Resource Usage Rate Research Articles

An intelligent task scheduling approach for the enhancement of collaborative learning in cloud computing

Cloud computing is the foremost technology that reliably connects end-to-end users. Task scheduling is a critical process affecting the performance enhancement of cloud computing. The scheduling of the enormous data results in increased response time, makespan time, and makes the system less efficient. Therefore, a unique Squirrel Search-based AlexNet Scheduler (SSbANS) is created for adequate scheduling and performance enhancement in cloud computing suitable for collaborative learning. The system processes the tasks that the cloud users request. Initially, the priority of each task is checked and arranged. Moreover, the optimal resource is selected using the fitness function of the squirrel search, considering the data rate and the job schedule. Further, during the scheduled task-sharing process, the system continuously checks for overloaded resources and balances based on the squirrel distribution function. The efficacy of the model is reviewed in terms of response time, resource usage, makespan time, and throughput. The model achieved a higher throughput and resource usage rate with a lower response and makespan time.

Concurrent service auto-scaling for Knative resource quota-based serverless system

Serverless computing platforms currently provide application developers with two ways to control their services’ resource usage cost: resource usage-based and resource quota-based. In usage-based systems, resources are assigned to services based on the amount they consume. Meanwhile, a fixed resource amount is reserved and shared between services for quota-based systems. Several studies have been proposed to enhancing the default serverless auto- scaling algorithms to optimize resource usage and service quality. However, almost all previous works targeted usage-based systems and optimized auto-scaling performance for each separate service. In contrast, this work targets auto-scaling of concurrent services in serverless quota-based systems. In serverless quota-based systems, over-quota resource usage and latency Service Level Objective (SLO) violation may occur when auto-scaling new instances during burst traffic moments. Hence, we aim to find an optimal hybrid auto-scaling decision to minimize over-quota resource usage and latency SLO violation. To solve this problem, We applied deep reinforcement learning and traffic prediction techniques. Our solution is developed and implemented based on the characteristics of the most popular open-source serverless platform Knative. For evaluation, we compared our solution with the default Knative auto-scaler and our previously proposed usage-based hybrid Knative auto-scaler. We configured the default Knative auto-scaler with two different settings that can optimize the total resource usage of all services or minimize service latency. Compared to the optimized resource usage version, our solution traded a slightly higher resource usage for an 11.3% latency SLO violation duration reduction. Meanwhile, compared with the optimized latency version, our solution reduced the over-quota resource usage rate by 18.25% while achieving a similar latency SLO preservation performance. Compared to the usage-based hybrid auto-scaler, the latency SLO violation rate and over-quota resource usage were approximately reduced by half.

Research on Cloud Computing-based Music Resource Sharing Platform Technology and Its Promotion of Employment and Entrepreneurship

Abstract Music resource-sharing platforms can increase students’ employment paths and enhance their employment ability. Load prediction and resource scheduling algorithms are designed in this paper to construct a music resource-sharing platform based on cloud computing technology. The mean predictor’s predictions are combined with the GRU-CNN model’s predictions, and the weighted average is calculated to enhance the accuracy and stability of the predictions. Then, the integrated migration scheduling method is designed to make the load of the cloud environment more balanced and the resource utilization more reasonable by the four processes of migration triggering, container selection, node selection, and container migration in a coarse-grained dynamic scheduling approach with nodes as objects. Following the completion of the construction, the algorithm that was designed is evaluated for load prediction and resource scheduling, and regression analysis is performed to examine the role of music resource-sharing platforms in promoting employment and entrepreneurship. It is found that after the resource scheduling by the algorithm in this paper, the node operation tension is greatly alleviated, and the CPU resource utilization and memory resource utilization of node Node1 are reduced from 80.96% and 62.69% to 58.03% and 43.36%, respectively. The resources of the low-load node Node4 are effectively utilized, the memory resource usage rate has increased from 6.86% to 49.52%, and the resource usage of each node in the cluster is more balanced and reasonable. At a statistical level of 1%, the impact of music resource-sharing platform development on employment quality is 0.194. In contrast, the industrial structure does not significantly contribute to the quality of employment, indicating that under the current economic situation, emerging economic forms such as music resource-sharing platforms are the main driving force to improve the quality of employment and the development of music resource sharing platforms generally improves the quality of labor force employment.

KubeAdaptor: A docking framework for workflow containerization on Kubernetes

As Kubernetes becomes the infrastructure of the cloud-native era, the integration of workflow systems with Kubernetes is gaining more and more popularity. However, when migrating the workflow systems to the Kubernetes platform, the inconsistency between the workflow scheduling algorithms and Kubernetes in task scheduling order seriously reduces the execution efficiency of the workflow systems. Besides, the integration of existing container-based workflow systems with Kubernetes lacks a general docking framework and requires various built-in tools and specialized technique supports, which brings high research costs and inconvenience to users. To this end, this paper proposes a cloud-native workflow engine named KubeAdaptor, a general docking framework to integrate workflow systems with Kubernetes and implement workflow containerization on Kubernetes, aiming to ensure a consistent task scheduling order between workflow scheduling algorithms in workflow systems and Kubernetes. We present the KubeAdaptor architecture and elaborate on the functionality implementation, fault tolerance management, and the event trigger mechanism within the KubeAdaptor. Experimental results on four real-world scientific workflows show that the KubeAdaptor as a docking framework ensures the consistency of the workflow scheduling algorithms and Kubernetes in the task scheduling order. Compared with the state-of-the-art Argo workflow engine, the KubeAdaptor achieves better performance in terms of the average execution time of the task pod, average workflow lifecycle, and resource usage rate.

LBAA: A novel load balancing mechanism in cloud environments using ant colony optimization and artificial bee colony algorithms

SummaryRecently, cloud computing has been recognized as an effective paradigm for offering an on‐demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non‐deterministic Polynomial hard (NP‐hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta‐heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively.

Challenges in service discovery for microservices deployed in a Kubernetes cluster – a case stud

With Kubernetes emerging as one of the most popular infrastructures in the cloud-native era, the utilization of containerization and tools alongside Kubernetes is steadily gaining traction. The main goal of this paper is to evaluate the service discovery mechanisms and DNS management (CoreDNS) of Kubernetes, and to present a general study of an experiment on service discovery challenges. In large scale Kubernetes clusters, running pods, services, requests, and workloads can be substantial. The increased number of HTTP-requests often result in resource utilization concerns, e.g., spikes of errors [24], [25]. This paper investigates potential optimization strategies for enhancing the performance and scalability of CoreDNS in Kubernetes. We propose a solution to address the concerns related to CoreDNS and provide a detailed explanation of how our implementation enhances service discovery functionality. Experimental results in a real-world case show that our solution for the CoreDNS ensures consistency of the workload. Compared with the default CoreDNS configuration, our customized approach achieves better performance in terms of number of errors for requests, average latency of DNS requests, and resource usage rate.

Enhanced Heuristic Method for Scheduling and Leveling Using Heuristic Indices

This paper presents an enhanced heuristic method based on five major heuristic indices used in project scheduling and leveling. Many researchers used some of these indices in project management, but few researchers combined multiple indices and used them in scheduling and leveling. The main purpose of this work is to find which of the five indices is the most important in leveling resources using the proposed method where the five heuristic indices were used together, and the method was applied using MATLAB software. The five heuristic indices are: Resource moment about the x-axis (<img src=image/14826095_1.gif>), Resource moment on the y-axis (<img src=image/14826095_2.gif>), Rate of Resource usage (<img src=image/14826095_3.gif>), Resource Improvement Coefficient (RIC), and the Maximum Value of the used resource (<img src=image/14826095_4.gif>). Moreover, every index has weight, which is referred to as importance weight. The method was tested on two projects. One of them is a hypothetical project consisting of 24 activities, and the other is an actual project where every index had an extremely bigger weight compared with the others. Also, a parameter (<img src=image/14826095_5.gif>) which refers to a partial value of the total float for the noncritical activities was used to enhance the heuristic method. It is found that the best (<img src=image/14826095_5.gif>) which gave the best possible resource histogram in the first project was <img src=image/14826095_5.gif>=1.0 and <img src=image/14826095_5.gif>=0.87 for the second. Also, it is found that the resource improvement coefficient (<img src=image/14826095_6.gif>) is the most important for the first project, while for the second project the Rate of Resource usage (<img src=image/14826095_3.gif>) was more important and gave better results. However, changing the value of (<img src=image/14826095_5.gif>) gave different results that are not related to a fixed relationship in terms of its increase or decrease. Finally, choosing the most important index depends on the nature of the project and its resources.

Predicting the results of competition between two breast cancer lines grown in 3-D spheroid culture

This study develops a novel model of a consumer–resource system with mobility included, in order to explain a novel experiment of competition between two breast cancer cell lines grown in 3D in vitro spheroid culture. The model reproduces observed differences in monoculture, such as overshoot phenomena and final size. It also explains both theoretically and through simulation the inevitable triumph of the same cell line in co-culture, independent of initial conditions. The mobility of one cell line (MDA-MB-231) is required to explain both the success and the rapidity with which that species dominates the population and drives the other species (MCF-7) to extinction. It is shown that mobility directly interferes with the other species and that the cost of that mobility is in resource usage rate.

PCN62 Budget IMPACT Analysis of Trastuzumab Emtansine for Residual Invasive HER2-Positive Breast Cancer in Argentina

To assess the 10 years-evolution of the annual budget impact of trastuzumab emtansine (T-DM1) compared with trastuzumab to the treatment of patients with HER2-positive early breast cancer and residual invasive disease at surgery, after completion of neoadjuvant chemotherapy plus HER2-targeted therapy from the whole health system perspective in Argentina. A budget impact model was first developed. Age-specific Her2-positive incidence rates published by GLOBOCAN were applied to Argentinian population estimates to determine its annual potential patients. Extrapolation of Kaplan-Meier curves, assuming a lognormal distribution, was used to project the number of relapses avoided by the adoption of T-DM1, and the treatment durations in both adjuvant and metastatic settings. Incremental benefit of T-DM1 was based on the Katherine study. Drug acquisition costs were calculated using publicly available prices and resource usage rates were estimated by conducting a Delphi Panel. Unitary costs were extracted out of the Unit Costs Database. Total Budget Impact is shown in US Dollars using the official exchange rate. The annual Budget Impact from the T-DM1 arm increased from $9,05 MM to $41,58 MM in 10 years compared with an increase from $4,1 MM to $40,51 MM in the Trastuzumab arm. The proportion of the metastatic setting direct costs, presented as percentages of the total variation, ranged from 2% to 44% in the T-DM1 arm and from 9% to 76% in the comparator arm. This analysis shows that purchase and monitoring costs of T-DM1 compared with trastuzumab tend to equate at a 10- year time horizon. This may be related to a lower relapse rate in the T-DM1 group. Although the driver of costs at the beginning of the analysis is related to the acquisition costs, relapse- related costs in the trastuzumab group represent a higher proportion of the total costs of care at the end of the analysis.

PMS11 EPIDEMIOLOGY, USE OF RESOURCES AND COST OF MODERATE TO SEVERE RHEUMATOID ARTHRITIS IN ARGENTINA: AN EXTENSIVE REVIEW WITH EXPERT ELICITATION

Rheumatoid arthritis (RA) is a disabling disease associated with a high use of resources for treatment (e.g. biologic drugs) and with costs related to productivity losses. There is scarce information on the epidemiology and economic burden of rheumatoid arthritis (RA) in Argentina. The objective was to collect information about key epidemiological parameters and estimate the cost of moderate to severe RA. We conducted a literature search on PUBMED and LILACS. In addition to this, we resorted to a consensus of experienced local rheumatologists using a modified Delphi panel method to find actual parameters for Argentina. We estimated resource usage rates and direct medical costs, based on the database of unit costs of the Institute for Clinical Effectiveness and Health Policy in Argentina, and indirect costs through the human capital approach. Costs were expressed in United States Dollars (USD), November 2017. The reported prevalence of RA in Argentina was 0.94% (95%CI: 0.86 – 1.02) with an estimated yearly incidence rate of 19 new cases every 100,000 persons/year (95%CI: 17 - 20). In 2017 the estimated cost of disease-modifying drugs was USD 54,405 per patient per year. The cost attributed to serious infections associated with the disease was nearly USD 2474. The per-patient cost of bilateral knee replacement exceeded USD 5276, and the cost of total hip replacement was USD 9196. Both types of costs increased with higher Health Assessment Questionnaire band. HAQ is a widely used disability assessment tool. We provide useful information on epidemiological and cost parameters of moderate to severe RA in the era of biologic agents in Argentina.

In brief

The execution of a workflow application can result in an imbalanced workload among allocated processors, ultimately resulting in a waste of resources and a higher cost to the user. Here, we consider a dynamic resource management system in which processors are reserved not for a job but only to run a task, thus allowing a higher resource usage rate. This paper presents a scheduling algorithm that manages concurrent workflows in a dynamic environment in which jobs are submitted by users at any moment in time, on shared heterogeneous resources, and constrained to a specified budget and deadline for each job. Recent research attempted to propose dynamic strategies for concurrent workflows but only addressed fairness in resource sharing among applications while minimizing the execution time. The Multi-QoS Profit-Aware scheduling algorithm (MQ-PAS) proposed here is able to increase the profit achieved by the provider by considering the budget available for each job to define tasks priorities. We study the scalability of the algorithm with different types of workflows and infrastructures. The experimental results show that our strategy improves provider revenue significantly and obtains comparable successful rates of completed jobs.

FPGA Prototyping and Design Evaluation of a NoC-Based MPSoC

Chip communication architectures become an important element that is critical to control when designing a complex MultiProcessor System-on-Chip (MPSoC). This led to the emergence of new interconnection architectures, like Network-on-Chip (NoC). NoCs have been proven to be a promising solution to the concerns of MPSoCs in terms of data parallelism. Field-Programmable Gate Arrays (FPGA) has some perceived challenges. Overcoming those challenges with the right prototyping solutions is easy and cost-effective leading to much faster time-to-market. In this paper, we present an FPGA based on rapid prototyping in hardware/software co-design and design evaluation of a mixed HW/SW MPSoC using a NoC. A case study of two-dimensional mesh NoC-based MPSoC architecture is presented with a validation environment. The synthesis and implementation results of the NoC-based MPSoC on a Virtex 5 ML 507 enable a reasonable frequency (151.5 MHz) and a resource usage rate equals to 58% (6,586 out of 11,200 slices used).

Reprint of “Multi-QoS constrained and Profit-aware scheduling approach for concurrent workflows on heterogeneous systems”

The execution of a workflow application can result in an imbalanced workload among allocated processors, ultimately resulting in a waste of resources and a higher cost to the user. Here, we consider a dynamic resource management system in which processors are reserved not for a job but only to run a task, thus allowing a higher resource usage rate. This paper presents a scheduling algorithm that manages concurrent workflows in a dynamic environment in which jobs are submitted by users at any moment in time, on shared heterogeneous resources, and constrained to a specified budget and deadline for each job. Recent research attempted to propose dynamic strategies for concurrent workflows but only addressed fairness in resource sharing among applications while minimizing the execution time. The Multi-QoS Profit-Aware scheduling algorithm (MQ-PAS) proposed here is able to increase the profit achieved by the provider by considering the budget available for each job to define tasks priorities. We study the scalability of the algorithm with different types of workflows and infrastructures. The experimental results show that our strategy improves provider revenue significantly and obtains comparable successful rates of completed jobs.

Multi-QoS constrained and Profit-aware scheduling approach for concurrent workflows on heterogeneous systems

The execution of a workflow application can result in an imbalanced workload among allocated processors, ultimately resulting in a waste of resources and a higher cost to the user. Here, we consider a dynamic resource management system in which processors are reserved not for a job but only to run a task, thus allowing a higher resource usage rate. This paper presents a scheduling algorithm that manages concurrent workflows in a dynamic environment in which jobs are submitted by users at any moment in time, on shared heterogeneous resources, and constrained to a specified budget and deadline for each job. Recent research attempted to propose dynamic strategies for concurrent workflows but only addressed fairness in resource sharing among applications while minimizing the execution time. The Multi-QoS Profit-Aware scheduling algorithm (MQ-PAS) proposed here is able to increase the profit achieved by the provider by considering the budget available for each job to define tasks priorities. We study the scalability of the algorithm with different types of workflows and infrastructures. The experimental results show that our strategy improves provider revenue significantly and obtains comparable successful rates of completed jobs.

Correlation-based virtual machine migration in dynamic cloud environments

Virtual machine (VM) migration enables flexible and efficient resource management in modern data centers. Although various VM migration algorithms have been proposed to improve the utilization of physical resources in data centers, they generally focus on how to select VMs to be migrated only according to their resource requirements and ignore the relationship between the VMs and servers with respect to their varying resource usage as well as the time at which the VMs should be migrated. This may dramatically degrade the algorithm performance and increase the operating and the capital cost when the resource requirements of the VMs change dynamically over time. In this paper, we propose an integrated VM migration strategy to jointly consider and address these issues. First, we establish a service level agreement-based soft migration mechanism to significantly reduce the number of VM migrations. Then, we develop two algorithms to solve the VM and server selection issues, in which the correlation between the VMs and the servers is used to identify the appropriate VMs to be migrated and the destination servers for them. The experimental results obtained from extensive simulations show the effectiveness of the proposed algorithms compared to traditional schemes in terms of the rate of resource usage, the operating cost and the capital cost.

Function points‐based resource prediction in cloud computing

SummaryAs a result of varying demands of computing resources by the users on cloud, resource provisioning in cloud computing has come out as a prominent topic of research. Many researchers have focused exclusively on the technical and security aspects of cloud computing, thereby neglecting the efficient provisioning of resources and the necessity of cloud services to be cost effective. Cloud consists of a large number of resources that are allocated to cloud customer's on‐demand. As demands cannot be static and constantly change with time, cloud service providers cannot adopt static provisioning of resources as there are chances of over‐provisioning or under‐provisioning. Therefore, to achieve efficient resource utilization, an optimized strategy that can deploy virtual machines on different physical machines according to resource requirements is the current need of cloud computing. That is, there must be a mechanism by which the total number of active physical nodes can be dynamically changed corresponding to their resource usage rate, thereby providing the efficient utilization of resources. In this paper, a linear regression‐based prediction model is proposed to predict the resource usage based on the number of function points computed from the users' requests. Thereafter, the artificial neural network is also used to predict the future resource requirements more accurately. The predicted resource usage results are used by a resource pool manager to manage the resources and allocate them to the users. The resource pool manager also uses an efficient load‐balancing algorithm to balance the load on each cloud service provider as well as to optimize cloud usage cost. With the help of this prediction model, the decision to allocate or release a virtual machine can be made proactively, thus making the cloud effective in terms of both cost and performance. Copyright © 2014 John Wiley &amp; Sons, Ltd.

A Value Based Dynamic Resource Provisioning Model in Cloud

Cloud computing has become an innovative computing paradigm, which aims at providing reliable, customized, Quality of Service (QoS) and guaranteed computing infrastructures for users. Efficient resource provisioning is required in cloud for effective resource utilization. For resource provisioning, cloud provides virtualized computing resources that are dynamically scalable. This property of cloud differentiates it from the traditional computing paradigm. But the initialization of a new virtual instance causes a several minutes delay in the hardware resource allocation. Furthermore, cloud provides a fault tolerant service to its clients using the virtualization. But, in order to attain higher resource utilization over this technology, a technique or a strategy is needed using which virtual machines can be deployed over physical machines by predicting its need in advance so that the delay can be avoided. To address these issues, a value based prediction model in this paper is proposed for resource provisioning in which a resource manager is used for dynamically allocating or releasing a virtual machine depending upon the resource usage rate. In order to know the recent resource usage rate, the resource manager uses sliding window to analyze the resource usage rate and to predict the system behavior in advance. By predicting the resource requirements in advance, a lot of processing time can be saved. Earlier, a server has to perform all the calculations regarding the resource usage that in turn wastes a lot of processing power thus decreasing its overall capacity to handle the incoming request. The main feature of the proposed model is that a lot of load is being shifted from the individual server to the resource manager as it performs all the calculations and therefore the server is free to handle the incoming requests to its full capacity.

A Value Based Dynamic Resource Provisioning Model in Cloud

Cloud computing has become an innovative computing paradigm, which aims at providing reliable, customized, Quality of Service (QoS) and guaranteed computing infrastructures for users. Efficient resource provisioning is required in cloud for effective resource utilization. For resource provisioning, cloud provides virtualized computing resources that are dynamically scalable. This property of cloud differentiates it from the traditional computing paradigm. But the initialization of a new virtual instance causes a several minutes delay in the hardware resource allocation. Furthermore, cloud provides a fault tolerant service to its clients using the virtualization. But, in order to attain higher resource utilization over this technology, a technique or a strategy is needed using which virtual machines can be deployed over physical machines by predicting its need in advance so that the delay can be avoided. To address these issues, a value based prediction model in this paper is proposed for resource provisioning in which a resource manager is used for dynamically allocating or releasing a virtual machine depending upon the resource usage rate. In order to know the recent resource usage rate, the resource manager uses sliding window to analyze the resource usage rate and to predict the system behavior in advance. By predicting the resource requirements in advance, a lot of processing time can be saved. Earlier, a server has to perform all the calculations regarding the resource usage that in turn wastes a lot of processing power thus decreasing its overall capacity to handle the incoming request. The main feature of the proposed model is that a lot of load is being shifted from the individual server to the resource manager as it performs all the calculations and therefore the server is free to handle the incoming requests to its full capacity.

A Pattern-Based Prediction Model for Dynamic Resource Provisioning in Cloud Environment

Cloud provides dynamically scalable virtualized computing resources as a service over the Internet. To achieve higher resource utilization over virtualization technology, an optimized strategy that deploys virtual machines on physical machines is needed. That is, the total number of active physical host nodes should be dynamically changed to correspond to their resource usage rate, thereby maintaining optimum utilization of physical machines. In this paper, we propose a pattern-based prediction model for resource provisioning which facilitates best possible resource preparation by analyzing the resource utilization and deriving resource usage patterns. The focus of our work is on predicting future resource requests by optimized dynamic resource management strategy that is applied to a virtualized data center in a Cloud computing environment. To this end, we build a prediction model that is based on user request patterns and make a prediction of system behavior for the near future. As a result, this model can save time for predicting the needed resource amount and reduce the possibility of resource overuse. In addition, we studied the performance of our proposed model comparing with conventional resource provisioning models under various Cloud execution conditions. The experimental results showed that our pattern-based prediction model gives significant benefits over conventional models.

Do diagnostic pathways contribute to the economic improvement of a hospital? Considerations from the perspective of laboratory medicine1)

Abstract Diagnostic pathways in the field of laboratory medicine could contribute economically to integrate modern “disease management” from the perspective of healthcare policy into a complex development with limited resources. The objective is to bridge the gap between medical and economic demand. Diagnostic pathways could be an innovative way to optimise processes in medicine with the objective of transparency of costs, results and performance, especially if technical prerequisites for web-based communication (i.e., order entry, digitised laboratory analysed findings) are given. The precondition is that laboratory diagnostics must be appreciated as a core procedure of a hospital. Furthermore, diagnostic pathways provide the opportunity to define processes concerning disease severity and rate of resource usage. The pathways support the physician by finding the probable or medically worthwhile primary and secondary diagnosis for coding the DRG (Diagnoses Related Groups). This results in guaranteeing revenues and an increase in the quality of therapy. The very visualisation of medical decisions by diagnostic pathways results in the reduction of ill-conceived activities and consequently in an improvement of health economics.