Mobile Edge as Part of the Multi-Cloud Ecosystem: A Performance Study (original) (raw)
Related papers
Why cloud applications are not ready for the edge (yet)
Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, 2019
Mobile Edge Clouds (MECs) are distributed platforms in which distant data-centers are complemented with computing and storage capacity located at the edge of the network. Their wide resource distribution enables MECs to ful ll the need of low latency and high bandwidth to o⇥er an improved user experience. As modern cloud applications are increasingly architected as collections of small, independently deployable services, they can be ⇤exibly deployed in various con gurations that combines resources from both centralized datacenters and edge locations. In principle, such applications should therefore be well-placed to exploit the advantages of MECs so as to reduce service response times. In this paper, we quantify the bene ts of deploying such cloud micro-service applications on MECs. Using two popular benchmarks, we show that, against conventional wisdom, end-to-end latency does not improve signi cantly even when most application services are deployed in the edge location. We developed a proler to better understand this phenomenon, allowing us to develop recommendations for adapting applications to MECs. Further, by quantifying the gains of those recommendations, we show that the performance of an application can be made to reach the ideal scenario, in which the latency between an edge datacenter and a remote datacenter has no impact on the application performance. This work thus presents ways of adapting cloud-native applications to take advantage of MECs and provides guidance for developing MEC-native applications. We believe that both these elements are necessary to drive MEC adoption. CCS CONCEPTS • Networks ⌅ Network measurement; • Computer systems organization ⌅ Cloud computing; • Software and its engineering ⌅ Software design engineering.
A Comprehensive Comparison between Cloud Computing and Mobile Edge Computing
International Journal of Research and Innovation in Applied Science
Cloud computing provides a user-convenient, low-expense, and powerful computing platform for sharing resources like online storage, applications, and software through the internet. But with the exponential growth of the Internet of Things (IoT) devices and massive amounts of private data in the network, the centralized and conventional architecture of cloud computing has become a bottleneck because of limited bandwidth and resources. At the same time security is also an open concern for cloud computing. Hence, Mobile Edge Computing (MEC) is an extended architecture of cloud computing that enables data processing and storing at the edge of mobile networks. Instead of having some unique features (distributed architecture, parallel processing, low latency), MEC has also brought some security threats and challenges. In this paper, a comprehensive comparison between cloud computing and MEC has been presented in terms of features and security threats. Also, the security mechanisms for han...
Towards a Fully Cloudified Mobile Network Infrastructure
IEEE Transactions on Network and Service Management, 2016
Cloud computing enables the on-demand delivery of resources for a multitude of services and gives the opportunity for small agile companies to compete with large industries. In the telco world, cloud computing is currently mostly used by mobile network operators (MNO) for hosting non-critical support services and selling cloud services such as applications and data storage. MNOs are investigating the use of cloud computing to deliver key telecommunication services in the access and core networks. Without this, MNOs lose the opportunities of both combining this with over-the-top (OTT) and value-added services to their fundamental service offerings and leveraging cost-effective commodity hardware. Being able to leverage cloud computing technology effectively for the telco world is the focus of mobile cloud networking (MCN). This paper presents the key results of MCN integrated project that includes its architecture advancements, prototype implementation, and evaluation. Results show the efficiency and the simplicity that a MNO can deploy and manage the complete service lifecycle of fully cloudified, composed services that combine OTT/IT-and mobile-networkbased services running on commodity hardware. The extensive performance evaluation of MCN using two key proof-of-concept scenarios that compose together many services to deliver novel converged elastic, on-demand mobile-based but innovative OTT services proves the feasibility of such fully virtualized deployments. Results show that it is beneficial to extend cloud computing to telco usage and run fully cloudified mobile-network-based systems with clear advantages and new service opportunities for MNOs and end-users.
Mobile edge cloud architecture for future low-latency applications
2020
OF THE DISSERTATION Mobile Edge Cloud Architecture for Future Low-latency Applications by Sumit Maheshwari Dissertation Director: Dipankar Raychaudhuri This thesis presents the architecture, design, and evaluation of the mobile edge cloud (MEC) system aimed at supporting future low-latency applications. Mobile edge clouds have emerged as a solution for providing low latency services in future generations (5G and beyond) of mobile networks, which are expected to support a variety of realtime applications such as AR/VR (Augmented/Virtual Reality), autonomous vehicles and robotics. Conventional cloud computing implemented at distant large-scale data centers incurs irreducible propagation delays of the order of 50-100ms or more that may be acceptable for current applications but may not be able to support emerging real-time needs. Edge clouds considered here promise to meet the stringent latency requirements of emerging classes of real-time applications by bringing compute, storage, and...
Multi-Objective Mobile Edge Provisioning in Small Cell Clouds
2019
In recent years, Mobile Cloud Computing (MCC) has been proposed as a solution to enhance the capabilities of user equipment (UE), such as smartphones, tablets and laptops. However, offloading to conventional Cloud introduces significant execution delays that are inconvenient in case of near real-time applications. Mobile Edge Computing (MEC) has been proposed as a solution to this problem. MEC brings computational and storage resources closer to the UE, enabling to offload near real-time applications from the UE while meeting strict latency requirements. However, it is very difficult for Edge providers to determine how many Edge nodes are required to provide MEC services, in order to guarantee a high QoS and to maximize their profit. In this paper, we investigate the static provisioning of Edge nodes in a area representing a cellular network in order to guarantee the required QoS to the user without affecting providers' profits. First, we design a model for MEC offloading consid...
IEEE Communications Surveys & Tutorials, 2017
Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multitenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.
CloudAware: A Context-Adaptive Middleware for Mobile Edge and Cloud Computing Applications
2016
The widespread use of mobile devices such as smartphones and tablets is accompanied by an ever increasing market for mobile applications, including resource demanding services like speech-or face recognition, that formerly were restricted to stationary devices. But as mobile devices remain comparatively limited in terms of resources (e.g., computation, storage and battery life), current approaches for augmentation have advocated the integration of cloud servers as well as other nearby devices to provide scalable computation-and storage resources to mobile end users. A current solution is the so-called computation offloading, which is the most prominent strategy used in Mobile Cloud Computing (MCC) and its successor known as Mobile Edge Computing (MEC). While MCC and MEC are receiving increasing attention, current work is often not able to cope with the quickly and constantly changing context (e.g., intermittent connectivity) of mobile devices. Therefore, this paper presents the evaluation of CloudAware, a context-adaptive mobile middleware for MCC as well as MEC that supports automated context adaptation by linking the distribution features of mobile middleware with context-aware self-adaptation techniques. In particular, we present a system software infrastructure and a data mining process which facilitate the development of elastic, scalable and context-adaptive mobile applications and present their evaluation using real usage data provided by the Nokia Mobile Data Challenge (MDC) dataset.
HMCC: A Hybrid Mobile Cloud Computing Framework Exploiting Heterogeneous Resources
2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, 2015
Hybrid Mobile Cloud Computing (HMCC) refers to a Mobile Computation Outsourcing (MCO) model that exploits hybrid granular cloud-based resources composed of coarse-, medium-, and fine-grained resources interconnected by wireless and wired networks to augment mobile devices. Leveraging single type of granules for augmentation (i.e., vertically heterogeneous) has its own deficiencies of low proximity or/and scalability that leads to communication or/and computation latency. Therefore, responsiveness and energy efficiency of cloud-connected Compute-intensive Mobile Applications (CiMA) are degraded. In this paper, we aim to enhance energy-time efficiency of executing CiMA using HMCC. Performance evaluation results show significant gains, 80%-96% round-trip time and 83%-96% energy saving when executing CiMA using HMCC.