Live migration in Mobile Edge Computing (MEC)
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In cellular networks, M stands for Mobile. Mobility in MEC demands that applications survive handover between MEC server nodes deployed at the network edge. The sweet-spot for MEC server deployments is at aggregation sites serving clusters of cells. To reduce latency and bandwidth overheads to perform a realtime live migration at MEC Server-to-MEC Server handover requires a deconstruction of the application into an idle/stateless portion and a (number of) per-session stateful portion(s) and involves a multi-step pre-emptive approach to transferring the application data to the new serving MEC Server. The pre-emptive instantiation of the application in "likely handover targets" (i.e. neighbouring MEC Servers) could leverage SON techniques such as automatic neighbour relations. Furthermore, container frameworks will further reduce these overheads compared with approaches based on VM migration.
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The European Commission, in a recent communication (April 19th), has identified 5G and Internet of Things (IoT) amongst the ICT standardisation priorities for the Digital Single Market (DSM). This session will discuss the emergence of the mobile edge computing paradigm to reduce the latency for processing near the source large quantities of data and the need of the emerging 5G technology to satisfy the requirements of different verticals. Mobile Edge Clouds have the potential to provide an enormous amount of resources, but it raises several research challenges related to the resilience, security, data portability and usage due to the presence of multiple trusted domains, as well as energy consumption of battery powered devices. Large and centralized clouds have been deployed and have shown how this paradigm can greatly improve performance and flexibility while reducing costs. However, there are many issues requiring solutions that are user and context aware, dynamic, and with the capability to handle heterogeneous demands and systems. This is a challenge triggered by the Internet of Things (IoT) scenario, which strongly requires cloud-based solutions that can be dynamically located and managed, on demand and with self-organization capabilities to serve the purposes of different verticals.
OpenStack NFV Edge computing for IOT microservices
This document discusses using OpenStack and OpenShift for IoT and edge computing. It proposes a three-tier IoT architecture with devices, intelligent gateways for real-time processing at the edge, and data centers. OpenShift allows for scalable microservices deployment across this architecture. OpenStack provides the virtualization infrastructure for NFV edge computing with the intelligent gateways. The combination provides a platform for applications like NB-IoT from the edge to the cloud.
Why edge computing is critical to hybrid IT and cloud success
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Application Delivery Platform Towards Edge Computing - Bukhary Ikhwan
Edge computing pushes applications and data processing closer to data sources like IoT devices to enable faster results, real-time analytics, and better decision making. Docker is well-suited for application delivery in edge computing due to its lightweight containers that have a small footprint and fast start times. A demo showed containers for a learning management system deploying in seconds versus minutes for virtual machines. Offloading an ETL application to edge resources also significantly reduced bandwidth usage and processing time compared to alternatives that transferred all data to the cloud. Docker's portability and layered images make it a good fit for distributed application delivery in edge computing environments.
Consideration of fixed mobile convergence in 5G
This presentation will introduce IMT-2020 fixed mobile convergence (FMC) scenarios and requirements, such as service continuity among different access networks in a 5G environment. Based on the requirements, a FMC solution, Unified Network Integrating Cloud (UNIC), will also be introduced, including the high level architecture and some key technologies, such as unified access control for both fixed and mobile, edge computing for user plane, converged subscriber database, Telecom Integrated Cloud (TIC), etc.
Author : Yachen Wang, China Mobile
Presented at ITU-T Focus Group IMT-2020 Workshop and Demo Day, 7 December 2016.
More details on the event : http://www.itu.int/en/ITU-T/Workshops-and-Seminars/201612/Pages/Programme.aspx
FOG COMPUTING
Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks.
Edge-Fog Cloud: Scaling IoT computations on the edge
Early description of EF clouds. Please see CIoT'16 ppt for more details. Presented in GI-Dagstuhl, Germany
Docker:- Application Delivery Platform Towards Edge Computing
Edge computing pushes applications and data processing closer to data sources like IoT devices to enable low latency and real-time insights. Docker containers are well-suited for edge computing due to their small size, fast deployment, and ability to run on resource-constrained edge devices. A demo showed containers for a learning management system deployed in seconds at an edge location versus minutes for virtual machines. Offloading an ETL application to edge resources also significantly reduced bandwidth usage versus processing in the cloud. Docker provides a lightweight container-based platform to efficiently deliver and manage applications at the edge.
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This document discusses how communication service providers can leverage edge cloud computing and virtualized transport (T-NFV) to offer new services and generate additional revenue streams. It notes that many emerging services are moving to or being created in the cloud but require communications connectivity. Edge cloud computing addresses this by situating services closer to end users with lower latency requirements. The document outlines how T-NFV can virtualize transport functions to enable fast, automated delivery of edge cloud services while differentiating the CSP. This virtualized transport is presented as key to enabling edge computing opportunities across sectors like IoT, smart cities, healthcare and more.
Fog computing
Fog computing is a model that processes data and applications at the edge of the network, rather than sending all data to the cloud. It helps address issues with IoT networks like high latency and bandwidth usage. Fog computing can overcome cloud limitations by keeping data local, reducing congestion and improving security. It is well-suited for applications that require real-time, localized processing like connected vehicles, smart grids, smart cities, and healthcare. Fog computing lowers costs and improves efficiencies compared to relying solely on cloud infrastructure.
Internet of Things: Programming on the edge
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Business Solutions - SIP Trunking
SIP Trunking allows customers to consolidate their voice and data traffic over their existing IP network. It offers improved reliability compared to traditional phone lines. SIP Trunking provides greater flexibility and scalability by allowing customers to increase phone lines on demand. It also provides cost savings through reducing dedicated phone lines and supporting a single network for voice and data.
Beat the content crunch enhancing video delivery with (mobile) edge computing
- The Streaming Challenge, or Bringing the Mountain to Mohammed
- Edge Computing for Network Operators: The Content-Delivery Business Case
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Learning Analytics – Opportunities for ISO/IEC JTC 1/SC36 standardisation
1) The document discusses opportunities for standardization in learning analytics, including harmonizing activity stream specifications, building vocabularies, storage designs, privacy and data protection, and sharing algorithms and predictive models.
2) It analyzes characteristics of educational big data like varied data sources and formats, and calls for standards to bring these diverse data together and make them interoperable and meaningful for learners and teachers.
3) The document reviews several emerging specifications and tools in learning analytics, and identifies challenges for standardization in areas like privacy, personal data stores, data analysis, and sharing models and algorithms.
Cryptocurrency & Blockchain Regulation
The document summarizes a FinTech workshop on cryptocurrency and blockchain regulation. It discusses the rising price of bitcoin due to demand from China and financial crises in Greece and Cyprus. It outlines the formation of the Blockchain Alliance between digital currency companies and law enforcement to address criminal uses. It also summarizes the final auction of bitcoins seized from Silk Road and issues around the cryptolocker virus, as well as publicly traded bitcoin companies. The document concludes with a note that the creator of bitcoin, Satoshi Nakamoto, has been nominated for the Nobel Prize in Economics.
Microservices on the Edge
How concepts from cloud native architecture can be used to drive edge computing/fog computing architecture
Data centers on the Edge
The worlds of IT and Telecommunications Networking are converging bringing with them new possibilities and capabilities that can be deployed into the network A key transformation has been the ability to run IT based servers at network edge, applying the concepts of cloud computing.
It’s a key decision most data center managers will face in the next couple of years:
Should you retrofit, build, colocate, or move to the cloud? Each has its benefits, and own inherent risks and costs
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- 2. Mobility today The core network is the session anchor. When a user moves, the session is rerouted from the core network. EPC Internet SGi S1
- 3. The mobility challenge for MEC EPC Internet SGi S1 MEC system App App MEC system App App Today, MEC operates as discrete systems. With no central anchor Session information is lost in the transition Mobility is limited.
- 4. MEC and mobility – the problem User1 is connected to App1 inside MEC System 1 VM App1 NFV + MEC U1 User 1 VM ? NFV + MEC MEC System 1 MEC System 2 eNodeB 1 eNodeB 2 Is App1 instantiated in the new MEC system? User1 is now connected MEC System 2……but What happens to the session information?
- 5. Mobility Groups & Mobility Services Each ME has its own mobility groupME ME ME ME ME ME ME ME ME ME ME ME ME ME ME ME ME Host VM App VM App Mobility Services Mesh connectivity within each group Mobility services added to the ME host
- 7. VM App1 U1 Mobilizing ME Mobility services: Use the Mobility Group mesh to connect the ME systems Stream the user app context from the previous ME to the new ME ME 1 User 1 eNodeB 1 eNodeB 2 Mobility Services NFV + MEC VM App1 U1 Mobility Services NFV + MEC ME 2 Copy the user context to the new ME Operate directly from the new ME