Mobile Edge Computing

Abstract

The ongoing development of the fifth-generation (5G) wireless technologies takes place in a unique landscape of recent advancement in information processing, marked by the emerging prevalence of cloud-based computing and smart mobile devices. These two technologies complement each other by design, with cloud servers providing the engine for computing and smart mobile devices naturally serving as human interface and untethered sensory inputs. Together, they are transforming a wide array of important applications such as telecommunication, industrial production, education, e-commerce, mobile healthcare, and environmental monitoring. We are entering a world where computation is ubiquitously accessible on local devices, global servers, and processors everywhere in between. Future wireless networks will provide communication infrastructure support to this ubiquitous computing paradigm, but at the same time they can also utilize the new-found computing power to drastically improve communication efficiency, expand service variety, shorten service delay, and reduce operation expenses. Previous generations of wireless networks are passive systems. Residing near the edge of the Internet, they serve only as communication access pathways for mobile devices to reach the Internet core and the public switched telephone network (PSTN). Improvements to these wireless networks have focused on the communication hardware and software, such as advanced electronics and signal processing in the transmitters and receivers. Even for 5G, substantial research effort has been devoted to densification techniques, such as small cells, device-todevice (D2D), and massive multi-input multi-output (MIMO). Successes of this communication-only wireless evolution reflect the classical view of an information age centered at information consumption through the Internet. Yet, in many emerging applications, communication and computation are no longer separated, but interactive and unified. For example, in an augmentedreality application, which might be displayed on smart eyeglasses , the user's mobile device continuously records its current view, computes its own location, and streams the combined information to the cloud server, while the cloud server performs pattern recognition and information retrieval and sends back to the mo

Resource Sharing of a Computing Access Point for Multi-User Mobile Cloud Offloading with Delay Constraints

“…That is, each user provides its information to the CAP, and the CAP will broadcast the obtained offloading decisions (and the corresponding resource allocations) to all users by solving problem (6).…”

“…It can be solved optimally using standard convex optimization approaches such as the interior-point method. Since there are a finite number of offloading decisions, a globally optimal solution for problem (6) can be obtained by exhaustive search among 3 N possible offloading decisions. However, the complexity grows exponentially with the number of users and thus impractical.…”

A matching game for tasks offloading in integrated edge‐fog computing systems

“…In particular, fog systems push computation capabilities down to the outer area level of a network architecture, usually processing data on one or more powerful computation nodes, named fog nodes (FNs), and deployed conveniently close to the end users to perform intensive computation tasks with low latency. Conversely, EC aims at pressing processing directly at the devices level at the edge of the network to enable collaborative sharing of storage and processing capabilities . Within this context, it is important to highlight that we have to take into account that an edge devices (EDs) may take advantage in computing its task on a more powerful neighbor ED or FN.…”

“…Conversely, EC aims at pressing processing directly at the devices level at the edge of the network to enable collaborative sharing of storage and processing capabilities. 9 Within this context, it is important to highlight that we have to take into account that an edge devices (EDs) may take advantage in computing its task on a more powerful neighbor ED or FN. In fact, EDs have usually a limited battery supply and computation capability, hence they may be sometimes unable to execute a given task.…”

Survey on Multi-Access Edge Computing for Internet of Things Realization

“…VII compares the reviewed state-of-the-art scalability feature in MEC enabled IoT. There are three main categories for the communication concerns about MEC[106]: Wireless access while offloading to the mobile edge host; Backhaul access while offloading to a remote cloud server; Communication among IoT devices, mobile edge host, and remote cloud servers when they collaboratively execute multiple jobs. The first and the second categories are the most renowned on behalf of the MEC servers which are the small scale data centers deployed by the network operators and can be co-located with the Wireless Access Points (WAPs).…”