System Resource Usage Research Articles

Job CPU Performance comparison based on MINIAOD reading options: Local versus remote

Job CPU Performance comparison based on MINIAOD reading options: Local versus remote

Implementation of Decoy Deception based Detection System for Ransomware Attack

Ransomware poses a dangerous threat to cybersecurity. Data as well as rights owned by the user are adversely impacted. The situation has become considerably more critical as a result of the emergence of new ransomware varieties and Ransomware-as-a-Service. In this paper, we presented a novel deception-based and behaviour-based method for real-time ransomware detection. In order to avoid any loss before ransomware is discovered, we build pretend files and directories for nefarious behaviours. We conducted a pilot study using Locky, and the results demonstrate the effectiveness of our strategy with little system resource usage and geographical cost.

Parent's preferences for unscheduled paediatric healthcare: A discrete choice experiment.

Unscheduled healthcare is a key component of healthcare delivery and makes up a significant proportion of healthcare access, with children being particularly high users of unscheduled healthcare. Understanding the relative importance of factors that influence this behaviour and decision-making is fundamental to ensuring the system is best designed to meet the needs of users and foster appropriate cost-effective usage of health system resources. The aim of the study was to identify the parent's preferences for unscheduled healthcare for a common mild childhood illness. A discrete choice experiment (DCE) was developed to identify the preferences of parents accessing unscheduled healthcare for their children. Data were collected from parents in Ireland (N = 458) to elicit preferences across five attributes: timeliness, appointment type, healthcare professional attended, telephone guidance before attending and cost. Using a random parameters logit model, all attributes were statistically significant, cost (β = -5.064, 95% confidence interval, CI [-5.60, -4.53]), same-day (β = 1.386, 95% CI [1.19, 1.58]) or next-day access (β = 0.857, 95% CI [0.73, 0.98]), coupled with care by their own general practitioner (β = 0.748, 95% CI [0.61, 0.89]), identified as the strongest preferences of parents accessing unscheduled healthcare for their children. The results have implications for policy development and implementation initiatives that seek to improve unscheduled health services as understanding how parents use these services can maximise their effectiveness. The development of the DCE included a qualitative research component to ensure that the content accurately reflected parents experiences when seeking healthcare. Before data collection, a pilot test was carried out with the target population to gather their views on the survey.

IoTSAX: A Dynamic Abstractive Entity Summarization Approach With Approximation and Embedding-Based Reasoning Rules in Publish/Subscribe Systems

Users are interested in entity information and may use paradigms like publish/subscribe systems in Internet of Things (IoT), where entity-centric data comes from multiple sources. The IoT application demands contributed to the data integration and semantic interoperability need while maintaining high usability and low processing system resources usage. Existing approaches dealt with semantic interoperability by using/extending ontologies with strict schemata/semantics and difficult to be updated. Other approaches tackled high usability by providing sensor data abstractions that infer high-level knowledge, but they are either complex for real-time processing or have semantic/syntactic coupling making them nonflexible. Therefore, our key question is: can a dynamic entity summarization system that provides high-level abstractions while ensuring semantic/syntactic decoupling, and low use of processing system resources, be defined? In this article, we address this question by proposing the abstractive publish/subscribe summarization system that provides abstractive summaries of numerical IoT data for user-friendly subscriptions by using IoTSAX, an enhanced symbolic aggregate approximation (SAX) methodology for dynamic IoT environments, and approximate rule-based reasoning by using embedding models. Our results for two use cases, medicine and smart cities, show that although abstraction can be 2 to 3 times slower in latency, it achieves up to 98% reduction in the notifications’ number. IoTSAX outperforms SAX in approximation error up to 2 to 3 times more and in compression space-saving percentage when data redundancy occurs, while it has a similar or better latency and throughput performance. Finally, concept hierarchy-based embedding models can achieve F-scores of up to 0.87 for approximate rule-based reasoning.

Time-Fuel Efficient Intermittent Feedback Control of LTI Systems

To derive analytical feedback solution of time- $L_{1}$ optimal control problem is a challenging task. In this regard, this letter proposes an alternative strategy that involves intermittent application of open loop time- $L_{1}$ optimal solution. When disturbances affect the system, such policy is shown to retain the system states to within a small bounded safe region about the origin. The key idea is to switch the processor in between active and idle mode. During the active mode, the states are measured, open-loop optimal control policy is computed and transmitted along the communication link to the actuator. While during the idle mode, the precomputed control profile is applied and rest of the system resources remain inactive. The open loop solution is obtained by solving a set of optimization problems with an additional bound constraint on the update time instances. The bound constraints are derived using an algorithm to ensure that the safe region (defined in terms of attainable set) is always inside the null controllable region or the reachable set. Since attainable and reachable sets are approximated as convex polytopes, the bound constraints are obtained through linear inequalities. Simulation results show that under the proposed control strategy, system achieves practical stability with reduced usage of system resources.

Resilient adaptive event-triggered H∞ control for networked stochastic control systems under denial-of-service attacks

This paper addresses the problem of resilient adaptive event-triggered control (AETC) for networked stochastic control systems (NSCSs) in the presence of the external disturbance and the energy-constrained, nonperiodic denial-of-service (DoS) attacks. A novel adaptive event-triggered scheme (AETS) that considers the effect of the energy-constrained, nonperiodic DoS attacks on the communication network is proposed to reduce the usage of system resources and adapt the variation of the plant state, and the model of closed-loop control system is established in the framework of time-delay systems and switched systems. Then, based on the Lyapunov stability theory, the stability criterion and the co-design algorithm are derived, which are used to ensure that the closed-loop control system is stochastically exponentially stable (SES) with an [Formula: see text] disturbance attenuation performance and to implement the co-design of state-feedback controller and proposed AETS, respectively. Unlike the network-based [Formula: see text] control approach for NSCSs in the existing literature, the resilient adaptive event-triggered network-based [Formula: see text] control approach proposed in this paper not only can considerably save the usage of system resources, but also can be resilient towards the energy-constrained, nonperiodic DoS attacks. Finally, a resilient AETC for the F16 aircraft system shows the effectiveness and superiority of proposed strategy.

TCLBM: A task chain-based load balancing algorithm for microservices

The microservices architecture has been proposed to overcome the drawbacks of the traditional monolithic architecture. Scalability is one of the most attractive features of microservices. Scaling in the microservices architecture requires the scaling of specified services only, rather than the entire application. Scaling services can be achieved by deploying the same service multiple times on different physical machines. However, problems with load balancing may arise. Most existing solutions of microservices load balancing focus on individual tasks and ignore dependencies between these tasks. In the present paper, we propose TCLBM, a task chain-based load balancing algorithm for microservices. When an Application Programming Interface (API) request is received, TCLBM chooses target services for all tasks of this API call and achieves load balancing by evaluating the system resource usage of each service instance. TCLBM reduces the API response time by reducing data transmissions between physical machines. We use three heuristic algorithms, namely, Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Genetic Algorithm (GA), to implement TCLBM, and comparison results reveal that GA performs best. Our findings show that TCLBM achieves load balancing among service instances and reduces API response times by up to 10% compared with existing methods.

A cloud-based monitoring system for performance analysis in IoT industry

As enterprise information systems grow in scale and computing resources remain limited, some computing system services run into occasional abnormalities such as degraded stability and the failure to respond in time. Fewer monitoring tools mean that system maintenance managers may not be notified when abnormal events occur. It becomes difficult to diagnose and manage the problems promptly, to decrease service interruptions and to fully grasp how computing resources are being utilized. Monitor systems that provide enterprise-wide services must perform better if they are to meet the needs of customers. Therefore, it is necessary for the system administrator to monitor the system. In this case study, we propose and develop a cloud-based monitor system that uses Java to run on the J2EE platform. We build a cloud-based performance analysis and monitoring mechanism whose system architecture has three components: a server resource performance monitor, an enterprise application systems monitor and an abnormal event notification system. The performance analysis and monitoring mechanisms are integrated and include an active diagnosis system and maintenance module to issue notifications when any system experiences abnormal operations. This results in an increase in enterprise system availability and effectively lowers the frequency of abnormal operations. System resource usage reports compiled from the data enable the optimal allocation of the enterprise’s limited computing resources. This monitoring system ensures high quality and lowers the operational cost of providing information services, enhancing the provider–customer relationship.

Analysis of Host Resources Utilization by OpenStack in Ubuntu Environment

Cloud computing is one of the frontier technologies, which over the last decade has gained a widespread commercial and educational user base. OpenStack is one of the popular open source cloud management platforms for establishing a private or public Infrastructure-as-a-Service (IAAS) cloud. Although OpenStack started with very few core modules, it now houses nearly 38 modules and is quite complex. Such a complex software bundle is bound to have an impact on the underlying hardware utilization of the host system. The objective is to monitor the usage of system resources by OpenStack on commodity hardware. This paper analyzes the effect of OpenStack on the host machine's hardware. An extensive empirical evaluation has been done on different types of hardware, at different virtualization levels and with different flavours of operating systems comparing the CPU utilization, memory consumption, disk I/O, network, and I/O requests. OpenStack was deployed using Devstack on a single node. The novel aspect of this work is monitoring the resource usage by OpenStack without creating virtual machines on commodity hardware. From the analysis of data, it is observed that standalone machine with Ubuntu server operating system is the least effected by OpenStack and thereby has more available resources for computation of user workloads. Doi: 10.28991/esj-2020-01246 Full Text: PDF

UAV based Mapping System for Precision Agriculture

Agriculture the backbone of our nation as well many nations is essential to feed the people and smart farming is the much-needed disruptive technology to help the farmers increase their yield. Airborne vehicles and relays supplement the automation system to monitor the growth of the plants, individually and record the observations to estimate the level of pesticides and herbicides to be applied. However, there are no simulation platforms designed to help these farmers to collect and process the data gathered. The proposed work consists of a sensor fusion framework that will give the user a complete farming scenario which is focused on data gathering with airborne UAV or relay mechanism, line of sight deployment, coverage area, identify sensor placement, and usage of fog computing paradigm as a backend computing support. The paper focuses on providing an evaluation benchmark for energy consumption, system resource usage, packet delivery ratio and transmission delay.

Stabilisation of networked control systems under a novel stochastic‐sampling‐based adaptive event‐triggered scheme

In order to save the usage of system resources and adapt the variation of plant state, this study first proposes a novel stochastic-sampling-based adaptive event-triggered scheme (AETS). Second, in the framework of time-delay systems, the closed-loop control system is modelled as a class of delayed stochastic systems where time-delay is distributed in some intervals with probability. Then, by employing stochastic analysis tool and Lyapunov stability theory, a stability criterion for this class of delayed stochastic systems is established to ensure that the system possesses stochastically asymptotic stability with an H ∞ disturbance attenuation performance. Also, a co-design of parameter matrices of the state-feedback controller and the stochastic-sampling-based AETS is implemented. Third, based on the obtained co-design condition, a convex optimisation algorithm for the tradeoffs between disturbance attenuation performance and resource utilisation of the closed-loop control system is further developed. Finally, the effectiveness and feasibility of the proposed control strategy are illustrated by two numerical examples of adaptive event-triggered control for networked control systems under stochastic sampling.

A workload-based nonlinear approach for predicting available computing resources

Performance degradation or system resource exhaustion can be attributed to inadequate computing resources as a result of software aging. In the real world, the workload of a web server varies with time, which will cause a nonlinear aging phenomenon. The nonlinear property often makes analysis and modelling difficult. Workload is one of the important factors influencing the speed of aging. This paper quantitatively analyzes the workload-aging relation and proposes a framework for aging control under varying workloads. In addition, this paper proposes an approach that employs prior information of workloads to accurately forecast incoming system exhaustion. The workload data are used as a threshold to divide the system resource usage data into multiple sections, while in each section the workload data can be treated as a constant. Each section is described by an individual autoregression (AR) model. Compared with other AR models, the proposed approach can forecast the aging process with a higher accuracy.

Leveraging Fog Computing for Sustainable Smart Farming Using Distributed Simulation

The concept of smart farming has led to the use of technology to enhance agricultural productivity. With access to low-cost sensors and management systems, more farmers are adopting this technology to achieve sustainable growth. However, in literature, there are no simulation platforms to help researchers and users understand sensor deployment, and data collection and processing. In this article, we propose a framework designed to provide a complete farming ecosystem. The toolkit facilitates users to simulate custom farming scenarios, specifically to identify sensor placement, coverage area, line-of-sight deployment, and data gathering through the relay mechanism or airborne systems, mobility models for mobile nodes, energy models for on-ground sensors and airborne vehicles, and backend computing support using the fog computing paradigm. Furthermore, in most of the existing works, network parameters are ignored, which can impact the overall performance of any deployed system. Therefore, the proposed framework also provides a benchmark in terms of transmission delay, packet delivery ratio, energy consumption, and system resources usage.

Analysing performance issues of open-source intrusion detection systems in high-speed networks

Driven by the growing data transfer needs, industry and research institutions are deploying 100 Gb/s networks. As such high-speed networks become prevalent, these also introduce significant technical challenges. In particular, an Intrusion Detection System (IDS) cannot process network activities at such a high rate when monitoring large and diverse traffic volumes, thus resulting in packet drops. Unfortunately, the high packet drop rate has a significant impact on detection accuracy. In this work, we investigate two popular open-source IDSs: Snort and Suricata along with their comparative performance benchmarks to better understand drop rates and detection accuracy in 100 Gb/s networks. More specifically, we study vital factors (including system resource usage, packet processing speed, packet drop rate, and detection accuracy) that limit the applicability of IDSs to high-speed networks. Furthermore, we provide a comprehensive analysis to show the performance impact on IDSs by using different configurations, traffic volumes and different flows. Finally, we identify challenges of using open-source IDSs in high-speed networks and provide suggestions to help network administrators to address identified issues and give some recommendations for developing new IDSs that can be used for high-speed networks.

Non-Orthogonal Random Access and Data Transmission Scheme for Machine-to-Machine Communications in Cellular Networks

In order to address the signalling overhead and resource allocation problems of Machine-to-Machine (M2M) communications with non-orthogonal multiple access (NOMA), we propose a hybrid non-orthogonal random access and data transmission (NORA-DT) scheme. A novel design of NORA-DT protocol for M2M communications in cellular networks is firstly proposed. A power back-off scheme is introduced to adjust machine-type communications device (MTCD)'s target arrived power, and a closed-form analytic formula for the relation of MTCD's transmission power is derived. Based on the transmission power relation, the devices are clustered into a set of NOMA clusters. In the hybrid NORA-DT protocol, the cluster center MTCD transmits a extended preamble on behalf of the MTCDs in the same NOMA cluster on the physical random access channel (PRACH) for connection request. Base station (BS) can perfectly detect the preamble collisions in advance and schedules physical uplink shared channel (PUSCH) only to the NOMA clusters without collision. Then the MTCDs in the same NOMA clusters transmit data packets right after preamble transmission on the PUSCH to reduce the signalling overhead. By finding the optimal power allocation, we propose a low-complexity energy efficiency maximization problem for NORA-DT scheme. Due to the relation of MTCD's transmission power, we transform the problem into the function of cluster center MTCD's transmission power and solve it by difference of convex (DC) programming under the maximum transmission power constraints and minimum rate requirements at the MTCDs. A computationally efficient adaptive resource allocation scheme is finally proposed to improve the system throughput and resource usage. The optimal resource allocation between PRACH and PUSCH for any number of MTCDs can be learned by BS in advance, which avoids frequent computation. The analytic model is validated by simulation results. We demonstrate that the proposed NORA-DT scheme can significantly improve the system throughput, resource efficiency and energy efficiency performance.

Fault prediction for distributed computing Hadoop clusters using real-time higher order differential inputs to SVM: Zedacross

Hadoop distributed computing clusters are used worldwide for high-performance computations. Often various hardware and software faults occur, leading to both data and computation time losses. This paper proposes the usage of a fault prediction software called 'Zedacross' which uses machine learning principles combined with cluster monitoring tools. Firstly, the paper suggests a model that uses the resource usage statistics of a normally functioning Hadoop cluster to create a machine learning model that can then be used to predict and detect faults in real time. Secondly, the paper explains the novel idea of using higher order differentials as inputs to SVM for highly accurate fault predictions. Predictions of system faults by observing system resource usage statistics in real-time with minimum delay will play a vital role in deciding the need for job rescheduling tasks or even dynamic up-scaling of the cluster. To demonstrate the effectiveness of the design a Java utility was built to perform cluster fault monitoring. The results obtained after running the system on various test cases demonstrate that the proposed method is accurate and effective.

Fault prediction for distributed computing Hadoop clusters using real-time higher order differential inputs to SVM: Zedacross

Hadoop distributed computing clusters are used worldwide for high-performance computations. Often various hardware and software faults occur, leading to both data and computation time losses. This paper proposes the usage of a fault prediction software called 'Zedacross' which uses machine learning principles combined with cluster monitoring tools. Firstly, the paper suggests a model that uses the resource usage statistics of a normally functioning Hadoop cluster to create a machine learning model that can then be used to predict and detect faults in real time. Secondly, the paper explains the novel idea of using higher order differentials as inputs to SVM for highly accurate fault predictions. Predictions of system faults by observing system resource usage statistics in real-time with minimum delay will play a vital role in deciding the need for job rescheduling tasks or even dynamic up-scaling of the cluster. To demonstrate the effectiveness of the design a Java utility was built to perform cluster fault monitoring. The results obtained after running the system on various test cases demonstrate that the proposed method is accurate and effective.

Adaptive event-triggered H∞ fuzzy filtering for interval type-2 T–S fuzzy-model-based networked control systems with asynchronously and imperfectly matched membership functions

This paper is concerned with the network-based H∞ fuzzy filtering for non-linear systems with parameter uncertainties under a novel adaptive discrete event-triggered communication scheme (DETCS). Based on interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy model, the non-linear systems with parameter uncertainties are represented as a class of IT2 T–S fuzzy systems. In the design process, a novel adaptive DETCS is proposed to reduce the usage of system resources and adapt the variation of plant output, and a novel networked IT2 T–S fuzzy filter is applied to improve the flexibility of filter design. By employing the time-delay systems modeling method, the filtering-error-system is modeled as a class of interval time-varying delayed IT2 T–S fuzzy systems with asynchronously and imperfectly matched membership functions, and further conditionally expressed as a favorable form. Then, some relaxed stability criteria are established to determine that this class of delayed IT2 T–S fuzzy systems is asymptotically stable with a prescribed H∞ disturbance attenuation performance. Also, the co-design of parameter matrices of adaptive DETCS and filter is implemented. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed method.

An Ontology-Based Method for HW/SW Architecture Reconstruction

To address the vast variety of computing requirements in recent ubiquitous computing ecosystem, there is a constant need for more complex computing systems that consist of integrated hardware (HW) and software (SW) systems. Providing an architectural insight into such systems helps in achieving a more efficient usage of system resources, verifying the characteristics of a platform and provisioning of its security and trust. Architecture reconstruction (AR) has been used in software engineering to gain a deeper insight into specific software. Neither software AR nor hardware reverse engineering techniques are sufficient to extract the architecture of a system that incorporates both HW/SW, since they are unable to recover the relationships between the HW and SW components. Inspired by the Symphony software AR framework, we propose a method to reconstruct the architecture of a computing platform as a whole. In order to cover the wide variety of existing HW/SW technologies, our method uses an ontology-based approach. Due to the lack of a comprehensive ontology in literature, we developed PLATOnt, a new ontology that has been shown to be more effective by OntoQA evaluation framework. We used our AR method to reconstruct the architecture of an ARM-based trusted execution environment and a Raspberry Pi platform, widely used in embedded systems and IoT devices.

Minimizing cost by reducing scaling operations in distributed stream processing

Elastic distributed stream processing systems are able to dynamically adapt to changes in the workload. Often, these systems react to the rate of incoming data, or to the level of resource utilization, by scaling up or down. The goal is to optimize the system's resource usage, thereby reducing its operational cost. However, such scaling operations consume resources on their own, introducing a certain overhead of resource usage, and therefore cost, for every scaling operation. In addition, migrations caused by scaling operations inevitably lead to brief processing gaps. Therefore, an excessive number of scaling operations should be avoided. We approach this problem by preventing unnecessary scaling operations and over-compensating reactions to short-term changes in the workload. This allows to maintain elasticity, while also minimizing the incurred overhead cost of scaling operations. To achieve this, we use advanced filtering techniques from the field of signal processing to pre-process raw system measurements, thus mitigating superfluous scaling operations. We perform a real-world testbed evaluation verifying the effects, and provide a break-even cost analysis to show the economic feasibility of our approach.