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Edge Computing Platforms and Protocols - Ph.D. thesis

N
Nitinder Mohan

The document discusses edge computing platforms and protocols, highlighting the need for low latency and context-aware computations as internet workloads increasingly rely on cloud infrastructure. It outlines various contributions like the edge-fog cloud architecture and innovative frameworks for optimizing task deployment and resource utilization at the edge. Additionally, it addresses challenges in edge computing, including hardware management, network constraints, and the necessity for standardized protocols.

Science
Related topics:
Edge Computing • Cloud Computing Insights • Internet of Things
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Edge Computing Platforms and Protocols Nitinder Mohan
Cloud Computing • Global network of inter-connected datacenters • Datacenters managed and operated by cloud providers, e.g. Google, Microsoft, Amazon etc. • Developers can deploy their applications on virtualized resources of the cloud worldwide 1 More than 60% of Internet workloads are cloud-based
Next Generation Applications Trends by year 2025 • More than 75 billion connected devices • Expected 11 trillion USD market share • On average 7 sensors per person worldwide Requirements 1. Latency-critical processing 2. Big data aggregation and analysis 3. Location and context aware computations Internet-of-Things 3
Problem: Network! Ø High transport cost Ø High data volume Ø High network latency Can Cloud support next-generation applications? 4
Edge Computing Small-scale server(s) deployed near the users to compute generated data Benefits: üDecreased latency for computation üReduced network load due to pre-processing üLocation and contextual awareness Network DatacenterEdge Server User 5
Problems in Edge computing 1. Unmanaged hardware 2. Constrained network 3. Inconsistent reliability and availability 4. Lack of standardized protocols 6
Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Parental Control, Firewall, Content Catalog, User Authentication Cloud Computing Service Model 7
Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Cloud Computing Service Model Edge Computing Service Model Hardware Organization? Installation? Maintenance? Security? Infrastructure Mobile hardware? Limited processing? Low storage? Wireless/Cellular NICs? Platform Server Discovery? Self-Organizing Containers? Parental Control, Firewall, Content Catalog, User Authentication Factory Automation, Intelligence, Autonomous vehicles, IoT Analytics Cloud providers? Crowdsourced? ISPs? Light-weight VM? Mirage OS? Unikernels? Smartphone manufactures? Specialized servers? 7
Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. 8
Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. 8
Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. How to do computations on edge clouds?
Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. 8
Requirement of Edge Computing Need of a reference architecture for designing protocols and platforms! Constraints? Capabilities? Capacities? 9
Edge-Fog Cloud (EFCloud) Edge Ø Collection of devices: i. Loosely-coupled ii. Voluntary/Crowdsourced iii. Owned by independent operators Ø Extremely close to sensors & clients Ø Device-to-device connectivity over WiFi/cellular/Bluetooth/Zigbee etc. Ø Varying processing capability e.g. desktops, laptops, workstations, nano data centers etc. 10
Edge-Fog Cloud (EFCloud) Fog Ø High capacity compute servers Ø Co-located with networking devices Ø Designed, manufactured, managed and deployed by cloud vendors Ø Lies farther from sensors and clients Ø Dense connectivity within layer Ø Support for virtualization technologies e.g. routers, switches, basestations etc. 11
Edge-Fog Cloud (EFCloud) Data Store Ø Data archival and storage Ø No computation on data Ø Reliable, ease-of-access, secure and global access to storage for Edge and Fog resources 12
Edge-Fog Cloud (EFCloud) Salient Features 1. All-inclusive edge architecture 2. Native support for mobility 3. Satisfies emerging application requirements 4. Context-aware computation 5. No single point-of-failure 6. No vendor lock-in 13
Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. 14
Edge-Fog Cloud Formalizing Task Deployment 15
Edge-Fog Cloud Formalizing Task Deployment𝜆/𝜋/BW 𝜆/𝜋/BW 𝜆/𝜋/BW 𝜆 / 𝜋/ BW 𝜆 / 𝜋/ BW 15
Edge-Fog Cloud Formalizing Task Deployment D1 D2 D3 D4 D5 1 4 34 1 15
Edge-Fog Cloud Formalizing Task Deployment D1 D2 D3 D4 D5 1 4 34 1 16
Edge-Fog Cloud J1 J2 J3 J4 J5 Job Graph Formalizing Task Deployment D1 D2 D3 D4 D5 1 4 34 1 16
J1 J2 J3 J4 J5 Formalizing Task Deployment D1 D2 D3 D4 D5 1 4 34 1 Objective: Find best placement of jobs on Edge and Fog devices 16
D1 D2 D3 D4 D5 1 4 34 1 J1 J2 J3 J4 J5 ∞ 1 8 4 5 1 ∞ 7 5 4 8 7 ∞ 4 3 4 5 4 ∞ 1 5 4 3 1 ∞ 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 Dconn[ i,j ] = Jconn[ i,j ] = 17 De-facto: Network Only Cost Assignment
De-facto: Network Only Cost Assignment Dconn[ i,j ] = 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 Jconn[ i,j ] = Quadratic Assignment Problem Minimize: NP-hard! • For 30 nodes, it will take 2500 servers to solve it • Optimal solution not guaranteed $ %(',))∈, 𝐽./00 𝑖, 𝑗 ∗ 𝐷./00(𝑓 𝑖 , 𝑓(𝑗)) ∞ 1 8 4 5 1 ∞ 7 5 4 8 7 ∞ 4 3 4 5 4 ∞ 1 5 4 3 1 ∞ 17
Our Approach Optimize Processing Time Search Space Reduction Least Network Cost Least Processing Cost First (LPCF) Optimize Processing Energy Search Space Reduction Least Network Energy energy-efficient Least Processing Cost First (eLPCF) Multi-Objective Optimization D1 D2 D3 D4 D5 1 4 34 1 : 3 : 2 : 2 : 6: 5 D1 D2 D3 D4 D5 1 4 34 1 : 3 : 2 : 2 : 6: 5 18
18 Our Approach Optimize Processing Time Search Space Reduction Least Network Cost Least Processing Cost First (LPCF) Optimize Processing Energy Search Space Reduction Least Network Energy energy-efficient Least Processing Cost First (eLPCF) Multi-Objective Optimization D1 D2 D3 D4 D5 1 4 34 1 : 3 : 2 : 2 : 6: 5 D1 D2 D3 D4 D5 1 4 34 1 : 3 : 2 : 2 : 6: 5
Least Processing Cost First (LPCF) D1:3 D2:2 D3:2 D4:5 D5:6 1 4 34 1 J1:4 J2:2 J3:5 J4:4 J5:2 3 2 2 5 6 4 2 5 4 2 Dproc [i] = Jsize [i] = 19
Least Processing Cost First (LPCF) I. Optimize Processing Time Minimize: Linear Assignment Problem $ ',)∈, 𝐶 𝐽7'89(𝑖) 𝐷:;/.(𝑗) 𝑥') 3 2 2 5 6 4 2 5 4 2 Dproc [i] = Jsize [i] = 20
I. Optimize Processing Time Minimize: Linear Assignment Problem • Solved using Kuhn-Munkres/ Hungarian algorithm • Optimal solution guaranteed in O(n3) Least Processing Cost First (LPCF) D1:3 D2:2 D3:2 D4:5 D5:6 1 4 34 1 J1:4 J2:2 J5:2 J4:4 J3:5 Least Processing Cost: 4.966 $ ',)∈, 𝐶 𝐽7'89(𝑖) 𝐷:;/.(𝑗) 𝑥') 20
Least Processing Cost First (LPCF) II. Create sub-problem space Interchange homogeneous devices running homogeneous jobs Search Space Calculation: 1. Same processing power → interchange jobs 2. Same job size → interchange devices D1:3 D2:2 D3:2 D4:5 D5:6 1 4 34 1 J1:4 J2:2 J5:2 J4:4 J3:5 J1:4 J5:2 J2:2 J4:4 J3:5 J4:4 J5:2 J2:2 J1:4 J3:5 21
Least Processing Cost First (LPCF) II. Create sub-problem space Interchange homogeneous devices running homogeneous jobs Search Space Calculation: 1. Same processing power → interchange jobs 2. Same job size → interchange devices Least Processing Cost: 4.966 D1 D2 D3 D4 D5 1. J1 J2 J5 J4 J3 2. J1 J5 J2 J4 J3 3. J4 J5 J2 J1 J3 4. J4 J2 J5 J1 J3 21
Least Processing Cost First (LPCF) III. Least Network Cost 1. Compute network cost of each assignment 2. Choose the assignment with least network cost 𝐽./00 𝑖, 𝑗 ∗ 𝐷./00(𝑓 𝑖 , 𝑓(𝑗)) D1 D2 D3 D4 D5 1. J1 J2 J5 J4 J3 2. J1 J5 J2 J4 J3 3. J4 J5 J2 J1 J3 4. J4 J2 J5 J1 J3 N/W 20 27 19 28 Least Processing Cost: 4.966 22
Performance Overview 5 20 40 60 80 100 Topology Size 0 1000 2000 3000 4000 NetworkCost NOC LPCF Minimum bound Maximum bound 20 40 60 80 100 Topology Size 0 50 100 150 200 250 ProcessingCost NOC LPCF eLPCF 5 20 40 60 80 100 Topology Size 5 10 15 20 25 30 %decreaseinenergy Edge Fog Network cost remains within 10% range of the optimal value Processing cost is multiple times lower than de-facto solvers 10% decrease in overall energy used. More than 20% energy saved for battery constrained devices 24
Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. 25
Hardware PublicationsInfrastructurePlatform Anveshak: Placing Edge Servers In The Wild. N. Mohan, A. Zavodovski, P. Zhou, and J. Kangasharju, MECOMM 2018 Placing it right!: optimizing energy, processing, and transport in Edge-Fog clouds. N. Mohan and J. Kangasharju, Annals of Telecommunication 2018 Managing Data in Computational Edge Cloud. N. Mohan, P. Zhou, K. Govindaraj and J. Kangasharju, MECOMM 2017 QAware: A Cross-Layer Approach to MPTCP Scheduling. T. Shreedhar, N. Mohan, S.K. Kaul, & J. Kangasharju. IFIP Networking 2018 ExEC: Elastic Extensible Edge Cloud. A. Zavodovski, N. Mohan, S. Bayhan, W. Wong and J. Kangasharju, EdgeSys 2019 ICON: Intelligent Container Overlays. A. Zavodovski, N. Mohan, S. Bayhan, W. Wong and J. Kangasharju, HotNets 2018 Is two greater than one?: Analyzing Multipath TCP over Dual-LTE in the Wild. N. Mohan, T. Shreedhar, A. Zavodovski, J. Kangasharju & S.K. Kaul. Manuscript 2019. 25
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Next Generation Applications Industrial Automation Autonomous Vehicles Augmented Reality Remote Monitoring & Collaboration Autonomous Drones & many more .. Smart Homes
2
Edge Computing Platforms and Protocols - Ph.D. thesis
Edge computing Multiple edge computing approaches have been proposed i. Cloudlets → Miniature datacenters Network
Edge computing Multiple edge computing approaches have been proposed i. Cloudlets → Miniature datacenters ii. Fog → Network devices (Base stations, Routers) Network
Edge computing Multiple edge computing approaches have been proposed i. Cloudlets → Miniature datacenters ii. Fog → Network devices (Base stations, Routers) iii. Edge → Crowdsourced (smartphones, smart speakers) Network
Edge computing Multiple edge computing approaches have been proposed i. Cloudlets → Miniature datacenters ii. Fog → Network devices (Base stations, Routers) iii. Edge → Crowdsourced (smartphones, smart speakers, ..) iv. Mist → Data generators (sensors, microcontrollers, ..) Network
Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Parental Control, Firewall, Content Catalog, User Authentication Cloud Computing Service Model Edge Computing Service Model
Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Cloud Computing Service Model Edge Computing Service Model Parental Control, Firewall, Content Catalog, User Authentication Factory Automation, Intelligence, Autonomous vehicles, IoT Analytics
Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Cloud Computing Service Model Edge Computing Service Model Hardware Organization? Installation? Maintenance? Security? Parental Control, Firewall, Content Catalog, User Authentication Factory Automation, Intelligence, Autonomous vehicles, IoT Analytics Cloud providers? Crowdsourced? Cellular providers?
Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Cloud Computing Service Model Edge Computing Service Model Hardware Organization? Installation? Maintenance? Security? Infrastructure Mobile hardware? Limited processing? Low storage? Wireless/Cellular NICs? Parental Control, Firewall, Content Catalog, User Authentication Factory Automation, Intelligence, Autonomous vehicles, IoT Analytics Cloud providers? Crowdsourced? Cellular providers?
Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Cloud Computing Service Model Edge Computing Service Model Hardware Organization? Installation? Maintenance? Security? Infrastructure Mobile hardware? Limited processing? Low storage? Wireless/Cellular NICs? Platform Server Discovery? Self-Organizing Containers? Unikernels? Cloud providers? Crowdsourced? Cellular providers? Software Parental Control, Firewall, Content Catalog, User Authentication Factory Automation, Intelligence, Autonomous vehicles, IoT Analytics
Hardware Thesis Research Questions RQ2: Can independent entities enroll their compute resources in an existing edge cloud platform? RQ1: Where should the cloud providers install compute servers in the physical world to satisfy the application requirements at the "edge"? Infrastructure RQ6: How do we assure datacenter-like network behavior over edge servers which operate on a public wireless network? RQ5: Can existing network technologies available at the edge support the requirements imposed by end-applications for optimal performance? RQ4: How to pre-cache computational data within edge servers to improve computations? RQ3: How do we utilize availability and variability of edge servers for computing tasks? Platform RQ9: How can independent edge providers generate revenue at par with cloud providers? RQ8: How can edge cloud ensure the promised Quality-of-Service despite variability in user requests and infrastructure hardware? RQ7: How can existing cloud virtualization technologies be exploited in edge clouds?
Hardware Thesis Research Questions RQ2: Can independent entities enroll their compute resources in an existing edge cloud platform? RQ1: Where should the cloud providers install compute servers in the physical world to satisfy the application requirements at the "edge"? Infrastructure RQ6: How do we assure datacenter-like network behavior over edge servers which operate on a public wireless network? RQ5: Can existing network technologies available at the edge support the requirements imposed by end-applications for optimal performance? RQ4: How to pre-cache computational data within edge servers to improve computations? RQ3: How do we utilize availability and variability of edge servers for computing tasks? Platform RQ9: How can independent edge providers generate revenue at par with cloud providers? RQ8: How can edge cloud ensure the promised Quality-of-Service despite variability in user requests and infrastructure hardware? RQ7: How can existing cloud virtualization technologies be exploited in edge clouds?
Can independent entities enroll their compute resources in an existing edge cloud platform? Hardware Infrastructure Platform
Hardware Infrastructure Platform How do we utilize availability and variability of edge servers for computing tasks?
Deploying Tasks on Edge Clouds Edge clouds cannot use existing datacenter-based assignment protocols as Edge and Fog recourses can: 1. operate in non-to-semi unmanaged environments with variable availability 2. be equipped with constrained processing hardware requiring multiple servers to complete single task 3. be inter-connected via wireless network links prone to latency and congestion 4. be powered by limited battery capacity
Task Deployment on EFCloud Network-Only Cost Solver 1. Finds job placement which has the least possible network cost 2. Very large problem search space 3. NP-hard optimization with no guarantee for optimal solution Least Processing Cost First 1. Finds job placement with least processing cost and almost-least networking cost 2. Reduced problem search space 3. Placement guaranteed for any problem size in linear time 23
Hardware Infrastructure Platform How can existing cloud virtualization technologies be exploited in edge clouds?
Deploying Cloud Applications Cloud applications are composed of multiple integrated (micro)services Catalog Retrieval Parental Control Application Metrics Exception Tracking Audit Logging Health Check … 700+
Deploying Cloud Applications Cloud applications are composed of multiple integrated (micro)services → Every microservice is encapsulated as virtualized container which is then deployed on the cloud Parental Control Catalog Retrieval Catalog Retrieval Parental Control Application Metrics Exception Tracking Audit Logging Health Check … 700+
Container Migration on Cloud
Container Migration on Cloud
Container Migration on Cloud
Container Migration on Cloud Does not work in Edge-Fog Clouds!
Fragmentation in EFCloud EFCloud is amalgamation of different managing entities with strict authoritative boundaries
Fragmentation in EFCloud EFCloud is amalgamation of different managing entities with strict authoritative boundaries 1. ISP-backed Fog + Cellular-based Edge EdgeFog
Fragmentation in EFCloud EFCloud is amalgamation of different managing entities with strict authoritative boundaries 1. ISP-backed Fog + Cellular-based Edge 2. WiFi router Fog + Crowdsourced Edge EdgeFog
Fragmentation in EFCloud EFCloud is amalgamation of different managing entities with strict authoritative boundaries 1. ISP-backed Fog + Cellular-based Edge 2. WiFi router Fog + Crowdsourced Edge 3. Fog of cloud providers + Independent Edge servers EdgeFog
Fragmentation in EFCloud EFCloud is amalgamation of different managing entities with strict authoritative boundaries 1. ISP-backed Fog + Cellular-based Edge 2. WiFi router Fog + Crowdsourced Edge 3. Fog of cloud providers + Independent Edge servers Multiple entities compete in/across every grouping for larger market share → Heterogeneity in Hardware and Software! EdgeFog
Intelligent Containers (ICONs) Self-managing virtualized services which can intelligently and automatically move to edge servers nearest to incoming user requests
Intelligent Containers (ICONs) Self-managing virtualized services which can intelligently and automatically move to edge servers* nearest to incoming user requests *irrespective of who owns/operates them
Operation of ICON ICON Edge/Fog End-users I. Initially, ICON is in the cloud One or multiple origination points
Operation of ICON ICON End-users Edge/Fog I. Initially, ICON is in the cloud One or multiple origination points II. ICON monitors incoming flows Where user requests are coming from?
Operation of ICON ICON End-users Discover Edge/Fog I. Initially, ICON is in the cloud One or multiple origination points II. ICON monitors incoming flows Where user requests are coming from? III. ICON discovers deployment locations In the domain of end-users or on a path to it
Operation of ICON ICON End-users Replicate Discover Edge/Fog I. Initially, ICON is in the cloud One or multiple origination points II. ICON monitors incoming flows Where user requests are coming from? III. ICON discovers deployment locations In the domain of end-users or on a path to it IV. ICON can take autonomous decisions i. Deploy replica of itself
Operation of ICON ICON End-users Use Edge Discover Edge/Fog I. Initially, ICON is in the cloud One or multiple origination points II. ICON monitors incoming flows Where user requests are coming from? III. ICON discovers deployment locations In the domain of end-users or on a path to it IV. ICON can take autonomous decisions i. Deploy replica of itself
I. Initially, ICON is in the cloud One or multiple origination points II. ICON monitors incoming flows Where user requests are coming from? III. ICON discovers deployment locations In the domain of end-users or on a path to it IV. ICON can take autonomous decisions i. Deploy replica of itself ii. Migrate closer to the end-users ICON End-users Migrate Operation of ICON Edge/Fog
Salient Features of ICONs • Capability to automatically move application services across administrative boundaries • Self-organizing architecture with zero management overhead • Developers can tune the Quality-of-Experience by easily setting budget and latency weights • Hierarchical overlay with minimal communication overhead • Automatic termination with budget reallocation based on thresholds

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Edge Computing Platforms and Protocols - Ph.D. thesis

  • 1. Edge Computing Platforms and Protocols Nitinder Mohan
  • 2. Cloud Computing • Global network of inter-connected datacenters • Datacenters managed and operated by cloud providers, e.g. Google, Microsoft, Amazon etc. • Developers can deploy their applications on virtualized resources of the cloud worldwide 1 More than 60% of Internet workloads are cloud-based
  • 3. Next Generation Applications Trends by year 2025 • More than 75 billion connected devices • Expected 11 trillion USD market share • On average 7 sensors per person worldwide Requirements 1. Latency-critical processing 2. Big data aggregation and analysis 3. Location and context aware computations Internet-of-Things 3
  • 4. Problem: Network! Ø High transport cost Ø High data volume Ø High network latency Can Cloud support next-generation applications? 4
  • 5. Edge Computing Small-scale server(s) deployed near the users to compute generated data Benefits: üDecreased latency for computation üReduced network load due to pre-processing üLocation and contextual awareness Network DatacenterEdge Server User 5
  • 6. Problems in Edge computing 1. Unmanaged hardware 2. Constrained network 3. Inconsistent reliability and availability 4. Lack of standardized protocols 6
  • 7. Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Parental Control, Firewall, Content Catalog, User Authentication Cloud Computing Service Model 7
  • 8. Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Cloud Computing Service Model Edge Computing Service Model Hardware Organization? Installation? Maintenance? Security? Infrastructure Mobile hardware? Limited processing? Low storage? Wireless/Cellular NICs? Platform Server Discovery? Self-Organizing Containers? Parental Control, Firewall, Content Catalog, User Authentication Factory Automation, Intelligence, Autonomous vehicles, IoT Analytics Cloud providers? Crowdsourced? ISPs? Light-weight VM? Mirage OS? Unikernels? Smartphone manufactures? Specialized servers? 7
  • 9. Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. 8
  • 10. Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. 8
  • 11. Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. How to do computations on edge clouds?
  • 12. Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. 8
  • 13. Requirement of Edge Computing Need of a reference architecture for designing protocols and platforms! Constraints? Capabilities? Capacities? 9
  • 14. Edge-Fog Cloud (EFCloud) Edge Ø Collection of devices: i. Loosely-coupled ii. Voluntary/Crowdsourced iii. Owned by independent operators Ø Extremely close to sensors & clients Ø Device-to-device connectivity over WiFi/cellular/Bluetooth/Zigbee etc. Ø Varying processing capability e.g. desktops, laptops, workstations, nano data centers etc. 10
  • 15. Edge-Fog Cloud (EFCloud) Fog Ø High capacity compute servers Ø Co-located with networking devices Ø Designed, manufactured, managed and deployed by cloud vendors Ø Lies farther from sensors and clients Ø Dense connectivity within layer Ø Support for virtualization technologies e.g. routers, switches, basestations etc. 11
  • 16. Edge-Fog Cloud (EFCloud) Data Store Ø Data archival and storage Ø No computation on data Ø Reliable, ease-of-access, secure and global access to storage for Edge and Fog resources 12
  • 17. Edge-Fog Cloud (EFCloud) Salient Features 1. All-inclusive edge architecture 2. Native support for mobility 3. Satisfies emerging application requirements 4. Context-aware computation 5. No single point-of-failure 6. No vendor lock-in 13
  • 18. Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. 14
  • 20. Edge-Fog Cloud Formalizing Task Deployment𝜆/𝜋/BW 𝜆/𝜋/BW 𝜆/𝜋/BW 𝜆 / 𝜋/ BW 𝜆 / 𝜋/ BW 15
  • 21. Edge-Fog Cloud Formalizing Task Deployment D1 D2 D3 D4 D5 1 4 34 1 15
  • 22. Edge-Fog Cloud Formalizing Task Deployment D1 D2 D3 D4 D5 1 4 34 1 16
  • 23. Edge-Fog Cloud J1 J2 J3 J4 J5 Job Graph Formalizing Task Deployment D1 D2 D3 D4 D5 1 4 34 1 16
  • 24. J1 J2 J3 J4 J5 Formalizing Task Deployment D1 D2 D3 D4 D5 1 4 34 1 Objective: Find best placement of jobs on Edge and Fog devices 16
  • 25. D1 D2 D3 D4 D5 1 4 34 1 J1 J2 J3 J4 J5 ∞ 1 8 4 5 1 ∞ 7 5 4 8 7 ∞ 4 3 4 5 4 ∞ 1 5 4 3 1 ∞ 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 Dconn[ i,j ] = Jconn[ i,j ] = 17 De-facto: Network Only Cost Assignment
  • 26. De-facto: Network Only Cost Assignment Dconn[ i,j ] = 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 Jconn[ i,j ] = Quadratic Assignment Problem Minimize: NP-hard! • For 30 nodes, it will take 2500 servers to solve it • Optimal solution not guaranteed $ %(',))∈, 𝐽./00 𝑖, 𝑗 ∗ 𝐷./00(𝑓 𝑖 , 𝑓(𝑗)) ∞ 1 8 4 5 1 ∞ 7 5 4 8 7 ∞ 4 3 4 5 4 ∞ 1 5 4 3 1 ∞ 17
  • 27. Our Approach Optimize Processing Time Search Space Reduction Least Network Cost Least Processing Cost First (LPCF) Optimize Processing Energy Search Space Reduction Least Network Energy energy-efficient Least Processing Cost First (eLPCF) Multi-Objective Optimization D1 D2 D3 D4 D5 1 4 34 1 : 3 : 2 : 2 : 6: 5 D1 D2 D3 D4 D5 1 4 34 1 : 3 : 2 : 2 : 6: 5 18
  • 28. 18 Our Approach Optimize Processing Time Search Space Reduction Least Network Cost Least Processing Cost First (LPCF) Optimize Processing Energy Search Space Reduction Least Network Energy energy-efficient Least Processing Cost First (eLPCF) Multi-Objective Optimization D1 D2 D3 D4 D5 1 4 34 1 : 3 : 2 : 2 : 6: 5 D1 D2 D3 D4 D5 1 4 34 1 : 3 : 2 : 2 : 6: 5
  • 29. Least Processing Cost First (LPCF) D1:3 D2:2 D3:2 D4:5 D5:6 1 4 34 1 J1:4 J2:2 J3:5 J4:4 J5:2 3 2 2 5 6 4 2 5 4 2 Dproc [i] = Jsize [i] = 19
  • 30. Least Processing Cost First (LPCF) I. Optimize Processing Time Minimize: Linear Assignment Problem $ ',)∈, 𝐶 𝐽7'89(𝑖) 𝐷:;/.(𝑗) 𝑥') 3 2 2 5 6 4 2 5 4 2 Dproc [i] = Jsize [i] = 20
  • 31. I. Optimize Processing Time Minimize: Linear Assignment Problem • Solved using Kuhn-Munkres/ Hungarian algorithm • Optimal solution guaranteed in O(n3) Least Processing Cost First (LPCF) D1:3 D2:2 D3:2 D4:5 D5:6 1 4 34 1 J1:4 J2:2 J5:2 J4:4 J3:5 Least Processing Cost: 4.966 $ ',)∈, 𝐶 𝐽7'89(𝑖) 𝐷:;/.(𝑗) 𝑥') 20
  • 32. Least Processing Cost First (LPCF) II. Create sub-problem space Interchange homogeneous devices running homogeneous jobs Search Space Calculation: 1. Same processing power → interchange jobs 2. Same job size → interchange devices D1:3 D2:2 D3:2 D4:5 D5:6 1 4 34 1 J1:4 J2:2 J5:2 J4:4 J3:5 J1:4 J5:2 J2:2 J4:4 J3:5 J4:4 J5:2 J2:2 J1:4 J3:5 21
  • 33. Least Processing Cost First (LPCF) II. Create sub-problem space Interchange homogeneous devices running homogeneous jobs Search Space Calculation: 1. Same processing power → interchange jobs 2. Same job size → interchange devices Least Processing Cost: 4.966 D1 D2 D3 D4 D5 1. J1 J2 J5 J4 J3 2. J1 J5 J2 J4 J3 3. J4 J5 J2 J1 J3 4. J4 J2 J5 J1 J3 21
  • 34. Least Processing Cost First (LPCF) III. Least Network Cost 1. Compute network cost of each assignment 2. Choose the assignment with least network cost 𝐽./00 𝑖, 𝑗 ∗ 𝐷./00(𝑓 𝑖 , 𝑓(𝑗)) D1 D2 D3 D4 D5 1. J1 J2 J5 J4 J3 2. J1 J5 J2 J4 J3 3. J4 J5 J2 J1 J3 4. J4 J2 J5 J1 J3 N/W 20 27 19 28 Least Processing Cost: 4.966 22
  • 35. Performance Overview 5 20 40 60 80 100 Topology Size 0 1000 2000 3000 4000 NetworkCost NOC LPCF Minimum bound Maximum bound 20 40 60 80 100 Topology Size 0 50 100 150 200 250 ProcessingCost NOC LPCF eLPCF 5 20 40 60 80 100 Topology Size 5 10 15 20 25 30 %decreaseinenergy Edge Fog Network cost remains within 10% range of the optimal value Processing cost is multiple times lower than de-facto solvers 10% decrease in overall energy used. More than 20% energy saved for battery constrained devices 24
  • 36. Hardware Thesis Contributions Edge-Fog Cloud: All-inclusive, node-oriented edge computing architecture which logically categorizes resources in layers. Anveshak: Deployment framework that assists service providers to identify best locations in a geographical region for installing edge servers. Infrastructure QAware: Cross-layer scheduler for Multipath TCP which allows edge servers to use multiple network paths simultaneously while overcoming excessive buffering and delays on any path. Data Grouping protocol: Edge caching mechanism which predicts and pre-caches prerequisite data in local caches of edge servers for upcoming computations. LPCF & eLPCF: Task allocation frameworks which distributes application jobs on a cluster of edge servers while minimizing processing, networking and energy costs. Platform ICON: Self-managing virtualized containers which can automatically migrate and replicate to servers experiencing more user traffic without involving application owner. ExEC: Open platform which allows cloud providers to discover and utilize resources offered by third- party edge providers operating in the network. 25
  • 37. Hardware PublicationsInfrastructurePlatform Anveshak: Placing Edge Servers In The Wild. N. Mohan, A. Zavodovski, P. Zhou, and J. Kangasharju, MECOMM 2018 Placing it right!: optimizing energy, processing, and transport in Edge-Fog clouds. N. Mohan and J. Kangasharju, Annals of Telecommunication 2018 Managing Data in Computational Edge Cloud. N. Mohan, P. Zhou, K. Govindaraj and J. Kangasharju, MECOMM 2017 QAware: A Cross-Layer Approach to MPTCP Scheduling. T. Shreedhar, N. Mohan, S.K. Kaul, & J. Kangasharju. IFIP Networking 2018 ExEC: Elastic Extensible Edge Cloud. A. Zavodovski, N. Mohan, S. Bayhan, W. Wong and J. Kangasharju, EdgeSys 2019 ICON: Intelligent Container Overlays. A. Zavodovski, N. Mohan, S. Bayhan, W. Wong and J. Kangasharju, HotNets 2018 Is two greater than one?: Analyzing Multipath TCP over Dual-LTE in the Wild. N. Mohan, T. Shreedhar, A. Zavodovski, J. Kangasharju & S.K. Kaul. Manuscript 2019. 25
  • 39. Next Generation Applications Industrial Automation Autonomous Vehicles Augmented Reality Remote Monitoring & Collaboration Autonomous Drones & many more .. Smart Homes
  • 40. 2
  • 42. Edge computing Multiple edge computing approaches have been proposed i. Cloudlets → Miniature datacenters Network
  • 43. Edge computing Multiple edge computing approaches have been proposed i. Cloudlets → Miniature datacenters ii. Fog → Network devices (Base stations, Routers) Network
  • 44. Edge computing Multiple edge computing approaches have been proposed i. Cloudlets → Miniature datacenters ii. Fog → Network devices (Base stations, Routers) iii. Edge → Crowdsourced (smartphones, smart speakers) Network
  • 45. Edge computing Multiple edge computing approaches have been proposed i. Cloudlets → Miniature datacenters ii. Fog → Network devices (Base stations, Routers) iii. Edge → Crowdsourced (smartphones, smart speakers, ..) iv. Mist → Data generators (sensors, microcontrollers, ..) Network
  • 46. Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Parental Control, Firewall, Content Catalog, User Authentication Cloud Computing Service Model Edge Computing Service Model
  • 47. Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Cloud Computing Service Model Edge Computing Service Model Parental Control, Firewall, Content Catalog, User Authentication Factory Automation, Intelligence, Autonomous vehicles, IoT Analytics
  • 48. Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Cloud Computing Service Model Edge Computing Service Model Hardware Organization? Installation? Maintenance? Security? Parental Control, Firewall, Content Catalog, User Authentication Factory Automation, Intelligence, Autonomous vehicles, IoT Analytics Cloud providers? Crowdsourced? Cellular providers?
  • 49. Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Software Cloud Computing Service Model Edge Computing Service Model Hardware Organization? Installation? Maintenance? Security? Infrastructure Mobile hardware? Limited processing? Low storage? Wireless/Cellular NICs? Parental Control, Firewall, Content Catalog, User Authentication Factory Automation, Intelligence, Autonomous vehicles, IoT Analytics Cloud providers? Crowdsourced? Cellular providers?
  • 50. Hardware Deployment, Cooling, Maintenance Infrastructure Processing, Storage, Networking Platform Virtual Machines, Containers, Databases Cloud Computing Service Model Edge Computing Service Model Hardware Organization? Installation? Maintenance? Security? Infrastructure Mobile hardware? Limited processing? Low storage? Wireless/Cellular NICs? Platform Server Discovery? Self-Organizing Containers? Unikernels? Cloud providers? Crowdsourced? Cellular providers? Software Parental Control, Firewall, Content Catalog, User Authentication Factory Automation, Intelligence, Autonomous vehicles, IoT Analytics
  • 51. Hardware Thesis Research Questions RQ2: Can independent entities enroll their compute resources in an existing edge cloud platform? RQ1: Where should the cloud providers install compute servers in the physical world to satisfy the application requirements at the "edge"? Infrastructure RQ6: How do we assure datacenter-like network behavior over edge servers which operate on a public wireless network? RQ5: Can existing network technologies available at the edge support the requirements imposed by end-applications for optimal performance? RQ4: How to pre-cache computational data within edge servers to improve computations? RQ3: How do we utilize availability and variability of edge servers for computing tasks? Platform RQ9: How can independent edge providers generate revenue at par with cloud providers? RQ8: How can edge cloud ensure the promised Quality-of-Service despite variability in user requests and infrastructure hardware? RQ7: How can existing cloud virtualization technologies be exploited in edge clouds?
  • 52. Hardware Thesis Research Questions RQ2: Can independent entities enroll their compute resources in an existing edge cloud platform? RQ1: Where should the cloud providers install compute servers in the physical world to satisfy the application requirements at the "edge"? Infrastructure RQ6: How do we assure datacenter-like network behavior over edge servers which operate on a public wireless network? RQ5: Can existing network technologies available at the edge support the requirements imposed by end-applications for optimal performance? RQ4: How to pre-cache computational data within edge servers to improve computations? RQ3: How do we utilize availability and variability of edge servers for computing tasks? Platform RQ9: How can independent edge providers generate revenue at par with cloud providers? RQ8: How can edge cloud ensure the promised Quality-of-Service despite variability in user requests and infrastructure hardware? RQ7: How can existing cloud virtualization technologies be exploited in edge clouds?
  • 53. Can independent entities enroll their compute resources in an existing edge cloud platform? Hardware Infrastructure Platform
  • 54. Hardware Infrastructure Platform How do we utilize availability and variability of edge servers for computing tasks?
  • 55. Deploying Tasks on Edge Clouds Edge clouds cannot use existing datacenter-based assignment protocols as Edge and Fog recourses can: 1. operate in non-to-semi unmanaged environments with variable availability 2. be equipped with constrained processing hardware requiring multiple servers to complete single task 3. be inter-connected via wireless network links prone to latency and congestion 4. be powered by limited battery capacity
  • 56. Task Deployment on EFCloud Network-Only Cost Solver 1. Finds job placement which has the least possible network cost 2. Very large problem search space 3. NP-hard optimization with no guarantee for optimal solution Least Processing Cost First 1. Finds job placement with least processing cost and almost-least networking cost 2. Reduced problem search space 3. Placement guaranteed for any problem size in linear time 23
  • 57. Hardware Infrastructure Platform How can existing cloud virtualization technologies be exploited in edge clouds?
  • 58. Deploying Cloud Applications Cloud applications are composed of multiple integrated (micro)services Catalog Retrieval Parental Control Application Metrics Exception Tracking Audit Logging Health Check … 700+
  • 59. Deploying Cloud Applications Cloud applications are composed of multiple integrated (micro)services → Every microservice is encapsulated as virtualized container which is then deployed on the cloud Parental Control Catalog Retrieval Catalog Retrieval Parental Control Application Metrics Exception Tracking Audit Logging Health Check … 700+
  • 63. Container Migration on Cloud Does not work in Edge-Fog Clouds!
  • 64. Fragmentation in EFCloud EFCloud is amalgamation of different managing entities with strict authoritative boundaries
  • 65. Fragmentation in EFCloud EFCloud is amalgamation of different managing entities with strict authoritative boundaries 1. ISP-backed Fog + Cellular-based Edge EdgeFog
  • 66. Fragmentation in EFCloud EFCloud is amalgamation of different managing entities with strict authoritative boundaries 1. ISP-backed Fog + Cellular-based Edge 2. WiFi router Fog + Crowdsourced Edge EdgeFog
  • 67. Fragmentation in EFCloud EFCloud is amalgamation of different managing entities with strict authoritative boundaries 1. ISP-backed Fog + Cellular-based Edge 2. WiFi router Fog + Crowdsourced Edge 3. Fog of cloud providers + Independent Edge servers EdgeFog
  • 68. Fragmentation in EFCloud EFCloud is amalgamation of different managing entities with strict authoritative boundaries 1. ISP-backed Fog + Cellular-based Edge 2. WiFi router Fog + Crowdsourced Edge 3. Fog of cloud providers + Independent Edge servers Multiple entities compete in/across every grouping for larger market share → Heterogeneity in Hardware and Software! EdgeFog
  • 69. Intelligent Containers (ICONs) Self-managing virtualized services which can intelligently and automatically move to edge servers nearest to incoming user requests
  • 70. Intelligent Containers (ICONs) Self-managing virtualized services which can intelligently and automatically move to edge servers* nearest to incoming user requests *irrespective of who owns/operates them
  • 71. Operation of ICON ICON Edge/Fog End-users I. Initially, ICON is in the cloud One or multiple origination points
  • 72. Operation of ICON ICON End-users Edge/Fog I. Initially, ICON is in the cloud One or multiple origination points II. ICON monitors incoming flows Where user requests are coming from?
  • 73. Operation of ICON ICON End-users Discover Edge/Fog I. Initially, ICON is in the cloud One or multiple origination points II. ICON monitors incoming flows Where user requests are coming from? III. ICON discovers deployment locations In the domain of end-users or on a path to it
  • 74. Operation of ICON ICON End-users Replicate Discover Edge/Fog I. Initially, ICON is in the cloud One or multiple origination points II. ICON monitors incoming flows Where user requests are coming from? III. ICON discovers deployment locations In the domain of end-users or on a path to it IV. ICON can take autonomous decisions i. Deploy replica of itself
  • 75. Operation of ICON ICON End-users Use Edge Discover Edge/Fog I. Initially, ICON is in the cloud One or multiple origination points II. ICON monitors incoming flows Where user requests are coming from? III. ICON discovers deployment locations In the domain of end-users or on a path to it IV. ICON can take autonomous decisions i. Deploy replica of itself
  • 76. I. Initially, ICON is in the cloud One or multiple origination points II. ICON monitors incoming flows Where user requests are coming from? III. ICON discovers deployment locations In the domain of end-users or on a path to it IV. ICON can take autonomous decisions i. Deploy replica of itself ii. Migrate closer to the end-users ICON End-users Migrate Operation of ICON Edge/Fog
  • 77. Salient Features of ICONs • Capability to automatically move application services across administrative boundaries • Self-organizing architecture with zero management overhead • Developers can tune the Quality-of-Experience by easily setting budget and latency weights • Hierarchical overlay with minimal communication overhead • Automatic termination with budget reallocation based on thresholds