Resource Usage Monitoring in Clouds

2012 ◽  
Author(s):  
Mohit Dhingra ◽  
J. Lakshmi ◽  
S. K. Nandy
Keyword(s):  
Resource Usage

A Simulation for Forecasting Compute Resource Usage

2021 ◽  
Author(s):  
Saurabh Adhikari ◽  
CHRISTIAN PLEWNIA ◽  
CHAYAKORN NETRAMAI ◽  
HORST LICHTER
Keyword(s):  
Resource Usage

scholarly journals Structure and Distribution of Health Care Costs across Age Groups of Patients with Multimorbidity in Lithuania

2021 ◽  
Vol 18 (5) ◽  
pp. 2767
Author(s):  
Laura Nedzinskienė ◽  
Elena Jurevičienė ◽  
Žydrūnė Visockienė ◽  
Agnė Ulytė ◽  
Roma Puronaitė ◽  
...  
Keyword(s):  
Health Care ◽  
Health Insurance ◽  
Chronic Disease ◽  
National Health Insurance ◽  
Health Care Services ◽  
Age Groups ◽  
Age Group ◽  
Resource Usage ◽  
Care Services ◽  
Care Costs

Background. Patients with multimorbidity account for ever-increasing healthcare resource usage and are often summarised as big spenders. Comprehensive analysis of health care resource usage in different age groups in patients with at least two non-communicable diseases is still scarce, limiting the quality of health care management decisions, which are often backed by limited, small-scale database analysis. The health care system in Lithuania is based on mandatory social health insurance and is covered by the National Health Insurance Fund. Based on a national Health Insurance database. The study aimed to explore the distribution, change, and interrelationships of health care costs across the age groups of patients with multimorbidity, suggesting different priorities at different age groups. Method. The study identified all adults with at least one chronic disease when any health care services were used over a three-year period between 2012 and 2014. Further data analysis excluded patients with single chronic conditions and further analysed patients with multimorbidity, accounting for increasing resource usage. The costs of primary, outpatient health care services; hospitalizations; reimbursed and paid out-of-pocket medications were analysed in eight age groups starting at 18 and up to 85 years and over. Results. The study identified a total of 428,430 adults in Lithuania with at least two different chronic diseases from the 32 chronic disease list. Out of the total expenditure within the group, 51.54% of the expenses were consumed for inpatient treatment, 30.90% for reimbursed medications. Across different age groups of patients with multimorbidity in Lithuania, 60% of the total cost is attributed to the age group of 65–84 years. The share in the total spending was the highest in the 75–84 years age group amounting to 29.53% of the overall expenditure, with an increase in hospitalization and a decrease in outpatient services. A decrease in health care expenses per capita in patients with multimorbidity after 85 years of age was observed. Conclusions. The highest proportion of health care expenses in patients with multimorbidity relates to hospitalization and reimbursed medications, increasing with age, but varies through different services. The study identifies the need to personalise the care of patients with multimorbidity in the primary-outpatient setting, aiming to reduce hospitalizations with proactive disease management.

scholarly journals Entropy-Based Approach in Selection Exact String-Matching Algorithms

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 31
Author(s):  
Ivan Markić ◽  
Maja Štula ◽  
Marija Zorić ◽  
Darko Stipaničev
Keyword(s):  
String Matching ◽  
Quantitative Measure ◽  
Search Pattern ◽  
Maximum Efficiency ◽  
Resource Usage ◽  
Specific Domain ◽  
Matching Problems ◽  
Algorithm Efficiency ◽  
Testing Algorithm ◽  
Algorithm Implementation

The string-matching paradigm is applied in every computer science and science branch in general. The existence of a plethora of string-matching algorithms makes it hard to choose the best one for any particular case. Expressing, measuring, and testing algorithm efficiency is a challenging task with many potential pitfalls. Algorithm efficiency can be measured based on the usage of different resources. In software engineering, algorithmic productivity is a property of an algorithm execution identified with the computational resources the algorithm consumes. Resource usage in algorithm execution could be determined, and for maximum efficiency, the goal is to minimize resource usage. Guided by the fact that standard measures of algorithm efficiency, such as execution time, directly depend on the number of executed actions. Without touching the problematics of computer power consumption or memory, which also depends on the algorithm type and the techniques used in algorithm development, we have developed a methodology which enables the researchers to choose an efficient algorithm for a specific domain. String searching algorithms efficiency is usually observed independently from the domain texts being searched. This research paper aims to present the idea that algorithm efficiency depends on the properties of searched string and properties of the texts being searched, accompanied by the theoretical analysis of the proposed approach. In the proposed methodology, algorithm efficiency is expressed through character comparison count metrics. The character comparison count metrics is a formal quantitative measure independent of algorithm implementation subtleties and computer platform differences. The model is developed for a particular problem domain by using appropriate domain data (patterns and texts) and provides for a specific domain the ranking of algorithms according to the patterns’ entropy. The proposed approach is limited to on-line exact string-matching problems based on information entropy for a search pattern. Meticulous empirical testing depicts the methodology implementation and purports soundness of the methodology.

scholarly journals An Incentive-based Mechanism for Volunteer Computing Using Blockchain

2021 ◽  
Vol 21 (4) ◽  
pp. 1-22
Author(s):  
Ismaeel Al Ridhawi ◽  
Moayad Aloqaily ◽  
Yaser Jararweh
Keyword(s):  
Resource Sharing ◽  
Secure Communication ◽  
Service Providers ◽  
Volunteer Computing ◽  
Resource Usage ◽  
Content Filtering ◽  
Computing Device ◽  
Blockchain Technology ◽  
Smart Agriculture ◽  
Industrial Solutions

The rise of fast communication media both at the core and at the edge has resulted in unprecedented numbers of sophisticated and intelligent wireless IoT devices. Tactile Internet has enabled the interaction between humans and machines within their environment to achieve revolutionized solutions both on the move and in real-time. Many applications such as intelligent autonomous self-driving, smart agriculture and industrial solutions, and self-learning multimedia content filtering and sharing have become attainable through cooperative, distributed, and decentralized systems, namely, volunteer computing. This article introduces a blockchain-enabled resource sharing and service composition solution through volunteer computing. Device resource, computing, and intelligence capabilities are advertised in the environment to be made discoverable and available for sharing with the aid of blockchain technology. Incentives in the form of on-demand service availability are given to resource and service providers to ensure fair and balanced cooperative resource usage. Blockchains are formed whenever a service request is initiated with the aid of fog and mobile edge computing (MEC) devices to ensure secure communication and service delivery for the participants. Using both volunteer computing techniques and tactile internet architectures, we devise a fast and reliable service provisioning framework that relies on a reinforcement learning technique. Simulation results show that the proposed solution can achieve high reward distribution, increased number of blockchain formations, reduced delays, and balanced resource usage among participants, under the premise of high IoT device availability.

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