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1. Joint Compression and Offloading Decisions for Deep Learning Services in 3-Tier Edge Systems

   https://doi.org/10.1109/DySPAN53946.2021.9677398  

   Hosseinzadeh, Minoo; Hudson, Nathaniel; Zhao, Xiaobo; Khamfroush, Hana; Lucani, Daniel E. (December 2021, 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN))

   Task offloading in edge computing infrastructure remains a challenge for dynamic and complex environments, such as Industrial Internet-of-Things. The hardware resource constraints of edge servers must be explicitly considered to ensure that system resources are not overloaded. Many works have studied task offloading while focusing primarily on ensuring system resilience. However, in the face of deep learning-based services, model performance with respect to loss/accuracy must also be considered. Deep learning services with different implementations may provide varying amounts of loss/accuracy while also being more complex to run inference on. That said, communication latency can be reduced to improve overall Quality-of-Service by employing compression techniques. However, such techniques can also have the side-effect of reducing the loss/accuracy provided by deep learning-based service. As such, this work studies a joint optimization problem for task offloading decisions in 3-tier edge computing platforms where decisions regarding task offloading are made in tandem with compression decisions. The objective is to optimally offload requests with compression such that the trade-off between latency-accuracy is not greatly jeopardized. We cast this problem as a mixed integer nonlinear program. Due to its nonlinear nature, we then decompose it into separate subproblems for offloading and compression. An efficient algorithm is proposed to solve the problem. Empirically, we show that our algorithm attains roughly a 0.958-approximation of the optimal solution provided by a block coordinate descent method for solving the two sub-problems back-to-back. 

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2. Load Profiling and Migration for Effective Cyber Foraging in Disaster Scenarios with FORMICA

   https://doi.org/10.1109/NETSOFT.2018.8459971  

   Ventrella, Agnese V.; Esposito, Flavio; Grieco, L. Alfredo (June 2018, 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft))

   Cyber foraging techniques have been proposed in edge computing to support resource-intensive and latency-sensitive mobile applications. In a natural or man-made disaster scenario, all cyber foraging challenges are exacerbated by two problems: edge nodes are scarce and hence easily overloaded and failures are common due to the ad-hoc hostile conditions. In this paper, we study the use of efficient load profiling and migration strategies to mitigate such problems. In particular, we propose FORMICA, an architecture for cyber foraging orchestration, whose goal is to minimize the completion time of a set of jobs offloaded from mobile devices. Existing service offloading solutions are mainly concerned with outsourcing a job out of the mobile responsibility. Our architecture supports both mobile-based offloading and backend-driven onloading i.e., the offloading decision is taken by the edge infrastructure and not by the mobile node. FORMICA leverages Gelenbe networks to estimate the load profile of each node of the edge computing infrastructure to make proactive load profiling decisions. Our evaluation on a proof-of-concept implementation shows the benefits of our policy-based architecture in several (challenged disaster) scenarios but its applicability is broad to other IoT-based latency-sensitive applications. 

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3. Collaborative Cloud-Edge-Local Offloading for Multi-Component Applications

   Gholami, Anousheh; Baras, John S. (December 2021, Proceedings of Sixth ACM/IEEE Symposium on Edge Computing - The 2nd Workshop on Edge Computing and Communications(EdgeComm 2021))

   With the explosion of intelligent and latency-sensitive applications such as AR/VR, remote health and autonomous driving, mobile edge computing (MEC) has emerged as a promising solution to mitigate the high end-to-end latency of mobile cloud computing (MCC). However, the edge servers have significantly less computing capability compared to the resourceful central cloud. Therefore, a collaborative cloud-edge-local offloading scheme is necessary to accommodate both computationally intensive and latency-sensitive mobile applications. The coexistence of central cloud, edge servers and the mobile device (MD), forming a multi-tiered heterogeneous architecture, makes the optimal application deployment very challenging especially for multi-component applications with component dependencies. This paper addresses the problem of energy and latency efficient application offloading in a collaborative cloud-edgelocal environment. We formulate a multi-objective mixed integer linear program (MILP) with the goal of minimizing the systemwide energy consumption and application end-to-end latency. An approximation algorithm based on LP relaxation and rounding is proposed to address the time complexity. We demonstrate that our approach outperforms existing strategies in terms of application request acceptance ratio, latency and system energy consumption. 

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4. Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks

   Poularakis, K.; Llorca, J.; Tulino, A.; Taylor, I.; Tassiulas, L. (April 2019, IEEE International Conference on Computer Communications)

   The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network-periphery, in proximity to end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC-enabled multi-cell networks with multidimensional (storage-computation-communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close-to-optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low-latency edge cloud servers. 

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5. ARISE: High-Capacity AR Offloading Inference Serving via Proactive Scheduling

   https://doi.org/10.1145/3643832.3661894  

   Kong, Z Jonny; Xu, Qiang; Hu, Y Charlie (June 2024, ACM)

   With faster wireless networks and server GPUs, offloading high-accuracy but compute-intensive AR tasks implemented in Deep Neural Networks (DNNs) to edge servers offers a promising way to support high-QoE Augmented/Mixed Reality (AR/MR) applications. A cost-effective way for AR app vendors to deploy such edge-assisted AR apps to support a large user base is to use commercial Machine-Learning-as-a-Service (MLaaS) deployed at the edge cloud. To maximize cost-effectiveness, such an MLaaS provider faces a key design challenge, \ie how to maximize the number of clients concurrently served by each GPU server in its cluster while meeting per-client AR task accuracy SLAs. The above AR offloading inference serving problem differs from generic inference serving or video analytics serving in one fundamental way: due to the use of local tracking which reuses the last server-returned inference result to derive results for the current frame, the offloading frequency and end-to-end latency of each AR client directly affect its AR task accuracy (for all the frames). In this paper, we present ARISE, a framework that optimizes the edge server capacity in serving edge-assisted AR clients. Our design exploits the intricate interplay between per-client offloading schedule and batched inference on the server via proactively coordinating offloading request streams from different AR clients. Our evaluation using a large set of emulated AR clients and a 10-phone testbed shows that \name supports 1.7x--6.9x more clients compared to various baselines while keeping the per-client accuracy within the client-specified accuracy SLAs. 

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