Research

The Smart Urban Networks (SUN) group is researching on edge computing and proposes a new framework called SmartEdge. A critical resource mismatch has been observed in the Internet-of-Things (IoT) context where large volumes of data that are constantly generated by massive devices need to be processed while those devices themselves are resource constrained. By involving the power of cloud computing, cloud-based solutions resolve this mismatch but bring about new concerns over latency, traffic, and privacy. To handle this situation, edge computing was recently proposed by introducing an intermediate tier equipped with computing resources at the network edge. The main goal of SmartEdge is to advance this research direction by identifying major scientific challenges in edge computing and providing a unified platform and solutions to address them. These challenges include edge runtime environment, edge control system, edge resource management, as well as distributed data analytics. Accordingly, theoretical foundations, efficient algorithms and mechanisms, as well as reference system architectures, will be produced to guide the design, development, and operation of a modern edge computing system for IoT.

Edge Computing System Control

While social Virtual Reality (VR) applications such as Facebook Spaces are becoming popular, they are not compatible with classic mobile- or cloud-based solutions due to their processing of tremendous data and exchange of delay-sensitive metadata. Edge computing may fulfill these demands better, but it is still an open problem to deploy social VR applications in an edge infrastructure while supporting economic operations of the edge clouds and satisfactory quality-of-service for the users. This paper presents the first formal study of this problem. We model and formulate a combinatorial optimization problem that captures all intertwined goals. We propose ITEM, an iterative algorithm with fast and big “moves” where in each iteration, we construct a graph to encode all the costs and convert the cost optimization into a graph cut problem. By obtaining the minimum s-t cut via existing max-flow algorithms, we can simultaneously determine the placement of multiple service entities, and thus, the original problem can be addressed by solving a series of graph cuts. Our evaluations with large-scale, real-world data traces demonstrate that ITEM converges fast and outperforms baseline approaches by more than 2× in one-shot placement and around 1.3× in dynamic, online scenarios where users move arbitrarily in the system.

  1. [INFOCOM’18] Lin Wang, Lei Jiao, Ting He, Jun Li, Max Mühlhäuser. Service Entity Placement for Social Virtual Reality Applications in Edge Computing. In: Proceedings of IEEE INFOCOM, Honolulu, HI, Apr 2018. [slides]

Online Resource Allocation for Edge Computing

Fig. 1: Edge computing scenario.

As clouds move to the network edge to facilitate mobile applications, edge cloud providers are facing new challenges on resource allocation. As users may move and resource prices may vary arbitrarily, resources in edge clouds must be allocated and adapted continuously in order to accommodate such dynamics. In this work, we first formulate this problem with a comprehensive model that captures the key challenges, then introduce a gap-preserving transformation of the problem, and propose a novel online algorithm that optimally solves a series of subproblems with a carefully designed logarithmic objective, finally producing feasible solutions for edge cloud resource allocation over time. We further prove via rigorous analysis that our online algorithm can provide a parameterized competitive ratio, without requiring any a priori knowledge on either the resource price or the user mobility. Through extensive experiments with both real-world and synthetic data, we further confirm the effectiveness of the proposed algorithm. We show that the proposed algorithm achieves near-optimal results with an empirical competitive ratio of about 1.1, reduces the total cost by up to 4x compared to static approaches, and outperforms the online greedy one-shot optimizations by up to 70%.

  1. [SECON’18] Lei Jiao, Lingjun Pu, Lin Wang, Xiaojun Lin, Jun Li. Multiple Granularity Online Control of Cloudlet Networks for Edge Computing. In: Proceedings of IEEE SECON, Hong Kong, China, Jun 2018.
  2. [ICDCS’17] Lin Wang, Lei Jiao, Jun Li, Max Mühlhäuser. Online Resource Allocation for Arbitrary User Mobility in Distributed Edge Clouds. In: Proceedings of IEEE ICDCS, Atlanta, GA, Jun 2017. [slides]
  3. [ICNPW’16] Lin Wang, Lei Jiao, Dzmitry Kliazovich, Pascal Bouvry. Reconciling Task Assignment and Scheduling in Mobile Edge Clouds. In: Proceedings of HotPNS@ICNP, Singapore, Nov 2016. (Best Paper Award)