COSMOS: An Orchestration Framework for Smart Computation Offloading in Edge Clouds

Abstract

The evolution of Internet of Things (IoT) has sparked significant research interest in edge computing. Within this scope and given the ever-increasing number of IoT and mobile devices, computation offloading is emerging as a cuttingedge and significant research area with enormous potential and practical applications.In this respect, we present the architecture design and experimental evaluation of an orchestration framework for smart computation offloading from IoT or mobile devices to edge cloud servers. The proposed orchestration platform, namely COSMOS, includes control-plane components for workload prediction, load balancing, and admission control. COSMOS is particularly tailored to the needs of an object identification service that receives images from a multitude of Points of Interest (PoIs), performs object identification using a trained model (based on Tensorflow), calculates the prediction accuracy, and finally returns to the end-users the identification outcome and accuracy along with useful information about the identified object. COSMOS has been deployed and evaluated in a large-scale experimental facility that employs OpenStack and OpenSourceMANO (OSM) for Network Function Virtualization (NFV) orchestration. Our experimental results indicate the feasibility of computation offloading for this object identification service and further uncover useful insights in terms of performance and scalability.

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

“…In this way, when users arrive at the coverage area of the next Edge site, they receive the product of their completed offloaded task, with the minimum additional delay [144]. As an example, a two-step offloading mechanism for smart touristic services [145], [146] is based on estimating the location and density of users. Every mobile device takes the initial offloading decision based on a dead reckoning technique and measurements of its WiFi signal strength.…”

A Framework for Cognitive, Decentralized Container Orchestration

“…AC DSWM CA-DL NMA MDM MiCADO-Edge [24] ✓ ✓ ✓ Sophos [25] ✓ ✓ ✓ Oakestra [26] ✓ ✓ DECIDE [27] ✓ ✓ ✓ ✓ HomeCloud [28] ✓ Apollo [29] ✓ ORCH [30] ✓ ECCO [31] ✓ COSMOS [32] ✓ ✓ LeSO [33] ✓ ✓ Caravela [34] ✓ ✓ BumbleBee [35] ✓ ✓ SODA [36] ✓ ✓ CHARIOT [37] ✓ ✓ ✓ FogBus2 [38] ✓ IaaSP-SDN [39] ✓ SDO [40] ✓ ✓ SD-ECC [41] ✓ SDRM [42] ✓ CTOSO [43] ✓…”

“…On the other hand, MiCADO-Edge automatically deploys complex sets of interconnected micro-services using KubeEdge 30 , an open-source orchestrator that extends Kubernetes clusters to non-Cloud workers [24]. Examples of other Edge-oriented K8s automation facilities include Knative 31 for rapid event-driven scalability and serverless deployment, KCP 32 for multi-cluster solutions designed for Edge-Cloud deployments, and Flotta 33 to meet the stringent requirements of even more constrained environments such as the far Edge.…”

“…In the adaptive provisioning process, reconfiguration has been studied to improve orchestration decisions. Several techniques have been proposed [27], [32], [47], [48], with the most common being reinforcement learning (RL) [44], [50], [51]. Distributed behavioral learning and inference techniques that can support decentralization across the Edge Cloud while preserving the privacy of the raw training data, such as Federated Learning and Decentralized Learning, have also been considered [46], [52].…”