Priority Classes Research Articles

A separate design principle for priority-aware packet collection in industrial cyber-physical systems

Industrial cyber-physical systems (ICPS) bridge the physical factory floor and the cyber computational space by leveraging the emerging techniques, such as wireless sensor networks for ubiquitous connection and perception, where sensors are deployed to monitor and collect industrial data according to different timeliness requirements. Priority-aware schemes are often used to tackle the differentiated data packet collection. However, it is challenging to coordinate the multichannel access while meeting the priority-aware transmission requirement in ICPS. In this paper, we propose a separate design principle (SDP) for priority-aware packet collection. In SDP, the transmission of each priority class of sensors is separately scheduled by a multichannel superframe which fully uses the available channels. As a result, each time slot on each channel is repeatedly scheduled to sensors of different priorities by different superframes. Then, a priority-aware transmission mechanism is devised to coordinate the transmissions of different sensors in the predefined priority order. Simulation results show that for four priorities, SDP achieves as low as 16, 18, 23, and 36 % of the mean packet transmission delay of non-overlap TDMA scheduling for each priority class, respectively. Moreover, SDP greatly outperforms IEEE 802.15.4e protocol for packet collection with two priorities. We also demonstrate the feasibility of SDP based on the implementation of software-defined radios.

Endowment additivity and the weighted proportional rules for adjudicating conflicting claims

We propose and study a new axiom, restricted endowment additivity, for the problem of adjudicating conflicting claims. This axiom requires that awards be additively decomposable with respect to the endowment whenever no agent’s claim is filled. For two-claimant problems, restricted endowment additivity essentially characterizes weighted extensions of the proportional rule. With additional agents, however, the axiom is satisfied by a great variety of rules. Further imposing versions of continuity and consistency, we characterize a new family of rules which generalize the proportional rule. Defined by a priority relation and a weighting function, each rule aims, as nearly as possible, to assign awards within each priority class in proportion to these weights. We also identify important subfamilies and obtain new characterizations of the constrained equal awards and proportional rules based on restricted endowment additivity.

A cyber-resilient architecture for critical security services

Authentication and authorization are two of the most important services for any IT infrastructure. Taking into account the current state of affairs of cyber warfare, the security and dependability of such services is a first class priority. For instance, the correct and continuous operation of identity providers (e.g., OpenID) and authentication, authorization and accounting services (e.g., RADIUS) is essential for all sorts of systems and infrastructures. As a step towards this direction, we introduce a functional architecture and system design artifacts for prototyping fault- and intrusion-tolerant identification and authentication services. The feasibility and applicability of the proposed elements are evaluated through two distinct prototypes. Our findings indicate that building and deploying resilient and reliable critical services is an achievable goal through a set of system design artifacts based on well-established concepts in the fields of security and dependability. Additionally, we provide an extensive evaluation of both resilient RADIUS (R-RADIUS) and OpenID (R-OpenID) prototypes. We show that our solution makes services resilient against attacks without affecting their correct operation. Our results also pinpoint that the prototypes are capable of meeting the needs of small to large-scale systems (e.g., IT infrastructures with 800k to 10M users).

Uplink Blocking Probabilities in Priority-Based Cellular CDMA Networks with Finite Source Population

Fast proliferation of mobile Internet and high-demand mobile applications necessitates the introduction of different priority classes in next-generation cellular networks. This is especially crucial for efficient use of radio resources in the heterogeneous and virtualized network environments. Despite the fact that many analytical tools have been proposed for capacity and radio resource modelling in cellular networks, only a few of them explicitly incorporate priorities among services. We propose a novel analytical model to analyse the performance of a priority-based cellular CDMA system with finite source population. When the cell load is above a certain level, low-priority calls may be blocked to preserve the quality of service of high-priority calls. The proposed model leads to an efficient closed-form solution that enables fast and very accurate calculation of resource occupancy of the CDMA system and call blocking probabilities, for different services and many priority classes. To achieve them, the system is modelled as a continuous-time Markov chain. We evaluate the accuracy of the proposed analytical model by means of computer simulations and find that the introduced approximation errors are negligible.

A Remote Sensing and GIS Approach for Prioritization of Wadi Shueib Mini-Watersheds (Central Jordan) Based on Morphometric and Soil Erosion Susceptibility Analysis

Recently watershed prioritization has become a pragmatic approach for watershed management and natural resources development. Wadi Shueib is a Jordan Rift valley and covers an area of 177.8 km2. The upper catchment is of dry Mediterranean climate, whereas the lower part is arid. The drainage network is sub-dendritic pattern, with a trellis pattern developed due to the influence of W. Shueib structure. Fourteen mini-watersheds were delineated and designated as (MW 1 to MW 14) for prioritization purposes. Morphometric analysis, and soil erosion susceptibility analysis were conducted, and their values were calculated for each mini-watersheds. Based on value/relationship with erodibility, different prioritization ranks were ascribed following the computation of compound factors. Based on morphometric and soil erosion susceptibility analysis, and the resultant ranks, the mini-watersheds have been classified into four categories in relation to their priority for soil conservation measures: very high, high, moderate, and low. It is found that 64.3% of the 3rd order mini-watersheds are classified in the categories of very high and high priority. Based on soil erosion susceptibility analysis, three mini-watersheds are of very high priority and three are of high priority. The integration of morphometric and soil erosion susceptibility methods shows that mini-watersheds no.2 and no.3 are common mini-watersheds, and can be classified in the class of moderate and low priority respectively. By contrast, two mini-watersheds (no.8 and no.13) are categorized in the class of high priority based on morphometric analysis, and are classified in the category of very high priority based on soil erosion susceptibility analysis. Similarly, mini-watershed no.14 can be placed in the category of very high priority based on morphometric analysis, and ranks in the category of high priority based on soil erosion susceptibility analysis. With reference to the integration of the two methods of prioritization, it can be concluded that most of the mini-watersheds can be categorized in the classes moderate, high, and very high priority. Consequently, the entire W. Shueib watershed must be prioritized for soil and water conservation to ensure future sustainable agriculture and development of natural resources.

Priority-based quality of service guaranteed radio resource allocation in long term evolution network

In this paper, radio resource allocation to users demanding priority for data transmission in the downlink of long term evolution LTE system is carried out. The proposed algorithm defines priority and non-priority user classes and considers minimum data rate requirement of each user. In this scenario, algorithm is implemented in a way to satisfy the priority users followed by allocating the radio resource to the remaining users. The performance analysis of existing conventional scheduling algorithms such as round robin RR, best channel quality indicator BCQI and proportional fair PF is carried out, followed by implementation of the proposed algorithm. The simulation results show that compared to the conventional scheduling algorithms, the proposed algorithm makes efficient utilisation of the scarce radio spectrum and the users with priority have higher chance of achieving their minimum data rate requirement which corresponds to their quality of service QoS requirements being met irrespective of their channel conditions.

On Enhancing Technology Coexistence in the IoT Era: ZigBee and 802.11 Case

ZigBee is often chosen as a technology to connect things because of characteristics, such as network resilience, interoperability, and low power consumption. In addition, Zigbee Pro, with its Green Power feature, allows low-power networking capable of supporting more than 64 000 devices on a single network, making it an excellent choice to connect things. However, in recent years, we have witnessed the proliferation of smart devices using either 802.11 or ZigBee technologies, which operate in the same frequency band. Proposing and developing techniques that may improve the fair operation and performance of these technologies in coexistence scenarios have been a major concern in industry and academia. In this paper, we propose the use of traffic prioritization for ZigBee nodes in order to improve their performance when coexisting with IEEE 802.11 nodes. We develop an analytical model based on Markov chains, which captures the behavior of channel access mechanisms for both 802.11 nodes and different ZigBee priority class nodes. Based on extensive simulations, we validate the accuracy of the proposed model, and demonstrate how traffic prioritization of ZigBee nodes effectively improves their performance when coexisting with 802.11 nodes. We also demonstrate that this improvement comes at the cost of negligible degradation in the performance of the 802.11 nodes.

Waiting Time Distributions in the Preemptive Accumulating Priority Queue

We consider a queueing system in which a single server attends to N priority classes of customers. Upon arrival to the system, a customer begins to accumulate priority linearly at a rate which is distinct to the class to which it belongs. Customers with greater accumulated priority levels are given preferential treatment in the sense that at every service selection instant, the customer with the greatest accumulated priority level is selected next for servicing. Furthermore, the system is preemptive so that the servicing of a customer is interrupted for customers with greater accumulated priority levels. The main objective of the paper is to characterize the waiting time distributions of each class. Numerical examples are also provided which exemplify the true benefit of incorporating an accumulating prioritization structure, namely the ability to control waiting times.

Analysis of a MAP/PH/1 Queue with Discretionary Priority Based on Service Stages

In this paper, we study a MAP/PH/1 queue with two classes of customers and discretionary priority. There are two stages of service for the low-priority customer. The server adopts the preemptive priority discipline at the first stage and adopts the nonpreemptive priority discipline at the second stage. Such a queuing system can be modeled into a quasi-birth-and-death (QBD) process. But there is no general solution for this QBD process since the generator matrix has a block structure with an infinite number of blocks and each block has infinite dimensions. We present an approach to derive the bound for the high-priority queue length. It guarantees that the probabilities of ignored states are within a given error bound, so that the system can be modeled into a QBD process where the block elements of the generator matrix have finite dimensions. The sojourn time distributions of both high and low priority customers are obtained. Some managerial insights are given after comparing the discretionary priority rule with the preemptive and nonpreemptive disciplines numerically.

First Ranked First to Serve: Strategic Agents in a Service Contest

We model a service provider whose agents endogenously determine their participation and performance level. This paper is motivated by two-sided marketplaces and work-from-home call centers, which crowdsource incoming demand to a pool of agents. The agents are ranked in a prespecified number of priority classes based on their relative performance. Higher performers are more utilized and agents' idle time is not compensated. We study which priority class structure creates the best incentives for agents' participation and performance. We show that the optimal design of such a “service contest” is often coarse, namely, it contains only a few priority classes. Discarding available information on agents' relative rankings, or, deploying coarser priority classes, can paradoxically provide higher incentives for agents' participation and performance.

Avoiding class warfare: managing continuous queries with differentiated classes of service

Data stream management systems (DSMSs) offer the most effective solution for processing data streams by efficiently executing continuous queries (CQs) over the incoming data. CQs inherently have different levels of criticality and hence different levels of expected quality of service (QoS) and quality of data (QoD). Adhering to such expected QoS/QoD metrics is even more important in cases of multi-tenant data stream management services. In this work, we propose DILoS, a framework that, through priority-based scheduling and load shedding, supports differentiated QoS and QoD for multiple classes of CQs. Unlike existing works that consider scheduling and load shedding separately, DILoS is a novel unified framework that exploits the synergy between scheduling and load shedding. We also propose ALoMa, a general, adaptive load manager that DILoS is built upon. By its design, ALoMa performs better than the state-of-the-art alternatives in three dimensions: (1) it automatically tunes the headroom factor, (2) it honors the delay target, (3) it is applicable to complex query networks with shared operators. We implemented DILoS and ALoMa in our real DSMS prototype system (AQSIOS) and evaluate their performance for a variety of real and synthetic workloads. Our experimental evaluation of ALoMa verified its clear superiority over the state-of-the-art approaches. Our experimental evaluation of the DILoS framework showed that it (a) allows the scheduler and load shedder to consistently honor CQs' priorities, (b) significantly increases system capacity utilization by exploiting batch processing, and (c) enables operator sharing among query classes of different priorities while avoiding priority inversion, i.e., a lower-priority class never blocks a higher-priority one.

New differentiated consensus problem with topology cost constraints

A new differentiated consensus problem is studied. The problem is, given a system with multiple classes, consensus is targeted for each class and the consensus values can be different among the classes. Specifically, differentiated consensus is studied in a distributed stochastic network of nodes (or agents), where tasks assigned with different priorities are serviced. The network is assumed to have a switching topology and involves noises, delays in measurements, and topology cost constraints. The goal is to reach a balanced load (i.e., consensus) across the network and, at the same time, to satisfy the topology cost constraint, both for each priority class. A new control protocol is proposed, with which the network resources are allocated in a randomized way with a probability assigned to each priority class. It is shown that the control protocol meets the topology cost constraint and can be used to reach an approximate consensus for each of the priority classes in the network.

Sufficient conditions for reachability in automata networks with priorities

In this paper, we develop a framework for an efficient under-approximation of the dynamics of Asynchronous Automata Networks (AANs). An AAN is an Automata Network with synchronised transitions between automata, where each transition changes the local state of exactly one automaton (but any number of synchronising local states are allowed). The work we propose here is based on static analysis by abstract interpretation, which allows to prove that reaching a state with a given property is possible, without the same computational cost of usual model checkers: the complexity is polynomial with the total number of local states and exponential with the number of local states within a single automaton. Furthermore, we address AANs with classes of priorities, and give an encoding into AANs without priorities, thus extending the application range of our under-approximation. Finally, we illustrate our method for the model checking of large-scale biological networks.

Priority classes and weighted constrained equal awards rules for the claims problem

We revisit the “claims problem” (O'Neill, 1982), where a group of individuals have claims on a resource but there is not enough of it to honor all of the claims. We characterize the rules satisfying three well-known invariance axioms: consistency, composition up, and claims truncation invariance. They are priority-augmented versions of the standard weighted constrained equal awards rules, also known as weighted gains methods (Moulin, 2000): individuals are sorted into priority classes; the resource is distributed among the individuals in the first priority class using a weighted constrained equal awards rule; if some of the resource is left over, then it is distributed among the individuals in the second priority class, again using a weighted constrained equal awards rule; the distribution carries on in this way until the resource is exhausted. Our characterization extends to a generalized version of the claims problem where there are multiple divisible and indivisible resources and individuals have claims on each of these.

Factors Contributing to Inappropriate Visits of Frequent Attenders and Their Economic Effects at an Emergency Department in Singapore.

This study aimed to determine which factors contribute to frequent visits at the emergency department (ED) and what proportion were inappropriate in comparison with nonfrequent visits. This study was a retrospective, case-control study comparing a random sample of frequent attenders and nonfrequent attenders, with details of their ED visits recorded over a 12-month duration. Frequent attenders were defined as patients with four or more visits during the study period. In comparison with nonfrequent attenders (median age = 45.0 years, interquartile range [IQR] = 28.0 to 61.0 years), frequent attenders were older (median = 57.5 years, IQR = 34.0 to 74.8 years; p = 0.0003). They were also found to have more comorbidities, where 53.3% of frequent attenders had three or more chronic illnesses compared to 14% of nonfrequent attenders (p < 0.0001), and were often triaged to higher priority (more severe) classes (frequent 52.2% vs. nonfrequent 37.6%, p = 0.0004). Social issues such as bad debts (12.7%), heavy drinking (3.3%), and substance abuse (2.7%) were very low in frequent attenders compared to Western studies. Frequent attenders had a similar rate of appropriate visits to the ED as nonfrequent attenders (55.2% vs. 48.1%, p = 0.0892), but were more often triaged to P1 priority triage class (6.7% vs. 3.2%, p = 0.0014) and were more often admitted for further management compared to nonfrequent attenders (47.5% vs. 29.6%, p < 0.001). The majority of frequent attender visits were appropriate (55.2%), and of these, 81.1% resulted in admission. For the same number of patients, total visits made by frequent attenders ($174,247.60) cost four times as much as for nonfrequent attenders ($40,912.40). This represents a significant economic burden on the health care system. ED frequent attenders in Singapore were associated with higher age and presence of multiple comorbidities rather than with social causes of ED use. Even in integrated health systems, repeat ED visits are frequent and expensive, despite minimal social causes of acute care. EDs in aging populations must anticipate the influx of vulnerable, elderly patients and have in place interventional programs to care for them.

Prioritizing regular demand while reserving capacity for emergency demand

This research is motivated by the capacity allocation problem at a major provider of customized products to the oil and gas drilling industry. We formulate a finite-horizon, discrete-time, dynamic programming model in which a firm decides how to reserve capacity for emergency demand and how to prioritize two classes of regular demand. While regular demand can be backlogged, emergency demand will be lost if not fulfilled within the period of its arrival. Since backlogging cost accumulates over time, we find it optimal for the firm to adopt a dynamic prioritization policy that evaluates the priorities of different classes of regular demand every period. The optimal prioritization involves metrics that measure backlogging losses from various perspectives. We fully characterize the firm’s optimal prioritization and reservation policy. Those characterizations shed light on managerial insights.

A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks.

Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour.

M/M/c Queue with Two Priority Classes

This paper provides the first exact analysis of a preemptive M/M/c queue with two priority classes having different service rates. To perform our analysis, we introduce a new technique to reduce the two-dimensionally infinite Markov chain (MC), representing the two class state space, into a one-dimensionally infinite MC, from which the generating function (GF) of the number of low-priority jobs can be derived in closed form. (The high-priority jobs form a simple M/M/c system and are thus easy to solve.) We demonstrate our methodology for the c = 1, 2 cases; when c &gt; 2, the closed-form expression of the GF becomes cumbersome. We thus develop an exact algorithm to calculate the moments of the number of low-priority jobs for any c ≥ 2. Numerical examples demonstrate the accuracy of our algorithm and generate insights on (i) the relative effect of improving the service rate of either priority class on the mean sojourn time of low-priority jobs; (ii) the performance of a system having many slow servers compared with one having fewer fast servers; and (iii) the validity of the square root staffing rule in maintaining a fixed service level for the low-priority class. Finally, we demonstrate the potential of our methodology to solve other problems using the M/M/c queue with two priority classes, where the high-priority class is completely impatient.

A simulation framework for priority scheduling on heterogeneous clusters

Executing heterogeneous workloads with different priorities, resource demands and performance objectives is one of the key operations for today’s data centers to increase resource as well as energy efficiency. In order to meet the performance objectives of diverse workloads, schedulers rely on evictions even resulting in waste of resources due to lost executions of evicted tasks. It is not straightforward to design priority schedulers which capture key aspects of workloads and systems and also to strike a balance between resource (in)efficiency and application performance tradeoff. To explore large space of designing such schedulers, we propose a trace-driven cluster management framework that models a comprehensive set of system configurations and general priority-based scheduling policies. In particular, we focus on the impact of task evictions on resource inefficiency and task response times of multiple priority classes driven by Google production cluster trace. Moreover, we propose a system design as a use case exploiting workload heterogeneity and introducing workload-awareness into the system configuration and task assignment.

A New Two‐Bin Policy for Inventory Systems with Differentiated Demand Classes

We consider an inventory system under continuous review with two demand classes that are different in terms of service level required (or penalty cost incurred for backordering of demand). Prior literature has proposed the critical level rationing (CLR) policy under which the demand from the lower priority class is backordered once inventory falls below the critical level. While this reduces the penalty cost for the higher demand class, the fill rate achieved for the lower priority demand class gets compromised. In this study, we propose a new class of two‐bin (2B) policy for the problem. The proposed 2B policy assigns separate bins of inventory for the two demand classes. The demand for each class is fulfilled from its assigned bin. However, when the bin intended for the higher demand class is empty, the demand from the higher class can still be fulfilled with the inventory from the other bin. The advantage of the 2B policy is that better fill rates are achieved, especially for the lower demand class. Computational results show that the proposed policy is able to provide a much higher service level for the lower priority class demand without increasing the total cost too much and without affecting the service level for the higher priority class. When a service level constrained optimization problem is considered, the 2B policy dominates the CLR policy when the service level difference for the two classes is not too high or the service levels required for both the classes are relatively lower.