Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein

Mobile, collaborative augmented reality using cloudlets

The evolution in mobile applications to support advanced interactivity and demanding multimedia features is still ongoing. Novel application concepts (e.g. mobile Augmented Reality (AR)) are however hindered by the inherently limited resources available on mobile platforms (not withstanding the dramatic performance increases of mobile hardware). Offloading resource intensive application components to the cloud, also known as "cyber foraging", has proven to be a valuable solution in a variety of scenarios. However, also for collaborative scenarios, in which data together with its processing are shared between multiple users, this offloading concept is highly promising. In this paper, we investigate the challenges posed by offloading collaborative mobile applications. We present a middleware platform capable of autonomously deploying software components to minimize average CPU load, while guaranteeing smooth collaboration. As a use case, we present and evaluate a collaborative AR application, offering interaction between users, the physical environment as well as with the virtual objects superimposed on this physical environment

Tripod of Requirements in Horizontal Heterogeneous Mobile Cloud Computing

Recent trend of mobile computing is emerging toward executing resource-intensive applications in mobile devices regardless of underlying resource restrictions (e.g. limited processor and energy) that necessitate imminent technologies. Prosperity of cloud computing in stationary computers breeds Mobile Cloud Computing (MCC) technology that aims to augment computing and storage capabilities of mobile devices besides conserving energy. However, MCC is more heterogeneous and unreliable (due to wireless connectivity) compare to cloud computing. Problems like variations in OS, data fragmentation, and security and privacy discourage and decelerate implementation and pervasiveness of MCC. In this paper, we describe MCC as a horizontal heterogeneous ecosystem and identify thirteen critical metrics and approaches that influence on mobile-cloud solutions and success of MCC. We divide them into three major classes, namely ubiquity, trust, and energy efficiency and devise a tripod of requirements in MCC. Our proposed tripod shows that success of MCC is achievable by reducing mobility challenges (e.g. seamless connectivity, fragmentation), increasing trust, and enhancing energy efficiency

A performance comparison of data-aware heuristics for scheduling jobs in mobile Grids

Given mobile devices ubiquity and capabilities, some researchers now consider them as resource providers of distributed environments called mobile Grids for running resource intensive software. Therefore, job scheduling has to deal with device singularities, such as energy constraints, mobility and unstable connectivity. Many existing schedulers consider at least one of these aspects, but their applicability strongly depends on information that is unavailable or difficult to estimate accurately, like job execution time. Other efforts do not assume knowing job CPU requirements but ignore energy consumption due to data transfer operations, which is not realistic for data-intensive applications. This work, on the contrary, considers the last as non negligible and known by the scheduler. Under these assumptions, we conduct a performance study of several traditional scheduling heuristics adapted to this environment, which are applied with the known information of jobs but evaluated along with job information unknown to the scheduler. Experiments are performed via a simulation software that employs hardware profiles derived from real mobile devices. Our goal is to contribute to better understand both the capabilities and limitations of this kind of schedulers in the incipient area of mobile Grids.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A performance comparison of data-aware heuristics for scheduling jobs in mobile Grids

Given mobile devices ubiquity and capabilities, some researchers now consider them as resource providers of distributed environments called mobile Grids for running resource intensive software. Therefore, job scheduling has to deal with device singularities, such as energy constraints, mobility and unstable connectivity. Many existing schedulers consider at least one of these aspects, but their applicability strongly depends on information that is unavailable or difficult to estimate accurately, like job execution time. Other efforts do not assume knowing job CPU requirements but ignore energy consumption due to data transfer operations, which is not realistic for data-intensive applications. This work, on the contrary, considers the last as non negligible and known by the scheduler. Under these assumptions, we conduct a performance study of several traditional scheduling heuristics adapted to this environment, which are applied with the known information of jobs but evaluated along with job information unknown to the scheduler. Experiments are performed via a simulation software that employs hardware profiles derived from real mobile devices. Our goal is to contribute to better understand both the capabilities and limitations of this kind of schedulers in the incipient area of mobile Grids.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A Dynamic Application Partitioning and Offloading Framework to Enhance the Capabilities of Transient Clouds Using Mobile Agents

Mobile cloud computing has emerged as a prominent area of research, a natural extension of cloud computing that proposes to offer solutions for enhancing the capabilities of smart mobile devices commonly plagued by resource constraints. As one of its promising models, transient clouds aim to address the internet connectivity shortfall inherent in most solutions through the formation of ad hoc networks by devices in close proximity, then the offloading some computations (Cyber Foraging) to the created cloud. However, transient clouds, at their current state, have several limitations, concerning their expansion on a local network having a large number of devices and the management of the instability of the network due to the constant mobility of the devices. Another issue is the fact code partitioning and offloading are not addressed to fit the need of such networks, thereby rendering the distributed computing mechanism barely efficient for the Transient Cloud. In this study, we propose a transient cloud-based framework that exploits the use of multi-agent systems, enabling a dynamic partitioning and offloading of code, and facilitating the movement and the execution of code partition packets in a multi-hop ad-hoc mesh network. When created and deployed, these intelligent mobile agents operate independently or collaboratively and adapt to the continual entry and exit of devices in the neighbourhood. The integration of these trending concepts in distributed computing within a framework offers a new architecture for resource-sharing among cooperating devices that addresses the varied issues that arise in dynamic environments