Edge/Fog Computing

1. Edge/Fog Computing CS 401/601 Computer Network Systems Mehmet Gunes Modified from P Peddabbu, A Levandoski, F Bonomi, C. Mouradian, K Kant

3. Need for edge/fog computing • Why can’t do all in cloud? • Cloud computing is • efficient, • inexpensive, • frees the enterprise and the end user from many details • This bliss becomes a problem for latency-sensitive applications • Why can’t do all in end systems? • Physical constraints • Energy, space, etc.

4. Edge/Fog Computing • Edge/Fog computing was conceived to address applications and services that do not fit with Cloud • Edge computing is a distributed computing paradigm in which computation is largely or completely performed on distributed device nodes known as smart devices or edge devices as opposed to primarily taking place in a centralized cloud environment • Why compute on the edge? • Latency: Importance of human interactivity • Privacy: Keep sensitive data local • Reliability: Keep computing during network partitions

5. Edge/Fog Computing IoT motivates the need for a hierarchical platform that extends from the edge to the core of the network. This is useful for: • Applications that require low and predictable latency • Geo-distributed applications • Fast mobile applications • Large-scale distributed control systems Fog does not substitute the cloud it compliments it!

6. Use Cases for Edge/Fog computing • Use Case 1: A smart Traffic Light System (STLS) • Use Case 2: Wind Farms • To abstract the major requirements to propose an architecture that addresses a vast majority of the IoT requirements

7. Use Case 1: A Smart Traffic Light System System Outline • Smart Traffic Light System (STLS) calls for deployment of a STL at each intersection • The STL is equipped with sensors that • Measure the distance and speed of approaching vehicles from every direction • Detect presence of pedestrians/other vehicles crossing the street • Issues “Slow down” warnings to vehicles at risk to crossing in red and even modifies its own cycle to prevent collisions

8. STLS: System outline continued.. STLS goals • Accidents prevention • Maintenance of steady flow of traffic • green waves along the main roads • Collection of relevant data to evaluate and improve the system Note: Goal (1) requires real-time reaction, (2) near-real time, and (3) relates to the collection and analysis of global data over long periods

9. Key requirements driven by STLS • Local Subsystem latency • Reaction time needed is in the order of < 10 milliseconds • Middleware orchestration platform • Middleware to handle a # of critical software components • Decision maker • message bus • Networking infrastructure • Edge/Fog nodes belongs to a family of modular compute and storage devices • Interplay with the cloud • Data must be injected into a Data center/ cloud for deep analysis to identify patterns in traffic, city pollutants

10. STLS Key requirements, cont’d. • Consistency of a highly distributed system • Need to be consistent between the different aggregator points • Multi-tenancy • It must provide strict service guarantees all the time • Multiplicity of providers • May extend beyond the borders of a single controlling authority • Orchestration of consistent policies involving multiple agencies is a challenge unique to Edge/Fog Computing

11. Use case 2: Wind Farm Brings up requirements shared by a number of Internet of Everything (IoE) deployments • Interplay between real time analytics and batch analytics • Tight interaction between sensors and actuators • in closed control loops • Wide geographical deployment of a large system consistent of a number of autonomous yet coordinated modules • which gives rise to the need of an orchestration platform Goals • Improve wind power capture and power quality • Reduce structural loading • Forecast wind accurately

12. System outline There are 4 typical regions • Wind speed is very low (say, 6m/sec) • not so economical to run the turbine • Normal operating condition (winds between 6-12m/sec) • so maximum conversion of wind power into electrical power • Winds exceed 12 m/sec • power is limited to avoid exceeding safe electrical and mechanical loads • Very high wind speeds above 25 m/sec • turbine is powered down to avoid excessive operating loads

13. Key requirements driven by Wind Farm • Network Infrastructure • An efficient communication network between sub-systems, system and the internet (cloud) • Global controller • gathering data, building the global state, determining the policy • Middle Orchestration platform • A middleware that mediates between sub-systems and the cloud • Data analytics • requires real-time reaction, • near-real time, and • relates to the collection and analysis of global data over long periods

14. Key attributes of Edge/Fog computing • Attributes that differentiate Edge/Fog computing platform from the Cloud • Applications that require very low and predictable latency • STLS, SCV • Geo-distributed applications • pipeline monitoring, STLS • Fast mobile applications • Smart connected vehicle, rail • Large-scale distributed control systems • STLS, smart grid • IoT also brings big data with a twist • rather than high volume, the number of data sources distributed geographically

15. Geo-distribution: A new Dimension of Big Data • 3 Dimensions • Volume, Velocity and Variety • IoT use cases • STLS, Connected Rail, pipeline monitoring are naturally distributed • This suggests to add a 4th dimension • geo-distribution • Since challenge is to manage number of sensors (and actuators) that are naturally distributed as a coherent whole • Call for “moving the processing to the data” • A distributed intelligent platform at the Edge that manages distributed compute, networking, and storage resources

16. Geo-Distribution Analyzing data close to the device that collected the data can make the difference between averting disaster and a cascading system failure • The main requirements to support IoT devices using Edge/Fog are to: • Minimize latency • Conserve network bandwidth • Address security concerns • Operate reliably • Collect and secure data across a wide geographic area with different environmental conditions • Move data to the best place for processing

17. Edge/Fog Computing

18. The Edge(Fog) and the core(Fog) interplay Days to months HMI Business Intelligence Key performance indicators, dashboards, reports Very high latency Business data repository Cloud Enterprise Technical Minutes to Days HMI, M2M Visualization, reporting, systems, and processes Transactional analytics Historical data Seconds to sub-minutes HMI, M2M Medium speed/medium latency real-time analytics Visualization systems and processes Operational and non-operation data Fog M2M Milliseconds to sub-seconds High speed/low latency real-time analytics Protection and control systems Very low latency Grid sensors and devices HMI = Human-Machine Interaction M2M = Machine to Machine

19. Edge/Fog Architecture • The Edge/Fog architecture should facilitate seamless resource management across a diverse set of platforms • Edge/Fog Nodes can be high-end servers, edge routers, access points, set-top boxes, vehicles, sensors, mobile phones, etc. • All of these platforms have varying levels of resources and run different operating systems and applications • Abstraction layer • provides generic APIs for monitoring, provisioning, and controlling CPU, memory, network, and energy resources • multi-tenancy features must be supported • Data and resource isolation • A single, consistent model across physical machines • Ability to expose the physical and logical network to administrators

20. Edge/Fog Software Architecture

21. Edge/Fog Architecture • Service orchestration layer • provides dynamic, policy-based life cycle management of Edge/Fog services • Management is achieved with • A software agent capable of bearing the orchestration functionality • Distributed storage to store policies and resource metadata capable of supporting high transaction rates • A scalable messaging bus • A distributed policy engine with a single global view and local enforcement

22. Service Orchestration Policy Manager Service Directory Do service instances satisfy policy constraints? • Retrieve relevant policies: • Performance • Security • Capability Policy DB No Life Cycle Manager Capability Engine Capabilities DB Provision service Find platforms that are capable of offering service

23. Fog Robotics on the Global Data Plane Tier 1 Trust Domain Cloud Computing `Copy Copy Copy Copy Data Data Data Data Data Data DataCapsules Replica DataCapsules Top-Level Resolver ℝC2 Domain Resolver EdgeComputing Domain Resolver GDP Routers EdgeComputing #7DFE #7DFE #7DFE #3AB3 #F543 #F543 #2201 #9001 #1543 #1543 EdgeComputing Trust Domain 1 Trust Domain 2

24. Conclusion • We looked at Edge/Fog computing and key aspects of it • How edge/fog complements and extends cloud computing • We looked at use cases that motivated the need for edge/fog • Seen a high-level description of Edge/Fog’s architecture

25. Application Provisioning

26. Resource Management

27. Communication

28. Resource Sharing

29. Task Scheduling

30. Response Time Reduction Response Time Reduction Agarwal et al. • A Fog server manager receives requests and is responsible for matching resources with demands • Depending on availability, it can: • Execute all tasks • Execute some tasks and delay others • Transfer demand to cloud nodes • Lowest existing response time in survey! Power Consumption ReductionDeng et al. • It accounts for • Computational capabilities • Communication bandwidth limitations • Delay constraints on user’s side • By sacrificing modest computational resources to save communication bandwidth and reduce transmission latency, the use of Fog can significantly improve the performance of Cloud computing

31. Adaptive Solutions • The authors propose a proactive resource allocation algorithm incorporating historical data and attributes. Users loyal to the requested service/service provider receive higher QoS! • Further, they complement this allocation strategy with a discriminative pricing scheme based on user loyalty. • Aazam et al.

32. Offloading and Load Redistribution

33. Dynamic Load Balancing • The authors use dynamic graph balancing to repartition the load of their Fog system model. • The strategy presented has additional overhead but outperforms previously presented hybrid load balancingstrategies. • Ningning et al.

34. Healthcare... • Elderly care (Stantchev et al.) • Support for COPD (Fratu et al., Masip-Bruin et al.) • Parkinson’s disease (Monteiro et al.) • Speech disorders (Dubey et al.) • ECG and EEG feature extraction (Gia et al., Zao et al.)

35. ...and More • Vehicular applications • Use of cellular networks and roadside units (RSUs) • Vehicular ad hoc networks (VANETs • Vehicular fog computing (VFC) • Smart living • Smart grids • Smart levee monitoring • Smart city infrastructure • Emergency alert management

36. Pricing, Pay-as-you-go, and Blockchain • Business models that determine levels of responsibility for users and agencies and tiers of compensation for services have yet to be fully discussed • A Blockchain-based smart contract system lends itself to the geo-distributed mobile nature of Edge/Fog computing

37. Key Questions • What are the emerging research problems? • What are the maturing research problems? • What are the research problems that need larger collaborative teams to make progress • What will future computing system look like and how will they be used

38. High Level Issues • Architecture • HW features, virtualization/containerization, hierarchy, mobility support, … • Performance, energy, reliability, resource sharing/mgmt., … • Distributed Computing Issues • Consensus, fault tolerance, verification, scalability, location, locality, distribution of intelligence, … • Applications • Computing/storage, Smart critical infrastructure, disaster response, human augmentation, augmented reality, … • Business models and economics • Ownership/control, accessibility, who pays?, charging, liability • Managing Services • Service provisioning/composition, auto-configuration, config verification, testing, and diagnosis. • Cross cutting • Security, privacy, access control, big data and analytics

39. Emerging Research Areas • Emerging areas • Incentivization (P2P model?) • Energy Usage • Host level security, privacy & trust • Issues with dynamism • Fault tolerance & reliability • Heterogeneity and Interoperability • No common app layers, resources, capabilities • Scalability • Cloud and edge management • Infra deployment, ownership, economics • Industry perspective • Edge + cloud – are they complementary

40. Research Needing Collaborations • Applied • Large scale testbeds • Industry involvement • Fundamental research • Domain/app-specific designs (needs cross disciplinary research) • End to end systems • Usability • Challenges • Incentives for collaborative research

41. Future Systems • Application centric systems • Consideration of human-in-the-loop issues • Autonomy, robustness, human behavior • Policies and mechanisms • Competing objectives for owners/providers for cloud, edge, & device level services. • Data is currency, insight is the objective • Security & privacy • Integrated sensing and actuation.

42. Cloud & Edge Computing – Summary • Emerging Research areas • Business models and incentivization • Infra deployment, ownership, economics, creating virtuous cycle of innovation/deployment • How do you deal with dynamism, heterogeneity and Interoperability • No common app layers, resources, capabilities • Scalable mgmt. and operation of Cloud and edge • Supporting real-time, safety-critical cyberphysical systems

43. Cloud & Edge Computing – Summary • Collaborative research • Domain/app-specific designs (needs cross disciplinary research) • Building end to end systems – cloud, edge, fog, device • Usability – configuration, policies, adaptation, … • Future Systems • Must be application centric & consider human-in-the-loop issues • Autonomy, robustness, human behavior • Policies and mechanisms • Competing objectives for owners/providers for cloud, edge, & device level services. • Must be inherently data driven • Adaptive behavior based on insights from the data • Security & privacy must be built in from ground up