Deployment Life Cycle Research Articles

Microservices and serverless functions—lifecycle, performance, and resource utilisation of edge based real-time IoT analytics

Edge Computing harnesses resources close to the data sources to reduce end-to-end latency and allow real-time process automation for verticals such as Smart City, Healthcare and Industry 4.0. Edge resources are limited when compared to traditional Cloud data centres; hence the choice of proper resource management strategies in this context becomes paramount. Microservice and Function as a Service architectures support modular and agile patterns, compared to a monolithic design, through lightweight containerisation, continuous integration/deployment and scaling. The advantages brought about by these technologies may initially seem obvious, but we argue that their usage at the Edge deserves a more in-depth evaluation. By analysing both the software development and deployment lifecycle, along with performance and resource utilisation, this paper explores microservices and two alternative types of serverless functions to build edge real-time IoT analytics. In the experiments comparing these technologies, microservices generally exhibit slightly better end-to-end processing latency and resource utilisation than serverless functions. One of the serverless functions and the microservices excel at handling larger data streams with auto-scaling. Whilst serverless functions natively offer this feature, the choice of container orchestration framework may determine its availability for microservices. The other serverless function, while supporting a simpler lifecycle, is more suitable for low-invocation scenarios and faces challenges with parallel requests and inherent overhead, making it less suitable for real-time processing in demanding IoT settings.

Robust ML model ensembles via risk-driven anti-clustering of training data

In this paper, we improve the robustness of Machine Learning (ML) classifiers against training-time attacks by linking the risk of training data being tampered with to the redundancy in the ML model's design needed to prevent it. Our defense mechanism is directly applicable to classifiers' training data, without any knowledge of the specific ML model to be hardened. First, we compute the training data proximity to class separation surfaces, identified via a reference linear model. Each data point is associated with a risk index, which is used to partition the training set by an unsupervised technique. Then, we train a learner for each partition and combine the learners' output in an ensemble. Our method treats the protected ML classifier as a black box and is inherently robust to transfer attacks. Experiments show that, for data poisoning rates between 6 and 25 percent of the training set, our method is more robust compared to benchmarks and to a monolithic version of the model trained on the whole training set. Our results make a convincing case for adopting training set partitioning and ensemble generation as a stage of ML models' development and deployment lifecycle.

Roadmap to Reasoning in Microservice Systems: A Rapid Review

Understanding software systems written by others is often challenging. When we want to assess systems to reason about them, i.e., to understand dependencies, analyze evolution trade-offs, or to verify conformance to the original blueprint, we must invest broad efforts. This becomes difficult when considering decentralized systems. Microservice-based systems are mainstream these days; however, to observe, understand, and manage these systems and their properties, we are missing fundamental tools that would derive various simplified system abstract perspectives. Microservices architecture characteristics yield many advantages to system operation; however, they bring challenges to their development and deployment lifecycles. Microservices urge a system-centric perspective to better reason about the system evolution and its quality attributes. This process review paper considers the current system analysis approaches and their possible alignment with automated system assessment or with human-centered approaches. We outline the necessary steps to accomplish holistic reasoning in decentralized microservice systems. As a contribution, we provide a roadmap for analysis and reasoning in microservice-based systems and suggest that various process phases can be decoupled through the introduction of system intermediate representation as the trajectory to provide various system-centered perspectives to analyze various system aspects. Furthermore, we cover different technical-based reasoning strategies and metrics in addition to the human-centered reasoning addressed through alternative visualization approaches. Finally, a system evolution is discussed from the perspective of such a reasoning process to illustrate the impact analysis evaluation over system changes.

Putting AI ethics to work: are the tools fit for purpose?

Bias, unfairness and lack of transparency and accountability in Artificial Intelligence (AI) systems, and the potential for the misuse of predictive models for decision-making have raised concerns about the ethical impact and unintended consequences of new technologies for society across every sector where data-driven innovation is taking place. This paper reviews the landscape of suggested ethical frameworks with a focus on those which go beyond high-level statements of principles and offer practical tools for application of these principles in the production and deployment of systems. This work provides an assessment of these practical frameworks with the lens of known best practices for impact assessment and audit of technology. We review other historical uses of risk assessments and audits and create a typology that allows us to compare current AI ethics tools to Best Practices found in previous methodologies from technology, environment, privacy, finance and engineering. We analyse current AI ethics tools and their support for diverse stakeholders and components of the AI development and deployment lifecycle as well as the types of tools used to facilitate use. From this, we identify gaps in current AI ethics tools in auditing and risk assessment that should be considered going forward.

Building and Testing Modular Programs for Programmable Data Planes

Programmable data planes, PDPs, enable an unprecedented level of flexibility and have emerged as a promising alternative to existing data planes. Despite the rapid development and prototyping cycles that PDPs promote, the existing PDP ecosystem lacks appropriate abstractions and algorithms to support these rapid testing and deployment life-cycles. In this paper, we propose P4Visor, a lightweight virtualization abstraction that provides testing primitives as a first-order citizen of the PDP ecosystem. P4Visor can efficiently support multiple PDP programs through a combination of compiler optimizations and program analysis-based algorithms. P4Visor’s algorithm improves over state-of-the-art techniques by significantly reducing the resource overheads associated with embedding numerous versions of a PDP program into hardware. To demonstrate the efficiency and viability of P4Visor, we implemented and evaluated P4Visor on both a software switch and an FPGA-based hardware switch using fourteen of different PDP programs. Our results demonstrate that P4Visor introduces minimal overheads and is one order of magnitude more efficient than existing PDPs primitives for concurrently supporting multiple programs.

Log++ logging for a cloud-native world

Logging is a fundamental part of the software development and deployment lifecycle but logging support is often provided as an afterthought via limited library APIs or third-party modules. Given the critical nature of logging in modern cloud, mobile, and IoT development workflows, the unique needs of the APIs involved, and the opportunities for optimization using semantic knowledge, we argue logging should be included as a central part of the language and runtime designs. This paper presents a rethinking of the logger for modern cloud-native workflows. Based on a set of design principles for modern logging we build a logging system, that supports near zero-cost for disabled log statements, low cost lazy-copying for enabled log statements, selective persistence of logging output, unified control of logging output across different libraries, and DevOps integration for use with modern cloud-based deployments. To evaluate these concepts we implemented the Log++ logger for Node.js hosted JavaScript applications.

ISEC: An Optimized Deep Learning Model for Image Classification on Edge Computing

Optimization strategies in deep learning models require different techniques for different use cases. Besides, various phases of the model deployment life-cycle specify possible and particular optimization strategies. In this paper, an optimized deep learning model on the edge computing environment is proposed for image classification cases. For preparing the dataset, the image preprocessing and data augmentation methods are utilized to prepare the data for the training process. To accelerate the deep learning training process, this system implemented CPU optimization and hyperparameter tuning. Tensorflow is applied as a framework for the training model. InceptionV3, VGG16, and MobileNet are applied as topology implemented in the deep learning training comparison. In this case, InceptionV3 was used for modeling the deep learning applications on edge. To optimize the trained model, a Model Optimizer is used on the edge device. It can be seen in the experiments, MobileNet was the least accurate model (85%) and the longest time to load the model (71s). VGG16 was the most reliable (91%) and the shortest time to load the model (50s). InceptionV3 has median accuracy (87%) and the average time to load the model (52s).

Industrial Wireless Systems Guidelines: Practical Considerations and Deployment Life Cycle

Industrial wireless has great potential to improve the monitoring and control of various processes and equipment in distributed automation systems because of advances in wireless networks and installation flexibility. However, harsh industrial environments and interference from the crowded electromagnetic frequency spectrum make it challenging for wireless to achieve the required performance. Thus, it is important to understand the benefits of wireless in certain industrial settings, the approach for reliable operations, the technologies to be used, and the best practices for successful deployment.

The Parenting Cycle of Deployment

Parents of dependent children comprise approximately 42% of Active Duty and National Guard/Reserve military members serving in Operation Iraqi Freedom/Operation Enduring Freedom. Recent estimates indicate that more than two million children have experienced parental deployment since the terrorist attacks on September 11, 2001. This article seeks to characterize the impact of the deployment life cycle on parenting roles among service members and at-home partners/caregivers of dependent children. Specifically, a new conceptual framework is presented for considering the ways in which parenting and co-parenting processes are affected by the demands and transitions inherent in contemporary deployment to a war zone. Although the phase-based emotional cycle of deployment continues to offer an instructive description of the broad challenges faced by military couples, a parenting cycle of deployment model shifts the perspective to the critical and largely ignored processes of parenting in the context of deployment and war, and to the realities faced by parents serving in the U.S. military. Implications for prevention, intervention, and future research related to military families are addressed.

Principles for Architecting Adaptable Command and Control Systems

Command and Control (C2) systems are complex and require large capital investments, asynchronous design and test periods for different elements, and long deployment lifecycles of substituent elements. During the deployment, or evolution, of a C2 system, the larger operational context is likely to change. This has been observed to force costly and often suboptimal architectural changes at run-time as the C2 system struggles to adapt. Adaptability has been defined as a sy stem's ability to change itself in resp onse to changin g env ironment s; unfortunately, not all changes will be positive. It remains conceivable that some instances of adaptation can lead to sub-optimization or even global failures. These failures need be minimized. Traditional C2 systems design attempts to minimize these risks through highly detailed design specifications, rules, and testing requirements. Unfortunately, such methods may not enable an adaptive architecture. A principles based approach, consisting of a relatively small set of strategic outcome oriented concepts, could serve to guide the development of an adaptable C2 architecture while simultaneously minimizing the corresponding risk to the C2 system. This paper proposes and examines a set of principles for architecting adaptable systems and offers areas for further research.

Performance Contracting in After-Sales Service Supply Chains

Performance-based contracting is reshaping service support supply chains in capital-intensive industries such as aerospace and defense. Known as “power by the hour” in the private sector and as “performance-based logistics” (PBL) in defense contracting, it aims to replace traditionally used fixed-price and cost-plus contracts to improve product availability and reduce the cost of ownership by tying a supplier's compensation to the output value of the product generated by the customer (buyer). To analyze implications of performance-based relationships, we introduce a multitask principal-agent model to support resource allocation and use it to analyze commonly observed contracts. In our model the customer (principal) faces a product availability requirement for the “uptime” of the end product. The customer then offers contracts contingent on availability to n suppliers (agents) of the key subsystems used in the product, who in turn exert cost reduction efforts and set spare-parts inventory investment levels. We show that the first-best solution can be achieved if channel members are risk neutral. When channel members are risk averse, we find that the second-best contract combines a fixed payment, a cost-sharing incentive, and a performance incentive. Furthermore, we study how these contracts evolve over the product deployment life cycle as uncertainty in support cost changes. Finally, we illustrate the application of our model to a problem based on aircraft maintenance data and show how the allocation of performance requirements and contractual terms change under various environmental assumptions.

9.4.3 Seamless Engineering Process to Enhance Systems Engineering Effectiveness

AbstractIn order to effectively execute large and complex programs, an engineering process must be established that eliminates multi‐disciplinary barriers. This process needs to be focused on the entire product development cycle and work to keep engineering disciplines from executing independently. This paper will describe the integration of the systems, hardware, and software engineering approach, the utilization of event driven, use case‐based, object oriented design practices, and the use of an integrated tool suite to perform the requirements decomposition, traceability, architecture design, and multi‐level documentation generation. The authors will also address how this process aids unit testing, system level testing, and regression testing methodologies throughout the product development and deployment lifecycles.

5.2.3 Obsolescence Management for System‐of‐System Hierarchies – A Technology‐based Approach

AbstractMany systems face a similar obsolescence issue, which is that the desired system deployment life cycle is much longer than the life cycle of the constituent parts (hardware and software) that make up the physical architecture. Thus, the system baseline must evolve over its operational life cycle just to remain functionally viable. This problem is compounded as the size of the system grows toward network‐centric system‐of‐systems and as the use of commercial technologies and products increases. Thus, the challenge is to perform technology assessments that facilitate the forward planning of technology evolution on the system of interest. A corollary challenge is to perform this planning while simultaneously optimizing the life cycle affordability for the customer.This paper provides a methodology for taking current technology information and transforming it into a system evolution plan (the term system being used in the most general sense to encompass sub‐assemblies through system‐of‐systems) using affordability as the optimizing variable for technology change incorporation.

Building a multisite Web architecture

Any large organizations that first came online in the late 1990s are now facing the decision whether to upgrade their Web systems or to start anew. Given the speed with which new technologies are introduced in the Web environment, system deployment life cycles have shrunk significantly-but so have system life spans. After only a few years, an organization's Internet infrastructure is likely to need a major overhaul. In late 2001, the systems architecture team to which I belong took on these issues for an organization that wanted to rebuild its Web infrastructure. The existing infrastructure contained multiple single points of failure, could not scale to expected usage patterns, was built on proprietary systems, and had a high management overhead. The legacy infrastructure had grown organically over the previous five years as administrators added unplanned features and functionality, and usage had grown 100-fold since the specifications were initially developed. Because of the age and condition of the legacy systems, we decided to redesign the solution from scratch to overcome the inherent limitations. This case study describes the process our systems architecture team followed for designing and deploying the new architecture. I detail the component selection rationale, with implementation details where allowed. Ours is just one successful approach to deploying a. multisite, fully redundant Web-based system for a large organization; other reasonable and viable ways to build such a system also exist.

The Economic Evaluation of Emerging Nuclear Energy Systems

Most emerging nuclear energy systems are in the early phases of the research, development, design, and deployment life cycle and/or represent pioneer or first-of-a-kind projects; hence, the uncerta...