[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2020.2974741]
[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3142265]
This is an extended abstract of our article 1 accepted for publication in the IEEE Transactions on Services Computing Journal proposing an approach for the conceptual modeling and design-time analysis of the data operations performed by a business process on a database. Our approach leverages a uniform formal representation that captures a process model, a conceptual database schema, and the data operations performed by the process on the database. We then show how such a formal model can be queried to obtain insights into the informational perspective of business process models and, in turn, support process modelers and analysts in reasoning about process (re) design and improvement. The approach is evaluated with a controlled experiment and a proof-of-concept implementation.1This extended abstract summarizes the approach and selected findings of the work referenced as [1] with doi:10.1109/TSC.2021.3134485
[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2021.3051771]
J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing, doi: 10.1109/TSC.2021.3086023
[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing]
In this panel contribution, I will discuss my vision on the need of developing new managing technologies to harness distributed "computing continuum" systems. These systems are concurrently executed in multiple computing tiers: Cloud, Fog, Edge and IoT. This simple idea develops manifold challenges due to the inherent complexity inherited from the underlying infrastructures of these systems. This makes inappropriate the use of current methodologies for managing Internet distributed systems, which are based on the early systems that were based on client/server architectures and were completely specified by the application software.
Deep learning services are extensively required and have powerful expected effects in a wide range of applications, such as auto-self driving, voice assistant and so on.Traditionally, deep learning services are mainly provided based on cloud computing, referred to as cloud intelligence services, where the deep learning model is deployed in the cloud, and endusers need to upload data through wireless and core network when requesting training and inference services. However, the cloud computing-based deep learning services have deficiencies in latency, privacy, etc. For example, the deep learning service users do not want their private data to leak, and the privacy problem is difficult to solve when the data is uploading to the cloud. With the widespread use of the Internet of Things (IoT) and the rapid development of mobile devices such as smartphones and IoT sensors, large amounts of data need to be used for a variety of real-time deep learning services, such as target recognition and voice recognition for smart cities, smart medical care, and the Internet of Vehicles (IoVs). With a certain network bandwidth, a large amount of data uploaded to the cloud will cause network congestion and greatly increase the response time. To meet the requirements of low latency, researchers have begun to consider the deployment of deep learning services in edges, i.e., edge intelligence service.In edge intelligence services, the computation capability and memory of processors (or devices) are different from a large. At the same time, the requirement of memory size of deep neural network (DNN) models is increasing, such as the memory usage for Alexnet and Resnet are 2.12G and 16.20G separately. Also, in DNN model design, the branches are becoming common, which brings the parallelism. Deploying deep learning models on multiple processors can support the large-scale DNN models and the parallel implementation of DNN model, where the computation of a deep learning model can be conducted in parallel is a possible solution to improve the efficiency of edge intelligence services. The key point in edge intelligence services is how to partition and assign the implementation of the DNN model.In this paper, we propose a novel latency-driven deep learning model placement method for efficient edge intelligence service. Model placement contains two procedures: model partition and sub-models assignment. In our method, we first convert a DNN model into an execution graph, which is a directed acyclic graph (DAG), and propose a novel latency-driven multilevel graph partition for the model. Then the partitioned submodels are heuristically assigned to available processors. To the best of our knowledge, it is the first work that proposes latency-driven graph partition algorithms for model placement. Extensive experiments on several commonly used DNN models and synthetic datasets show that our method can achieve the lowest execution latency with low complexity compared with other state-of-the-art model placement methods.
[J1C2 Presentation Abstract at IEEE SERVICES 2021 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2021.3111208]
J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3142853
[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2019.2955486]
[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3180067]
[J1C2 Presentation Abstract at IEEE SERVICES 2021 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2021.3128137]
Traditional data sharing systems are facing new challenges when implementing access control with more and more complex data sharing requirements. Flexibility of user revocation in completely decentralized environments needs to be taken into account. In this article, we propose a Key-Policy Attribute-Based Encryption scheme with Multiple and Flexible Revocation (MAFR-KP-ABE) to achieve the features of decentralized authorization and flexible revocation. We prove the security of our MAFR-KP-ABE scheme in the standard model and provide the comparison with relevant schemes to demonstrate its efficiency. Then we propose a fine-grained access control system based on MAFR-KP-ABE scheme and blockchain that matches the need of paid data sharing services with several security properties enhanced. Security analysis and system implementation are given subsequently to demonstrate our system efficient and secure.
With the development of complex networks and with increasing service demands, service use is becoming more complex and the composition of services is becoming more complicated. In the XaaS (X as a Service) environment, users only care about the QoE of a service and do not care about the composition process of the service. Therefore, it is important to evaluate the reliability of the entire service. In this article, we use Petri Net as a basis for modeling the composition of services. In addition, we consider the problems of shared resources and common cause faults. Both of these problems can cause network congestion and regional failures. We use distance to assess the effects of regional faults and queuing theory to simulate the network congestion process. Moreover, in the simulation, we verify the impacts of regional failures and network congestion on service reliability. We choose the Tree-Based Search algorithm and the Semi-Markov Model as comparison algorithms. The results of our algorithm are related to service time. Our algorithm can timely reflect the impact of regional failure or network congestion, and it can feedback different evaluation results according to environmental changes. Therefore, our algorithm is more comprehensive and has better performance.
The ongoing COVID-19 pandemic highlights the importance of dashboards for providing critical real-time information. In order to enable people to obtain information in time and to understand complex statistical data, many developers have designed and implemented public-oriented COVID-19 “information dashboards” during the pandemic. However, development often takes a long time and developers are not clear about many people’s information needs, resulting in gaps between information needs and supplies. According to our empirical study and observations with popular developed COVID-19 dashboards, this seriously impedes information acquirement. Our study compares people’s needs on Twitter with existing information suppliers. We determine that despite the COVID-19 information that is currently on existing dashboards, people are also interested in the relationship between COVID-19 and other viruses, the origin of COVID-19, vaccine development, fake new about COVID-19, impact on women, impact on school/university, and impact on business. Most of these have not yet been well addressed. We also summarise the visualization and interaction patterns commonly applied in dashboards, finding key patterns between data and visualization as well as visualization and interaction. Our findings can help developers to better optimize their dashboard to meet people’s needs and make improvements to future crisis management dashboard development.
Deep learning services based on cloud computing have deficiencies in latency, privacy, etc. To meet the requirements of low latency, researchers have begun to consider the deployment of deep learning services in edges, i.e., edge intelligence service. Deploying deep learning models on multiple processors or devices so that the computation of a deep learning model can be conducted in parallel is a possible solution to improve the efficiency of edge intelligence services. In this article, we propose a novel latency-driven deep learning model placement method for efficient edge intelligence service. Model placement contains two procedures: model partition and sub-models assignment. In our method, we first convert the model into execution graphs and propose a novel latency-driven multilevel graph partition for the model. Then the partitioned sub-models are heuristically assigned to available processors. To the best of our knowledge, it is the first work that proposes latency-driven graph partition algorithms for model placement. Extensive experiments on several commonly used DNN (deep neural network) models and synthetic datasets show that our method can achieve the lowest execution latency with low complexity compared with other state-of-the-art model placement methods.
Currently, the world is experiencing the rapid spread of Coronavirus Disease 2019 (COVID-19). Since the epidemic continues to take a devastating impact on the society, economy, and healthcare, the real-time detection of COVID-19 is essential for fast and cost-effective diagnosis services. Fortunately, deep learning (DL), as a promising technology, enables the COVID-19 diagnosis services on chest X-ray (CXR) images. The training task of DL model is generally implemented at the centralized cloud. However, due to the geo-distributed data sources and the transmission of large amounts of raw data to the centralized cloud, the transmission latency becomes a bottleneck of the COVID-19 diagnosis model training. In this paper, we propose a Dis tributed COV ID-19 detection model training method on CXR images with edge-cloud collaboration, named DisCOV. Specifically, to improve the training efficiency and guarantee the model accuracy, a distributed lightweight model-based training algorithm is designed with the cooperation of edge computing and cloud computing. In addition, a resource allocation algorithm is developed during the training to jointly minimize the time cost and energy consumption. Extensive experiments based on real-world CXR image datasets demonstrate that DisCOV is better performed and more promising than the existing baselines.
We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment – Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make realtime, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results based on public and self-collected data sets demonstrate that our approach can effectively improve forecasting performance and protect user privacy.
The increasing heterogeneity of the VM offerings on public IaaS clouds gives rise to a very large number of deployment options for constructing distributed, multi-component cloud applications. However, selecting an appropriate deployment variant, i.e., a valid combination of deployment options, to meet required performance levels is non-trivial. The combinatorial explosion of the deployment space makes it infeasible to measure the performance of all deployment variants to build a comprehensive empirical performance model. To address this problem, we propose Feature-Oriented Cloud (FOCloud), a performance engineering approach for deployment configurable cloud applications. FOCloud (i) uses feature modeling to structure and constrain the valid deployment space by modeling the commonalities and variations in the different deployment options and their inter-dependencies, (ii) uses sampling and machine learning to incrementally and cost-effectively build a performance prediction model whose input variables are the deployment options, and the output variable is the performance of the resulting deployment variant, and (iii) uses Explainable AI techniques to provide explanations for the prediction outcomes of valid deployment variants in terms of the deployment options. We demonstrate the practicality and feasibility of FOCloud by applying it to an extension of the RuBiS benchmark application deployed on Google Cloud.
