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Mobile Computing – A Distributed Systems Perspective

Mobile Computing – A Distributed Systems Perspective. Prof. Nalini Venkatasubramanian Department of Computer Science University of California, Irvine. Outline. The Future: Mobile and Pervasive Computing An Applications View A Content View Multimedia Content

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Mobile Computing – A Distributed Systems Perspective

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  1. Mobile Computing – A Distributed Systems Perspective Prof. Nalini Venkatasubramanian Department of Computer Science University of California, Irvine

  2. Outline • The Future: Mobile and Pervasive Computing • An Applications View • A Content View • Multimedia Content • QoS Basics and End-to-end support • A Systems View • Device, Operating System • Networks, Middleware • Application, Content • A Cross-Layer Approach to Systems Support • Supporting Cross Cutting Concerns (Security, Reliability, Heterogeneous Interoperability)

  3. Pervasive Computing and Communication • Proliferation of devices • System support for multitude of smart (mobile) devices that • attach and detach from a distribution infrastructure • produce a large volume of information at a high rate • limited by communication and power constraints • Require a customizable global networking backbone. • That can handle heterogeneous needs of applications • Quality of Service (QoS), security, reliability • That can make effective use of the underlying infrastructure • Intelligent Middleware is key to supporting application needs in a highly dynamic environment

  4. Distributed Mobile Applications

  5. In the last 5 years….. • New devices w/ new capabilities • Data capture, storage, presentation • New apps • Social networking,…. • Multimedia applications, gaming, data capturing, download/upload/streaming • Information seek/notification • New network infrastructures • DTN, WiMax, UWB,… • New computational infrastructure • Cloud computing, grids… New problems and challenges…

  6. Laptop Users Applications BS Mobile Devices Networks GSM or CDMA ad hoc network PDA WLAN Access Point Cell Phone Introduction Mobile Ecosystem

  7. Technology Incentive • Growth in computational capacity • MM workstations with audio/video processing capability • Dramatic increase in CPU processing power • Dedicated compression engines for audio, video etc. • Rise in storage capacity • Large capacity disks (several gigabytes) • Increase in storage bandwidth,e.g. disk array technology • Surge in available network bandwidth • high speed fiber optic networks - gigabit networks • fast packet switching technology

  8. Enabler: Mobile Communication Networks • Cellular - GSM (Europe+), TDMA & CDMA (US) • FM: 1.2-9.6 Kbps; Digital: 9.6-14.4 Kbps (ISDN-like services) • Public Packet Radio - Proprietary • 19.2 Kbps (raw), 9.6 Kbps (effective) • Private and Share Mobile Radio • Wireless LAN - wireless LAN bridge (IEEE 802.11) • Radio or Infrared frequencies: 1.2 Kbps-15 Mbps • Paging Networks – typically one-way communication • low receiving power consumption • Satellites – wide-area coverage (GEOS, MEOS, LEOS) • LEOS: 2.4 Kbps (uplink), 4.8Kbps (downlink)

  9. Mobile Network Architecture

  10. Wireless network characteristics • Variant Connectivity • Low bandwidth and reliability • Frequent disconnections • predictable or sudden • Asymmetric Communication • Broadcast medium • Monetarily expensive • Charges per connection or per message/packet • Connectivity is weak, intermittent and expensive

  11. Device Characteristics • Battery Power restrictions • Transmit/receive, disk spinning, display, CPUs, memory consume power • Resource constraints • Mobile computers are resource poor • Reduce program size • Computation and communication load cannot be distributed equally • Small screen sizes

  12. Mobility Characteristics • Location changes • location management - cost to locate is added to communication • Heterogeneity in services • bandwidth restrictions and variability • Dynamic replication of data • data and services follow users • Querying data - location-based responses • Security and authentication • System configuration is no longer static

  13. Challenges • Application Context • Increasing QoS., security, reliability demands • Soft Real Time Constraints • Support for traditional media (text, images) and continuous media (audio/video) • Synchronization (e.g. lip sync., floor control) • System support for multitude of components • Attach and detach from a distributed infrastructure • Deal with large vol. of information at a high rate • Changing global system state Application Context Global Context Challenges Device Context Network Context • Need high degree of “network awareness” and “customizability” • congestion rates, mobility patterns etc. • QoS driven resource provisioning • Heterogeneous networks • Heterogeneous devices • Limited battery lifetime • Size/weight limitations • Computation/Communication constraints

  14. Wireless Network Wide Area Network Mobile Multimedia – Rich Media applications on mobile devices OBJECTIVE : Stream rich multimedia content at highest possible quality (user experience) over wired and wireless networks Low-power mobile device Video stream Video request MEDIA SERVER Access point • Challenges • Soft Real time Requirements • High demands on CPU / Network • Loss in performance directly affects user perception • Opportunities • Predictable regular behavior allows for interesting optimizations and adaptations

  15. Multimedia Information Systems: Challenges • Sheer volume of data • Need to manage huge volumes of data • Timing requirements • among components of data computation and communication. • Must work internally with given timing constraints - real-time performance is required. • Integration requirements • need to process traditional media (text, images) as well as continuous media (audio/video). • Media are not always independent of each other - synchronization among the media may be required.

  16. High Data Volume of Multimedia Information

  17. Quality of Service in MM Applications User • QoS: A Design Parameter for MM • Minor violations of performance requirements • Generally used to express constraints on Timing, availability, reliability,security, resource utilization (Perceptual QoS) Application (Application QoS) System (Operating and Communication System) (System QoS) (Network QoS) (Device QoS) MM devices Network

  18. QoS Classes • QoS Service Classes determine • reliability of offered QoS • utilization of resources • Guaranteed Service Class • QoS guarantees are provided based on deterministic and statistical QoS parameter values. • Predictive Service Class • QoS parameter values are estimated and based on the past behavior of the service • Best-effort Service Class • No guarantees or only partial guarantees provided • No QoS parameters are specified or some minimal bounds are given.

  19. Supporting continuous media: Approaches • Admission control • Provide different service classes • Fast storage and retrieval of Multimedia content • Optimized organization (placement) of multimedia files on disk • Special disk scheduling algorithms • Efficient Memory Management • Sufficient buffers to avoid jitter • Intelligent Caching

  20. End-to-end QoS for Wired Applications • Usually achieved through QoS Brokers • Coordinates interactions between multiple sessions with QoS needs • Typical Functions of a QoS broker • Resource Provisioning • Deal adaptively with incoming requests • Allocate server, network and client resources • Predictive and Adaptive Data Placement • Re(configure) data to service requests more efficiently • Must maintain resource allocation invariants

  21. Supporting QoS in Mobile Applications • New challenges • Constantly changing system conditions • Network connectivity, user mobility • Device constraints • Energy, CPU, display, bandwidth • This needs • Provisioning, re-provisioning based on local conditions of wireless network • Data Placement based on current and projected data access patterns • Cross-layer awareness required • Resource provisioning algorithms utilize current system resource availability information to ensure that applications meet their QoS requirements

  22. s1 s1 s2 O s2 CD s3 s3 QoS-based resource provisioning • Provisioning Network and Server Resources Effectively • State information enables decision making for resource provisioning - e.g. Routing, Scheduling and Placement • Maintaining accurate and current system information is important • Existing Approaches • In network : QoS Based Routing • At Server : Server Load Balancing • Future Middleware will support CPSS (Combined Path and Server Scheduling) O

  23. S1 v1 v2 v3 v4 S3 v1 v2 v3 v4 Initial placement S2 v5 v6 v1 v2 S1 v1 v2 v3 v4 S2 v5 v6 v1 v2 S3 v6 v5 v3 v4 After replication degree enforcement Data Placement in Mobile Environments • Design replication and intelligent data placement mechanisms that • Ensure effective resource management • Ensure QoS for admitted clients • Design caching mechanisms • Partition data/processing between mobile clients and infrastructure • Allows disconnected operation • Efficient power management

  24. Need for a Cross Layer Approach • Information exchange needed • To provide information good enough for resource provisioning tasks such as admission control, load balancing etc. • Need an information collection mechanism that is : • is aware of multiple levels of imprecision in data • is aware of quality requirements of applications • makes optimum use of the system (network and server) resources • Sample Parameters • Network link status, Data server capacity (Remote disk bandwidth, Processor capacity), device constraints (battery power, memory limitations)

  25. Misc. Display CPU NETWORK CARD DISPLAY NIC CPU Case Study: Power Management • Power Optimization in battery operated mobile devices is a crucial research challenge • Devices operate in dynamic distributed environments • Power Management strategies need to be aware of global system state and exploit it.

  26. User/Application Architecture (cpu, memory) Network Interface Card DVS, DPM, Driver Interfaces, system calls Quality of Service Application/user feedback Operating System Architecture Network Existing Work in Power Optimization • Flinn (ICDSP 2001), Yau (ICME 2002) • Krintz, Wolski (ISLPED 2004) • Noble (SOSP 97, MCSA 1999) • Li (CASES 2002), Othman (1998) • Abeni (RTSS 98) • Rudenko ( ACM SAC 99), Satyanarayan (2001) • Ellis, Vahdat (IEEE pervasive 05, Usenix 03, ASPLOS 02) • Hao, Nahrstedt (ICMCS 99, HPDC 99, Globecom 01) • Yuan (MMCN 03,04, SOSP 03, ACM MM 04), • Rajkumar (03), Anand (Mobicom 03, Mobisys 04) • DVS (Shin, Gupta, Weiser, Srivastava, Govil et. al.) • DPM (Douglis, Hembold, Delaluz, Kumpf et. al.) • Chandra (MMCN 04,02, USENIX 2002, ICPP 04) • Shenoy (ACM MM 2002) • Feeney, Nilson ( Infocom 2001) • Katz (IEICE 97) • Soderquist (ACM Multimedia 97) • Azevedo (AWIA 2001) • Hughes, Adve (MICRO 01, ICSA 01) • Brooks (ISCA 2000), Choi (ISLPED 02) • Leback (ASPLOS 2000), Microsoft’s ACPI Power Optimization has been extensively researched

  27. Illustration Client Wired Network Wireless Network Client congestion Server Proxy Client Client Limitations of Current Approaches • Limited co-ordination between the different system layers • Address concerns at one or two system levels • Make assumptions about adaptations at other system levels (lack awareness) • Device Centric • Cannot exploit global system knowledge (e.g. congestion, mobility, location) • Reactive Adaptations • Lack of generalized framework What if new applications are started or residual battery energy changes?

  28. Caching Compression Use proxy to dynamically adapt to global changes Traffic Shaping State Monitor Compositing Transcoding Wireless Network Wide Area Network Execute Remote Tasks Coordinate techniques on device Power-aware middleware • A power-cognizant distributed middleware framework can • dynamically adapt toglobal changes • co-ordinate techniques at different system levels • maximize the utility (application QoS, power savings)of a low-power device. • study and evaluate cross layer adaptation techniques for performance vs. quality vs. power tradeoffs for mobile handheld devices. Low-power mobile device proxy server Use a Proxy-Based Architecture

  29. User/Application Architecture (cpu, memory) Quality of Service Application/user feedback Network Middleware Distributed Adaptation Cross-Layer Adaptation Appl. specific Adaptation Operating System Architecture Power-Aware Adaptive Middleware Distributed Adaptation Cross-Layer Adaptation Power-Aware API • DYNAMO SYSTEM • Mohapatra et. al (ICN 05, ITCC 05, ACM Middleware 04, DATE 04,ICDCS 03, MWCN 03, ACM MM 03, RTAS/RTSS Workshops 03, Estimedia 03, CIPC 03, ICDCS 01)

  30. LOCAL CROSS LAYER ADAPTATION Cross-Layer Approach User/Application device Distributed Middleware Middleware network Operating System Architecture Proxy GLOBAL (End-to-End) PROXY BASED ADAPTATION Expose “state” information to other layers Design strategies at each layer to dynamically adapt to changes at other layers • Design end-to-end adaptations that • can exploit global state (network noise, • mobility patterns, device state etc.) • 2. Use control information to notify • mobile device of adaptations • 3. Adapt strategies on device

  31. Energy-Sensitive Video Transcoding for Mobile devices VIDEO TRANSCODING PARAMETERS QUALITY Video transformation parameters Avg. Power (Windows CE) Avg. Power (Linux) Q1 (Like original) SIF, 30fps, 650Kbps 4.42 W 6.07 W Q2 (Excellent) SIF, 25fps, 450Kbps 4.37 W 5.99 W Q3 (Very Good) SIF, 25fps, 350Kbps 4.31 W 5.86 W Q4 (Good) HSIF, 24fps, 350Kbps 4.24 W 5.81 W Q5 (Fair) HSIF, 24fps, 200Kbps 4.15 W 5.73 W Q6(Poor) HSIF, 24fps, 150Kbps 4.06 W 5.63 W Q7 (Bad) QSIF, 20fps, 150Kbps 3.95 W 5.5 W Q8 (Terrible) QSIF, 20fps,100kbps 3.88 W 5.38 W Quality/Power Matrix for COMPAQ IPAQ 3600 ( Grand Theft Auto Action Video Sequence) • We conducted a survey to subjectively assess human perception of video quality on handhelds. • Hard to programmatically identify video quality parameters • We identified 8 perceptible video quality levels that produced noticeable difference in power consumption (Compaq iPaq 3600)

  32. Energy-Sensitive Video Transcoding: Visual Comparison Quality 1 (like original) Avg. Power : 6.24 W Quality 4 (good) Avg. Power : 5.81 W Quality 7 (bad) Avg. Power : 5.41 W • Parameters varying: frame size, bit-rate, frame rate • Profile power for each quality • Optimize system for each quality

  33. Video Stream (QSTREAM) Residual Energy (ERES) User defined Quality (Qthreshold) QSTREAM = Max(Qi) such that PSTREAM * T < ERES QSTREAM > QTHRESHOLD QSTREAM = Video streamed by proxy Qthreshold = Threshold quality level acceptable to the user PSTREAM = Avg. Power consumption for video playback ERES = Residual Energy of device T = Time of playback Energy-Aware Video Transcoding Mobile Device Proxy

  34. User Profile Negotiation QoS Monitor Transcoder E-Q profile Communication CPU NIC Wireless Network NIC CPU Energy-Aware Application Adaptation Application Dynamo Middleware Dynamo Middleware Linux OS DISPLAY Proxy Wireless Network Mobile Device

  35. Network Card Optimization • Wireless NIC cards can operate in various power modes • Avg. power consumption in sleep mode (0.184 W) whereas idle/receive modes consume (1.34/1.435 W) respectively. • NIC can be transitioned to sleep mode (high energy savings) • Packets can get lost (quality drop) • Adaptation • Proxy buffers video and sends it in bursts to the device • Control info added - when device should wake up • Allows for long sleep intervals of the network card on device • Limitations • Large bursts can result in high packet loss rates • Access point and mobile device buffering limitations

  36. Video Stream (QSTREAM) # of frames, buffer size, quality Residual Energy, Quality threshold + Noise (SL), Buffer capacity (Bf), Decode rate (Fd) Adaptation at Proxy QSTREAM = Max(Qi) such that PSTREAM * T < ERES QSTREAM > QTHRESHOLD Mobile Device Proxy Set local buffer size based on noise level (empirical) Fix quality level (Qi) to be streamed (QSTREAM ... QTHRESHOLD) Let N = number of transcoded frames in local buffer Burst Size (I) = N / Fd Send next burst after I seconds.

  37. Adaptation at Mobile Device Burst Size (I) = N / Fd Video Stream (QSTREAM) # of frames (N), buffer size, quality, σ Mobile Device Proxy total number of packets at the Access Point Worst case transmission delay of burst (D) = σ x TAP Therefore total sleep time (δ) for NIC at the device δ = I – D + γ. DEtoE Switch network card to “sleep” mode for δ seconds after receiving video Psaved ≥ δ x (PIDLE - PSLEEP)

  38. User Profile Negotiation Network Transmission QoS NIC adaptation Monitor Transcoder E-Q profile Communication CPU NIC Wireless Network NIC CPU Network Card Adaptation in Dynamo Application Dynamo Middleware Dynamo Middleware Linux OS DISPLAY Proxy Wireless Network Mobile Device

  39. Network Energy Savings NIC Backlight 1738 Frames Energy Savings – 35% - 57% (over no optimization) Frames Lost – < 12% CPU 399 Frames Energy Savings – 50% - 75% (over no optimization) Frames Lost – < 5% 2924 Frames Energy Savings – 25% - 45% (over no optimization) Frames Lost – < 10%

  40. Backlight Compensation • Adaptation 1) Enhance luminance of video frames at proxy 2) Dim backlight on the device to compensate luminance enhancement • Problems • Technique to achieve the suitable (optimal) backlight dimming factor • Reduce flicker induced by frequent backlight switching

  41. Backlight Adaptation Backlight Level Quality (PSNR) Power Savings Fair 149 30.17 41.8% Good 162 34.28 36.7% Excellent 205 42.31 27.3% Backlight Level Power Savings Fair 80 44.8% Good 125 39.7% Excellent 186 30.3% Backlight Level Power Savings Fair 172 34.8% Good 162 27.7% Excellent 205 21.4%

  42. Video Stream (QSTREAM) # of frames, buffer size, quality + Backlight Setting Residual Energy, Quality threshold + Noise (SL), Buffer capacity (Bf), Decode rate (Fd) Backlight Adaptation in Dynamo Mobile Device Proxy Use backlight quantization table to set backlight Stream luminance compensated video

  43. User Profile Negotiation Network Transmission Backlight adaptor NIC adaptor QoS Monitor Transcoder E-Q profile B/L Scaling Communication CPU NIC Wireless Network NIC CPU Backlight Adaptation using Dynamo NIC Backlight CPU Application Dynamo Middleware Dynamo Middleware Linux OS DISPLAY Proxy Wireless Network Mobile Device

  44. I B B P B B P Voltage MPEG Stream no DVS with DVS 0 D Fd time Architecture/CPU Adaptation • DVS idea: trade off processor speed for power • MPEG frame decoding – good candidate • Frame decoding takes less than the frame delay • Decoding time – depends on frame type: I, P, B

  45. Residual Energy, Quality threshold + Noise (SL), Buffer capacity (Bf), Decode rate (Fd) CPU Adaptation using Dynamo Video Stream (QSTREAM) # of frames, buffer size, quality Backlight Setting + Profiled WCET, BCET, Avg. Execution time Mobile Device Proxy • Middleware communicates execution characteristics to scheduler • Scheduler can now dynamically re-compute slowdown parameters for the new video quality • Determine video quality • Use a rule base of profiled data to send execution parameters to the mobile device

  46. User Profile Negotiation Network Transmission CPU adaptation NIC adaptor Backlight adaptation QoS Monitor E-Q profile Transcoder B/L Scaling Rule Base Communication scheduler Wireless Network NIC CPU CPU Adaptation Application Dynamo Middleware Dynamo Middleware Linux OS DISPLAY Proxy Wireless Network Mobile Device

  47. Other (8.2%) Other (8.2%) Network (15.09%) CPU Savings (13.6%) Network (37.7%) CPU (27.2%) NIC Savings (22.64%) CPU (13.6%) Display (26.9%) Display Savings (12.1%) Display (14.8%) Overall Energy Savings After Cross-Layer Optimizations Energy Distribution (After Optimization) Energy Distribution (Before Optimization) Note: These are avg. energy savings Total energy savings ~ 48% (for a medium action video clip called Foreman) Implications for a commercial PDA • Avg. Battery lifetime of the mobile device increases by approximately 2 times its original lifetime under similar load (i.e. battery power consumption).

  48. Insecure network 90 80 70 60 Measured Energy (Joules) 50 40 30 20 Experimental Study 10 Negligible Energy Overhead 0 FOREMAN.qcif NEWS.qcif Video Clips Encoding without Encryption Encoding with Encryption (Selective) Encoding with Encryption (Naïve) Secure Mobile Multimedia – Energy Implications Problem and Motivation Attacks Battery -Operated Devices • Mobile multimedia applications are vulnerable to security attacks in wireless networks • Significant computation for video encryption is expected onbattery-operated mobiles Video Encoder Video Decoder Symmetric Encryption Technique Symmetric Decryption Technique • Evaluate symmetric video encryption schemes w.r.t. energy Secure Video Encoder Secure Video Decoder

  49. Problem and Motivation Idea: ME (Motion Estimation) is.. Multiplexing, Packetizing & Channel Encoding Video Encoder Raw Video ME DCT Q VLC Lossy Network • Video streams over a wireless network • Data loss  Error control • Channel coding : FEC, Retransmission • Source coding: Error-resilient coding • Most energy consuming operation • Avoiding ME can improve energy profile • at the cost of encoding efficiency • High impact on image quality • Making ME algorithm robust against packet loss • can improve error resiliency • Probability Based Partial intra-coding • trade-offs among error resiliency, encoding efficiency, energy consumption Experimental Study Fast recovery Energy efficiency Flexibility Reliable Mobile Multimedia – Energy Implications

  50. Mobile Grid/Cloud Computing

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