Collaborative Reuse of Streaming Dataflows in IoT Applications

Distributed Stream Processing Systems (DSPS) like Apache Storm and Spark Streaming enable composition of continuous dataflows that execute persistently over data streams. They are used by Internet of Things (IoT) applications to analyze sensor data from Smart City cyber-infrastructure, and make active utility management decisions. As the ecosystem of such IoT applications that leverage shared urban sensor streams continue to grow, applications will perform duplicate pre-processing and analytics tasks. This offers the opportunity to collaboratively reuse the outputs of overlapping dataflows, thereby improving the resource efficiency. In this paper, we propose \emph{dataflow reuse algorithms} that given a submitted dataflow, identifies the intersection of reusable tasks and streams from a collection of running dataflows to form a \emph{merged dataflow}. Similar algorithms to unmerge dataflows when they are removed are also proposed. We implement these algorithms for the popular Apache Storm DSPS, and validate their performance and resource savings for 35 synthetic dataflows based on public OPMW workflows with diverse arrival and departure distributions, and on 21 real IoT dataflows from RIoTBench.Comment: To appear in IEEE eScience Conference 201

THE COLUMBUS GROUND SEGMENT – A PRECURSOR FOR FUTURE MANNED MISSIONS

In the beginning the space programs were self standing national activities, often in competition to other nations. Today space flight becomes more and more an international task. Complex space mission and deep space explorations are not longer to be stemmed by one agency or nation alone but are joint activities of several nations. The best example for such a joint (ad-) venture at the moment is the International Space Station ISS. Such international activities define complete new requirements for the supporting ground segments. The world-wide distribution of a ground segment is not any longer limited to a network of ground stations with the aim to provide a good coverage of the space craft. The coverage is sometimes – like for the ISSanyway ensured by using a relay satellite system instead. In addition to the enhanced down- and uplink methods a ground segment is aimed to connect the different centres of competence of all participating agencies/nations. From the space craft operations point of view such transnational ground segments are required to support distributed and shared operations in a predefined decision/commanding hierarchy. This has to be taken into account in the technical topology as well as for the operational set-up and teaming. Last not least increases the duration of missions, which requires a certain flexibility of the ground segment and long-term maintenance strategies for the ground segment with a special emphasis on nonintrusive replacements. The Russian space station MIR has been in the orbit for about 15 years, the ISS is currently targeted for 2020, to be for over 20 years in space

Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources

The growing deployment of sensors as part of Internet of Things (IoT) is generating thousands of event streams. Complex Event Processing (CEP) queries offer a useful paradigm for rapid decision-making over such data sources. While often centralized in the Cloud, the deployment of capable edge devices on the field motivates the need for cooperative event analytics that span Edge and Cloud computing. Here, we identify a novel problem of query placement on edge and Cloud resources for dynamically arriving and departing analytic dataflows. We define this as an optimization problem to minimize the total makespan for all event analytics, while meeting energy and compute constraints of the resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for such dynamic dataflows, and validate them using detailed simulations for 100 - 1000 edge devices and VMs. The results show that our heuristics offer O(seconds) planning time, give a valid and high quality solution in all cases, and reduce the number of query migrations. Furthermore, rebalance strategies when applied in these heuristics have significantly reduced the makespan by around 20 - 25%.Comment: 11 pages, 7 figure

Knowledge visualizations: a tool to achieve optimized operational decision making and data integration

The overabundance of data created by modern information systems (IS) has led to a breakdown in cognitive decision-making. Without authoritative source data, commanders’ decision-making processes are hindered as they attempt to paint an accurate shared operational picture (SOP). Further impeding the decision-making process is the lack of proper interface interaction to provide a visualization that aids in the extraction of the most relevant and accurate data. Utilizing the DSS to present visualizations based on OLAP cube integrated data allow decision-makers to rapidly glean information and build their situation awareness (SA). This yields a competitive advantage to the organization while in garrison or in combat. Additionally, OLAP cube data integration enables analysis to be performed on an organization’s data-flows. This analysis is used to identify the critical path of data throughout the organization. Linking a decision-maker to the authoritative data along this critical path eliminates the many decision layers in a hierarchal command structure that can introduce latency or error into the decision-making process. Furthermore, the organization has an integrated SOP from which to rapidly build SA, and make effective and efficient decisions.http://archive.org/details/knowledgevisuali1094545877Outstanding ThesisOutstanding ThesisMajor, United States Marine CorpsCaptain, United States Marine CorpsApproved for public release; distribution is unlimited

A Survey on the Evolution of Stream Processing Systems

Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and numerous worldwide open-source communities. This survey provides a comprehensive overview of fundamental aspects of stream processing systems and their evolution in the functional areas of out-of-order data management, state management, fault tolerance, high availability, load management, elasticity, and reconfiguration. We review noteworthy past research findings, outline the similarities and differences between early ('00-'10) and modern ('11-'18) streaming systems, and discuss recent trends and open problems.Comment: 34 pages, 15 figures, 5 table

Towards Digital Twin-enabled DevOps for CPS providing Architecture-Based Service Adaptation & Verification at Runtime

Industrial Product-Service Systems (IPSS) denote a service-oriented (SO) way of providing access to CPS capabilities. The design of such systems bears high risk due to uncertainty in requirements related to service function and behavior, operation environments, and evolving customer needs. Such risks and uncertainties are well known in the IT sector, where DevOps principles ensure continuous system improvement through reliable and frequent delivery processes. A modular and SO system architecture complements these processes to facilitate IT system adaptation and evolution. This work proposes a method to use and extend the Digital Twins (DTs) of IPSS assets for enabling the continuous optimization of CPS service delivery and the latter's adaptation to changing needs and environments. This reduces uncertainty during design and operations by assuring IPSS integrity and availability, especially for design and service adaptations at CPS runtime. The method builds on transferring IT DevOps principles to DT-enabled CPS IPSS. The chosen design approach integrates, reuses, and aligns the DT processing and communication resources with DevOps requirements derived from literature. We use these requirements to propose a DT-enabled self-adaptive CPS model, which guides the realization of DT-enabled DevOps in CPS IPSS. We further propose detailed design models for operation-critical DTs that integrate CPS closed-loop control and architecture-based CPS adaptation. This integrated approach enables the implementation of A/B testing as a use case and central concept to enable CPS IPSS service adaptation and reconfiguration. The self-adaptive CPS model and DT design concept have been validated in an evaluation environment for operation-critical CPS IPSS. The demonstrator achieved sub-millisecond cycle times during service A/B testing at runtime without causing CPS operation interferences and downtime.Comment: Final published version appearing in 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2022

협업 로봇을 위한 서비스 기반과 모델 기반의 소프트웨어 개발 방법론

학위논문(박사)--서울대학교 대학원 :공과대학 전기·컴퓨터공학부,2020. 2. 하순회.가까운 미래에는 다양한 로봇이 다양한 분야에서 하나의 임무를 협력하여 수행하는 모습은 흔히 볼 수 있게 될 것이다. 그러나 실제로 이러한 모습이 실현되기에는 두 가지의 어려움이 있다. 먼저 로봇을 운용하기 위한 소프트웨어를 명세하는 기존 연구들은 대부분 개발자가 로봇의 하드웨어와 소프트웨어에 대한 지식을 알고 있는 것을 가정하고 있다. 그래서 로봇이나 컴퓨터에 대한 지식이 없는 사용자들이 여러 대의 로봇이 협력하는 시나리오를 작성하기는 쉽지 않다. 또한, 로봇의 소프트웨어를 개발할 때 로봇의 하드웨어의 특성과 관련이 깊어서, 다양한 로봇의 소프트웨어를 개발하는 것도 간단하지 않다. 본 논문에서는 상위 수준의 미션 명세와 로봇의 행위 프로그래밍으로 나누어 새로운 소프트웨어 개발 프레임워크를 제안한다. 또한, 본 프레임워크는 크기가 작은 로봇부터 계산 능력이 충분한 로봇들이 서로 군집을 이루어 미션을 수행할 수 있도록 지원한다. 본 연구에서는 로봇의 하드웨어나 소프트웨어에 대한 지식이 부족한 사용자도 로봇의 동작을 상위 수준에서 명세할 수 있는 스크립트 언어를 제안한다. 제안하는 언어는 기존의 스크립트 언어에서는 지원하지 않는 네 가지의 기능인 팀의 구성, 각 팀의 서비스 기반 프로그래밍, 동적으로 모드 변경, 다중 작업(멀티 태스킹)을 지원한다. 우선 로봇은 팀으로 그룹 지을 수 있고, 로봇이 수행할 수 있는 기능을 서비스 단위로 추상화하여 새로운 복합 서비스를 명세할 수 있다. 또한 로봇의 멀티 태스킹을 위해 '플랜' 이라는 개념을 도입하였고, 복합 서비스 내에서 이벤트를 발생시켜서 동적으로 모드가 변환할 수 있도록 하였다. 나아가 여러 로봇의 협력이 더욱 견고하고, 유연하고, 확장성을 높이기 위해, 군집 로봇을 운용할 때 로봇이 임무를 수행하는 도중에 문제가 생길 수 있으며, 상황에 따라 로봇을 동적으로 다른 행위를 수행할 수 있다고 가정한다. 이를 위해 동적으로도 팀을 구성할 수 있고, 여러 대의 로봇이 하나의 서비스를 수행하는 그룹 서비스를 지원하고, 일대 다 통신과 같은 새로운 기능을 스크립트 언어에 반영하였다. 따라서 확장된 상위 수준의 스크립트 언어는 비전문가도 다양한 유형의 협력 임무를 쉽게 명세할 수 있다. 로봇의 행위를 프로그래밍하기 위해 다양한 소프트웨어 개발 프레임워크가 연구되고 있다. 특히 재사용성과 확장성을 중점으로 둔 연구들이 최근 많이 사용되고 있지만, 대부분의 이들 연구는 리눅스 운영체제와 같이 많은 하드웨어 자원을 필요로 하는 운영체제를 가정하고 있다. 또한, 프로그램의 분석 및 성능 예측 등을 고려하지 않기 때문에, 자원 제약이 심한 크기가 작은 로봇의 소프트웨어를 개발하기에는 어렵다. 그래서 본 연구에서는 임베디드 소프트웨어를 설계할 때 쓰이는 정형적인 모델을 이용한다. 이 모델은 정적 분석과 성능 예측이 가능하지만, 로봇의 행위를 표현하기에는 제약이 있다. 그래서 본 논문에서 외부의 이벤트에 의해 수행 중간에 행위를 변경하는 로봇을 위해 유한 상태 머신 모델과 데이터 플로우 모델이 결합하여 동적 행위를 명세할 수 있는 확장된 모델을 적용한다. 그리고 딥러닝과 같이 계산량을 많이 필요로 하는 응용을 분석하기 위해, 루프 구조를 명시적으로 표현할 수 있는 모델을 제안한다. 마지막으로 여러 로봇의 협업 운용을 위해 로봇 사이에 공유되는 정보를 나타내기 위해 두 가지 모델을 사용한다. 먼저 중앙에서 공유 정보를 관리하기 위해 라이브러리 태스크라는 특별한 태스크를 통해 공유 정보를 나타낸다. 또한, 로봇이 자신의 정보를 가까운 로봇들과 공유하기 위해 멀티캐스팅을 위한 새로운 포트를 추가한다. 이렇게 확장된 정형적인 모델은 실제 로봇 코드로 자동 생성되어, 소프트웨어 설계 생산성 및 개발 효율성에 이점을 가진다. 비전문가가 명세한 스크립트 언어는 정형적인 태스크 모델로 변환하기 위해 중간 단계인 전략 단계를 추가하였다. 제안하는 방법론의 타당성을 검증하기 위해, 시뮬레이션과 여러 대의 실제 로봇을 이용한 협업하는 시나리오에 대해 실험을 진행하였다.In the near future, it will be common that a variety of robots are cooperating to perform a mission in various fields. There are two software challenges when deploying collaborative robots: how to specify a cooperative mission and how to program each robot to accomplish its mission. In this paper, we propose a novel software development framework that separates mission specification and robot behavior programming, which is called service-oriented and model-based (SeMo) framework. Also, it can support distributed robot systems, swarm robots, and their hybrid. For mission specification, a novel scripting language is proposed with the expression capability. It involves team composition and service-oriented behavior specification of each team, allowing dynamic mode change of operation and multi-tasking. Robots are grouped into teams, and the behavior of each team is defined with a composite service. The internal behavior of a composite service is defined by a sequence of services that the robots will perform. The notion of plan is applied to express multi-tasking. And the robot may have various operating modes, so mode change is triggered by events generated in a composite service. Moreover, to improve the robustness, scalability, and flexibility of robot collaboration, the high-level mission scripting language is extended with new features such as team hierarchy, group service, one-to-many communication. We assume that any robot fails during the execution of scenarios, and the grouping of robots can be made at run-time dynamically. Therefore, the extended mission specification enables a casual user to specify various types of cooperative missions easily. For robot behavior programming, an extended dataflow model is used for task-level behavior specification that does not depend on the robot hardware platform. To specify the dynamic behavior of the robot, we apply an extended task model that supports a hybrid specification of dataflow and finite state machine models. Furthermore, we propose a novel extension to allow the explicit specification of loop structures. This extension helps the compute-intensive application, which contains a lot of loop structures, to specify explicitly and analyze at compile time. Two types of information sharing, global information sharing and local knowledge sharing, are supported for robot collaboration in the dataflow graph. For global information, we use the library task, which supports shared resource management and server-client interaction. On the other hand, to share information locally with near robots, we add another type of port for multicasting and use the knowledge sharing technique. The actual robot code per robot is automatically generated from the associated task graph, which minimizes the human efforts in low-level robot programming and improves the software design productivity significantly. By abstracting the tasks or algorithms as services and adding the strategy description layer in the design flow, the mission specification is refined into task-graph specification automatically. The viability of the proposed methodology is verified with preliminary experiments with three cooperative mission scenarios with heterogeneous robot platforms and robot simulator.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Contribution 7 1.3 Dissertation Organization 9 Chapter 2. Background and Existing Research 11 2.1 Terminologies 11 2.2 Robot Software Development Frameworks 25 2.3 Parallel Embedded Software Development Framework 31 Chapter 3. Overview of the SeMo Framework 41 3.1 Motivational Examples 45 Chapter 4. Robot Behavior Programming 47 4.1 Related works 48 4.2 Model-based Task Graph Specification for Individual Robots 56 4.3 Model-based Task Graph Specification for Cooperating Robots 70 4.4 Automatic Code Generation 74 4.5 Experiments 78 Chapter 5. High-level Mission Specification 81 5.1 Service-oriented Mission Specification 82 5.2 Strategy Description 93 5.3 Automatic Task Graph Generation 96 5.4 Related works 99 5.5 Experiments 104 Chapter 6. Conclusion 114 6.1 Future Research 116 Bibliography 118 Appendices 133 요약 158Docto