Online Service Management Algorithm for Cellular/WALN Multimedia Networks

1. Online Service Management Algorithmfor Cellular/WALN Multimedia Networks SOFSEM 2007 Sungwook Kim Sogang University Department of Computer Science Seoul, South Korea

2. Introduction • Efficient network resource management -key to enhance network performance & QoS • Next generation networks - support heterogeneous multimedia services • Support heterogeneous multimedia data - while ensuring QoS for higher priority traffic services • Traffic pattern is difficult to predict - online approach is essential • Adaptive network management - while maintaining a well-balanced network performance

3. Online Algorithm • Online algorithm - dealing with the online computation problem • Online computation problem - based on past events without future information - make decisions in real time • Many QoS problems in network management - online computation problems • The online resource management & control algorithm - natural candidate for multimedia network operations

4. Traffic Service • Traffic services  new and handoff call services in cellular network - give higher priority to handoff services  class I (real-time) and class II (non real-time) call services in multimedia communication networks - class I data service : Voice telephony, Video-phone - class II data service : E-mail, ftp, Data on demand, etc : give higher priority to class I call services

5. Bandwidth Reservation • The traffic window size can be adjustable. If CDPclass_I is higher (lower) than Pclass_I, - traffic window size is increased (decreased) - in steps equal to unit_time. • Bandwidth reservation amountis estimated dynamically - the sum of requested bandwidth by class I calls during the traffic window

7. Buffer Management • Active Queue Management algorithm : network router is responsible - for detecting network congestion - for notifying end hosts of congestion to adapt their sending rates • RED and BLUE algorithms - avoid global synchronization - adjust the packet dropping probability in response to congestion - pushing most of the complexity and state of differentiated services : to the network edges

8. RED Algorithm (1) • The RED (Random Early Detection) Algorithm - queue length is used as threshold to detect network situation - try to maintain an average queue length under congestion • Based on recent buffer history - drops incoming packets in a random probabilistic manner - provide a more equitable distribution of packet loss - improve the utilization of the network • Major problem - heavily depend on the system parameter values - average queue length is only the index for network situation

9. RED Algorithm (2) • for each incoming packet - calculate the average queue length (Avg) : exponential weighted average • if Avg < MINh - do nothing • if MINh< Avg < MAXh - calculate packet dropping probability Pa - mark packets with probability Pa • if MAXh < Avg - mark packet

10. Blue Algorithm (1) • Recently developed simple algorithm - retain all the desirable features of RED algorithm • Main indices of network congestion - directly on packet loss and current link utilization • Queue overflow and idle event - update the packet marking probability - learn the correct rate and send back congestion notification • Major problem - queue length variation for bursty traffic changes : difficult to control temporal traffic fluctuations

11. Blue Algorithm (2) • For each packet loss:  if ((now – last_update) > freeze_time ) - Pm = Pm + Di - last_update = now • For link idle event:  if ((now – last_update) > freeze_time ) - Pm = Pm - Dd - last_update = now

12. Orange (Online range) Algorithm (1) • Three parameter values for QoS and congestion control : adaptive decision by online manner  bandwidth range for the reservation (RESb)  queue range (Qr) packet marking probability (Mp) • Main issue - adaptive range adjustment for bandwidth and buffer control • Orange (Online range) control algorithm - adaptive online control for service differentiation - to provide a ‘better effort’ service for class II traffics while ensuring QoS for the admission controlled class I services

13. Orange (Online range) Algorithm (2) • Adjusts system parameters - in adaptive online fashion • Bandwidth reservation range (RESb) • Queue range (Qr) - unused reserved bandwidth can be temporarily allocated for buffered class II service - same as the RESb to maximize network performance • Packet marking probability (Mp) - decided proportional to the current queue length - adaptively characterized by threshold values

14. Orange (Online range) Algorithm (3) • If L < Qr - congestion free : no arriving packets are dropped • L > T - all arriving class II data packets are dropped • Qr < L < T - class II data packets can be marked with probability • Packet marking probability Mp - L : current queue length - T : maximum buffer size

15. Simulation Model • Consists of 7 clusters, each cluster consists of 7 micro cells • In the even traffic situation, new call arrivals are Poisson with rate (0-3 calls/s/cell), which is uniform in all the cells • In the uneven traffic situation, the arrival rate of hot cell is Poisson with rate 3 • Capacity of each cell is C (=30Mbps) • One base station per cluster is selected randomly as the faulty base station and this occurs at a random time • Mobiles can travel in one of 6 directions with equal probability with three cases of user velocity • Eight different data groups are assumed based on call duration, bandwidth requirement and class of service • Durations of calls are exponentially distributed with different means for different multimedia data types

16. Simulation Results Fig.1 Call Blocking Probability Fig.2 Call Dropping Probability

17. Concluding Remarks • Development of efficient bandwidth management - for QoS sensitive multimedia networks • Proposed integrated online approach - provides excellent network performance while ensuring QoS guarantees under widely different traffic scenarios • On-line decisions based on real time estimates - mutually dependent each other - adaptable and quite flexible to traffic changes • Strike the appropriate balanced network performance - among contradictory QoS requirements while other existing schemes cannot offer such an attractive trade off

18. . Internet Communication Control (ICC) Research Lab.Prof. Sungwook Kim

19. Internet • Differentiated Services (DiffServ)  Complexity & Scalability - easy to implement - no state information is needed in the core routers does not suffer from the scalability problems - concentrates on packet forwarding using appropriate queue management • Major problem  QoS control - not to provide guaranteed QoS for higher priority traffic services : growing interest in Internet QoS

20. Bandwidth Reservation (1) • guarantee QoS for class I data traffic services  maintain the reserved bandwidth close to the optimal value  on-line estimate by traffic window - based on real time measurement - keeps the history of class I task - learn the pattern of coming requests - close to the optimal value - partition the time axis into equal interval : unit_time

21. Bandwidth Reservation (2)

22. Bandwidth Reservation (3) • The traffic window size can be adjustable. If CBPclass_I is higher (lower) than Pclass_I, - traffic window size is increased (decreased) - in steps equal to unit_time. • Bandwidth reservation amountis estimated dynamically - the sum of requested bandwidth by class I calls during the traffic window

23. Online management for Internet • Guarantee QoS for class I data traffic services  maintain the reserved bandwidth close to the optimal value  on-line estimate by traffic window - based on real time measurement ABlink = MABpath(i,j)=

24. Call Admission Control (1) • CAC is responsible to decide - granted, declined or renegotiated • Two system parameters are used: One-way packet Delivery Time (ODT) : packet delay time of setting path the Acceptance Threshold (AT) : the predefined bit sending rate • Network probing - to determine if all routers along the path have available bandwidth

25. Call Admission Control (2) • For a new class I request, - a probing packet estimates the available network bandwidth SRbits/sec ( = BU ×)≥ ATi bits/sec • For a new class II request, - a probing packet only estimates the unused network bandwidth SRbits/sec ( = BU ×)≥ M_ATj bits/sec • Guarantee QoS for class I data traffic services

26. Internet • The rapid growth of data communication network - Internet Protocol (IP) : Internet - QoS sensitive multimedia data services : based on different priority • Major Problem - difficult to support guaranteed QoS : bounded delay & minimum throughput for higher priority real time applications

27. Intserv Model • Integrated Services (IntServ) - in order to provide QoS in Internet. - signal to the network through a reservation request • ReSerVation Protocol (RSVP) - end-to-end signaling protocol - receiver-oriented protocol for setting up resource reservations - reservations have to be refreshed periodically • Major problem  Complexity & Scalability - router has to keep state information on all reservations

28. Diffserv Model • Differentiated Services (DiffServ)  Complexity & Scalability - easy to implement - no state information is needed in the core routers does not suffer from the scalability problems - concentrates on packet forwarding using appropriate queue management • Major problem  QoS control - not to provide guaranteed QoS for higher priority traffic services : growing interest in Internet QoS

29. AQM Algorithms • Active Queue Management algorithm : network router is responsible - for detecting network congestion - for notifying end hosts of congestion to adapt their sending rates • RED and BLUE algorithms - avoid global synchronization - adjust the packet dropping probability in response to congestion - pushing most of the complexity and state of differentiated services : to the network edges

30. RED Algorithm (1) • The RED (Random Early Detection) Algorithm - queue length is used as threshold to detect network situation - try to maintain an average queue length under congestion • Based on recent buffer history - drops incoming packets in a random probabilistic manner - provide a more equitable distribution of packet loss - improve the utilization of the network • Major problem - heavily depend on the system parameter values - average queue length is only the index for network situation

31. RED Algorithm (2) • for each incoming packet - calculate the average queue length (Avg) : exponential weighted average • if Avg < MINh - do nothing • if MINh < Avg < MAXh - calculate packet dropping probability Pa - mark packets with probability Pa • if MAXh < Avg - mark packet

32. BLUE Algorithm (1) • Recently developed simple algorithm - retain all the desirable features of RED algorithm • Main indices of network congestion - directly on packet loss and current link utilization • Queue overflow and idle event - update the packet marking probability - learn the correct rate and send back congestion notification • Major problem - queue length variation for bursty traffic changes : difficult to control temporal traffic fluctuations

33. BLUE Algorithm (2) • For each packet loss:  if ((now – last_update) > freeze_time ) - Pm = Pm + Di - last_update = now • For link idle event:  if ((now – last_update) > freeze_time ) - Pm = Pm - Dd - last_update = now

34. Online Control in Internet • Basic idea of the cellular network management - can be applied to Internet • Online strategy based on real time measurements - due to the uncertain network environment : do not require advance knowledge or prediction • Major advantage of an online approach - adaptability, flexibility, responsiveness to current traffic conditions • Online algorithm based on DiffServ model - provides QoS guarantees for higher priority calls while accommodating as many call connections as possible

35. Multimedia Internet Management • Online management algorithm  the QoS provisioning mechanism - guarantee QoS based on call admission control : for class Idata service  the congestion control mechanism - adaptive bandwidth allocation for higher network performance : for class II data services • Integrated online approach - both mechanisms act cooperatively : in order to simultaneously satisfy the conflicting requirements

36. Orange (Online range) Algorithm • Three parameter values for QoS and congestion control : adaptive decision by online manner  bandwidth range for the reservation (RESb)  queue range (Qr) packet marking probability (Mp) • Main issue - adaptive range adjustment for bandwidth and buffer control • Orange (Online range) control algorithm - adaptive online control for service differentiation - to provide a ‘better effort’ service for class II traffics while ensuring QoS for the admission controlled class I services

37. Online Control Algorithm for Internet • QoS guarantee for higher priority service - no reduction in network capacity • Ability to adaptively congestion control - to maximize network performance • Low complexity - practical for real network implementation • Ability to respond to current network traffic conditions - for the appropriate performance balance between contradictory QoS requirements

38. QoS provisioning mechanism (1) • During network congestion - QoS provisioning problem is further intensified • Admission control management - provide good QoS in Internet • Link bandwidth is shared dynamically - between class I and class II data services - each service has different operational requirements • Different admission control rules -strict admission control rule for class I data services -non-controlled admission rule for class II data services

39. QoS provisioning mechanism (2) • Bandwidth is partitioned by range - some part is reserved for higher priority traffic service - partition range can be movable • Bandwidth range (RESb) for reservation - adaptive adjustment by traffic window  online computational problem • Admission decisions for class I traffic services - controlled by the moving range : get the benefit from reservations for QoS guarantees

40. Congestion control mechanism (1) • On-line control for network congestion : unable to optimally control the network congestion exactly  try to close to optimal network performance - responsive to current traffic changes in link loads - adaptive balance between traffic history and recent traffic changes • Dropping packet rate - provide feedback information : the congestion level of the gateways through the path

41. Congestion control mechanism (2) • Adjusts system parameters - in adaptive online fashion • Bandwidth reservation range (RESb) • Queue range (Qr) - unused reserved bandwidth can be temporarily allocated for buffered class II service - same as the RESb to maximize network performance • Packet marking probability (Mp) - decided proportional to the current queue length - adaptively characterized by threshold values

42. Congestion control mechanism (3) • If L < Qr - congestion free : no arriving packets are dropped • L > T - all arriving class II data packets are dropped • Qr < L < T - class II data packets can be marked with probability • Packet marking probability Mp - L : current queue length - T : maximum buffer size

43. Congestion control mechanism (4) • Recent traffic patterns reflect effectively the current condition - during recent unit_time [ tc - unit_time, tc] • Traffic management in next interval - adaptively control packets during [tc, tc + unit_time] • L < Qr - packet queuing rate (Ip_r) in current interval : packet incoming rate - packet clearing rate  if (T – Qr ) < Ip_r then Mp1 • Qr < L < T  if (0 < Ip_r ) then Mp2  if (Ip_r<0) & | Ip_r | > (L – Qr) thenno packet drop

44. Online Control Practical Applications • Dynamic QoS priority control in multimedia networks - call priority can be changed based on online requests and current network conditions • Main concept of this dissertation  integrated online approach based on real-time measurement - develop other adaptive control algorithms - inter-process communication, disk and memory file and I/O systems, CPU scheduling, power control, distributed operating system

45. Concluding Remarks • QoS guarantee for higher priority service - no reduction in network capacity • Ability to adaptively congestion control - to maximize network performance • Low complexity - practical for real network implementation • Ability to respond to current network traffic conditions - for the appropriate performance balance between contradictory QoS requirements