Networks


Samsung acquires network analysis firm Zhilabs for 5G prep

ZDNet

Samsung has acquired a Spanish network analysis firm to enhance its 5G capabilities, the company has announced. The South Korean tech giant acquired Zhilabs for an undisclosed sum with full ownership, saying it will use the latter's artificial intelligence (AI)-based network and service analytics to further enhance its 5G capabilities. Zhilabs was formed in 2008 and provides services such as root cause analysis and automated troubleshooting and optimisation to more than 50 telecommunications carriers around the world. The two will collaborate to create new technology that can be applied in the transformation from 4G to 5G, the conglomerate added. In July, Samsung unveiled its 3.5GHz and 28GHz spectrum 5G equipment and promised a timely rollout to local and global telcos.


Spatio-temporal Edge Service Placement: A Bandit Learning Approach

arXiv.org Artificial Intelligence

Shared edge computing platforms deployed at the radio access network are expected to significantly improve quality of service delivered by Application Service Providers (ASPs) in a flexible and economic way. However, placing edge service in every possible edge site by an ASP is practically infeasible due to the ASP's prohibitive budget requirement. In this paper, we investigate the edge service placement problem of an ASP under a limited budget, where the ASP dynamically rents computing/storage resources in edge sites to host its applications in close proximity to end users. Since the benefit of placing edge service in a specific site is usually unknown to the ASP a priori, optimal placement decisions must be made while learning this benefit. We pose this problem as a novel combinatorial contextual bandit learning problem. It is "combinatorial" because only a limited number of edge sites can be rented to provide the edge service given the ASP's budget. It is "contextual" because we utilize user context information to enable finer-grained learning and decision making. To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore, SEEN is extended to scenarios with overlapping service coverage by incorporating a disjunctively constrained knapsack problem. In both cases, we prove that our algorithm achieves a sublinear regret bound when it is compared to an oracle algorithm that knows the exact benefit information. Simulations are carried out on a real-world dataset, whose results show that SEEN significantly outperforms benchmark solutions. Mobile cloud computing (MCC) supports mobile applications in resource-constrained mobile devices by offloading computation-demanding tasks to the resource-rich remote cloud. L. Chen and J. Xu are with Department of Electrical and Computer Engineering, University of Miami, USA. S. Ren is with Department of Electrical and Computer Engineering, University of California, Riverside, USA.


Intelligent Edge Analytics: 7 ways machine learning is driving edge computing adoption in 2018

#artificialintelligence

Edge services and edge computing have been in talks since at least the 90s. When Edge computing is extended to the cloud it can be managed and consumed as if it were local infrastructure. It's the same as how humans find it hard to interact with infrastructure that is too far away. Edge Analytics is the exciting area of data analytics that is gaining a lot of attention these days. While traditional analytics, answer questions like what happened, why it happened, what is likely to happen and options on what you should do about it Edge analytics is data analytics in real time.


8 Trends of IoT in 2018

#artificialintelligence

The Internet of things (IoT) is growing rapidly and 2018 will be a fascinating year for the IoT industry. IoT technology continues to evolve at an incredibly rapid pace. Consumers and businesses alike are anticipating the next big innovation. They are all set to embrace the ground-breaking impact of the Internet of Things on our lives like ATMs that report crimes around them, forks that tell you if you are eating fast, or IP address for each organ of your body for doctors to connect and check. Digitally connected devices are fast becoming an essential part of our everyday lives.


Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing

arXiv.org Artificial Intelligence

Abstract--Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge. However, with the sinking of computing capabilities, the new challenge incurred by user mobility arises: since end-users typically move erratically, the services should be dynamically migrated among multiple edges to maintain the service performance, i.e., user-perceived latency. Tackling this problem is nontrivial since frequent service migration would greatly increase the operational cost. To address this challenge in terms of the performance-cost tradeoff, in this paper we study the mobile edge service performance optimization problem under long-term cost budget constraint. To address user mobility which is typically unpredictable, we apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time optimization problems which do not require a priori knowledge such as user mobility. As the decomposed problem is NPhard, we first design an approximation algorithm based on Markov approximation to seek a nearoptimal solution. To make our solution scalable and amenable to future 5G application scenario with large-scale user devices, we further propose a distributed approximation scheme with greatly reduced time complexity, based on the technique of best response update. Rigorous theoretical analysis and extensive evaluations demonstrate the efficacy of the proposed centralized and distributed schemes. With the explosive growth of mobile devices, the recent years have witnessed an unprecedented shift of user preferences from traditional desktops and laptops to smartphones and other connected devices. Subsequently, more and more new mobile applications, as exemplified by augmented reality and interactive gaming [1], emerge and catch public attention. In general, these kinds of applications demand intensive computation resources and high energy consumption for real-time processing. However, due to the physical size constraint, the end device can not efficiently support theses applications alone within our expectation.


Label-less Learning for Traffic Control in an Edge Network

arXiv.org Artificial Intelligence

Abstract--With the development of intelligent applications (e.g., self-driving, real-time emotion recognition, etc), there are higher requirements for the cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed as LLTC. By the use of the limited computing and storage resources at edge cloud, LLTC evaluates the value of data, which will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission.


Fog Computing Test Bed: Cutting Costs and Latency in Data Transmission

#artificialintelligence

This feature is accessible to IEEE Members only, with an IEEE Account. If you are an IEEE Member please sign in to enable this feature. In addition to exclusive access to IEEE.tv programming, IEEE members have file download, and can save favorite videos with myTV. This feature is accessible to IEEE Members only, with an IEEE Account. If you are an IEEE Member please sign in to enable this feature.


5 Reasons Why Azure IoT Edge Is Industry's Most Promising Edge Computing Platform

#artificialintelligence

Last week, Microsoft announced the general availability of Azure IoT Edge, the edge computing platform that has been in works for more than a year. Out of the top 5 public cloud platforms – AWS, Azure, Google Cloud Platform, IBM Cloud and Alibaba Cloud – only Microsoft and Amazon have a sophisticated edge computing strategy. Other players are yet to figure out their story for edge computing. Amazon's edge platform is delivered through AWS Greengrass – a service that was announced at re:Invent event in 2016 and became generally available in June 2017. AWS recently added the ability to perform inferencing of machine learning models.


5 Reasons Why Azure IoT Edge Is Industry's Most Promising Edge Computing Platform

Forbes Technology

Last week, Microsoft announced the general availability of Azure IoT Edge, the edge computing platform that has been in works for more than a year. Out of the top 5 public cloud platforms – AWS, Azure, Google Cloud Platform, IBM Cloud and Alibaba Cloud – only Microsoft and Amazon have a sophisticated edge computing strategy. Other players are yet to figure out their story for edge computing. Amazon's edge platform is delivered through AWS Greengrass – a service that was announced at re:Invent event in 2016 and became generally available in June 2017. AWS recently added the ability to perform inferencing of machine learning models.


Cloud Computing and AI transform the Banking sector

#artificialintelligence

With the adoption and development of cognitive computing capabilities, the way customers interact with their banks will ultimately change for good. Artificial intelligence and cloud computing will empower banks to efficiently redefine the workflow, create innovative products and services, and transform customer experiences. Many banks have adopted AI, infusing it into their customer experience. This development is witness to AI's role in banking becoming increasingly crucial and visible over the next few years. The introduction of cloud computing and AI will permit the banking workforce to discard repetitive, process driven tasks towards the more strategic and innovative kinds of work that will ultimately drive the industry forward.