User Priorities Research Articles

Towards completely fair scheduling on asymmetric single-ISA multicore processors

Single-ISA asymmetric multicore processors (AMPs), which combine high-performance big cores with low-power small cores, were shown to deliver higher performance per watt than symmetric CMPs (Chip Multi-Processors). Previous work has highlighted that this potential of AMP systems can be realizable via OS scheduling. To date, most existing scheduling schemes for AMPs have been designed to optimize the system throughput, but they are inherently unfair. Although fairness-aware schedulers have been also proposed, they fail to effectively deal with user priorities and do not always ensure that equal-priority applications experience a similar slowdown. To overcome these limitations, we propose ACFS, an asymmetry-aware completely fair scheduler that seeks to optimize fairness while ensuring acceptable throughput. Our evaluation on real AMP hardware and using scheduler implementations in the Linux kernel demonstrates that ACFS achieves an average 23% fairness improvement over two state-of-the-art schemes, while providing higher system throughput.

Performance Analysis of IEEE 802.15.6 CSMA/CA Protocol for WBAN Medical Scenario through DTMC Model.

The newly drafted IEEE 802.15.6 standard for Wireless Body Area Networks (WBAN) has been concentrating on a numerous medical and non-medical applications. Such short range wireless communication standard offers ultra-low power consumption with variable data rates from few Kbps to Mbps in, on or around the proximity of the human body. In this paper, the performance analysis of carrier sense multiple access with collision avoidance (CSMA/CA) scheme based on IEEE 802.15.6 standard in terms of throughput, reliability, clear channel assessment (CCA) failure probability, packet drop probability, and end-to-end delay has been presented. We have developed a discrete-time Markov chain (DTMC) to significantly evaluate the performances of IEEE 802.15.6 CSMA/CA under non-ideal channel condition having saturated traffic condition including node wait time and service time. We also visualize that, as soon as the payload length increases the CCA failure probability increases, which results in lower node's reliability. Also, we have calculated the end-to-end delay in order to prioritize the node wait time cause by backoff and retransmission. The user priority (UP) wise DTMC analysis has been performed to show the importance of the standard especially for medical scenario.

Identifying the most suitable road crossing infrastructure based on estimated delay to pedestrians and vehicle users

Worldwide guidelines have been studied to identify the most common parameters being considered for making a decision regarding identification of most suitable road crossing infrastructure. Delay being the most common parameter needs to be considered to make a decision regarding most suitable crossing infrastructure. This study proposes a methodology to select road crossing infrastructure type based on the delay caused to pedestrians and vehicle passengers. To estimate the average delay caused to pedestrians and vehicle users, data through videography have been collected at six locations in Delhi. At all six locations, average delay caused to pedestrians and vehicles users has been estimated using existing methods suitable for Indian traffic conditions and behavior. Total delay caused to all pedestrians and vehicle users has been calculated based on average delay, traffic volume count and number of pedestrians. Estimated gross delay for all users (total delay of pedestrians and vehicle users) for alternate road crossing infrastructures (at-grade signalized and grade-separated) has been compared to identify the one which causes minimum gross delay. Total delay has been compared for various scenarios developed considering the equal and higher priority for non-motorized and public transport users compared to personalized vehicle users.

Multiple Objective Optimization in Reliability Demonstration Tests

Reliability demonstration tests are usually performed in product design or validation processes to demonstrate whether a product meets specified requirements on reliability. For binomial demonstration tests, the zero-failure test has been most commonly used due to its simplicity and use of minimum sample size to achieve an acceptable consumer's risk level. However, this test can often result in unacceptably high risk for producers as well as a low probability of passing the test even when the product has good reliability. This paper explicitly explores the interrelationship between multiple objectives that are commonly of interest when planning a demonstration test and proposes structured decision-making procedures using a Pareto front approach for selecting an optimal test plan based on simultaneously balancing multiple criteria. Different strategies are suggested for scenarios with different user priorities and graphical tools are developed to help quantify the trade-offs between choices and to facilitate informed decision making. Potential impacts of some subjective user inputs on the final decision are studied to offer insights and useful guidance for general applications.

Equilibrium analysis of an opportunistic spectrum access mechanism with imperfect sensing results

In order to reduce the average delay of secondary user (SU) packets and adapt to various levels of tolerance for transmission interruption, we propose a novel opportunistic channel access mechanism with admission threshold and probabilistic feedback in cognitive radio networks (CRNs). Considering the preemptive priority of primary user (PU) packets, as well as the sensing errors of missed detection and false alarm caused by SUs, we establish a type of priority queueing model in which two classes of customers may interfere with each other. Based on this queueing model, we evaluate numerically the proposed mechanism and then present the system performance optimization. By employing a matrix-geometric solution, we derive the expressions for some important performance measures. Then, by building a reward function, we investigate the strategies for both the Nash equilibrium and the social optimization. Finally, we provide a pricing policy for SU packets to coordinate these two strategies. With numerical experiments, we verify the effectiveness of the proposed opportunistic channel access mechanism and the rationality of the proposed pricing policy.

Performance Optimization of Priority Assisted CSMA/CA Mechanism of 802.15.6 under Saturation Regime

Due to the recent development in the field of Wireless Sensor Networks (WSNs), the Wireless Body Area Networks (WBANs) have become a major area of interest for the developers and researchers. Human body exhibits postural mobility due to which distance variation occurs and the status of connections amongst sensors change time to time. One of the major requirements of WBAN is to prolong the network lifetime without compromising on other performance measures, i.e., delay, throughput and bandwidth efficiency. Node prioritization is one of the possible solutions to obtain optimum performance in WBAN. IEEE 802.15.6 CSMA/CA standard splits the nodes with different user priorities based on Contention Window (CW) size. Smaller CW size is assigned to higher priority nodes. This standard helps to reduce delay, however, it is not energy efficient. In this paper, we propose a hybrid node prioritization scheme based on IEEE 802.15.6 CSMA/CA to reduce energy consumption and maximize network lifetime. In this scheme, optimum performance is achieved by node prioritization based on CW size as well as power in respective user priority. Our proposed scheme reduces the average back off time for channel access due to CW based prioritization. Additionally, power based prioritization for a respective user priority helps to minimize required number of retransmissions. Furthermore, we also compare our scheme with IEEE 802.15.6 CSMA/CA standard (CW assisted node prioritization) and power assisted node prioritization under postural mobility in WBAN. Mathematical expressions are derived to determine the accurate analytical model for throughput, delay, bandwidth efficiency, energy consumption and life time for each node prioritization scheme. With the intention of analytical model validation, we have performed the simulations in OMNET++/MIXIM framework. Analytical and simulation results show that our proposed hybrid node prioritization scheme outperforms other node prioritization schemes in terms of average network delay, average throughput, average bandwidth efficiency and network lifetime.

User Association With Unequal User Priorities in Heterogeneous Cellular Networks

In heterogeneous networks (HetNets), the load between macrocell base stations (MBSs) and small-cell base stations (SBSs) is imbalanced due to their different transmission powers and locations. This load imbalance significantly impacts system performance and affects the experience of mobile users (MUs) with different priorities. In this paper, we aim to distributively optimize the user association in HetNets with various user priorities to solve the load balancing problem. Since the user association is a binary matching problem, which is NP-hard, we propose a distributed belief propagation (BP) algorithm to approach the optimal solution. We first develop a factor graph model, using the network topology, to represent this user association problem. With this factor graph, we propose a novel distributed BP algorithm by adopting the proportional fairness as the objective. Next, we theoretically prove the existence of the fixed point in our BP algorithm. To be more practical, we develop an approximation method to significantly reduce the computational and communication complexity of the BP algorithm. Furthermore, we analyze some properties of the factor graph relevant to the performance of the BP algorithm using the stochastic geometry. Simulation results show that 1) the proposed BP algorithm well approaches the optimal system performance and achieves a much better performance compared with other association schemes and that 2) the analytical results on the average degree distribution and sparsity of the factor graph match with those obtained from the Monte Carlo simulations.

Modelling and multi objective optimization of WEDM of commercially Monel super alloy using evolutionary algorithms

In this research work, development of a multi response optimization technique has been undertaken, using traditional desirability analysis and non-traditional particle swarm optimization techniques (for different customer's priorities) in wire electrical discharge machining (WEDM). Monel 400 has been selected as work material for experimentation. The effect of key process parameters such as pulse on time (TON), pulse off time (TOFF), peak current (IP), wire feed (WF) were on material removal rate (MRR) and surface roughness(SR) in WEDM operation were investigated. Further, the responses such as MRR and SR were modelled empirically through regression analysis. The developed models can be used by the machinists to predict the MRR and SR over a wide range of input parameters. The optimization of multiple responses has been done for satisfying the priorities of multiple users by using Taguchi-desirability function method and particle swarm optimization technique. The analysis of variance (ANOVA) is also applied to investigate the effect of influential parameters. Finally, the confirmation experiments were conducted for the optimal set of machining parameters, and the betterment has been proved.

Search Engine Behavior and Satisfaction:A Survey-Based Study among Users in Malaysia

The birth of the internet in the 1990‟s did not only change the way of how people communicate but also of how they access information. The amount of information that the internet offers is enormous, it grows by the day and can be retrieved at any time – only technical issues may interrupt its constant availability. That is why the World Wide Web is nowadays the first port for information retrieval. Information retrieval tools are indispensible in this environment. They filter the information mass and allow users to locate the desired information.Search engines are one of the most popular web services.Most of what is known about user search engine behavior comes from query log analysis and clickthrough analysis. Only few studies have so far examined search engine behavior and user satisfaction from a mere user perspective.The number still shrinks when it comes to narrowing down the research focus on users of a specific country or language community. This study investigates user search engine behavior and satisfaction from the perspective of Chinese-speaking users in Malaysia. A survey made among 62 Malaysian Chinese users assesses search engine usage frequency, preferred search engines, and user priorities for search engine selection. It also assesses linguistic aspects such as preferred search language and search engine interface language. The study evaluates the users‟ satisfaction with the search results in general and the search results on the first page, with the search results of English and Chinese search queries, andthe necessity for query rephrasing. Where possible, the study compares the results with existing studies conducted in Malaysia.

Personalised Information Recommender Using Framework for Ontology Alignment Among Digital Libraries

In the digital erantology is considered as one of the powerful tools for knowledge representation and efficient information retrieval. Ontology alignment is a process that discovers mapping between source and target ontologies, where each mapping is a relationship based on some similarity measure. This paper, has presented a new context aware alignment approach that needs little human intervention and it can map multiple ontologies to generate user interest dynamically. The objective is to design and develop an ontology alignment model that provides more benefits to its stakeholders in sharing resources and searching across digital libraries based on priorities of users. The experimental results evidently indicate significant improvement in search results when user profile and navigational pattern ontologies are aligned with digital library ontology.

Management of loads in residential buildings installed with PV system under intermittent solar irradiation using mixed integer linear programming

One of the challenges of utilizing solar PV in residential buildings is the management of load in accordance to the intermittent nature of solar resource. In this paper, Mixed Integer Linear Programming (MILP) is proposed to effectively plan and allocate load for consumption in such a way that the user defined priority of loads are conserved. In this way, the loads are made to follow the battery state of charge in accordance to the intermittent nature of solar resource without adversely affecting the quality of life of the user. In doing this, energy audit of a typical 4-bedroom residential building belonging to a medium-income family is carried out and classified into controllable and non-controllable loads. The energy audit is also used to size the PV system and the battery bank taking into consideration the financial status of the user (i.e. adequately sized and under-sized PV system based on financial status of the user). A load manager based on MILP with solar resource load following capability is thereafter developed and implemented in a computational program to manage the load based on user’s priority. Six scenarios are created to gain insights into the effectiveness of the proposed load manager. The results reveal that the proposed technique is effective in allocating loads based on the available solar resource in such a way that the user priority is maintained.

A paper recommender system based on user’s profile in big data scholarly

Users encounter a huge volume of papers in digital libraries and paper search engines such as IEEE Explore, ACM Digital library, Google scholar and etc. these high number of papers make some difficulties for researchers for finding proper information and items. Recommender systems contain successful tools for knowledge of users and identification of their priorities. These systems present a personalized proposal to users who seek to find a special kind of relevant data or their priorities through the big number of data. Recommendersystem based on personalization uses the user profile and in view of the fact that the user profile encompass information pertaining to the user priorities; so it is a very active district in data recovery. Recommendersystem is an attitude that presented in order to encounter difficulties caused by abundant data and it helps users to attain their goals quickly through huge number of data. In this paper, we have presented an approach that received entire of available information in a paper, and formed a profile for each user by short and long inquiries; this profile is personalized for user and then recommends the closest paperto the user by the user profile. Findings indicate that suggested approach outperformsthe similar approaches. Keywords: recommender system; bigdata; user profile; content-based recommender system; hadoop

Distributed Late-binding Scheduling and Cooperative Data Caching

Pull-based overlays are used in some of today’s largest computational grids. Job agents are submitted to resources with the duty of retrieving real workload from a central queue at runtime and executing it. This model helps overcome the problems of direct job submission in the highly complex grid environments, namely, heterogeneity, imprecise status information, relatively high failure rates and slow adaptation to changes of grid conditions or user priorities. This article presents a distributed scheduling architecture for such late-binding overlays. In this architecture, execution nodes share a distributed hash table and cooperatively perform job assignment. As our experiments prove, scalability problems of centralized matching are avoided, achieving low and predictable scheduling overheads even for execution of large workflows, and total turnaround times are improved. This is in line with the predictions of a theoretical model of grid workflow execution that the article also discusses. Scalability makes fine-grained scheduling possible and enables new functionalities, like a distributed data cache shared by the execution nodes, which helps alleviate the commonly congested storage services. In addition, we show that our system is more resilient to problems like communication breakdowns between computation centres. Moreover, the new architecture is better prepared to deal with demanding scenarios like intense demand of popular data files or remote data processing.

Developing an Environmental Public Health Surveillance System for England

Introduction: The preventable environmental burden of disease estimated for the UK is 14%. To support primary prevention of non-communicable disease, robust information leading to actions that reduce environmental hazard exposures is required, that could support an environmental public health service and a Health in All Policies approach. In England, no comprehensive population-based surveillance exists that integrates environmental hazard and health information. Public Health England (PHE) decided to develop an Environmental Public Health Surveillance System (EPHSS), with a first phase to build capabilities for surveillance of acute incidents, and a second phase to extend to datasets relevant to chronic exposures. Methods: A governance system was established with representation across PHE groups. EPHSS development started from consultation of stakeholders in public health service, government, academia and NGOs. In 2014 user requirements were identified for phase one leading to development of software as a web-based system capturing information on environmental events and exposures of interest to public health and to be shared across contributing agencies. In 2015, 106 stakeholders were consulted for phase two, and user priorities and requirements related to chronic exposure were documented, with related software development expected for 2017-18. Results: EPHSS software for Phase one is expected to be completed in 2016, several topics were identified of interest to specific users, who are expected to pilot applications of acute incident information surveillance on topics including poisonings, housing-related chemical hazards, and exposures from waste sites. Phase two stakeholder consultation highlighted several priority topic areas. Conclusions: EPHSS has developed a tool expected to lead to improved intelligence across a range of sectors and thus support public health practitioners aiming to prevent acute and chronic exposures and related health effects.

Multiple Response Optimization for Higher Dimensions in Factors and Responses

When optimizing a product or process with multiple responses, a two‐stage Pareto front approach is a useful strategy to evaluate and balance trade‐offs between different estimated responses to seek optimum input locations for achieving the best outcomes. After objectively eliminating non‐contenders in the first stage by looking for a Pareto front of superior solutions, graphical tools can be used to identify a final solution in the second subjective stage to compare options and match with user priorities. Until now, there have been limitations on the number of response variables and input factors that could effectively be visualized with existing graphical summaries. We present novel graphical tools that can be more easily scaled to higher dimensions, in both the input and response spaces, to facilitate informed decision making when simultaneously optimizing multiple responses. A key aspect of these graphics is that the potential solutions can be flexibly sorted to investigate specific queries, and that multiple aspects of the solutions can be simultaneously considered. Recommendations are made about how to evaluate the impact of the uncertainty associated with the estimated response surfaces on decision making with higher dimensions. Copyright © 2016 John Wiley & Sons, Ltd.

Biomedical nemesis? Critical deliberations with regard to health and social care integration for social work with older people

This article questions ongoing moves towards integration into health care for social work with older people in the United Kingdom. While potentially constructing clearer pathways to support, integration risks reducing welfare provisions for a traditional low priority user group, while further extending the principles of privatisation. Integration models also understate the ideological impact of biomedical perspectives within health and social care domains, conflate roles and undermine the potential positive role of ‘holistic’ multi-agency care. Constructive social work for older people is likely to further dilute within aggressive integrated models of welfare, which will be detrimental for meeting many of the complex needs of ageing populations.

Delay Aware and Users Categorizing-Based Call Admission Control for Multi-Services LTE-A Networks

In this paper, we introduce the user’s privileges and traffic maximum delay tolerance as additional dimensions in the call admission control processes to efficiently control the utilization of LTE-A network resources. Based on this idea, we propose an efficient call admission control scheme named “Delay Aware and User Categorizing-based CAC with Adaptive Resource Reservation”, where the user priority is adjusted dynamically based on the current network conditions and the users’ categorizations and traffic delay tolerances, to increase the network’s resource utilization and at the same time to maximize the operators’ revenue. In this proposed scheme, the users are classified into Golden users and Silver users, and the type of service per user is classified as real-time (RT) and non-real-time (NRT) services. We compare the performance of the proposed scheme with the corresponding results of previous schemes, referred to as the adaptive resource reservation-based Call Admission Control (ARR-CAC), where user categorization and delay were not taken into consideration in the call admission control process. Simulation results indicate the superiority of the proposed scheme because it is able to achieve a better balance between system utilization, users’ privileges provided by network operators and QoS provisioning compared to the ARR-CAC scheme.

A Mobile Care Coordination System for the Management of Complex Chronic Disease.

There is global concern about healthcare cost, quality, and access as the prevalence of complex and chronic diseases, such as heart disease, continues to grow. Care for patients with complex chronic disease involves diverse practitioners and multiple transitions between medical centers, physician practices, clinics, community resources, and patient homes. There are few systems that provide the flexibility to manage these varied and complex interactions. Participatory and user-centered design methodology was applied to the first stage of building a mobile platform for care coordination for complex, chronic heart disease. Key informant interviews with patients, caregivers, clinicians, and care coordinators were conducted. Thematic analysis led to identification of priority user functions including shared care plan, medication management, symptom management, nutrition, physical activity, appointments, personal monitoring devices, and integration of data and workflow. Meaningful stakeholder engagement contributes to a person-centered system that enhances health and efficiency.

Adaptive OFDMA Resource Allocation using Modified Multi-Dimension Genetic Algorithm

A considerable work has been conducted to cope with orthogonal frequency division multiple access (OFDMA) resource allocation with using different algorithms and methods. However, most of the available studies deal with optimizing the system for one or two parameters with simple practical condition/constraints. This paper presents analyses and simulation of dynamic OFDMA resource allocation implementation with Modified Multi-Dimension Genetic Algorithm (MDGA) which is an extension for the standard algorithm. MDGA models the resource allocation problem to find the optimal or near optimal solution for both subcarrier and power allocation for OFDMA. It takes into account the power and subcarrier constrains, channel and noise distributions, distance between user's equipment (UE) and base stations (BS), user priority weight – to approximate the most effective parameters that encounter in OFDMA systems. In the same time multi dimension genetic algorithm is used to allow exploring the solution space of resource allocation problem effectively with its different evolutionary operators: multi dimension crossover, multi dimension mutation. Four important cases are addressed and analyzed for resource allocation of OFDMA system under specific operation scenarios to meet the standard specifications for different advanced communication systems. The obtained results demonstrate that MDGA is an effective algorithm in finding the optimal or near optimal solution for both of subcarrier and power allocation of OFDMA resource allocation.

Mils-Cloud: A Sensor-Cloud-Based Architecture for the Integration of Military Tri-Services Operations and Decision Making

Spatially distributed sensor nodes in wireless sensor networks (WSNs) can be used to monitor large unmanned areas. However, there are many limitations to WSNs, and the influence and accessibility of the sensors in these networks are limited to localized areas. Another popular technology today is cloud computing (CC). CC can provide a potent and scalable processing and storage infrastructure that can be used to perform the analysis of online as well as offline data streams provided by the sensors. It is possible to virtualize the sensor networks to provide these networks as a utility service. In this paper, we propose “Mils-Cloud,” which is a sensor-cloud architecture utilizing this infrastructure for developing architecture for the integration of military tri-services in a battlefield scenario. We propose a hierarchical architecture of sensor-cloud with users having different levels of priority. The results show that reserving about 20%–25% of resources actually boosts the performance of the system for priority users without compromising the availability for normal users.