Research Area: Edge Computing
Over the last decade, cloud computing has realized the long-held dream of computing as a utility, in which computational and storage services are made available via the Internet to anyone at any time and from anywhere. This has transformed Information Technology (IT) and given rise to new ways of designing and purchasing hardware and software. However, the rapid development of the Internet of Things (IoTs) and mobile technology has brought a new wave of disruptive applications and services whose performance requirements are stretching the limits of current cloud computing systems and platforms. In particular, novel large scale mission-critical IoT systems and latency-intolerant applications strictly require very low latency and strong guarantees of privacy, and can generate massive amounts of data that are only of local interest. These requirements are not readily satisfied using modern application deployment strategies that rely on resources from distant large cloud data centers because they easily cause network congestion and high latency in service delivery. This has provoked a paradigm shift leading to the emergence of new distributed computing infrastructures known as Mobile Edge Clouds (MECs) in which resource capabilities are widely distributed at the edge of the network, in close proximity to end-users.
Based on our model, we proposed two online application placement algorithms that take these factors into account to minimize the total cost of operating the application.The methods and algorithms proposed in this thesis were evaluated by implementing prototypes on simulated test beds and conducting experiments using workloads based on real mobility traces. These evaluations showed that the proposed approaches outperformed alternative state-of-the-art approaches and could thus help improve the efficiency of resource allocation in MECs.
Name of the Researcher: Chanh Nguyen
Name of the Supervisor(s): Erik Elmroth
Year of Completion: 2021
University: Umea University
Thesis Link: Home Page Url
