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1. Edge computing framework for distributed smart applications

   https://doi.org/10.1109/UIC-ATC.2017.8397448  

   Liu, Kaikai ; Gurudutt, Abhishek ; Kamaal, Tejeshwar ; Divakara, Chinmayi ; Prabhakaran, Praveen ( August 2017 , The IEEE Smart World Congress 2017 (IEEE SWC 2017))

   The rapid growth in technology and wide use of internet has increased smart applications such as intelligent transportation control system, and Internet of Things, which heavily rely on an efficient and reliable connectivity network. To overcome high bandwidth work load on the network, as well as minimize latency for real-time applications, the computation can be moved from the central cloud to a distributed edge cloud. The edge computing benefits various smart applications that uses distributed network for data analytics and services. Different from the existing cloud management solutions, edge computing needs to move cloud management services towards distributed heterogeneous edge nodes for multi-tenant user applications. However, existing cloud management services do not offer remote deployment of multi-tenant user applications on the cloud of edge nodes. In this paper, we propose a practical edge cloud software framework for deploying multi-tenant distributed smart applications. Having multiple distributed end nodes, auto discovery of all active end nodes is required for deploying multi-tenant user applications. However, existing cloud solutions require either private network or fixed IP address, which is not achievable for the distributed edge nodes. Most of the edge nodes connected through the public internet without fixed IP, and some of them even connect through IEEE 802.15 based sensor networks. We propose to build a software platform to manage the distributed edge nodes as well as support services to deploy and launch isolated, multi-tenant user applications through a lightweight container. We propose an architectural solution to remotely access edge cloud management services through intermittent internet connections. We open sourced our whole set of software solutions, and analyzed the major performance metrics of the edge cloud platform. 

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2. Nomenclature over 5 years in TaxonWorks: Approach, implementation, limitations and outcomes

   https://doi.org/10.3897/biss.5.75441  

   Yoder, Matthew ; Dmitriev, Dmitry ( September 2021 , Biodiversity Information Science and Standards)

   We are now over four decades into digitally managing the names of Earth's species. As the number of federating (i.e., software that brings together previously disparate projects under a common infrastructure, for example TaxonWorks) and aggregating (e.g., International Plant Name Index, Catalog of Life (CoL)) efforts increase, there remains an unmet need for both the migration forward of old data, and for the production of new, precise and comprehensive nomenclatural catalogs. Given this context, we provide an overview of how TaxonWorks seeks to contribute to this effort, and where it might evolve in the future. In TaxonWorks, when we talk about governed names and relationships, we mean it in the sense of existing international codes of nomenclature (e.g., the International Code of Zoological Nomenclature (ICZN)). More technically, nomenclature is defined as a set of objective assertions that describe the relationships between the names given to biological taxa and the rules that determine how those names are governed. It is critical to note that this is not the same thing as the relationship between a name and a biological entity, but rather nomenclature in TaxonWorks represents the details of the (governed) relationships between names. Rather than thinking of nomenclature as changing (a verb commonly used to express frustration with biological nomenclature), it is useful to think of nomenclature as a set of data points, which grows over time. For example, when synonymy happens, we do not erase the past, but rather record a new context for the name(s) in question. The biological concept changes, but the nomenclature (names) simply keeps adding up. Behind the scenes, nomenclature in TaxonWorks is represented by a set of nodes and edges, i.e., a mathematical graph, or network (e.g., Fig. 1). Most names (i.e., nodes in the network) are what TaxonWorks calls "protonyms," monomial epithets that are used to construct, for example, bionomial names (not to be confused with "protonym" sensu the ICZN). Protonyms are linked to other protonyms via relationships defined in NOMEN, an ontology that encodes governed rules of nomenclature. Within the system, all data, nodes and edges, can be cited, i.e., linked to a source and therefore anchored in time and tied to authorship, and annotated with a variety of annotation types (e.g., notes, confidence levels, tags). The actual building of the graphs is greatly simplified by multiple user-interfaces that allow scientists to review (e.g. Fig. 2), create, filter, and add to (again, not "change") the nomenclatural history. As in any complex knowledge-representation model, there are outlying scenarios, or edge cases that emerge, making certain human tasks more complex than others. TaxonWorks is no exception, it has limitations in terms of what and how some things can be represented. While many complex representations are hidden by simplified user-interfaces, some, for example, the handling of the ICZN's Family-group name, batch-loading of invalid relationships, and comparative syncing against external resources need more work to simplify the processes presently required to meet catalogers' needs. The depth at which TaxonWorks can capture nomenclature is only really valuable if it can be used by others. This is facilitated by the application programming interface (API) serving its data (https://api.taxonworks.org), serving text files, and by exports to standards like the emerging Catalog of Life Data Package. With reference to real-world problems, we illustrate different ways in which the API can be used, for example, as integrated into spreadsheets, through the use of command line scripts, and serve in the generation of public-facing websites. Behind all this effort are an increasing number of people recording help videos, developing documentation, and troubleshooting software and technical issues. Major contributions have come from developers at many skill levels, from high school to senior software engineers, illustrating that TaxonWorks leads in enabling both technical and domain-based contributions. The health and growth of this community is a key factor in TaxonWork's potential long-term impact in the effort to unify the names of Earth's species. 

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3. Optimal Accuracy-Time Trade-off for Deep Learning Services in Edge Computing Systems

   https://doi.org/10.1109/ICC42927.2021.9500744  

   Hosseinzadeh, Minoo ; Wachal, Andrew ; Khamfroush, Hana ; Lucani, Daniel E. ( June 2021 , EEE International Conference on Communications, 2021)

   With the increasing demand for computationally intensive services like deep learning tasks, emerging distributed computing platforms such as edge computing (EC) systems are becoming more popular. Edge computing systems have shown promising results in terms of latency reduction compared to the traditional cloud systems. However, their limited processing capacity imposes a trade-off between the potential latency reduction and the achieved accuracy in computationally-intensive services such as deep learning-based services. In this paper, we focus on finding the optimal accuracy-time trade-off for running deep learning services in a three-tier EC platform where several deep learning models with different accuracy levels are available. Specifically, we cast the problem as an Integer Linear Program, where optimal task scheduling decisions are made to maximize overall user satisfaction in terms of accuracy-time trade-off. We prove that our problem is NP-hard and then provide a polynomial constant-time greedy algorithm, called GUS, that is shown to attain near-optimal results. Finally, upon vetting our algorithmic solution through numerical experiments and comparison with a set of heuristics, we deploy it on a testbed implemented to measure for real-world results. The results of both numerical analysis and real-world implementation show that GUS can outperform the baseline heuristics in terms of the average percentage of satisfied users by a factor of at least 50%. 

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4. AggNet: Cost-Aware Aggregation Networks for Geo-distributed Streaming Analytics

   Kumar, Dhruv ; Ahmad, Sohaib ; Chandra, Abhishek ; Sitaraman, Ramesh K. ( January 2021 , ACM/IEEE Symposium on Edge Computing (SEC'21))

   null (Ed.)

   Large-scale real-time analytics services continuously collect and analyze data from end-user applications and devices distributed around the globe. Such analytics requires data to be transferred over the wide-area network (WAN) to data centers (DCs) capable of processing the data. Since WAN bandwidth is expensive and scarce, it is beneficial to reduce WAN traffic by partially aggregating the data closer to end-users. We propose aggregation networks for per- forming aggregation on a geo-distributed edge-cloud infrastructure consisting of edge servers, transit and destination DCs. We identify a rich set of research questions aimed at reducing the traffic costs in an aggregation network. We present an optimization formula- tion for solving these questions in a principled manner, and use insights from the optimization solutions to propose an efficient, near-optimal practical heuristic. We implement the heuristic in AggNet, built on top of Apache Flink. We evaluate our approach using a geo-distributed deployment on Amazon EC2 as well as a WAN-emulated local testbed. Our evaluation using real-world traces from Twitter and Akamai shows that our approach is able to achieve 47% to 83% reduction in traffic cost over existing baselines without any compromise in timeliness. 

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5. AggNet: Cost-Aware Aggregation Networks for Geo-distributed Streaming Analytics

   https://doi.org/10.1145/3453142.3491276  

   Dhruv Kumar ; Sohaib Ahmad ; Abhishek Chandra ; Ramesh K. Sitaraman ( December 2021 , IEEE/ACM Symposium on Edge Computing (SEC))

   Large-scale real-time analytics services continuously collect and analyze data from end-user applications and devices distributed around the globe. Such analytics requires data to be transferred over the wide-area network (WAN) to data centers (DCs) capable of processing the data. Since WAN bandwidth is expensive and scarce, it is beneficial to reduce WAN traffic by partially aggregating the data closer to end-users. We propose aggregation networks for performing aggregation on a geo-distributed edge-cloud infrastructure consisting of edge servers, transit and destination DCs. We identify a rich set of research questions aimed at reducing the traffic costs in an aggregation network. We present an optimization formulation for solving these questions in a principled manner, and use insights from the optimization solutions to propose an efficient, near-optimal practical heuristic. We implement the heuristic in AggNet, built on top of Apache Flink. We evaluate our approach using a geo-distributed deployment on Amazon EC2 as well as a WAN-emulated local testbed. Our evaluation using real-world traces from Twitter and Akamai shows that our approach is able to achieve 47% to 83% reduction in traffic cost over existing baselines without any compromise in timeliness. 

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