# Aika: A Distributed Edge System for AI Inference
## 5 Conclusions  
Monitoring of resources in world oceans and the Arctic is a technological challenge that requires domain-specific systems. The edge environments that offshore fishing vessels traverse are characterized by their lack of stable connections, high latency, low bandwidth, and difficulty of manual intervention. This paper describes Aika: a prototype system for executing distributed AI applications in these domain-specific edge environments. We investigate how a system supporting AI inference in these environments can be built to support a wide range of computational graphs through a DAG computation model, while making the system tolerant to failures.  
Aika provides application developers with generalized building blocks that can be used to construct complex distributed computational tasks with robustness and privacy  
Figure 16: Results obtained from performing feature extraction with pre-trained VGG-16, DenseNet-121, and ResNet-50 models, where the models are either distributed among three workers (Distributed Feature Extraction), or put in a sequence on a single worker (Sequential Feature Extraction). The images have been processed with a batch size of 500. The standard deviation is shown as an error bar and value at each data point.  
properties. Through a hierarchical design, we utilize local controllers on physical nodes to perform quick recovery when failure occurs. A cluster controller is used to further invoke node recovery, where agents from a failed physical node are moved to a replica. The cluster controller is replicated in a chain to avoid having a single point of failure, and communicates with a remote monitor that logs failures and classifies the system state when sufficient bandwidth is available.  
Our experimental evaluations demonstrate that Aika has a stable throughput despite potential agent crashes. For data-intensive tasks, we show that persistent queues can be beneficial compared to in-memory queues, in cases where the workload on an item exceeds the time spent transporting the item. We demonstrate how Aika can be used to create DAGs of varying complexity with load balancing, and distribute work among agents to optimize performance. We implement two experiments with computational tasks relevant for the targeted domain, namely a MapReduce task and a deep-learning-based feature extraction task. The results from these experiments demonstrate that Aika is scalable and supports different computational graph designs that can be used in the domain of fishery monitoring and surveillance.  
Conceptualization, J.A.A., A.B.O. and D.J.; methodology, J.A.A. and D.J.; software, J.A.A. and A.B.O.; validation, J.A.A., investigation, J.A.A., A.B.O. and D.J.; data curation, J.A.A.; writing--original draft preparation, J.A.A. and M.A.R; writing--review and editing, J.A.A., A.B.O., T.-A.S.N., H.D.J., M.A.R., P.H. and D.J.; visualization, J.A.A.; supervision, H.D.J. and D.J.; project administration, H.D.J., M.A.R., P.H. and D.J.; funding acquisition, H.D.J. and D.J. All authors have read and agreed to the published version of the manuscript.  
This work is partially funded by the Research Council of Norway project numbers 274451 and 263248, and Lab Nord-Norge (\"Samfunnsloftef\").  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.