Appropriate Amount Of Resources Research Articles

Popularity-Aware Service Provisioning Framework in Cloud Environment

To balance the tradeoff between quality of service (QoS) and operating expenditure (OPEX), the service provider should request the appropriate amount of resources to the cloud operator based on the estimated variation of service requests. This paper proposes a popularity-aware service provisioning framework (PASPF), which leverages the network data analytics function (NWDAF) to obtain analytics on service popularity variations. These analytics estimate the congestion level and list of top services contributing most of the traffic change. Based on the analytics, PASPF enables the service provider to request the appropriate amount of resources for each service for the next time period to the cloud operator. To minimize the OPEX of the service provider while keeping the average response time of the services below their requirements, we formulate a constrained Markov decision process (CMDP) problem. The optimal stochastic policy can be obtained by converting the CMDP model into a linear programming (LP) model. Evaluation results demonstrate that the PASPF can achieve less than 50% OPEX of the service provider compared to a popularity-non-aware scheme while keeping the average response time of the services below the requirement.

Joint Traffic Offloading and Aging Control in 5G IoT Networks

The widespread adoption of 5G cellular technology will evolve as one of the major drivers for the growth of IoT-based applications. In this paper, we consider a Service Provider (SP) that launches a smart city service based on IoT data readings: in order to serve IoT data collected across different locations, the SP dynamically negotiates and rescales bandwidth and service functions. 5G network slicing functions are key to lease appropriate amount of resources over heterogeneous access technologies and different site types. Also, different infrastructure providers will charge slicing service depending on specific access technology supported across sites and IoT data collection patterns. We introduce a pricing mechanism based on Age of Information (AoI) to reduce the cost of SPs. It provides incentives for devices to smooth traffic by shifting part of the traffic load from highly congested and more expensive locations to lesser charged ones, while meeting QoS requirements of the IoT service. The proposed optimal pricing scheme comprises a two-stage decision process, where the SP determines the pricing of each location and devices schedule uploads of collected data based on the optimal uploading policy. Simulations show that the SP attains consistent cost reductions tuning the trade-off between slicing costs and the AoI of uploaded IoT data.

PRIMARY DISABILITY CAUSED BY INJURIES AND DISEASES OF THE MUSCULOSKELETAL SYSTEM AS ONE OF THE PREDICTORS OF THE NEED FOR THE PROVISION OF REHABILITATION SERVICES TO THE POPULATION OF THE REGION

Diseases of the musculoskeletal system and injuries are identified as one of the main causes of disability of the country's population, which leads to a permanent loss of working capacity of adults. This leads to an increase in production losses, an increase in social benefits, an increase in the share of costs for restorative treatment and ensuring an adequate quality of life for the particular part of the population. Strengthening the rehabilitation direction in the health care system may be a possible solution to the problem. A systematic analysis of the levels of primary disability of the population as a result of injuries and diseases of the musculoskeletal system can become a reliable basis for determining the need for rehabilitation services in the region and planning the appropriate amount of resources for their provision.
 The aim: to study the features of changes in the levels of disability of the population of the Transcarpathian region during 2011-2020 and to provide an assessment of the needs for rehabilitation services for people with injuries and diseases of the musculoskeletal system in the region.
 Materials and methods. The data of the Centre for Medical and Social Expertise of the Transcarpathian region for the years from 2011 to 2020 served as research materials. The medical-statistical method and the method of structural-logical analysis were used during the study.
 The results. It was established that in the region the third and fourth places in the structure of causes of disability for ten years were stably taken by diseases of the musculoskeletal system (13.0-16.9%) and injuries (9.4-10.8%). In total, 3,807 people were recognized as disabled for the first time as a result of these pathologies, and their average annual number was 983±49 people. The positive increase in the absolute number of cases of disability due to diseases of the musculoskeletal system and injuries during 2011-2019 outweighed the negative by 37.8%. With a fairly insignificant increase in the number of cases of newly established disability due to diseases of the musculoskeletal system, their share in the structure of causes of general disability increased from 13.0% to 16.9%, and the decrease of 23.6% in cases of annual disability due to injuries was also accompanied by a decrease in their share in the structure of causes of disability from 10.2% to 9.7%, which is connected with the trends of changes in the total number of cases of disability.
 The results of the analysis showed the preservation of the relevance of diseases of the musculoskeletal system and injuries as causes of permanent disability, which requires the response of the health care system of the Transcarpathian region by creating effective methods to improve the situation, one of which can be the provision of timely, full-fledged and high-quality rehabilitation assistance to such patients.
 The used methodological approach to the analysis and interpretation of the dynamics of the population's disability levels can serve as a management tool for regular calculations of the region's population's need for rehabilitation services.
 Conclusions. Disability of the population of the Transcarpathian region due to injuries and diseases of the musculoskeletal system remains an important issue for the regional health care system, which causes a high level of rehabilitation needs. Administrative analysis of the average annual number of people with a newly established disability for the specified reasons and a detailed interpretation of the dynamics of disability levels will allow to plan the rehabilitation services and their resource provision in accordance with the needs of the population.

Application of the Harmony Search Algorithm for Optimization of WDN and Assessment of Pipe Deterioration

The relevance of the optimal design of water distribution networks lies in its applicability. Thus, the design must be as efficient and affordable as possible, meaning that it achieves a minimum level of serviceability using an appropriate amount of resources. In this context, an ideal water network requires the use of minimum-size elements to reach the minimum head pressure required for each node of the system with the lowest energy consumption. For this purpose, a relatively new meta-heuristic algorithm, called Harmony Search, was used in this study to optimize the water distribution network of Cheongna International City, Korea, in terms of the pipe diameter. Furthermore, using the same algorithm under a different approach, an assessment of the deterioration of conduits over time was conducted by analyzing the optimal roughness coefficient for the original pipe material, which provides a useful tool for decision making. Other heuristic and meta-heuristic algorithms have been applied to this type of problem; however, Harmony Search provides a convenient implementation at a reasonable computational cost. In this study, Harmony Search is demonstrated to be a valuable tool for water distribution network optimization as well as for pipe aging assessment.

On revisiting energy and performance in microservices applications: A cloud elasticity-driven approach

Monolithic applications are a subject that includes several knowledge areas. Sometimes it can be a challenge to optimize CPU or IO requirements because it is not trivial to recognize the problem itself and improve it. There are many approaches to resolve this situation, where a trending one is the microservices. As a variant of the service-oriented architecture, microservices is a technique that arranges an application as a collection of loosely coupled services. This decomposition enables better software management in cloud-based environments since we can replicate each part individually using cloud elasticity to avoid execution bottlenecks. Also, since elasticity mitigates resource overprovisioning, it favors better energy consumption: the cloud owner can redistribute finite available resources among different tenants, and users can pay less to use the infrastructure. However, elasticity tuning is not trivial and depends on several factors, such as user experience, application architecture, and parameter modeling. Today, we observe a lack of initiatives in the literature that address both performance and energy perspectives to support the execution of microservices applications in the cloud. Concerning this context, this article introduces Elergy as a lightweight proactive elasticity model that provides resource reorganization for a cloud-based microservices application. Its differential approach appears in improving energy consumption by periodically handling the most appropriate amount of resources to execute an application while maintaining or yet improving the performance of CPU-bound applications. Elergy performs these functions proactively, in such a way of preventing future problems related to either resource under- or overprovisioning. The results showed energy consumption reduction and a competitive cost (application time x consumed resources) when comparing Elergy with a non-elastic scenario. Elergy obtained savings from 1.93% to 27.92% for energy consumption.

“When” Does It Pay to Be Good? Attributions Mediate the Way CSR Elements Impact on Consumer Responses, and Are Controllable

Responding to the appeal for more research on the contingencies that shape the relationship between CSR and corporate performance, this paper incorporates environmental CSR, sets up an experimental survey and employs multiple mediation analysis with the aim to test the mediating role of consumer attributions on the CSR elements–consumer responses relationship; and further to examine the degree to which attributions are controllable, i.e., specific CSR elements activate specific type of attributions. Results support that attributions have a strong predicting power on consumer outcomes. The right time of appearance and the appropriate amount of resources committed to a CSR campaign, through the dual type of attributions they activate (more positive, i.e., values-driven and less negative, i.e., egoistic), impact positively on consumer reactions. In this respect, the study adds to past research showing that attributions are controllable, i.e., specific CSR initiative characteristics of a impact on the dimensionality of attributions and, through that, on specific target-types of consumer responses. This study thus shows that the activation of a dual-level attributions’ system is ambivalent, dependent on the character of the CSR campaign. The fact that specific CSR elements (i.e., CSR Timing) activate dual-level CSR motives that act complementarily indicates that managers should be clear about the capabilities of the elements of their CSR initiatives and how much impact they expect those elements to have on consumer response.

Multi-policy-aware MapReduce resource allocation and scheduling for smart computing cluster

Abstract When a user submit a MapReduce job in the smart computing cluster, we first need to allocate cluster resource for the job. It is widely concerned that how to save time and resource costs to provide users with computing capacity and services. Here, we propose a multi-policy-aware Resources Allocation Algorithm that can allocate the appropriate amount of resources to the job to meet the execution deadline in private cloud and an extension in public cloud. Then when job running in the allocated cluster, in order to guarantee the performance of smart computing framework, we further propose a multi-policy-aware resource scheduling optimization model under YARN. If users use default policy, considering the difference of the heterogeneity of jobs and the resource request of tasks, we propose a global task dynamic resource scheduling algorithm—LRD algorithm, based on data locality, resource demand and task dependency. Further, if users set the deadline policy for jobs, we propose a DA (deadline-aware) scheduling algorithm based on performance prediction model. It can optimize the overall execution time of Hadoop jobs and improve the resource utilization of the entire cluster. Finally, we conduct different experiments to evaluate and verify the proposed models and algorithms in this paper.

SLA-aware energy-efficient scheduling scheme for Hadoop YARN

Apache Hadoop becomes ubiquitous for cloud computing which provides resources as services for multi-tenant applications. YARN (a.k.a. MapReduce 2.0) is one of the key features in the second-generation Hadoop, which provides resource management and scheduling for large-scale MapReduce environments. Two enormous challenges in the YARN scheduler are the abilities to automatically tailor and control resource allocations to different jobs for achieving their Service Level Agreements (SLAs), and minimize energy consumption of the overall cloud computing system. In this work, we propose an SLA-aware energy-efficient scheduling scheme which allocates appropriate amount of resources to MapReduce applications with YARN architecture. In our task scheduling policy, We consider the data locality information to save the MapReduce network traffic. Furthermore, the slack time between the actual execution time of completed tasks and expected completion time of the application is utilized to improve the energy-efficiency of the system. An online userspace governor-based dynamic voltage and frequency scaling (DVFS) scheme is designed in the YARN per-application ApplicationMaster to dynamically change the CPU frequency for upcoming tasks given the slack time from previous completed tasks. Experimental evaluation shows that our proposed scheme outperforms the existing MapReduce scheduling policies in terms of both resource ultization and energy-efficiency.

The US Army Corps of Engineers Scalable Approach to Estimating Loss of Life from Flooding

Although flooding can lead to many types of severe consequences, the primary objective of the US Army Corps of Engineers (USACE) dam and levee safety programs are to manage risk to the public who rely on those structures to keep them reasonably safe from flooding. Thus, reducing the risk associated with loss of life is paramount. USACE employs a scalable approach to estimating loss of life, where the goal is to invest an appropriate amount of resources to answer the question at hand. Given the large number of dams and levees within the USACE portfolio (over 700 dams and 15,000 miles of levees), a “one size fits all” approach does not work. Screening level risk assessments are carried out to initially characterize risk. That initial risk characterization informs prioritization of additional efforts such as interim risk management activities and more detailed risk assessments. More detailed risk assessments inform investment in long-term risk reduction measures, which include analysis and selection of major modification activities and implementation of non-structural measures including emergency management and evacuation planning. This paper describes the USACE scalable approach for estimating loss of life from flood events.

Radio resources allocation for decentrally controlled relay stations

To enhance throughput and to extend coverage of wireless networks based on IEEE 802.16 standards, relay stations can be implemented. The crucial point influencing the overall system performance is allocation of appropriate amount of resources to individual relay stations depending on their current needs. If resources are not allocated properly, relay stations can experience congestion situations resulting in system throughput decrease and packet delays increase. If the base station is enhanced purely by centrally controlled relay stations, the base station itself is able to dynamically schedule data transmissions of its relays. Nevertheless, decentrally controlled relays schedule data for its users on its own and the base station is no longer capable to allocate the right amount of radio resources for them. The decentrally controlled relays have to ask for radio resources by means of existing scheduling services defined in IEEE 802.16 standard. While utilization of the conventional unsolicited grant service decreases the system capacity, the use of real-time polling or extended real-time polling services results in higher signaling overhead and longer packet delays. In order to maximize system throughput and to minimize packet delays and signaling overhead, we suggest a mechanism that pre-allocates to decentrally controlled relay stations a certain amount of default radio resources while the remaining resources are dynamically shared. The obtained results show that for low traffic load, overhead and packet delays are significantly smaller as relays do not have to ask base stations for resources. On the other hand, at heavy traffic load the system throughput slightly decreases whereas packet delays and signaling overhead are still significantly smaller.

Improving existing delay analysis techniques for the establishment of delay liabilities

When construction delays occur, it is necessary to ascertain the liabilities of the contracting parties and to direct the appropriate amount of resources to recover the schedule. Unfortunately, delay analysis and schedule compression are normally treated as separate or independent aspects. This paper examines the feasibility of integrating the delay analysis and schedule compression functions into a broad‐scoped two‐stage process. The main issue is shown to be the kind of delay analysis required for each stage of the process and seven existing techniques are illustrated for use in conjunction with schedule compression. Since the current form and assumptions of delay analysis techniques are unlikely to provide the necessary level of feedback reliability for recovering delays, it is necessary to modify these techniques by incorporating some means of delay type scrutiny, excusable delays updating, and treatment of concurrent delays. The modified delay analysis techniques can serve as a basis for negotiation between the client and contractor and hence improve the interdisciplinary relations.

Adaptive allocation of resources and call admission control for wireless ATM using genetic algorithms

In wireless ATM-based networks, admission control is required to reserve resources in advance for calls requiring guaranteed services. In the case of a multimedia call, each of its substreams (i.e., video, audio, and data) has its own distinct quality of service (QoS) requirements (e.g., cell loss rate, delay, jitter, etc.). The network attempts to deliver the required QoS by allocating an appropriate amount of resources (e.g., bandwidth, buffers). The negotiated QoS requirements constitute a certain QoS level that remains fixed during the call (static allocation approach). Accordingly, the corresponding allocated resources also remain unchanged. We present and analyze an adaptive allocation of resources algorithm based on genetic algorithms. In contrast to the static approach, each substream declares a preset range of acceptable QoS levels (e.g., high, medium, low) instead of just a single one. As the availability of resources in the wireless network varies, the algorithm selects the best possible QoS level that each substream can obtain. In case of congestion, the algorithm attempts to free up some resources by degrading the QoS levels of the existing calls to lesser ones. This is done, however, under the constraint of achieving maximum utilization of the resources while simultaneously distributing them fairly among the calls. The degradation is limited to a minimum value predefined in a user-defined profile (UDP). Genetic algorithms have been used to solve the optimization problem. From the user perspective, the perception of the QoS degradation is very graceful and happens only during overload periods. The network services, on the other hand, are greatly enhanced due to the fact that the call blocking probability is significantly decreased. Simulation results demonstrate that the proposed algorithm performs well in terms of increasing the number of admitted calls while utilizing the available bandwidth fairly and effectively.

Efficient call admission control for heterogeneous services in wireless mobile ATM networks

An efficient call admission control scheme for handling heterogeneous services in wireless ATM networks is proposed. Quality-of-service provisioning of jitter bounds for constant bit rate traffic and delay bounds for variable bit rate traffic is used in the CAC scheme to guarantee predefined QoS levels for all traffic classes. To reduce the forced handoff call dropping rate, the CAC scheme gives handoff calls a higher priority than new calls by reserving an appropriate amount of resources for potential handoff calls. Resource reservation in the CAC scheme makes use of user mobility information to ensure efficient resource utilization. Simulation results show that the proposed CAC scheme can achieve both low handoff call dropping rate and high resource utilization.