An Adaptive Deployment Algorithm for IaaS Cloud Virtual Machines Based on Q Learning Mechanism

When deploying infrastructure as a service (IaaS) cloud virtual machines using the existing algorithms, the deployment process cannot be simplified, and the algorithm is difficult to be applied. This leads to the problems of high energy consumption, high number of migrations, and high average service-level agreement (SLA) violation rate. In order to solve the above problems, an adaptive deployment algorithm for IaaS cloud virtual machines based on Q learning mechanism is proposed in this research. Based on the deployment principle, the deployment characteristics of the IaaS cloud virtual machines are analyzed. The virtual machine scheduling problem is replaced with the Markov process. The multistep Q learning algorithm is used to schedule the virtual machines based on the Q learning mechanism to complete the adaptive deployment of the IaaS cloud virtual machines. Experimental results show that the proposed algorithm has low energy consumption, small number of migrations, and low average SLA violation rate.

Frequency of enforcement is more important than the severity of punishment in reducing violation behaviors

External enforcement policies aimed to reduce violations differ on two key components: the probability of inspection and the severity of the punishment. Different lines of research offer different insights regarding the relative importance of each component. In four studies, students and Prolific crowdsourcing participants (Ntotal = 816) repeatedly faced temptations to commit violations under two enforcement policies. Controlling for expected value, we found that a policy combining a high probability of inspection with a low severity of fines (HILS) was more effective than an economically equivalent policy that combined a low probability of inspection with a high severity of fines (LIHS). The advantage of prioritizing inspection frequency over punishment severity (HILS over LIHS) was greater for participants who, in the absence of enforcement, started out with a higher violation rate. Consistent with studies of decisions from experience, frequent enforcement with small fines was more effective than rare severe fines even when we announced the severity of the fine in advance to boost deterrence. In addition, in line with the phenomenon of underweighting of rare events, the effect was stronger when the probability of inspection was rarer (as in most real-life inspection probabilities) and was eliminated under moderate inspection probabilities. We thus recommend that policymakers looking to effectively reduce recurring violations among noncriminal populations should consider increasing inspection rates rather than punishment severity.

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.

ANALISIS EFEKTIVITAS RUANG HENTI KHUSUS (RHK) SEPEDA MOTOR MODEL STARTING GRID PADA SIMPANG BERSINYAL DI TAMAN MULI – MEKHANAI (TAMAN KOTA LUNGSIR) KOTA BANDAR LAMPUNG DI MASA PANDEMI COVID – 19

In the performance of intersections with signals, traffic lights play an important role in their performance, and motorcycle RHK (Special Stopping Rooms) is one of the solutions to help the performance of the intersection with signals running well. And during the 2019 Corona Virus Disease Pandemic, the role of RHK was very influential on the spread of the virus, therefore the government, Traffic Police and Transportation Agency collaborated to improvise the RHK by adding a Starting Grid Mark to the RHK. This study analyzes the effectiveness of RHK with Starting Grid markers by calculating the violation rate and RHK filling levels with starting grid markers. The analysis is carried out on Weekend and Weekdays during the morning, afternoon and evening rush hour. Obtained data. On weekdays RHK 1 morning 89.57%, noon, 62.5% and 50.99% afternoon, while at Wekeend RHK 1 morning 71.74%, 55.3% afternoon and 60.1% afternoon. From the results of this research, at the intersection of the city park lungsir bandar Lampung, it works effectively, can reduce the length of queues and can minimize the spread of Corona Virus Disease 2019.

Clinical Validity of 3 Different Grading Systems for Facet Joint Violation: A Retrospective Study and In-Depth Review

Study Design: Retrospective. Objectives: To study the violation rate of 3 different types of facet joint violation (FJV) grading systems (Babu, Shah, and Park), and to evaluate the accuracy, reliability, and association with clinical outcomes of the above 3 grading systems. Methods: 152 patients of lumbar spinal stenosis treated with percutaneous pedicle screw placement were enrolled in our study. FJV was evaluated on 3-dimensional lumbar CT reconstruction. Three types of grading systems were used to evaluate FJV: Babu’s system (grading by the severity of violation), Shah’s system (grading by side of violation), and modified Park’s system (grading by different components to cause violation). The violation rate and observer consistency of the 3 grading systems were analyzed. Clinical outcomes were evaluated by visual analog score (VAS), Oswestry disability index (ODI) score. Results: Kappa coefficients of interobserver consistency on Babu, Shah, and Park grading systems were 0.726,0.849,0.692, respectively. The violation rate of Babu, Shah, and Park grading systems were comparable, which were 34.54%, 32.57%, 33.55%, respectively. In all 3 grading systems, the postoperative VAS low-back pain and ODI scores in non-FJV groups were lower than those in FJV groups ( P < .05), and there were no significant differences between 2 groups in VAS leg pain( P >.05). Conclusions: Babu, Shah and modified Park grading system are reliable grading systems, and it reported comparable violation rate. The self-reported clinical outcomes of patients with FJV were worse at 2-year follow-up. For clinical application, it is recommended to use 2 or even 3 different grading systems together to evaluate the FJV.

Probabilistic-QoS-Aware Multi-Workflow Scheduling Upon the Edge Computing Resources

Edge computing is an evolving decentralized computing infrastructure by which end applications are situated near the computing facilities. While the edge servers leverage the close proximity to the end-users for provisioning services at reduced latency and lower energy costs, their capabilities are constrained by limitations in computational and radio resources, which calls for smart, quality-of-service (QoS) guaranteed, and efficient task scheduling methods and algorithms. For addressing the edge-environment-oriented multi-workflow scheduling problem, the authors consider a probabilistic-QoS-aware approach to multi-workflow scheduling upon edge servers and resources. It leverages a probability-mass function-based QoS aggregation model and a discrete firefly algorithm for generating the multi-workflow scheduling plans. This research conducted an experimental case study based on varying types of workflow process models and a real-world dataset for edge server positions. It can be observed the method clearly outperforms its peers in terms of workflow completion time, cost, and deadline violation rate.

Who Gets a Second Chance? Effectiveness and Equity in Supervision of Criminal Offenders

Most convicted offenders serve their sentences under “community supervision” at home instead of in prison. Under supervision, however, a technical rule violation such as not paying fees can result in incarceration. Rule violations account for 25% of prison admissions nationally and are significantly more common among black offenders. I test whether technical rules are effective tools for identifying likely reoffenders and deterring crime and examine their disparate racial impacts using administrative data from North Carolina. Analysis of a 2011 reform reducing prison punishments for technical violations on probation reveals that 40% of rule breakers would go on to commit crimes if spared harsh punishment. The same reform also closed a 33% black-white gap in incarceration rates without substantially increasing the black-white reoffending gap. These effects combined imply that technical rules target riskier probationers overall, but disproportionately affect low-risk black offenders. To justify black probationers’ higher violation rate on efficiency grounds, their crimes must be roughly twice as socially costly as that of white probationers. Exploiting the repeat-spell nature of the North Carolina data, I estimate a semiparametric competing-risks model that allows me to distinguish the effects of particular types of technical rules from unobserved probationer heterogeneity. Rules related to the payment of fees and fines, which are common in many states, are ineffective in tagging likely reoffenders and drive differential impacts by race. These findings illustrate the potentially large influence of ostensibly race-neutral policies on racial disparities in the justice system.

Potensi Pelanggaran Lalu Lintas Di Simpang Mayang Tujuan Jambi Town Square Mall

One of the biggest factors causing traffic accidents is a violation committed by drivers who are less orderly in traffic. This research was conducted on Jalan Kapten A. Bakarudin Simpang Mayang, Jambi Town Square Mall (Jamtos mall). The research objective is to find out how much the traffic sign violations at the Intersection of Jamtos mall. The results show that the highest traffic sign violations occurred on Sunday, August 16, 2020, with 1996 vehicles : 1494 two-wheeled vehicles and 502 four-wheeled vehicles. Meanwhile, the vehicles that enter Jamtos mall consist of 519 two-wheeled vehicles and 237 four-wheeled vehicles with the highest violation rate of two-wheeled vehicles at 19.00-20.00 WIB totaling 145 vehicles and 52 four-wheeled vehicles at 13.00-14. 00 WIB. The largest percentage of traffic violations to the Jamtos mall destination occurred on Tuesday, August 18, 2020, amounting to 41%, hereby stating that there was a large turn-around traffic sign violation.

CPU-Memory Aware VM Consolidation for Cloud Data Centers

The unbalanced usage of resources in cloud data centers cause an enormous amount of power consumption. The Virtual Machine (VM) consolidation shuts the underutilized hosts and makes the overloaded hosts as normally loaded hosts by selecting appropriate VMs from the hosts and migrates them to other hosts in such a way to reduce the energy consumption and to improve physical resource utilization. Efficient method is needed for VM selection and destination hosts selection (VM placement). In this paper, a CPU-Memory aware VM placement algorithm is proposed for selecting suitable destination host for migration. The VMs are selected using Fuzzy Soft Set (FSS) method VM selection algorithm. The proposed placement algorithm considers both CPU, Memory, and combination of CPU-Memory utilization of VMs on the source host. The proposed method is experimentally compared with several existing selection and placement algorithms and the results show that the proposed consolidation method performs better than existing algorithms in terms of energy efficiency, energy consumption, SLA violation rate, and number of VM migrations.