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![Mobile Cloud Computing: Definition
Definition
An integration of cloud computing technology with mobile devices to
make the mobile devices resource-full in terms of computational power,
memory, storage, energy, and context awareness.
[1] Khan, A.R.; Othman, M.; Madani, S.A.; Khan, S.U., “A Survey of Mobile Cloud Computing Application
Models," IEEE Communications Surveys & Tutorials, vol.16, no.1, pp.393,413, First Quarter 2014
doi: 10.1109/SURV.2013.062613.00160
Claudio Fiandrino | Mobile Cloud Computing - Introduction 4 of 37](https://image.slidesharecdn.com/distributed-systems-and-middleware-150114022221-conversion-gate01/85/Distributed-systems-and-middleware-7-320.jpg)
![Mobile Cloud Computing: Definition (II)
Definition (II)
Mobile cloud computing is a model for transparent elastic augmentation
of mobile device capabilities via ubiquitous wireless access to cloud
storage and computing resource, with context-aware dynamic adjusting
of offloading in respect to change in operating conditions, while
preserving available sensing and interactivity capabilities of mobile
devices.
[1] Dejan Kovachev, Yiwei Cao, Ralf Klamma: Mobile Cloud Computing: A Comparison of Application
Models. CoRR, abs/1107.4940, 2011.
(http://arxiv.org/abs/1107.4940v1)
Claudio Fiandrino | Mobile Cloud Computing - Introduction 5 of 37](https://image.slidesharecdn.com/distributed-systems-and-middleware-150114022221-conversion-gate01/85/Distributed-systems-and-middleware-8-320.jpg)
![List of Approaches
CloudLets
CloneCloud
MAUI
MobiCloud
Odessa
Cuckoo
Hyrax
µCloud
ThinkAir
eXCloud
[1] Khan, A.R.; Othman, M.; Madani, S.A.; Khan, S.U., “A Survey of Mobile Cloud Computing Application
Models," IEEE Communications Surveys & Tutorials, vol.16, no.1, pp.393,413, First Quarter 2014
doi: 10.1109/SURV.2013.062613.00160
[2] Shiraz, M.; Gani, A.; Khokhar, R.H.; Buyya, R., “A Review on Distributed Application Processing
Frameworks in Smart Mobile Devices for Mobile Cloud Computing,” IEEE Communications Surveys &
Tutorials, vol.15, no.3, pp.1294,1313, Third Quarter 2013
doi: 10.1109/SURV.2012.111412.00045
Claudio Fiandrino | Mobile Cloud Computing - Distributed Application Processing 22 of 37](https://image.slidesharecdn.com/distributed-systems-and-middleware-150114022221-conversion-gate01/85/Distributed-systems-and-middleware-30-320.jpg)
![A brief list of Architectures
Fat-Tree
“Al-Fares”
Portland
Hedera
Clos (VL2)
Hypercube
BCube
DCell
DPillar
FiConn
FlatNet
Helios
C-Through
Petabit
GreenCloud (Not the Simulator)
[1] Ali Hammadi, Lotfi Mhamdi, A survey on architectures and energy efficiency in Data Center
Networks, Computer Communications, Volume 40, 1 March 2014, Pages 1-21, ISSN 0140-3664,
http://dx.doi.org/10.1016/j.comcom.2013.11.005.
(http://www.sciencedirect.com/science/article/pii/S0140366413002727)
[2] Bari, M.F.; Boutaba, R.; Esteves, R.; Granville, L.Z.; Podlesny, M.; Rabbani, M.G.; Qi Zhang; Zhani,
M.F., “Data Center Network Virtualization: A Survey," IEEE Communications Surveys & Tutorials, vol.15,
no.2, pp.909,928, Second Quarter 2013 doi: 10.1109/SURV.2012.090512.00043
Claudio Fiandrino | Data Center Network Architectures - Definition and Classification 26 of 37](https://image.slidesharecdn.com/distributed-systems-and-middleware-150114022221-conversion-gate01/85/Distributed-systems-and-middleware-35-320.jpg)
![Evaluation Criteria: Computing Servers
Power consumption (DVFS)
P(l) = Pidle +
Ppeak − Pidle
2
· (1 + l − e−( l
τ ))
l server load
τ in the range [0.5, 0.8]
Key idea:
Load= 0
Servers
Access Layer
Aggregation Layer
Core Layer
→ →
Increasing Load
→ →
Load= 1
Idle link; Active link; Idle device; Active device;
Number of computing servers: 4096
Idle and peak power from Dell PowerEdge R720, Huawei Tecal
RH2288H V2 and IBM System x3500 M4
Claudio Fiandrino | Data Center Network Architectures - Energy Consumption and Communication Latency Evaluation 33 of 37](https://image.slidesharecdn.com/distributed-systems-and-middleware-150114022221-conversion-gate01/85/Distributed-systems-and-middleware-44-320.jpg)
Uploaded byClaudio Fiandrino
Distributed systems and middleware
This document discusses mobile cloud computing and data center network architectures. It begins with an introduction to mobile cloud computing, including definitions and key elements. It describes how mobile devices can offload computation and data to augment their capabilities. It then covers mobile networks and technologies for cloud access. The document outlines approaches to distributed application processing and partitioning. Finally, it defines data center network architectures and classification schemes before analyzing examples like Fat-Tree, BCube and DCell. Evaluation criteria for energy consumption and communication latency in data centers are also presented.
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- 1. University of Luxembourg Distributedsystems and middleware Claudio Fiandrino claudio.fiandrino@uni.lu November 17, 2014
- 2. Agenda Mobile Cloud Computing DataCenter Network Architectures Claudio Fiandrino | Agenda - 1 of 37
- 3. Scenario Mobile Operator Network CloudInternetP-GWS-GW MME HSSPCRFeNodeB IP Network WiFi AP WiFi APUEs Claudio Fiandrino | Agenda - 2 of 37
- 4. Outline Mobile Cloud Computing Introduction MobileDevices Mobile Networks Distributed Application Processing Data Center Network Architectures Claudio Fiandrino | Mobile Cloud Computing - Introduction 2 of 37
- 5. Mobile Cloud Computing MobileCloud Computing vs. Mobile Computing In Mobile Cloud Computing the devices run cloud-based apps In Mobile Computing the devices run native apps In Mobile Cloud Computing users have to access remotely stored applications and their associated data over the Internet Claudio Fiandrino | Mobile Cloud Computing - Introduction 3 of 37
- 6. Mobile Cloud Computing MobileCloud Computing vs. Mobile Computing In Mobile Cloud Computing the devices run cloud-based apps In Mobile Computing the devices run native apps In Mobile Cloud Computing users have to access remotely stored applications and their associated data over the Internet Mobile Cloud Computing: Common Understanding Access to cloud computing services through mobile devices Claudio Fiandrino | Mobile Cloud Computing - Introduction 3 of 37
- 7. Mobile Cloud Computing:Definition Definition An integration of cloud computing technology with mobile devices to make the mobile devices resource-full in terms of computational power, memory, storage, energy, and context awareness. [1] Khan, A.R.; Othman, M.; Madani, S.A.; Khan, S.U., “A Survey of Mobile Cloud Computing Application Models," IEEE Communications Surveys & Tutorials, vol.16, no.1, pp.393,413, First Quarter 2014 doi: 10.1109/SURV.2013.062613.00160 Claudio Fiandrino | Mobile Cloud Computing - Introduction 4 of 37
- 8. Mobile Cloud Computing:Definition (II) Definition (II) Mobile cloud computing is a model for transparent elastic augmentation of mobile device capabilities via ubiquitous wireless access to cloud storage and computing resource, with context-aware dynamic adjusting of offloading in respect to change in operating conditions, while preserving available sensing and interactivity capabilities of mobile devices. [1] Dejan Kovachev, Yiwei Cao, Ralf Klamma: Mobile Cloud Computing: A Comparison of Application Models. CoRR, abs/1107.4940, 2011. (http://arxiv.org/abs/1107.4940v1) Claudio Fiandrino | Mobile Cloud Computing - Introduction 5 of 37
- 9. Keywords Mobile devices Cloud Computing Augmentation Preservingcapabilities Resource-full Claudio Fiandrino | Mobile Cloud Computing - Introduction 6 of 37
- 10. Key Elements Mobile Cloud Computing Mobile Devices Network Cloud Applications Claudio Fiandrino| Mobile Cloud Computing - Introduction 7 of 37
- 11. Outline Mobile Cloud Computing Introduction MobileDevices Mobile Networks Distributed Application Processing Data Center Network Architectures Claudio Fiandrino | Mobile Cloud Computing - Mobile Devices 7 of 37
- 12. Type of Devices Notonly smartphones and tablets Claudio Fiandrino | Mobile Cloud Computing - Mobile Devices 8 of 37
- 13. Issues of mobiledevices Resource scarceness Battery constrained Low connectivity Claudio Fiandrino | Mobile Cloud Computing - Mobile Devices 9 of 37
- 14. Solution Offloading to theCloud Computation Data Outcome: Application partitioning Cope with mobile device issues Gather more data Use idle processing power Claudio Fiandrino | Mobile Cloud Computing - Mobile Devices 10 of 37
- 15. Outline Mobile Cloud Computing Introduction MobileDevices Mobile Networks Distributed Application Processing Data Center Network Architectures Claudio Fiandrino | Mobile Cloud Computing - Mobile Networks 10 of 37
- 16. Technologies Cellular (3G/4G) WiFi Bluetooth Comparison TECHNOLOGY MAXDATA RATE ENERGY RANGE SPECTRUM Cellular Up to 300 Mb/s1 High More than 10 km (1 km)2 Licensed WiFi (802.11n) Up to 100 Mb/s Medium Up to 250 m (120 m) Unlicensed Bluetooth Up to 3 Mb/s Low Up to 100 m (20-30 m) Unlicensed 1. Considering 4x4 MIMO, 20 MHz channel 2. Macro cell radius and typical urban cell radius Claudio Fiandrino | Mobile Cloud Computing - Mobile Networks 11 of 37
- 17. Cellular Coverage http://opensignal.com/ Claudio Fiandrino| Mobile Cloud Computing - Mobile Networks 12 of 37
- 18. Health State ofthe Global Mobile Traffic 4.4 billion people will use mobile cloud applications by 2017 Mobile data traffic will reach 15 EB (1018 ) per month in 2018 22 million wearable devices generated 1.7 PB (1015 ) per month traffic in 2013 Claudio Fiandrino | Mobile Cloud Computing - Mobile Networks 13 of 37
- 19. What about Energy? Theimportance of the technologies “Our calculations show that, in 2015, the wireless networks we use to access cloud services will command around 90% of the energy needed to power the entire wireless cloud services ecosystem. By comparison, data centres will account for only 9% or less. Industry needs to focus on the real issues with wireless network technologies if it wants to solve this problem.” CEET (Centre for Energy-Efficient Telecommunications), Massive energy cost hidden in wireless cloud boom, 2013 (http://www.ceet.unimelb.edu.au/news/media/2013-04-09.html) Claudio Fiandrino | Mobile Cloud Computing - Mobile Networks 14 of 37
- 20. What about Energy? CloudWireless Access Burns More Energy Than Data Centres – Report (http://www.techweekeurope.co.uk/workspace/cloud-wireless-energy-use-data-centre-113227) Claudio Fiandrino | Mobile Cloud Computing - Mobile Networks 14 of 37
- 21. What about Energy?(II) Interesting Statistics One Google search is equivalent to about 0.2 grams of CO2 (http://googleblog.blogspot.com/2009/01/powering-google-search.html) Raffi (Twitter API team) says: “One tweet consumes around 100 J” http://goo.gl/uMnV0f http://www.slideshare.net/raffikrikorian/energy-tweet Claudio Fiandrino | Mobile Cloud Computing - Mobile Networks 15 of 37
- 22. Diagnosis Networking can notbe neglected Communications may impact dramatically on performance Carefully select proper technologies Claudio Fiandrino | Mobile Cloud Computing - Mobile Networks 16 of 37
- 23. Outline Mobile Cloud Computing Introduction MobileDevices Mobile Networks Distributed Application Processing Data Center Network Architectures Claudio Fiandrino | Mobile Cloud Computing - Distributed Application Processing 16 of 37
- 24. Application Partitioning: Objectives Enhancemobile devices’ performance Energy saving Claudio Fiandrino | Mobile Cloud Computing - Distributed Application Processing 17 of 37
- 25. High level listof applications Commerce Healthcare Gaming Searching (Keyword, Voice, Image, Location, Tag) Claudio Fiandrino | Mobile Cloud Computing - Distributed Application Processing 18 of 37
- 26. Application Partitioning Classification StaticPartitioning A set of task is always run remotely Dynamic Partitioning Tasks are run where it is most convenient Claudio Fiandrino | Mobile Cloud Computing - Distributed Application Processing 19 of 37
- 27. Where? Mobile Operator Network CloudInternetP-GWS-GW MME HSSPCRFeNodeB IP Network WiFi AP WiFi APUEs Claudio Fiandrino | Mobile Cloud Computing - Distributed Application Processing 20 of 37
- 28. Where? Mobile Operator Network CloudInternetP-GWS-GW MME HSSPCRFeNodeB IP Network WiFi AP WiFi APUEs Remote Clouds Local Clouds Other mobile devices Claudio Fiandrino | Mobile Cloud Computing - Distributed Application Processing 20 of 37
- 29. A practical example:Face Recognition Flow Detection on picture Determine match on database Partitioning 1. Local detection/remote recognition 2. Remote detection/remote recognition Beware: what is remote? Claudio Fiandrino | Mobile Cloud Computing - Distributed Application Processing 21 of 37
- 30. List of Approaches CloudLets CloneCloud MAUI MobiCloud Odessa Cuckoo Hyrax µCloud ThinkAir eXCloud [1]Khan, A.R.; Othman, M.; Madani, S.A.; Khan, S.U., “A Survey of Mobile Cloud Computing Application Models," IEEE Communications Surveys & Tutorials, vol.16, no.1, pp.393,413, First Quarter 2014 doi: 10.1109/SURV.2013.062613.00160 [2] Shiraz, M.; Gani, A.; Khokhar, R.H.; Buyya, R., “A Review on Distributed Application Processing Frameworks in Smart Mobile Devices for Mobile Cloud Computing,” IEEE Communications Surveys & Tutorials, vol.15, no.3, pp.1294,1313, Third Quarter 2013 doi: 10.1109/SURV.2012.111412.00045 Claudio Fiandrino | Mobile Cloud Computing - Distributed Application Processing 22 of 37
- 31. Take-home messages Mobile CloudComputing Augmenting devices capabilities Heterogeneity (applications/technologies/user behaviour) Application Partitioning To keep in mind Not simply “Access to cloud computing services through mobile devices.” Claudio Fiandrino | Mobile Cloud Computing - Distributed Application Processing 23 of 37
- 32. Outline Mobile Cloud Computing DataCenter Network Architectures Definition and Classification Architectures Analysis Energy Consumption and Communication Latency Evaluation Claudio Fiandrino | Data Center Network Architectures - Definition and Classification 23 of 37
- 33. Data Center Definitions DataCenter Infrastructure The data center is home to the computational power, storage, management and dissemination of data and information necessary to a particular body of knowledge or pertaining to a particular business. Data Center Network Architecture The data center network architecture is the set of network nodes and links that characterize the interconnectivity among the computing servers and to the external world. Claudio Fiandrino | Data Center Network Architectures - Definition and Classification 24 of 37
- 34. Design Criteria Scalability Flexibility Resiliency Maintenance Claudio Fiandrino| Data Center Network Architectures - Definition and Classification 25 of 37
- 35. A brief listof Architectures Fat-Tree “Al-Fares” Portland Hedera Clos (VL2) Hypercube BCube DCell DPillar FiConn FlatNet Helios C-Through Petabit GreenCloud (Not the Simulator) [1] Ali Hammadi, Lotfi Mhamdi, A survey on architectures and energy efficiency in Data Center Networks, Computer Communications, Volume 40, 1 March 2014, Pages 1-21, ISSN 0140-3664, http://dx.doi.org/10.1016/j.comcom.2013.11.005. (http://www.sciencedirect.com/science/article/pii/S0140366413002727) [2] Bari, M.F.; Boutaba, R.; Esteves, R.; Granville, L.Z.; Podlesny, M.; Rabbani, M.G.; Qi Zhang; Zhani, M.F., “Data Center Network Virtualization: A Survey," IEEE Communications Surveys & Tutorials, vol.15, no.2, pp.909,928, Second Quarter 2013 doi: 10.1109/SURV.2012.090512.00043 Claudio Fiandrino | Data Center Network Architectures - Definition and Classification 26 of 37
- 36. Classifications Electronic/Optical Which technology isused in the forwarding? Switch/Server Centric Who performs the forwarding? Ali Hammadi, Lotfi Mhamdi, A survey on architectures and energy efficiency in Data Center Net- works, Computer Communications, Volume 40, 1 March 2014, Pages 1-21, ISSN 0140-3664, http://dx.doi.org/10.1016/j.comcom.2013.11.005. (http://www.sciencedirect.com/science/article/pii/S0140366413002727) Claudio Fiandrino | Data Center Network Architectures - Definition and Classification 27 of 37
- 37. Electronic vs Optical Fullyelectronic Fully optical Hybrid Claudio Fiandrino | Data Center Network Architectures - Definition and Classification 28 of 37
- 38. Swich Centric vsServer Centric Switch Centric: switches are the key components in forwarding Switches are complex Servers no forwarding functionalities Bandwidth oversubscription Server Centric: servers are the key components in forwarding Switches are very simple Servers with forwarding functionalities No bandwidth oversubscription Trade off Servers devoted to computational purposes only Exploiting full link capacity Claudio Fiandrino | Data Center Network Architectures - Definition and Classification 29 of 37
- 39. Outline Mobile Cloud Computing DataCenter Network Architectures Definition and Classification Architectures Analysis Energy Consumption and Communication Latency Evaluation Claudio Fiandrino | Data Center Network Architectures - Architectures Analysis 29 of 37
- 40. Three-tier Hierarchical structure Bandwidth oversubscription ComputingServers Access Layer Aggregation Layer Core Layer Gateway Router Internet Claudio Fiandrino | Data Center Network Architectures - Architectures Analysis 30 of 37
- 41. BCube Main parameters: n: numberof ports per switch k + 1: number of ports per server Number of servers: nk+1 Number of switches: (k + 1) · nk Example with n = 4 and k = 1 Internet Computing Servers Commodity Switches Level 0 Commodity Switches Level k + 1 Load Balancers Gateway Router Claudio Fiandrino | Data Center Network Architectures - Architectures Analysis 31 of 37
- 42. DCell Main parameters: n: numberof servers per DCell0 k: number of DCell levels, server degree of connectivity (k + 1) Number of servers: tj+1 = tj · (tj + 1), t0 = n Number of switches (#DCell0): gj = tj−1 + 1 or #servers/n Example with n = 3 and k = 1 Internet Computing Servers Commodity Switches Load Balancers Gateway Router Claudio Fiandrino | Data Center Network Architectures - Architectures Analysis 32 of 37
- 43. Outline Mobile Cloud Computing DataCenter Network Architectures Definition and Classification Architectures Analysis Energy Consumption and Communication Latency Evaluation Claudio Fiandrino | Data Center Network Architectures - Energy Consumption and Communication Latency Evaluation 32 of 37
- 44. Evaluation Criteria: ComputingServers Power consumption (DVFS) P(l) = Pidle + Ppeak − Pidle 2 · (1 + l − e−( l τ )) l server load τ in the range [0.5, 0.8] Key idea: Load= 0 Servers Access Layer Aggregation Layer Core Layer → → Increasing Load → → Load= 1 Idle link; Active link; Idle device; Active device; Number of computing servers: 4096 Idle and peak power from Dell PowerEdge R720, Huawei Tecal RH2288H V2 and IBM System x3500 M4 Claudio Fiandrino | Data Center Network Architectures - Energy Consumption and Communication Latency Evaluation 33 of 37
- 45. Evaluation Criteria: Network Three-tier: 128racks 16 aggregation switches 8 core switches BCube: n = 8, k = 3 2048 switches DCell: n = 8 2 < k < 3 Other parameters: 1 Gbits link (Three-Tier, BCube and DCell) 10 Gbits link (Three-Tier) Test packets of 40 B and 1500 B Methodology One way delay Transmission and Propagation Claudio Fiandrino | Data Center Network Architectures - Energy Consumption and Communication Latency Evaluation 34 of 37
- 46. Energy: Results 0 0.20.4 0.6 0.8 1 0 1 2 3 4 ·105 Load (l) PowerConsumption(W) Dcell Three-tier BCube Claudio Fiandrino | Data Center Network Architectures - Energy Consumption and Communication Latency Evaluation 35 of 37
- 47. Communication: Results PERFORMANCE INDEX ARCHITECTURES Three-tierBCube DCell Latency (40 B) 1.98 µs 3.93 µs 4.73 µs Latency (1500 B) 28.34 µs 73.72 µs 93.92 µs Hop distance 5.78 7.00 8.94 Server Degree Connectivity 1 4 2.79 Beware: we counted as 2 the number of links between any pair of servers within a DCell0 and in BCube. Claudio Fiandrino | Data Center Network Architectures - Energy Consumption and Communication Latency Evaluation 36 of 37
- 48. Take-home messages Data CenterNetwork Architectures Different Architectures have: Different impact on energy costs Different impact on communication processes Claudio Fiandrino | Data Center Network Architectures - Energy Consumption and Communication Latency Evaluation 37 of 37
- 49.