Runtime Changes Research Articles

Impact of refrigerant undercharge faults on building indoor conditions and HVAC system operation in residential Buildings: A simulation study

This study investigates the impact of refrigerant undercharge on indoor temperature and HVAC system performance in residential buildings. Simulation models for typical residential buildings in Orlando, FL and Indianapolis, IN were developed using the ResStock database. A refrigerant undercharge fault model was then applied to the simulations with varying levels of fault intensity. The paper offers an extensive analysis, revealing that variations in supply air temperature, equipment runtime, and cooling energy consumption due to the level of refrigerant undercharge faults are notably significant on a summer representative day. Similarly, on a winter representative day, changes in supply air temperature and runtime are significant as well as changes in supplemental heat energy consumption. We find that occupants may remain oblivious to these faults during the cooling season, particularly when the HVAC system is oversized; in that case, supply air temperature data could help detect a fault. Another challenge is that during the heating season, when the supplemental heater operates, it is difficult to identify a refrigerant undercharge fault using only indoor and supply air temperature data. This study finds that supply air temperature, equipment runtime, and supplemental heater energy consumption data can help in detecting refrigerant undercharge faults.

Domain-Independent Lifelong Problem Solving Through Distributed ALife Actors.

A domain-independent problem-solving system based on principles of Artificial Life is introduced. In this system, DIAS, the input and output dimensions of the domain are laid out in a spatial medium. A population of actors, each seeing only part of this medium, solves problems collectively in it. The process is independent of the domain and can be implemented through different kinds of actors. Through a set of experiments on various problem domains, DIAS is shown able to solve problems with different dimensionality and complexity, to require no hyperparameter tuning for new problems, and to exhibit lifelong learning, that is, to adapt rapidly to run-time changes in the problem domain, and to do it better than a standard, noncollective approach. DIAS therefore demonstrates a role for ALife in building scalable, general, and adaptive problem-solving systems.

Metascheduling Using Discrete Particle Swarm Optimization for Fault Tolerance in Time-Triggered IoT-WSN

In time-triggered systems, adaptation is performed by using metascheduling approaches to ensure temporal predictability. Wireless sensor networks (WSNs) are growing to be used as a reliable, an energy-efficient, and a scalable network infrastructure for numerous Internet of Things (IoT) applications. Because of run-time changes because of failure events, a metascheduling system is required to address the changes by precomputing schedules for several context events at design time. Therefore, this paper proposes a metascheduler that solves a new scheduling problem for each adaptation scenario in IoT-WSN using our offline algorithm named discrete particle swarm optimization for reliable task allocation (DPSO-TA), resulting in a multi-schedule graph that combines the repeated schedules. In this work, a time-triggered IoT-WSN metascheduler is proposed that takes into considerations the node or link failures that are common in WSN. Single and two failure events are considered, where a graph for all feasible schedules is formed at the network design time. Such a large number of schedules causes a state space explosion problem, which is managed using a re-convergence method. Different application model sizes and network topologies are used to assess the proposed metascheduler under various failure event scenarios. The results show a reduction in the size of the multi-schedule graph by applying inputs with different deadline values and different number of tasks. Furthermore, the validity suggested by the proposed metascheduler, which is the ratio of valid to invalid schedules, is improved when compared to algorithms that schedule tasks to hosts with the shortest completion time or algorithms that select hosts for a set of sorted tasks based on energy consumed and task arrival time.

IP Detection Using Different Approaches Using VPN/Proxy

Abstract: The number of cyberattacks worldwide since the Covid-19 epidemic has reached an all-time high as they continue to rise day by day. This assault is typically carried out by criminals who want to steal personal data or commit financial fraud. As can be seen, data is the new kind of currency. Therefore, maintaining privacy is crucial. Since the attackers utilise techniques for concealment, such as anonymizing proxies or Virtual Private Networks (VPN), spotting unauthorised users might be challenging. There is a model to identify how network consumption can be categorised, detected, and tracked using that manner. Using these services to conceal their identity, the attacker launches attacks using SSH and HTTP. Other applications with the same criteria can use this strategy. The most frequent data breaches result in too much loss for businesses. Any person who hits a website's server is considered an attacker. typically to collect user data, shut down the website, or make inappropriate runtime changes to the website. Cyberpunks or crackers will employ a honeypot to find this and will be able to obtain their IP address. Once it has the IP address, it can follow the attacker's geolocation and determine their latitude and longitude. Even though they are utilising a proxy or VPN. This strategy will aid in locating the attacker, allowing access to their system logs and other pertinent information. As a result, it may monitor IP hidden behind VPN or a proxy and learn more about the attacker

A Flexible Approach to Multi-party Business Process Execution on Blockchain

In modern business scenarios, more and more organisations have to deal with the critical requirements of trustworthiness and flexibility, when collaborating in multi-party business processes. This calls for new kinds of systems able to manage collaborative processes in untrusted and dynamic environments. Concerning the collaborative perspective, the Business Process Management discipline has provided effective and standardised solutions for a long time, now. Regarding the trustworthiness perspective, blockchain is advocated as one of the most prominent technologies to guarantee trust in a multi-party setting. However, while the immutability of blockchain provides transparent and secure proof of past business interactions, it hinders the flexibility of the business process execution, as the business logic regulating the process execution is immutably stored in the blockchain. On the other hand, flexibility is a property that is becoming crucial in such a setting due to the high dynamism of the business scenarios. In fact, it permits to modify a process at run-time to deal with internal or external changes. In this paper, we face this issue by proposing an architecture for the flexible blockchain-based execution of multi-party business processes. In our approach, business processes are modelled by BPMN choreography diagrams translated into code, whose execution state is then stored in the blockchain. Flexibility is achieved by decoupling the business process’s logic from its execution state, thus allowing run-time changes to the process execution without losing the fundamental properties of trust provided by the blockchain. To show the effectiveness of our approach, we provide a prototypical implementation, called FlexChain, and we use it on a case study from the healthcare application domain. The results obtained by the analysis of cost for the reported case study show the feasibility of the approach. In particular, major costs to sustain relate to one-time operations, such as the deployment and the run-time update of the model, while the most frequent actions are quite efficient.

Continuous QoS-aware adaptation of Cloud-IoT application placements

Cloud-Internet of Things computing paradigms call for novel and efficient methodologies to decide where to place application services in continuity with Continuous Integration/Continuous Deployment pipelines and infrastructure monitoring. In this article, we present Continuous Adaptation (CA), a new DevOps practice for (1) detecting runtime changes in the application requirements or the infrastructure that, due to their change in resource consumption or their effects on the Quality of Service (QoS), can affect the validity and dependability of the current application placement, and for (2) locally intervening on them by suggesting new placements that ensure all (functional and non-functional) application requirements are met. We assess a prototype of CA, ConDADO, and analyze its performance over a motivating use case. ConDADO adapts the application placement to environmental changes through the use of continuous reasoning, reducing the size of the problem to be solved to optimize its performance. The evaluation shows that ConDADO is able to obtain nearly optimal QoS up to 4.2times faster than alternative techniques, also minimizing the cost of service migration.

RLReC: Towards Reinforcement Learning-based Dynamic Design of Reconfiguration Control

The design of Reconfigurable Control Systems (RCS) is a tremendous task since that their increasing need for reconfiguration imposes the anticipation of all potential runtime changes at design time, which is in most cases infeasible. To deal with the high dynamicity of RCS at design-time we propose a novel design approach, RLReC (Reinforcement Learning-based Reconfiguration Control) allowing for the improvement of reconfiguration knowledge through online (runtime) exploration. This approach combines the benefits of meta-modelling with Reinforcement Learning (RL) principals for reconfiguration knowledge modelling in order to handle early and effective exploration of various design choices. In this work, the reconfiguration controller is an autonomous software agent designed as a RL agent (RLRA) that handles both offline (exploitation) and online (exploration) learning through interactions with the controlled system at runtime. In particular, the RLRA has to select reconfiguration rules that were not selected before, which is known as exploration. The main aim of this proposal is to cope with design-time unplanned changes by giving the RLRA the ability of improving its knowledge through online learning feedback. We illustrate the use of our contributions with a running example from the manufacturing domain.

Trying out a million genes to find the perfect pair with RTIST.

Consensus methods can be used for reconstructing a species tree from several gene trees, which exhibit incompatible topologies due to incomplete lineage sorting. Motivated by the fact that there are no anomalous rooted gene trees with three taxa and no anomalous unrooted gene trees with four taxa in the multispecies coalescent model, several contemporary methods form the gene tree consensus by finding the median tree with respect to the triplet or quartet distance-i.e. estimate the species tree as the tree which minimizes the sum of triplet or quartet distances to the input gene trees. These methods reformulate the solution to the consensus problem as the solution to a recursively solved dynamic programming (DP) problem. We present an iterative, easily parallelizable approach to finding the exact median triplet tree and implement it as an open source software package that can also find suboptimal consensus trees within a specified triplet distance to the gene trees. The most time-consuming step for methods of this type is the creation of a weights array for all possible subtree bipartitions. By grouping the relevant calculations and array update operations of different bipartitions of the same subtree together, this implementation finds the exact median tree of many gene trees faster than comparable methods, has better scaling properties with respect to the number of gene trees and has a smaller memory footprint. RTIST (Rooted Triple Inference of Species Trees) finds the exact median triplet tree of a set of gene trees. Its runtime and memory footprints scale better than existing algorithms. RTIST can resolve all the non-unique median trees, as well as sub-optimal consensus trees within a user-specified triplet distance to the median. Although it is limited in the number of taxa (≤20), its runtime changes little when the number of gene trees is changed by several orders of magnitude. RTIST is written in C and Python. It is freely available at https://github.com/glebzhelezov/rtist.

Performance analyses of mesh-based local Finite Element Method and meshless global RBF Collocation Method for solving Poisson and Stokes equations

Steady and unsteady Poisson and Stokes equations are solved using mesh dependent Finite Element Method and meshless Radial Basis Function Collocation Method to compare the performances of these two numerical techniques across several criteria. The accuracy of Radial Basis Function Collocation Method with multiquadrics is enhanced by implementing a shape parameter optimization algorithm. For the time-dependent problems, time discretization is done using Backward Euler Method. The performances are assessed over the accuracy, runtime, condition number, and ease of implementation. Three error kinds considered; least square error, root mean square error and maximum relative error. To calculate the least square error using meshless Radial Basis Function Collocation Method, a novel technique is implemented. Imaginary numerical solution surfaces are created, then the volume between those imaginary surfaces and the analytic solution surfaces is calculated, ensuring a fair error calculation. Lastly, all results are put together and trends are observed. The change in runtime vs. accuracy and number of nodes; and the change in accuracy vs. the number of nodes is analyzed. The study indicates the criteria under which Finite Element Method performs better and conditions when Radial Basis Function Collocation Method outperforms its mesh dependent counterpart.

Load shifting and energy conservation using smart thermostats in contemporary high-rise residential buildings: Estimation of runtime changes using field data

As the proportion of the population who resides in urban centers grows, the number of dwellings in high-rise residential buildings is also increasing. In Canada, heating, ventilation, and air conditioning (HVAC) typically accounts for over half of all energy use in this building type and, as such, consideration of these loads is key to effective energy demand-side management. Smart thermostats present a new opportunity conserve energy and shift electricity load to off-peak periods in these buildings through modifications to HVAC control strategies. In this study, three thermostat control strategies were tested in 49 suites across two contemporary high-rise residential buildings: 1) baseline operation (thermostat operating as a standard programmable thermostat); 2) occupancy-based control (thermostat operating using an occupancy-reactive control strategy); 3) load shifting control (thermostat operating using a pre-conditioning strategy). HVAC runtime data was collected from smart thermostats installed in the suites and was used to develop data-driven regression models to estimate baseline suite HVAC runtime based on weather conditions and suite characteristics. Thermostat-measured HVAC runtime data for periods in which occupancy-based or load shifting control strategies were in use in the suite was then compared with estimated baseline runtime for similar outdoor conditions. Occupancy-based control was found to reduce HVAC system runtime during the cooling seasonby 5.9% ± 46% on average. On aggregate, the load shifting control strategy was not effective at reducing peak period HVAC load during the cooling season. The strategy was able to reduce peak period loads in a subset of suites substantially, which may be leveraged to reduce electricity demand during capacity-constrained periods of grid operation, however, based on marginal emissions factors for Ontario’s electricity grid, even in these suites, the strategy was not effective in reducing GHG emissions overall.

Fault-Resilience for Bandwidth Management in Industrial Software-Defined Networks

Industrial Cyber-Physical Systems (ICPS) expect assurances of timely delivery of data even during the occurrence of distinct faults. It is a challenge to manage the required bandwidth by providing resilience to link failures and dynamically changing bandwidth requirements. In this paper, we address the aforementioned challenge by exploring Software-Defined Networks (SDN). We present a framework coined SDN-RMbw (Software-Defined Networking Resilience Management for Bandwidth), which is a contract-based framework, where the components are bound to bandwidth contracts and a resilience manager. The bandwidth contracts state the bandwidth requirements of traffic flows. With each such contract, a monitor is associated, which is responsible to detect two events, run-time changes and link failures. Directly after receiving the event trigger reports from the monitor, new routes are calculated by a path-finding algorithm. Based on newly calculated routes, an observer detects whether the contract requirements are still satisfied, or the contract gets violated (termed as fault). To provide resilience to such faults in the network, a resilience manager integrated with control logic decides and executes a suitable response strategy. The proposed SDN-based framework aims at providing fault-resilience as well as adapting to different network-state changes. The proposed framework is evaluated using a Ryu SDN controller on a hardware testbed. Our results show that the proposed framework provides enhanced network resilience as compared to baseline mechanisms and improves the success rate up to 21% and bandwidth up to 111 Mbps under distinct network scenarios. Furthermore, extensive experimental emulations on the Mininet tool depicts the scalability of the proposed framework.

Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, round-trip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.

Any-Precision Deep Neural Networks

We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-widths, by truncating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low-bits, we show that the model achieved accuracy comparable to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learning models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures and applicable to multiple vision tasks. Our code is released at https://github.com/SHI-Labs/Any-Precision-DNNs.

Evaluating verification awareness as a method for assessing adaptation risk

Self-integration requires a system to be self-aware and self-protecting of its functionality and communication processes to mitigate interference in accomplishing its goals. Incorporating self-protection into a framework for reasoning about compliance with critical requirements is a major challenge when the system’s operational environment may have uncertainties resulting in runtime changes. The reasoning should be over a range of impacts and tradeoffs in order for the system to immediately address an issue, even if only partially or imperfectly. Assuming that critical requirements can be formally specified and embedded as part of system self-awareness, runtime verification often involves extensive on-board resources and state explosion, with minimal explanation of results. Model-checking partially mitigates runtime verification issues by abstracting the system operations and architecture. However, validating the consistency of a model given a runtime change is generally performed external to the system and translated back to the operational environment, which can be inefficient.This paper focuses on codifying and embedding verification awareness into a system. Verification awareness is a type of self-awareness related to reasoning about compliance with critical properties at runtime when a system adaptation is needed. The premise is that an adaptation that interferes with a design-time proof process for requirement compliance increases the risk that the original proof process cannot be reused. The greater the risk to limiting proof process reuse, the higher the probability that the requirement would be violated by the adaptation. The application of Rice’s 1953 theorem to this domain indicates that determining whether a given adaptation inherently inhibits proof reuse is undecidable, suggesting the heuristic, comparative approach based on proof metadata that is part of our approach. To demonstrate our deployment of verification awareness, we predefine four adaptations that are all available to three distinct wearable simulations (hearables, stress, and insulin delivery). We capture metadata from applying automated theorem proving to wearable requirements and assess the risk among the four adaptations for limiting the proof process reuse for each of their requirements. The results show that the adaptations affect proof process reuse differently on each wearable. We evaluate our reasoning framework by embedding checkpoints for requirement compliance within the wearable code and log the execution trace of each adaptation. The logs confirm that the adaptation selected by each wearable with the lowest risk of inhibiting proof process reuse for its requirements also causes the least number of requirement failures in execution.

Geometric Partitioning Mode in Versatile Video Coding: Algorithm Review and Analysis

This paper presents an overview of the geometric partitioning mode (GPM) algorithm that is a part of the most recent Versatile Video Coding (VVC) standard. The GPM algorithm aims to increase the partitioning precision of moving objects using non-rectangular and asymmetric rectangular partitions on top of the conventional rectangular block partitioning structure of VVC. Novel features of GPM contributing to the increase in coding efficiency and the reduction in encoder and decoder complexity are detailed and analyzed in this paper. Evaluated with VVC test model version 8.0 under the joint video experts team common test conditions, experimental results show that the presented GPM algorithm provides luma Bjøntegaard Delta rate reduction of 0.70% for random access and of 1.55% for low delay with B slices configurations, with roughly 3% to 5% additional encoding time and negligible decoder runtime change. Furthermore, as GPM provides more precise partitions for the boundaries of the moving objects, an improvement of visual quality is seen in GPM coded sequences.

Agents and robots for collaborating and supporting physicians in healthcare scenarios.

Monitoring patients through robotics telehealth systems is an interesting scenario where patients' conditions, and their environment, are dynamic and unknown variables. We propose to improve telehealth systems' features to include the ability to serve patients with their needs, operating as human caregivers. The objective is to support the independent living of patients at home without losing the opportunity to monitor their health status. Application scenarios are several, and they spread from simple clinical assisting scenarios to an emergency one. For instance, in the case of a nursing home, the system would support in continuously monitoring the elderly patients. In contrast, in the case of an epidemic diffusion, such as COVID-19 pandemic, the system may help in all the early triage phases, significantly reducing the risk of contagion. However, the system has to let medical assistants perform actions remotely such as changing therapies or interacting with patients that need support. The paper proposes and describes a multi-agent architecture for intelligent medical care. We propose to use the beliefs-desires-intentions agent architecture, part of it is devised to be deployed in a robot. The result is an intelligent system that may allow robots the ability to select the most useful plan for unhandled situations and to communicate the choice to the physician for his validation and permission.

Cost effective stream workflow scheduling to handle application structural changes

Stream workflow is a network of big data streaming applications that acts as key enabler for real-time analysis from Internet of Things data. Smart traffic management and smart grid are examples of stream workflow. The focus of existing work is on streaming operator graphs which differs from stream workflow and handling data fluctuations without significant consideration of different dynamic forms that could happen in the structure of data pipelines. This paper investigates the scheduling problem of stream workflow to support runtime alterations of stream workflow deployment, so that scheduling plans will be revised to handle stream workflow applications with continuously changing characteristics. It proposes a pluggable dynamic scheduling technique that accepts user-defined algorithms to handle stream workflow runtime changes. It also presents three different plug-in algorithms and methods to enable auto-scaling of this workflow in a Multicloud environment. The experimental results of the quality of the solution showed that the proposed plug-in optimisation technique is more efficient than baseline and dynamic fair-share techniques to handle runtime changes.

RoboPlanner: a pragmatic task planning framework for autonomous robots

Robotic automation has proliferated various industrial deployments including manufacturing, retail warehousing and logistics supply chains. In order for robots to advance to the next stage of cognitive autonomy, a robust framework for planning, execution and adaptation is needed. While there have been advances in abstract automated planning systems, they are still ill-suited to be applied within runtime robotic executions, which take place in uncertain environments. In this study, the authors provide a deliberative robotic planning and simulated execution framework called RoboPlanner that provides a pragmatic integration of automated planning, orchestration and adaptive deployments. This is coupled with an execution monitor and plan repair module, that allows reconfiguration to various template actions with runtime changes. Structured rules for re-planning in the case of state changes, unforeseen obstacles or execution failures are provided. They demonstrate their simulation framework on a realistic example of mobile pick & delivery robots in Industry 4.0 warehouses, that plan, execute, adapt and re-plan in sync with a knowledge base.

Ensemble-Based Software Engineering for Modern Computing Platforms

Software systems have evolved from being 'stand-alone systems' to 'systems of systems' to meet the challenging needs of societies. Contemporary software systems such as socio-technical systems are composed of distributed and heterogeneous agents, the em- bedded environment, and software components. Addressing the disruptions caused by run-time changes in the environment while designing software systems remains a challenging task in practice. Ensembles enable systems with collective adaptability to be built as emergent aggregations of autonomous and self-adaptive agents. The phenomenal aspect of this type of systems allows agents (i.e, services, people and things) a ected by an issue to adapt collabo- ratively with minimal impact on their own preferences through a collective resolution process. In this paper we report the outcomes of the 2nd ACM SIGSOFT International Workshop on Ensemble- Based Software Engineering for Modern Computing Platforms (EnSEmble 2019), which was held as part of the 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2019) in Tallinn, Estonia, on August 26th, 2019.

Adaptive Compiler Strategies for Mitigating Timing Side Channel Attacks

Existing compiler techniques can transform code to make its timing behavior independent of sensitive values to prevent information leakage through time side channels. Those techniques are hampered, however, by their static nature and dependence on details of the processor targeted during the compilation. This paper presents a dynamic compiler approach based on offline profiles and JIT compiler strategies. This approach reduces overhead significantly and enables a trade-off between provided protection and overhead. Furthermore, it supports adaptive policies in which the protection adapts to run-time changes in the requirements. A prototype implementation in the Jikes Research VM is evaluated on RSA encryption, HMAC key verification, and IDEA encryption.