COOPERATIVE TASK SCHEDULING

Transcription

1 A. Brodlos and F.H.P. Fitzek and P. Koch. Energy Aware Computing in Cooperative Wireless Networks in Cooperative Networks, WirelessCom 2005, pages Maui, Hawaii, USA. COOPERATIVE TASK SCHEDULING

2 dynamic voltage scaling DVS++ Distributed task scheduling among terminals Power savings Overcoming local ~ 275 1/2 ~ 80 1/3 ~ 50 1/4 ~ 45 mw title

3 Chenguang Lu and Frank H.P. Fitzek and Patrick C.F. Eggers and Ole Kiel Jensen and Gert F. Pedersen and Torben Larsen. Terminal-Embedded Beamforming for Wireless Local Area Networks IEEE Wireless Communications. COOPERATIVE BEAMFORMING

4 Cooperative beamforming Packet 1 Packet 1 Packet 2 Packet 2 Packet Packet Packet N Packet N Client 1 Client 2

5 Cooperative beamforming

6 User Interface and demo script (fully automatically) Monitoring signal strength and data rate (CBR traffic) Display the antenna configuration Note, the directed beam performance includes the steering Cooperative beamforming

7 Cooperative beamforming

8 energy saving capacity Cooperative beamforming

9 G.P. Perrucci and F.H.P. Fitzek and M.V. Petersen Chapter in Heterogeneous Wireless Access Networks: Architectures and Protocols -- Energy Saving Aspects for Mobile Device Exploiting Heterogeneous Wireless Networks. Springer. ENERGY SAVING IN COOPERATIVE WIRELESS NETWORKS

10 different scenarios

11 general notation

12 wlan streaming

13 wlan download

14 Bluetooth w/o streaming

15 Bluetooth w/o broadcast download

16 Bluetooth w streaming

17 Bluetooth w broadcast download

18 energy consumption download WLAN BT wobc BT w BC

19 power levels for wlan

20 power levels for Bluetooth

21 power levels for cellular

22 energy consumption download

23 energy consumption download

24 energy savings download

25 delay download

26 energy consumption streaming WLAN BT wobc BT w BC

27 energy consumption streaming

28 energy consumption streaming

29 energy saving streaming

30 delay streaming

31 multi modality example Using GPRS and Bluetooth for cooperation among two terminals for file sharing N70 Mobile Phones Symbian/C++ implementation Tit for Tat Strategy Benefit in the download time (reduced by a factor of two), the energy (going down to 56%) and (perhaps) reduced service costs Fit with analytical results

32 Netlogo again Works with Netlogo 3 and 4.

33 Cooperative Wireless Networking PART III

34 MOBILE P2P MEASUREMENTS

35 Measurement Setup and Methodology Nokia N70 running a Python S60 script sniffing Bluetooth neighborhood GSM position (GPS planed) Date (hh:mm:ss) Address, Name, Major Service Class, Major Device Class and Minor Device Class of neighboring devices Device placed in different locations (busses, conferences, airports, bars, etc)

36 Measurement Setup and Methodology Scanning repeated every 30 seconds Information stored in an XML file

37 Measurement Setup and Methodology Public places classified into 2 categories: Slowly changing node distribution: buses, trains, clubs, etc. indoor static places Rapidly changing node distribution: downtown, shopping malls, stations, etc.highly dynamic places

38 Results: BT Classes 07

39 Results: Neighbors 07 No change in measurement position few new devices

40 Results: Neighbors 07

41 Results: Scanning Time 07

42 Results: Cluster Characteristic 07

43 Conclusion Surprisingly high number of short-range communication links found Depending on country (DK /D) Most devices within phone categorycommon phones Scanning time proportional to number of devices found long time to discover neighboring devices Improved Short Range would be nice All results and tools are publically available

44 SOCIAL MOBILE NETWORKS

45 Social networks The need for social networks in the Internet is motivated by Diversity of interests Mobility of users / Spread over the globe Facebook and friends have a huge success, but...

46 Social networks supports? Economic domain supports Social domain Technological domain

47 Social mobile networks Lets enter the mobile domain Of course existing (fixed) social networks can be used on mobile devices But being mobile offers even more Establish relationship with context (place) Relationships are not anymore life-long SMN offers quick establishment of new relationships Mobile 2.0 (buzz-word alarm)

48 Social mobile networks Context awareness with the neighbourhood is achieved by In-built sensors Microphone/Camera Accelerometer GPS Short range communication Bluetooth WLAN IEEE Cellular Mobile sensors Short Range Cellular

49 Nokia Sensor 2005 Nokia Sensor

50 By courtesy of aka aki! aka-aki.com

51 Social mobile networks: future Economic domain Technological domain supports Social domain

52 Social mobile networks Non altruistic cooperation among mobile device Altruistic cooperation among mobile devices Socially motivated cooperation among mobile device

53 Social Mobile Clouds Social Mobile Cloud (Focus) Network coding Social Forced Cooperation Technology enabled Socially enabled Altruism B and C does not matter B>C B >C Hamilton Rule B*r>C There is no understanding of costs and benefits (see LTE multi hop or sensor networks) Users see the instantaneous gain in cooperation Users do not gain by cooperation as such but show off in their social cycles Users do not care too much about their own gain but happy to help B: benefit C:cost

54 3G Social Mobile Clouds WiFi Frank grants Anna access to the IP world INVITE: WiFi hotspot facebook ID Technology domain Frank Anna Social domain After the technical cooperation, Anna is rewarding Frank on facebook! Anna Meyer Thanks Frank for letting me uses your WiFi connection! It was very helpful! Hope I can be helpful for you next time... THANKS AGAIN!

55 content sharing content sharing Bob Lilly Anna commenting on the sharing social networks Anna Meyer Just watch the new Big Bang Theory Episode with Bob and Lilly. Thanks Lilly for shring this great episode with us!

56 Conclusion

57 F. Albieroand F.H.P. Fitzekand M. Katz.Cooperative Power Saving Strategies in Wireless Neworks: an Agent-based model inieee International Symposium on Wireless Communication Systems GAME THEORY FOR COOPERATIVE WIRELESS NETWORKS

58 Game Theory: One Example Strategy B Strategy A 60.80% 68.00% 100%

59 game theory energy strategy A time strategy B

60 game theory energy strategy A time strategy B

61 game theory energy number of stations

62 game theory energy number of stations

63 game theory energy number of devices Real egoistic behavior is to cooperate! K.Edwin

64 BIO-INSPIRED ENERGY-AWARE MEDIUM ACCESS CONTROL PROTOCOL

65 Scenario

66 NetLogo

67 Bio-Inspired MAC

68 Results

69 COGNITIVE WIRELESS NETWORKS

70 cognitive wireless networks Mobile devices become more and more flexible Communication environment is much more dynamic

71 cognitive wireless networks

72 cognition for cooperative devices

73 cognitive mobile device title

74 GENETIC ALGORITHMS AND EVOLUTION THEORY

75 The scenario A cooperative cluster for file sharing service Some nodes in the cluster act as sources of the information, as they download the data over a cellular link Each source has different shares of the same file At each instant of time all the nodes forward to their neighbours the data they possess All the nodes in the cluster will receive all the portions of the file

76 RLNC in addition to simple cooperation With RLNC we benefit in the service time value, the time needed for all the nodes to obtain the complete file (in the figures L stands for loss on the WLAN short range link)

77 RLNC in addition to simple cooperation We benefit also in the overall energy consumption value for the cluster

78 Cluster head election If we want to select the best subset of nodes acting as cluster heads for the cooperative group we need to consider that: Nodes are randomly deployed in the network Nodes have different cellular throughput values and cellular access (GPRS, UMTS, HSDPA) Both parameters (position and cellular link) strongly influence the overall performances (service time, energy consumption) in electing the cluster head nodes

79 How can we train the network? We can use training packets at the beginning of the file sharing process Testing all the different combinations of nodes in the cluster results in a number of tests equal to: where nis the number of cluster heads and kis the total number of nodes. With this solution we find the optimum value, but this is highly resource and time demanding especially for a large number of nodes!

80 Evolution Theory Evolution is an optimization strategy Recombination of existing solutions to create new solutions Better solutions will proceed due to their better fitness This step alone would only find local optima Mutation (some kind of revolution) Mutation is not by default negative!

81 Evolutionary theory Genetic Algorithms permit to converge in a shorter time to a good solution (the example on the right reaches the optimal solution obtained with an exhaustive search).

82 Evolutionary theory Example for 12 nodes and 4 cluster heads: 495 tests for an Exhaustive search about 40 with GA and 200 Generations

83 Comparing GA with Random choice The GA solution gives a 5% gain compared to a Random choice solution within the same training time (noteworthy, the gain strongly depends on the solution domain)

84 Tutorial on Cooperative Wireless Networks Free Online Tutorial IEEE Tutorial NOW Fitzek/Katz Recorded at GC 09

85 Contact Frank Fitzek Aalborg University