mobile data access. • Broadband cellular access becoming most common and pervasive world-wide. • Fueled by introduction of user-friendly smart phones, notebooks, tablets, eBook readers.

  6. Background - A look at smartphone technology Courtesy: Technology Review, Published by MIT, May 9th 2012

  7. Background - Broadband Cellular Networks • Has led to innovative & flashy mobile applications like gaming, video streaming, social networking, etc. • Use of several and various types of middleboxes to manage the scarce resources (because same resources are shared mostly) in the network and to protect them e.g. Network Address Translation (NAT) boxes, firewalls, etc.

  8. Background - On usage of middleboxes • Many times, cellular network middleboxes (deployed by carriers like AT&T, T-Mobile) and mobile applications (application developers) – managed independently. • Knowledge mismatch -> End-to-end performance degradation, Increase in energy consumption, Introduce security vulnerabilities. • E.g. Carrier setting aggressive timeout for inactive TCP connections in the firewall and disrupting long lived and occasionally idle connections maintained by applications like instant messaging, push-based email, etc. • Need for understanding the effects of middleboxes in cellular network. • Paper 2 specifically focuses on NAT boxes, their policies & firewall and its policies.

  9. Background - Broadband Cellular Networks (Contd.) • Expectations in increase in the volume of data seen exponentially. • Supporting such an increase requires good understanding of traffic dynamics and its impact on resource allocation on the service provider’s network. • Leading to better resource planning, network designs, spectrum allocation and energy savings.

  10. Background - Broadband Cellular Networks (Contd.) • For some exciting numbers, refer to a white paper by Cisco on global mobile data traffic forecast for 2011-2016: http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-520862.html

  11. Paper 1 - Short Summary • Discuss traffic dynamics specific to 3G cellular networks. • End user perspective: Study subscriber traffic patterns, number of distinct base stations visited by subscribers, relate mobility and traffic, subscriber temporal activity & relate subscriber activity and traffic. • Network component perspective: Study aggregated load at base stations, base station load distributions, spatial characteristics, temporal characteristics and spatio-temporal characteristics of network load at base stations.

  12. Paper 1 - Short Summary (Contd.) • Provide implications on the measurements and observations made. • Test conducted in 2007 for a week over a US nation-wide network with thousands of base stations and with entire subscriber base (order of hundreds of thousands i.e. close to a million). • Performed measurements on all generated data packet headers (not including payloads) and on signaling & accounting packets.

  13. Paper 1 - Subscriber Traffic Dynamics • Subscriber Traffic Distribution: • KEY OBSERVATIONS • Heavy users: Users who generate as high as 10GB of traffic per day (10^5 times median). • Light users: Users who generate less than 1KB per day. • CDF shifts left over weekends. • INFERENCE • Less traffic on weekends relative to traffic on working days. Fig. CDF of total traffic volume per subscriber per day.

  14. Paper 1 - Subscriber Traffic Dynamics • Subscriber Traffic Distribution (Contd.): • KEY OBSERVATIONS • 1% of the subscribers create more than 60% of the daily network traffic. • 10% of subscribers create 90% of the daily network traffic. • INFERENCE • Imbalance in network usage with few subscribers (10%) using much of the network resources. Fig. CDF of normalized traffic volume over the percentage of subscribers per day.

  15. Paper 1 - Subscriber Traffic Dynamics • Implications of Subscriber Traffic Distribution: • An unlimited data plan with flat rate pricing is not efficient both from the carrier’s perspective and subscriber’s perspective. • CDF graphs shown in previous two slides can be used to create a ‘tiered’ rate plan for data. • Tiered rate plan deals with providing different pricing options based on data usage.

  16. Paper 1 - Subscriber Traffic Dynamics • Implications of Subscriber Traffic Distribution (Contd.): 4) To alleviate the problem of high volume subscribers creating poor experience for other subscribers, high volume subscribers can be provided with some incentives. 5) Paper doesn’t consider optimal pricing schemes based on subscriber usage and network capacity. It only provides heuristic implications for subscriber traffic distribution.

  17. Paper 1 - Subscriber Traffic Dynamics • Subscriber Mobility (i.e. Base Stations Visited): • KEY OBSERVATIONS • Distribution similar on weekdays and different on weekends. • 60% of users are stationary (i.e. constrained within a cell) and over 95% of users travel across less than 10 base stations in a day. • Highly mobile users (who visit more than 50 distinct base stations in a day) are about 0.01%. Fig. CDF of number of distinct base stations visited by a subscriber each day.

  18. Paper 1 - Subscriber Traffic Dynamics • Subscriber Mobility (i.e. Base Stations Visited): • INFERENCE • Tendency of lesser degree of mobility on weekends. • In terms of the number of distinct base stations visited, the overall mobility is low. Fig. CDF of number of distinct base stations visited by a subscriber each day.

  19. Paper 1 - Subscriber Traffic Dynamics • Subscriber Mobility (Radius of Gyration): • Radius of Gyration is the linear size occupied by a subscriber’s trajectory. Requires certain duration of time (t) for computation from subscriber’s trajectory. • It is basically a root mean square value. • Calculated with respect to the center of mass point of the user’s trajectory. Fig. CDF of radius of gyration.

  20. Paper 1 - Subscriber Traffic Dynamics • Subscriber Mobility (Radius of Gyration): • KEY OBSERVATIONS: • 53% of subscribers are practically static and almost 98% of the subscribers have radius of gyration less than 100 miles. • INFERENCE: • Shows the low level of mobility of majority of subscribers (half of them). Fig. CDF of radius of gyration.

  21. Paper 1 - Subscriber Traffic Dynamics • Subscriber Mobility (Radius of Gyration): • KEY OBSERVATIONS: • Radius of gyration on an average comes to a saturation point in just few days (based on no. of hours). • Saturation indicates that some sort of boundary in the movement area has been reached. Quick saturation measured in terms of ‘return probability’ in next slide. • Users with larger radius of gyration need longer time to saturate. Fig. Radius of gyration versus duration of computation for subscribers categorized into 4 groups according to their final rg at the end of seven-day period.

  22. Paper 1 - Subscriber Traffic Dynamics • Subscriber Mobility (Radius of Gyration): • KEY OBSERVATIONS: • Distribution has peaks at 24th, 48th and 72nd hours. • INFERENCE: • Periodic nature of human mobility with a 24 hour period (like coming back home) and tendency to return to the same location periodically. This infers the saturation of radius of gyration. Fig. Probability distribution of time to returning to the same location.

  23. Paper 1 - Subscriber Traffic Dynamics • Subscriber Mobility (Radius of Gyration): • KEY OBSERVATIONS: • Location with rank, L = 1 indicates the most visited base station for a subscriber. • Subscribers spend 30% of their time in the top two preferred locations. • INFERENCE: • Subscribers are found at their favorite location with high probability even there is high mobility among them. Fig. Probability of finding a subscriber at different locations that are ranked on the basis of their frequency of visits. Shows four categories of subscribers who visit 5, 10, 30 and 50 distinct base stations.

  24. Paper 1 - Subscriber Traffic Dynamics • Inferences on Subscriber Mobility so far • Large fraction of subscribers have limited mobility (roughly half of them are static moving within just 1 mile). • Subscriber mobility also exhibits periodic behavior with high probability of returning to same base station at same time of the day. • Overall mobility is predictable. • More mobile users tend to generate more traffic.

  25. Paper 1 - Subscriber Traffic Dynamics • Implications on Subscriber Mobility • Idea of caching content and delivering it to subscribers who exhibit a predictable mobility behavior - Innovative cloud-based content delivery applications. • Optimizing the location based services and targeted ad-services through predictable mobility pattern.

  26. Paper 1 - Subscriber Traffic Dynamics • Relating subscriber mobility and traffic they generate: Fig. CDF of traffic generated per day by subscribers based on number of locations (base stations) visited in a day. Fig. CDF of traffic generated per day by subscribers based on radius of gyration.

  27. Paper 1 - Subscriber Traffic Dynamics • Relating subscriber mobility and traffic they generate: • KEY OBSERVATIONS FROM PREVIOUS SLIDE: • Though the plot lines appear similar, they differ in traffic volume for different number of base stations visited and traffic volume for different radii of gyration. • INFERENCE: • More traffic is generated by more subscribers. • Median traffic generated by subscribers in the highest mobility category is roughly twice that of the subscribers in the lowest mobility category.

  28. Paper 1 - Subscriber Traffic Dynamics • Implications relating to subscriber mobility and traffic they generate: 1) Planning resources dynamically based on traffic generated by subscribers specific to subscriber timings of movements. 2) Spectrum management based on timings of traffic generated and in different cells.

  29. Paper 1 - Subscriber Traffic Dynamics • Subscriber Temporal Activity: It is the number of days (or hours) in a week (or in a day), subscribers generate traffic. • KEY OBSERVATIONS • About 28% of the subscribers generate traffic only in single hour during the peak hours. • A typical subscriber (i.e. median) is active in the 4 different hours during the peak hours. (Consider a straight line -50% line- across the graph) Fig. CDF of number of hours among peak hours (8 AM to 8 PM) subscribers generate traffic.

  30. Paper 1 - Subscriber Traffic Dynamics • Subscriber Temporal Activity: • INFERENCE: • Large fraction of subscribers generate traffic only in few hours within a day. • That is, more of number of subscribers generating traffic is for a lesser duration of time (for the week / for a day). Fig. CDF of number of hours among peak hours (8 AM to 8 PM) subscribers generate traffic.

  31. Paper 1 - Subscriber Traffic Dynamics • Subscriber Temporal Activity: Airtime: Amount of time a subscriber holds onto a radio channel regardless of whether it communicates or not. • KEY OBSERVATIONS: • Median usage is about 100 sec. • For all 24 hrs (86,400 sec), very few i.e. less than 1% of subscribers use the radio channel. • Weekend usage again lower compared to weekday usage. Fig. CDF of airtime among subscribers.

  32. Paper 1 - Subscriber Traffic Dynamics • Relating subscriber temporal activity and traffic they generate: • KEY OBSERVATIONS: • A typical heavy user appears in 4 to 6 different hours during peak hours in the days they generate traffic. • INFERENCE: • Most heavy users are actually quite sporadic in traffic generation and not habitual. Fig. CDF of occurrence for heavy users (within top 5000 in atleast one day in the week with regard to traffic) in peak hours.

  33. Paper 1 - Subscriber Traffic Dynamics • Relating subscriber temporal activity and traffic they generate: Effective bit rate is the ratio of amount of actual traffic generated by the subscribers to the airtime. Metric for efficient radio channel use. • KEY OBSERVATIONS: • Subscribers generating less traffic (<= 30 KB) have poorer effective bit rate compared to more traffic ones. May be due to the kind of application they use (next slide). Fig. CDF of effective bit rate for subscribers categorized by traffic generated per day.

  34. Paper 1 - Subscriber Traffic Dynamics • Relating subscriber temporal activity and traffic they generate: • KEY OBSERVATIONS: • P2P and http:yahoo have the best channel efficiencies. • VPN, https and http for Google, Microsoft have poorest efficiencies. • INFERENCE: • Enterprise applications generate less traffic compared to other applications for the same airtime. • All applications have significantly poorer effective bit rates compared to nominal rates (phy channel). Fig. Effective bit rate for popular TCP based applications.

  35. Paper 1 - Subscriber Traffic Dynamics • Relating subscriber temporal activity and traffic they generate: • REASONING for INFERENCE: • Enterprise applications (VPN) tend to use network sporadically like keep-alive messages and typically not high throughput applications. • Considering dormancy/sleep modes, effective bit rate is poor for VPN-like applications. • High throughput applications like P2P use the channel better. Fig. Effective bit rate for popular TCP based applications.

  36. Paper 1 - Subscriber Traffic Dynamics • Implications on effective bit rates: • Inefficiency in the usage of the radio channel airtime drives the need for an innovative protocol to use wireless channel efficiently. • Inefficiency arises because of wired-internet protocols used to access wireless channel and hence better network protocols need to be designed.

  37. BASE STATION TRAFFIC DYNAMICS We focus on network behavior as a whole or in terms of network components (base stations) instead of focusing on subscribers. • Aggregate Load • Base Station Load Distribution • Spatial Characteristics • Temporal Characteristics • Load • Auto-correlation • Spatiotemporal Characteristics

  38. BASE STATION TRAFFIC DYNAMICS - Contd. • Total traffic split into upload and download for each day of the week. • Favorite weekends see a lesser load • Downloads dominate relative to uploads with more than 75% of daily load coming from download traffic Aggregate Load:

  39. BASE STATION TRAFFIC DYNAMICS- Contd. Aggregate Load: Load on the network is relatively low in the early morning hours, and roughly similar during the day and the evening.

  40. BASE STATION TRAFFIC DYNAMICS- Contd. Base Station Load Distribution: Volume of daily traffic load for each base station 80% of the base stations are loaded in the range of 1- 100MB per day and 10% of the base stations are highly loaded (more than 100MB per day). • shows the CDF of daily base station loads normalized by the total network load. • 10% of the base stations experience roughly about 50-60% of the aggregate traffic load. In both cases, weekend behavior is slightly different than weekday behavior. The load imbalance seems more pronounced in weekends. Great imbalance of the base station loads indicates that a more careful cell planning is possibly needed. Network providers may use smaller cells or microcells at the hotspots to even out the imbalance.

  41. BASE STATION TRAFFIC DYNAMICS- Contd. Spatial Characteristics • Goal is to identify whether or how much spatially correlated the network load is. • Estimates can potentially help the provider to allocate resources appropriately. • Use of Voronoi cells to conduct the experiments • Voronoi cell corresponds to the geographic region of each base station’s coverage. E.g. 10 shops in a flat city and their Voronoi cells

  42. BASE STATION TRAFFIC DYNAMICS- Contd. More on Voronoi cells: • Voronoi cells in certain areas (city centers) signifying some degree of cell planning. • We can readily see again that the cells are not uniformly loaded in space. The load differentials can extend several orders of magnitude. • There does appear to be some degree of negative correlation between the Voronoi cellsize and load. • Large Voronoi cells mean sparsely located base stations, implying sparer population density. No significant spatial correlation between adjacent cells is observed via visual inspection of similar plots for all days. Region1 Region2

  43. BASE STATION TRAFFIC DYNAMICS- Contd. Temporal Characteristics: correlation or predictable relationship between signals observed at different moments in time. 1. Load: • Hourly aggregate load of the entire network and highly loaded base stations. • Aggregate network load exhibits a nice periodic behavior with relatively high loads during the day and the lowest load during midnight. • Individual base station loads do not show that much periodicity. • load curve varies significantly among individual base stations with their peaks occurring at different times of the day.

  44. BASE STATION TRAFFIC DYNAMICS- Contd. • Auto-correlation: • Rigorous analysis of the periodic behavior describing the network • load is done using temporal correlation for a load metric. • Helps in understanding the underlying trends and seasonal variations better. • Auto-correlation function of the time series • at different lags. • Notice the plot shows a high degree of • temporal correlation. • High peaks occur at 24 hour intervals and low peaks at 12 hour intervals. • Isn’t this consistent with diurnal human activity patterns. 

  45. BASE STATION TRAFFIC DYNAMICS- Contd. Spatiotemporal Characteristics: • Use of Moran I to investigate spatial behavior. • Moran's I is a measure of spatial auto correction. •  Spatial autocorrelation is characterized by a correlation in a signal among nearby locations in space. Spatial autocorrelation is more complex than one-dimensional autocorrelation because spatial correlation is multi-dimensional (i.e. 2 or 3 dimensions of space) and multi-directional. • It’s defined as 𝑥 is the is the hourly load on a base station(random variable). --𝑥(x bar)mean of x 𝑥𝑖 ’s are the observations. 𝑤𝑖𝑗 is the weight associated with each pair (𝑥𝑖, 𝑥𝑗) 𝑁 is the number of observations.

  46. BASE STATION TRAFFIC DYNAMICS- Contd. More on Moran I: • Binary weights: 𝑤𝑖𝑗 = 1, when the base stations are in close proximity (a threshold of 2 miles is used), else 𝑤𝑖𝑗 = 0. • Moran’s I metric is plotted for hourly loads of all base stations in • the network on a temporal scale. • Periodic behavior with a diurnal cycle is interesting. • Appears that while temporal usage patterns of base stations may be very different • and might even miss periodicity there is a • general tendency for proximate base • station loads to be more correlated when • the loads are high. • Correlation is fairly small, rarely exceeding 0.15. • Min close to zero, showing almost independent loading behavior around midnights when generally the loads are small.

  47. Implication of variability in Base station Load • High degree of variability in base station loads has important implication on spectrum allocation and energy saving schemes in the network. • Adaptively turning on/off certain carriers or radios in base stations based on the load experienced need to be developed. • Peak hours of different cells vary a lot • Dynamic allocation of spectrum resources to highly loaded cells during their peak hours • Future Work: model the demand characteristics on different cells in cellular data networks based on measurements for a long period of time and feed the model as inputs to dynamic spectrum allocation algorithms. Study the observation

  48. Paper 2 – NetPiculet – Untold Story of middleboxes • Cellular networks becoming more and more ubiquitous and \ • pervasive. • Two major players involved in such networks – • - Network providers • - Application developers • Cellular Networks also face problems similar to their Internet counterparts such as IP address space depletion and security loopholes • Moreover cellular networks have limited resources • To make best use of their limited resources, number of middleboxes deployed by providers to enforce policies

  49. NetPiculet • An Android Application opened to mret place in January 2011 • in order to record policies • Major policies tested are NAT and Firewall • Tested over 6 continents and 107 different carriers. • Made lucrative by making the user know his network shortcomings and loopholes

  50. NetPiculet - System Architecture

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