As smartphones, tablets and wearable devices are gaining enormous popularity, more and more new mobile applications such as face recognition, natural language processing, interactive gaming, and augmented reality are also emerging and attracting greater attention. These kinds of mobile applications are computing intensive and drain a lot of energy in the mobile devices. The mobile devices can hardly cope due to limitations in terms of battery life, storage, processing power and display size [51]. Extending battery life is one of the key requirements by mobile phone users as compared to memory, storage and display size. Hence there is a need for improvement of energy efficiency in mobile devices. One possible approach is to offload the application computation to the remote public clouds such as Amazon Elastic Compute Cloud (EC2) and Windows Azure using Mobile Cloud Computing (MCC) paradigm as shown in Figure 2-19, in which computation is outsourced
to cloud datacentres in the core network. This could help save some amount of energy in the mobile device.
Figure 2-19. An illustration of Mobile Cloud Computing.
These cloud datacentres provide virtually unlimited computation capacity to augment the processors in mobile devices. However, in MCC, the communication between mobile users and remote cloud centres is often over a long distance, adding to the latency in cloud computation. To overcome this limitation, MEC [52], also termed FOG computing [53] was proposed as shown in Figure 2-20.
Figure 2-20. An illustration of MEC architecture.
MEC is envisioned as a promising approach to improve the offloading efficiency. In the MEC framework, cloud computing capabilities are provided within the RAN in close proximity to these mobile devices. In other words, with
the aid of MEC, mobile devices are enabled to offload their application tasks to the MEC servers on the edge of the network, rather than utilising the servers in the core network in the cloud datacentres. This MEC paradigm can provide low latency, high bandwidth, high computing agility and improve the energy performance of the mobile devices [52]. Table 2 shows the difference between the MEC and traditional cloud computing.
Table 2. Difference between MEC and traditional cloud computing.
MEC Traditional cloud
Latency Low latency High latency
Resource and service location
At the RAN e.g. BSs, access points, routers and mobile devices.
Dedicated datacentres on the internet.
Mobility Mobile clients Mobile and fixed clients Services User request and
network initiated services
User requested services Computation capability Limited computation
capability
High computation capability
Service context awareness
Aware of the radio network status and user context
Context available through application reporting
Motivation for MEC
The main motivations for MEC [54] are:
i) Computation offloading ii) Dynamic content optimisation iii) Mobile big data analytics
1. Computation offloading
As the recent exposure of wearable devices, low processing power IoT devices, and smartphones becomes prevalent, computing intensive
applications cannot be performed in the device itself due to limited storage and computation and processing. This problem can be solved by splitting the application into small tasks then some of the tasks can be performed in the cloud provided that the delay deadlines are met. The tasks can be offloaded to the edge server in closer proximity to the device itself. The key of challenges in MEC computation offloading are: i) How to split the application and ii) How to identify whether a task should be offloaded or not.
2. Dynamic content optimisation
To fulfil the customer’s expectations, traditional content optimisation is performed at the web hosting site where the content optimiser uses the user’s
web surfing history stored in the database [55]. These traditional methods incur some delays and inaccuracies. In MEC, content optimisation can be done based on the user’s context aware information dynamically where the
content optimiser can be located at the edge server. The optimiser in MEC can acquire accurate RAN information like network status and network load dynamically and these information can be used for optimisation [54]. This MEC content optimisation can improve network performance, QoE and new services can be added easily.
3. Mobile big data analytics
Big data is a collection of large volumes of both structured and unstructured data and big data analytics is the process of analysing such big data for better decisions and gaining strategic business insights [54]. Data collection from the edge devices in big data analytics are transferred to the core network and this process takes high bandwidth and latency. The MEC platform can be used to perform big data analytic at the edge of the network thereby saving large
amount of bandwidth required. After the analysis, the results can be sent to the core network. As such, the scenario will reduce bandwidth consumption and improve the network latency.
Taxonomy of MEC
The reader is directed to comprehensive surveys on MEC in [54, 56]. Figure 2-21 shows the taxonomy of MEC which is based on: i) Characteristics, ii) Actors, iii) Access technologies, iv) Applications, v) Objectives, vi) Computation platforms and vii) Key enablers. These parameters are described below.
Figure 2-21. The taxonomy for MEC [54].
1. Characteristics
The key characteristics of MEC are as follows:
i) Proximity: Where the mobile device is closer to MEC server in the
RAN. The MEC server can also be another mobile device through device to device (D2D) communication forming a mobile cloud. Since, edge server is nearby to devices; it can extract device information and
ii) Dense geographic distribution: Where the MEC hosts the cloud computing services at the edge network which is located at numerous locations. As such, these geographically dispersed infrastructure contribute to the MEC in many ways.
iii) Low latency: It takes a long time when transmitting data to the core network but with MEC, transmission is faster as the MEC servers are closer to the mobile devices.
iv) Location awareness: Where application developers can use the user’s
locations to provide context aware services. User mobility patterns can be collected easily at the MEC to predict future network status.
v) Network context information: Where real time RAN information such as subscriber location, radio condition, network load etc. is used to provide context related services to the mobile subscriber. RAN information is used by the application developers and content providers thus improving user satisfaction.