Design Of Scalable Systems Research Articles

Optimizing Resource Management for Shared Microservices: A Scalable System Design

A common approach to improving resource utilization in data centers is to adaptively provision resources based on the actual workload. One fundamental challenge of doing this in microservice management frameworks, however, is that different components of a service can exhibit significant differences in their impact on end-to-end performance. To make resource management more challenging, a single microservice can be shared by multiple online services that have diverse workload patterns and SLA requirements. We present an efficient resource management system, namely Erms, for guaranteeing SLAs with high probability in shared microservice environments. Erms profiles microservice latency as a piece-wise linear function of the workload, resource usage, and interference. Based on this profiling, Erms builds resource scaling models to optimally determine latency targets for microservices with complex dependencies. Erms also designs new scheduling policies at shared microservices to further enhance resource efficiency. Experiments across microservice benchmarks as well as trace-driven simulations demonstrate that Erms can reduce SLA violation probability by 5× and more importantly, lead to a reduction in resource usage by 1.6×, compared to state-of-the-art approaches.

Directly‐Deposited Ultrathin Solid Polymer Electrolyte for Enhanced CO2 Electrolysis

AbstractThe economic viability of carbon dioxide electroreduction (CO2R) relies on improved performance accompanied by scalable system design. Membranes are commonly used for the separation of reduction and oxidation products as well as to provide a suitable micro‐environment for CO2R. Commercial membranes often address only one of the key challenges in CO2R: either they offer a suitable micro‐environment for CO2R (e.g., anion exchange membrane) or suppress carbonate cross‐over (e.g., cation exchange membrane and bipolar membrane). This work presents a cation‐infused ultrathin (≈3 µm) solid polymer electrolyte (CISPE) that concomitantly addresses both challenges via a bidirectional ion transport mechanism and suppressed cathode flooding. This directly‐deposited CISPE (that substitutes the commonly used pre‐made membrane) enables record high full‐cell energy efficiency of 28% at 100 mA cm−2 for one‐step CO2 electrolysis to ethylene (C2H4) with ≈110 h of stable operation. This translates into a record low energy cost of 290 GJ per ton C2H4 for the end‐to‐end process (i.e., CO2 capture and electroreduction, carbonate regeneration, CO2 separation from anode and cathode streams) in a membrane electrode assembly CO2R. The present work offers a versatile design paradigm for functional polymer electrolytes, opening the door to stable, and efficient electrolysis of high‐value feedstock chemicals and fuels using low‐cost catalysts.

Technological measures to improve the environmental friendliness of alcohol industry enterprises

The article concerns technological measures to improve the environmental friendliness of food industry enterprises: in particular, aspects of processing waste from the alcohol industry with further use in agriculture (in animal feed). It is important to use methods to optimize the feeding of farm animals, allowing one to involve feed resources that do not have a harmful effect on health and productivity, while significantly reducing the consumption of grain components. This resource is dry distillery grains, which is a waste product of distillery production. Technologies of distillery bard processing include its evaporation with further drying. The main waste at the ethanol plant is whole stillage. It is centrifuged to separate thin stillage from wet grains. Thin stillage contains 5-10% of solids. For reducing energy consumption during the concentration process, it is possible to use a direct contact evaporator (bubbling evaporator). In this article, the bubbling evaporator design was developed. To justify the size of the evaporator, the temperature difference profile along the column length was determined. It is less than 1 and remains constant, which determines the high efficiency of heat transfer. The most effective parameters of the evaporation process according to moisture removal are a gas flow rate of 1.3–1.5 m3/h, air temperature of 400±15. Work results can be used in the design of scalable systems for thin stillage processing and applicable in medium-sized ethanol plants.

TeMoto: A Software Framework for Adaptive and Dependable Robotic Autonomy With Dynamic Resource Management

For widespread deployment of robots in challenging environments (fire fighting, search and rescue, planetary exploration, etc), the software of the robot must allow for reliability and adaptability. For many existing systems, an unexpected change in mission specification, component failures, or energy conservation requires downtime for adaption (redesign of mission logic, switching sensor data processing pipeline, etc.). This is because software and hardware components for robotic applications are commonly chosen or designed based on task requirements and integrated either directly in the source code or via system configuration scripts, such as ROS launch files, leading to a fixed monolithic design. As the necessity and extent of adaptive behaviors is not always known prior to deployment, the structure of a robot’s software needs to support it by design. In this paper, we propose TeMoto, a novel architecture for adaptive autonomous robots, and a ROS-based framework of openly available software tools that implement the TeMoto architecture. TeMoto is a developer tool which combines dynamic (run-time) task and resource management, encourages modular and scalable system design and is task and platform agnostic - TeMoto provides the foundation for an adaptive robotic system. The feasibility of the TeMoto framework is qualitatively assessed via experiments on single and multi-robot setups spanning common scenarios (teleoperation, autonomous surveillance, cargo delivery, etc.). TeMoto-based systems exhibit increased fault tolerance and dynamic reconfigurability of software and hardware resources, as well as up to a 47% reduction in power consumption compared to the non TeMoto-enabled reference setup.

An Intelligent IoT-Based System Design for Controlling and Monitoring Greenhouse Temperature

The Kingdom of Saudi Arabia is known for its extreme climate where temperatures can exceed 50 °C, especially in summer. Improving agricultural production can only be achieved using innovative environmentally suitable solutions and modern agricultural technologies. Using Internet of Things (IoT) technologies in greenhouse farming allows reduction of the immediate impact of external climatic conditions. In this paper, a highly scalable intelligent system controlling, and monitoring greenhouse temperature using IoT technologies is introduced. The first objective of this system is to monitor the greenhouse environment and control the internal temperature to reduce consumed energy while maintaining good conditions that improve productivity. A Petri Nets (PN) model is used to achieve both monitoring of the greenhouse environment and generating the suitable reference temperature which is sent later to a temperature regulation block. The second objective is to provide an Energy-Efficient (EE) scalable system design that handles massive amounts of IoT big data captured from sensors using a dynamic graph data model to be used for future analysis and prediction of production, crop growth rate, energy consumption and other related issues. The design tries to organize various possible unstructured formats of raw data, collected from different kinds of IoT devices, unified and technology-independent fashion using the benefit of model transformations and model-driven architecture to transform data in structured form.

GreyCat: Efficient what-if analytics for data in motion at scale

Over the last few years, data analytics shifted from adescriptive era, confined to the explanation of past events, to the emergence of predictive techniques. Nonetheless, existing predictive techniques still fail to effectively explore alternative futures, which continuously diverge from current situations when exploring the effects of what-if decisions. Enabling prescriptive analytics therefore calls for the design of scalable systems that can cope with the complexity and the diversity of underlying data models. In this article, we address this challenge by combining graphs and time series within a scalable storage system that can organize a massive amount of unstructured and continuously changing data into multi-dimensional data models, called Many-Worlds Graphs. We demonstrate that our open source implementation, GreyCat, can efficiently fork and update thousands of parallel worlds composed of millions of timestamped nodes, such as what-if exploration.

A Modular Framework for a Bus Monitoring & Passenger Notification System

Vehicle tracking is a very powerful ingredient in developing location based services such as passenger notification system. It provides value to passenger and gives tools for analysis, evaluation, and management strategies for businesses. A major challenge is for businesses to implement such systems. Tracking systems are either too simple to do the task or too complex and expensive to develop. In this paper a flexible and scalable system design for building such solutions is proposed using University Putra Malaysia (UPM) student bus scheme. System specifications has been calculated, designed and simulated providing an efficient system with the capabilities of monitoring buses, inform student with current bus location and time to arrive and also send phone messages and e-mails to students upon request.

High-performance computing in mobile services

With the ever increasing popularity of smart phones, mobile services have been evolving rapidly to allow users to enjoy localized and personalized experiences. Users can discover local information and keep connected with family and friends on the go, and ultimately to experience the convergence of cyber space and physical world where digital technologies are interwoven into the day-to-day life. A pivotal component of such a cyber-physical convergence is the contextual intelligence. The extraction and dissemination of contextual information around users is the key for the cyber capabilities to be applied to physical activities and for the cyber world to better reflect the physical reality. In this talk, we shall address some issues arising from context-based mobile services. In particular, we discuss how mobility impacts contextual relevancy and personalization in mobile services. The relevancy and timeliness of contextual information not only are essential for these services to deliver great user experiences, but also put significant computation pressure on service infrastructure that processes continuous data streams in real time and disseminate relevant data to a large amount of mobile users. This talk will explore the challenges and opportunities for high-performance computing in mobile services. Based on key findings from large-scale mobile measurement data, the talk will analyze the tradeoff of different computing architectures, present case studies of scalable system design and implementation for personalized mobile services, and conclude with open challenges for the broad research community in performance measurement and modeling.

Energy scalable system design

We introduce the notion of energy-scalable system-design. The principal idea is to maximize computational quality for a given energy constraint at all levels of the system hierarchy. The desirable energy-quality (E-Q) characteristics of systems are discussed. E-Q behavior of algorithms is considered and transforms that significantly improve scalability are analyzed using three distinct categories of commonly used signal-processing algorithms on the StrongARM SA-1100 processor as examples (viz., filtering, frequency domain transforms and classification). Scalability hooks in hardware are analyzed using similar examples on the Pentium III processor and a scalable programming methodology is proposed. Design techniques for true energy scalable hardware are also demonstrated using filtering as an example.