Get in Touch

CONTACT US

I Agree to the Privacy Policy
Please enter your name. Please enter alphabets only for Name. Please enter Organization. Please enter valid email id. Please enter numeric only for Phone number.

Introduction

As technologies continue to shift shape and evolve to suit market needs, you might have noticed Mobile Edge Computing metamorphosing into Multi-access Edge Computing (MEC) while still retaining its moniker. This change was necessitated as the latent benefits of edge technology extended beyond mobile to Wi-Fi and fixed access technologies.

ETSI defines Multi-access edge computing (MEC) as a cloud-based IT services environment at the edge of the network. It checks all the right boxes by providing ultra-low latency and high-bandwidth while enabling applications to benefit from real-time radio network information.

MEC provides a new ecosystem and value chain. Operators can open their Radio Access Network (RAN) edge to authorized third-parties, allowing them to flexibly and rapidly deploy innovative applications and services towards mobile subscribers, enterprises, and vertical segments.

MEC today develops purely from a software point of view, overlooking the hardware since it is based on virtualization technology. The objective is to define a set of APIs that empowers the making of virtual network functions (VNFs) that will respond to all the needs of a mobile communications network, including security, orchestration, and portability while leaving the actual implementation to the respective provider.

The end goal of MEC is to provide an optimized and low latency computing infrastructure with deployment agility that can scale horizontally or vertically based on requirements. With MEC we can move services and content closer to end-users and get more QoE, QoS while reducing backhaul congestion and optimizing gateway interconnectivity costs.

Benefits of MEC

  • Real-time: Lowest robust application latency end-to-end.
  • Video caching and Analytics: Real-time insights from data at the point of capture, minimum cloud ingress bandwidth.
  • Private: Local communications to private networks for performance, privacy, and security.
  • Interactive: Maximum transaction rate between the device and local cloud.
  • Distributed: the Rapid introduction of network and other functions in the RAN, dynamic filtering rules.
  • It supports IoT and M2M applications.
  • Application virtualization and coordination between cloud and edge.

 

Use Cases

  • Enterprise applications include asset tracking, video surveillance and analytics, local voice and data routing. In case an enterprise wants to provide connectivity directly from the RAN for security reasons, the edge extends its unique capabilities for running such processes. Likewise, the edge with the combination of vCPE provides an excellent location to implement branch connectivity and other enterprise services. Privacy can also be addressed by some third-party application, for example, a medical app that anonymizes Personal Health Information (PHI) storing it in the cloud.
  • Real-time: Increasing number of applications are real-time and cannot tolerate latency more than in the order of 10’s of milliseconds. Applications are also sensitive to jitter (the variation in latency). AR/VR connected cars, tactile internet, Industry 4.0 and smart cities are some other cases which come under real-time segment.
  • Immersive: The available bandwidth from the MEC to the UE/CPE will create a wide range of new immersive applications. Premium HD, 360°, and 4K video can be cached and optimized at the edge. Network level metrics (round-trip-delay, packet loss, etc.) can improve by 30-60%. Multimedia content delivery where video can specifically benefit from caching and transcoding.
  • Cost reduction: Video surveillance, face recognition, vehicle number plate recognition, IoT gateway, big-data analytics. For example, it will be costly to send all the video feeds directly from the camera to the cloud. In such case, Edge can perform motion detection and threat recognition and send only relevant frames to the cloud. For example, in an IoT gateway, where the available bandwidth is not so high, sending billions of events to the cloud would be expensive and inefficient versus handling them at the edge with an IoT gateway.
  • Self-contained: Some of the best use-cases are cruise ships, planes, mines, trains offering movies and Wi-Fi on board. When the cloud connectivity is available, the data from these locations could be synchronized, for example when the plane lands or the ship docks.
  • Places where the edge can offer local services like in the stadiums, airports, concerts, universities or any smart buildings, the application allows the viewers to perform the same action from different perspectives based on their personal preferences. For example, during a game, the application could allow the stadium viewers to view a game from numerous perspectives at the same time offer them some high defined personalized content without burdening the upstream bandwidth. Providing the same services from the cloud would be impractical.
  • As part of Retail Services, the edge can offer, Ad delivery and footprints analysis in shopping malls among other application.
  • IoT applications defined into two categories:
    • Massive IoT connectivity where MEC streamlines device connectivity with the core network to reduce overhead communications and improves response time.
    • High-responsiveness applications where low latency is critical. This includes smart grid switching of power and alternative energy supplies and fault detection applications.
  • Critical communications: This category contains multiple applications in various sectors like traffic safety and control systems, Precision farming using autonomous vehicles and real-time analytics, Industrial IoT applications for monitoring and time-critical process control, Automotive applications related to hazard warning and cooperative autonomous driving, Healthcare applications requiring high responsiveness.
  • Analytics: Edge technology collects a significant amount of data from all its networks and connections that can be invaluable for the process of machine learning, automation, and Big Data applications.
  • Compliance: Compliance issues diverges from copyright enforcement to geographical data placement. During a concert or a sports event or a play, the copyright enforcement comes into play. The audience in such events doesn’t have the rights to transmit any video of the show through the internet or their cell phone. An edge application in such cases comes to the rescue by either disabling the upstream transmission or reduce the resolution to make it transmission complaint.
  • Security: Edge computing allows for applications such as DDOS and cybersecurity to prevent these types of attacks and moves the security perimeter closer to the source.
  • NFV: Network Functional Virtualization (NFV) is not an edge application, strictly speaking. However, access virtual network functions (VNFs) such as vRAN, C-RAN, vCMTS, vOLT need to run at the same location as edge computing.

 

mec

Key Challenges

  • Standard protocol: Being a recent technology, MEC is evolving through distinct phases of implementation and needs requires standardization that originates from the collaboration of industry and researchers over a common platform.
  • Efficient deployment: The efficient implementation of MEC will minimize the latencies through optimal utilization. However, it is difficult to optimize the spectrum usage with dependence on complex system components.
  • User mobility and transparency: Providing nonstop services to the continuously moving client is a big challenge in the MEC environment with transparent process migration and platform heterogeneity.
  • Availability and security: Constant service delivery and security of the resources is usually dependent on the server capacity and wireless access medium and physical measures.
  • Data Management: The data management capabilities required include Data normalization, Filtering and querying data, Integration with edge analytics, Aggregating data or abstract Meta data.

 

--------------or--------------






Contact us contact us