In recent years, there are increasing technologies that benefit from cloud computing and edge computing, especially the application in the Deep Learning and Internet of Things topics. Functionalities such as load balancing, traffic routing, and task scheduling, which used to be part of the software, are now enabled and provided as API or microservices on the underlying infrastructure. Therefore, when we want to deliver a container application to the users either on the cloud cluster or edge cluster, developers do not need to worry about these issues and only focus on the development of the functionality. Nevertheless, for the deployment stage, the system architect should design the fundamental architecture to build an ecosystem for the application. In this paper, a practical approach is proposed to develop a flexible and on-demand system for deep learning applications based on container and service mesh technologies. Our experiment chooses Kubernetes as the container orchestration platform and introduces Istio to implement the service mesh. We packaged one Flask based Deep learning model into a Docker container, and successfully deploy and provision this image classifier application to the public users with Functionalities like load balancing and task scheduling. Furthermore, we empower the system with an evaluation of the resource utilization services, and traffic flows inside the Kubernetes cluster in a visualized manner.

Subgraph queries have been a fundamental query for retrieving patterns from graph data. Due to the well known NP hardness of subgraph queries, those queries may sometimes take a long time to complete. Our recent investigation on real- world datasets revealed that the performance of queries on graphs generally varies greatly. In other words, query clients may occasionally encounter “unexpectedly” long execution from a subgraph query processor. This paper aims to demonstrate a tool that alleviates the problem by monitoring subgraph query progress. Specifically, we present a novel subgraph query progress indicator called PIGEON that exploits query-time information to report to users accurate estimated query progress. In the demonstration, users may interact with PIGEON to gain insights on the query evaluation, which include the following: Users are enabled to (i) monitor query progress; (ii) analyze the causes of long query times; and (iii) abort queries that run abnormally long, which may sometimes contain human errors.

Genomic science is now facing an explosive increase of data thanks to the fast development of sequencing technology. This situation poses serious challenges to genomic data storage and transferring. It is desirable to compress data to reduce storage and transferring cost, and thus to boost data distribution and utilization efficiency. Up to now, a number of algorithms / tools have been developed for compressing genomic sequences. Unlike the existing algorithms, most of which treat genomes as one-dimensional text strings and compress them based on dictionaries or probability models, this paper proposes a novel approach called CoGI (the abbreviation of Compressing Genomes as an Image) for genome compression, which transforms the genomic sequences to a two-dimensional binary image (or bitmap), then applies a rectangular partition coding algorithm to compress the binary image. CoGI can be used as either a reference-based compressor or a reference-free compressor. For the former, we develop two entropy-based algorithms to select a proper reference genome. Performance evaluation is conducted on various genomes. Experimental results show that the reference-based CoGI significantly outperforms two state-of-the-art reference-based genome compressors GReEn and RLZ-opt in both compression ratio and compression efficiency. It also achieves comparable compression ratio but two orders of magnitude higher compression efficiency in comparison with XM--one state-of-the-art reference-free genome compressor. Furthermore, our approach performs much better than Gzip--a general-purpose and widely-used compressor, in both compression speed and compression ratio. So, CoGI can serve as an effective and practical genome compressor. The source code and other related documents of CoGI are available at: http://admis.fudan.edu.cn/projects/cogi.htm.