Cider: a Rapid Docker Container Deployment System through Sharing Network Storage

Container technology has been prevalent and widely-adopted in production environment considering the huge benefits to application packing, deploying and management. However, the deployment process is relatively slow by using conventional approaches. In large-scale concurrent deployments, resource contentions on the central image repository would aggravate such situation. In fact, it is observable that the image pulling operation is mainly responsible for the degraded performance. To this end, we propose Cider - a novel deployment system to enable rapid container deployment in a high concurrent and scalable manner at scale. Firstly, on-demand image data loading is proposed by altering the local Docker storage of worker nodes into all-nodes-sharing network storage. Also, the local copy-on-write layer for containers can ensure Cider to achieve the scalability whilst improving the cost-effectiveness during the holistic deployment. Experimental results reveal that Cider can shorten the overall deployment time by 85% and 62% on average when deploying one container and 100 concurrent containers respectively

Breaking barriers for breaking ground: A categorisation of public sector challenges to smart city project implementation

Smart city technologies provide promising solutions for local governments to tackling societal challenges and enhancing public service provision. The global embrace of these digital innovations represents a new era in public sector advancements. However, it has also brought to light difficulties that existing public sector innovation (PSI) theories struggle to address. One key issue is the lack of comprehensive knowledge regarding the most critical barriers to implementing smart city projects and their intensity. We address this knowledge gap with a systematic literature review within the smart city domain, focusing on literature reporting on the barriers that local governments commonly encounter. This effort has culminated in the development of a conceptual framework that categorize smart city project barriers, forming a taxonomy that builds on and expand the most recent development in the PSI literature. This study contributes to PSI theory refinement by offering a more nuanced understanding of the barriers that local governments might experience when attempting to sustain digital innovation efforts. Moreover, this insight into PSI dynamics is a valuable resource for local governments as they seek to devise realistic mitigation strategies tailored to local development needs

EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices

Real-time video analytics on edge devices for changing scenes remains a difficult task. As edge devices are usually resource-constrained, edge deep neural networks (DNNs) have fewer weights and shallower architectures than general DNNs. As a result, they only perform well in limited scenarios and are sensitive to data drift. In this paper, we introduce EdgeMA, a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time, addressing the data drift problem. EdgeMA extracts the gray level co-occurrence matrix based statistical texture feature and uses the Random Forest classifier to detect the domain shift. Moreover, we have incorporated a method of model adaptation based on importance weighting, specifically designed to update models to cope with the label distribution shift. Through rigorous evaluation of EdgeMA on a real-world dataset, our results illustrate that EdgeMA significantly improves inference accuracy.Comment: Accepted by 30th International Conference on Neural Information Processing (ICONIP 2023

A Cache-Aware Approach to Adaptive Mesh Refinement in Parallel Stencil-based Solvers

In prior-research the authors have demonstrated that, for stencil-based numerical solvers for Partial Differential Equations (PDEs), the parallel performance can be significantly improved by selecting sub-domains that are not cubic in shape (Saxena et. al., HPCS 2016, pp. 875-885). This is achieved through accounting for cache utilization in both the message passing and the computational kernel, where it is demonstrated that the optimal domain decompositions not only depend on the communication and load balance but also on the cache-misses, amongst other factors. In this work we demonstrate that those conclusions may also be extended to more advanced numerical discretizations, based upon Adaptive Mesh Refinement (AMR). In particular, we show that when basing our AMR strategy on the local refinement of patches of the mesh, the optimal patch shape is not typically cubic. We provide specific examples, with accompanying explanation, to show that communication minimizing strategies are not necessarily the best choice when applying AMR in parallel. All numerical tests undertaken in this work are based upon the open source BoxLib library

ReLeaSER: A Reinforcement Learning Strategy for Optimizing Utilization Of Ephemeral Cloud Resources

International audienceCloud data center capacities are over-provisioned to handle demand peaks and hardware failures which leads to low resources' utilization. One way to improve resource utilization and thus reduce the total cost of ownership is to offer unused resources (referred to as ephemeral resources) at a lower price. However, reselling resources needs to meet the expectations of its customers in terms of Quality of Service. The goal is so to maximize the amount of reclaimed resources while avoiding SLA penalties. To achieve that, cloud providers have to estimate their future utilization to provide availability guarantees. The prediction should consider a safety margin for resources to react to unpredictable workloads. The challenge is to find the safety margin that provides the best trade-off between the amount of resources to reclaim and the risk of SLA violations. Most state-of-the-art solutions consider a fixed safety margin for all types of metrics (e.g., CPU, RAM). However, a unique fixed margin does not consider various workloads variations over time which may lead to SLA violations or/and poor utilization. In order to tackle these challenges, we propose ReLeaSER, a Reinforcement Learning strategy for optimizing the ephemeral resources' utilization in the cloud. ReLeaSER dynamically tunes the safety margin at the host-level for each resource metric. The strategy learns from past prediction errors (that caused SLA violations). Our solution reduces significantly the SLA violation penalties on average by 2.7x and up to 3.4x. It also improves considerably the CPs' potential savings by 27.6% on average and up to 43.6%