Service-Sharing Approach Using a Mutual Exclusion Algorithm for Smart Cashier Systems

Distributed algorithms for multiple entries to a critical section with priority

In distributed systems,cooperating process essh are both local and remoter sources. Chance is very high that multiple processes makesimultaneous requests to the same resource. If the resourcerequires mutually exclusive access (critical section - CS), then some regulation is needed to access it for ensuring synchronizedaccess of the resources thatonly one process could use the resource at a given time. This is the distributed mutual exclusion problem. The problem of coordinating the execution of critical sections by eachprocessissolved by providing mutually exclusive access to the CS. Mutual exclusion ensures that concurrent processes make a serialized access to shared resources.Quorum-based algorithms offer the advantage of protocol symmetry, spreading effort and responsibility uniformly across the distributed systems. In thispaper, we have proposed a permission baseddistributedmutual exclusion algorithmwhichis an improvement of Maekawa’s algorithm. The number of messages required by the improvisedalgorithmis in the range 3Mto 5Mper critical section invocation whereMis the number of intersection nodes1 in the system. A reduction in number of message by restricting the communication of anynodewith the intersection nodes of the quorums, withoutany modification of the basic structure of the algorithm.

Mutual exclusion is one of the fundamental problems in mobile ad hoc networks (MANETs). The solutions to the mutual exclusion problem ensure exclusive access to a shared resource(s). In MANETs, one of the variants of mutual exclusion problem is local mutual exclusion (LME) problem. In LME problem, the non-neighboring nodes may enter CS simultaneously. Whereas, the group local mutual exclusion (GLME) problem in MANETs is the generalization of LME problem, where, unlike LME problem, the processes requesting same group (resource) may be in a critical section (CS) simultaneously. In the presented exposition, an extension of GLME problem, named h-group local mutual exclusion (h-GLME), has been proposed. The h-GLME problem, where the number of active groups can be h (h ≥ 1) and the processes requesting any of h groups may be in their respective CS simultaneously. It is applicable to MANETs. To the best of our knowledge, h-GLME is the first wireless extension of the GLME problem.

Hypercube have been commercially available in the past few years to their high degree of connectivity, symmetry, and low degree of diameter. In this paper, we analyse the performance in number of messages on d-dimensional hypercube, for two groups of distributed algorithms for mutual exclusion, a permission-based mutual exclusion group, and a token-based mutual exclusion group.In the permission-based mutual exclusion algorithm, a node enters in the critical section only after receiving permission from all others nodes, this algorithm requires d2 d messages.In the token-based mutual exclusion algorithm, a node is allowed to access its critical section if and only if it holds the token. In this algorithm, there is a node, called root, which knows the last node to get the token among the current requesting nodes. When a node wants to enter critical section, it sends request message to the root, which in turn informs the last node that new node will get the token next, and updates its last node. As result, the requesting nodes form a distributed queue, each of which records only the element next to it, this algorithm requires 2d messages in the worst case.

A mutual exclusion (ME) algorithm consists of a trying protocol (TP)and exit protocol (EP)that surround a critical section (CS)and satisfy the following properties: mutual exclusion : at most one process is allowed to use the CS at a given time; lockout freedom : any process that enters the TP eventually enters the CS; and bounded exit : a process can complete the EP in a bounded number of its own steps. A First-Come-First-Served (FCFS) ME algorithm [1] additionally requires processes to enter the CS in roughly the order in which they start the TP. Once a process has started executing the TP of a ME algorithm, it has committed itself to entering the CS, since the correctness of the algorithm may depend on every process properly completing its TP and EP.

The asynchronous group mutual exclusion (GME) problem [5] is a generalization of the (ordinary) mutual exclusion problem. Each process requests a forum. When a process is in the critical section (CS), we say its forum is held. The GME problem (i) allows at most one forum to be held at any time and, (ii) when a forum is held, encourages any number of processes requesting that forum to participate in that forum, that is, to be in the CS simultaneously. An extension of this problem, called the group mutual l-exclusion with multiple forum requests (GMLE-M) problem [8], allows each process to request multiple forums and up to l forums to be held simultaneously. In this paper, we give a simple GMLE-M algorithm that facilitates a highly concurrent participation of processes in forums. The algorithm uses a new technique, called automatic joining, that enables processes to enter the CS directly when their forums are being held. This technique can be used in other extensions of the GME problem also, to increase concurrency.

Cloud computing is the emerging domain in academia and IT Industry. It is a business framework for delivering the services and computing power on-demand basis. Cloud users have to pay the service providers based on their usage. For enterprises, cloud computing is the worthy of consideration and they try to build business systems with lower costs, higher profits and quality-of-service. Considering cost optmization, service provider may initially try to use less number of CPU cores and data centers. For that reason, this paper deals with CloudSim simulation tool which has been utilized for evaluating the number of CPU cores and execution time. Minimization of waiting time is also a considerable issue. When a large number of jobs are requested, they have to wait for getting allocated to the servers which in turn may increase the queue length and also waiting time. This paper also deals with queuing model with multi-server and finite capacity to reduce the waiting time and queue length.

Public transportation schedules are designed by agencies to optimize service quality under multiple constraints. However, real service usually deviates from the plan. Therefore, transportation analysts need to identify, compare and explain both eventual and systemic performance issues that must be addressed so that better timetables can be created. The purely statistical tools commonly used by analysts pose many difficulties due to the large number of attributes at trip- and station-level for planned and real service. Also challenging is the need for models at multiple scales to search for patterns at different times and stations, since analysts do not know exactly where or when relevant patterns might emerge and need to compute statistical summaries for multiple attributes at different granularities. To aid in this analysis, we worked in close collaboration with a transportation expert to design TR-EX, a visual exploration tool developed to identify, inspect and compare spatio-temporal patterns for planned and real transportation service. TR-EX combines two new visual encodings inspired by Marey's Train Schedule: Trips Explorer for trip-level analysis of frequency, deviation and speed; and Stops Explorer for station-level study of delay, wait time, reliability and performance deficiencies such as bunching. To tackle overplotting and to provide a robust representation for a large numbers of trips and stops at multiple scales, the system supports variable kernel bandwidths to achieve the level of detail required by users for different tasks. We justify our design decisions based on specific analysis needs of transportation analysts. We provide anecdotal evidence of the efficacy of TR-EX through a series of case studies that explore NYC subway service, which illustrate how TR-EX can be used to confirm hypotheses and derive new insights through visual exploration.

Background: Understanding predictors of patient satisfaction in primary care is critical to improving healthcare quality. The OECD Patient Reported Indicators Survey (PaRIS) seeks to fill this knowledge gap by exploring how individuals with chronic conditions perceive the quality and outcomes of primary care in Ontario. This pilot study focuses on identifying key factors that influence patient satisfaction with primary care, examining patient-reported experiences and outcomes to better understand the elements that drive care satisfaction. Approach: Two surveys were administered: one to nurse practitioners and physicians at 16 practices in Ontario and a second to patients within these practices (n=867 respondents). Guided by Andersen’s Behavioral Model of Health Services Use, predisposing and enabling factors that contribute to care satisfaction were selected for analysis. Univariate analyses were used to identify variables that were significantly associated with patient reported satisfaction with care. A random effects multivariate ordinal logistic regression model was conducted using satisfaction of care as the outcome and significant predictors (p <0.15) from the univariate analysis (gender, income, self-rated health, processes of care). Results: Patients who preferred not to reveal their income had significantly lower satisfaction scores compared to those who had an income of up to $60,000 annually (OR=0.606, p=0.03). Further, patients who were always or usually stressed/worried about having enough money to pay rent/mortgage had significantly lower satisfaction scores (OR = 0.659, p=0.02 and OR=0.469, p=0.04 respectively) than those who were never worried/stressed about having to pay rent/mortgage. Patients who rated their health as “very good” or “excellent” had significantly higher satisfaction with care in comparison to those who rated their health as “good” (OR=1.563, p=0.02 and OR=2.533, p=0.01, respectively). Wait times for appointments also seems to influence patient satisfaction, with shorter wait times resulting in higher satisfaction. Patients who had appointments that took place between a few days/up to a week (OR=2.577, p<0.01), on the next day (OR=4.711, p<0.01), and on the same day (OR=2.772, p=0.01) after initially booking the appointment had significantly higher satisfaction scores than those whose appointments took place more than one month later. Lastly, patients who went to a clinic where the head of practice was a nurse practitioner had significantly higher satisfaction scores than those who saw a physician (OR=1.768, p=0.05). Implications: Patient satisfaction is now commonly viewed as an important indicator of quality in health care. The findings of this study shed light on several factors that enhance patient satisfaction in primary care – income, self assessed health status, wait times for appointments, and professional background of the provider. Insights into these factors underlying patient satisfaction can help health policymakers and providers to design services and allocate resources to improve patients’ experience. Structuring primary care in ways that address social determinants of health, drawing on the skills and leadership of multidisciplinary teams, creating efficient systems for reducing wait times and attending to patients’ perceptions of their health and ability to function in their daily lives are actions that may enhance patient satisfaction.

Designing irregular parallel algorithms with mutual exclusion and lock-free protocols

Mutual exclusion is one of the most commonly used techniques to handle contention in concurrent systems. Traditionally, mutual exclusion algorithms have been designed under the assumption that a process does not fail while acquiring/releasing a lock or while executing its critical section. However, failures do occur in real life, potentially leaving the lock in an inconsistent state. This gives rise to the problem of recoverable mutual exclusion (RME) , which involves designing a mutual exclusion (ME) algorithm that can tolerate failures while maintaining required safety and liveness properties. In this work, we present a framework that transforms any algorithm that solves the RME problem into an algorithm that can also simultaneously adapt to (i) the number of processes concurrently competing for the lock, as well as (ii) the number of unresolved failures in the system, while maintaining the correctness properties and performance characteristics of the underlying RME algorithm. Additionally, the algorithm constructed as a result of this transformation adds certain desirable properties such as bounded recovery and fairness. One of the important performance measures of any ME algorithm, including an RME algorithm, is the number of remote memory references (RMRs) made by a process—to acquire and release a lock, as well as to recover the lock structure after a failure. Let R(n) denote the RMR complexity of a critical section request in the underlying RME algorithm, where n denotes the number of processes in the system. Then, our framework yields an RME algorithm for which the RMR complexity of a critical section request is given by \(\mathcal {O}(\min \lbrace \ddot{c},\sqrt {F+1},\ R(n) \rbrace)\) , where \(\ddot{c}\) denotes the point contention of the request and F denotes the failure-density of the request. We further extend our framework by presenting a novel memory reclamation algorithm to bound the space complexity of the RME algorithm. Our memory reclamation algorithm maintains the correctness, performance, and fairness properties of our transformation. Our approach is general enough that it may also be used to bound the space complexity of other RME algorithms. In addition to read and write instructions, our algorithm uses compare-and-swap ( CAS ) and fetch-and-store ( FAS ) hardware instructions, both of which are commonly available in most modern processors.

Energy consumption is a significant factor in wireless networks with battery-powered nodes and transceivers as a dominant energy consumer .Also mutual exclusion is a fundamental problem in distributed systems.“Mutual exclusion” allows only one process at a time to access the shared resource and the others have to be locked and wait for their turns.. Retaining mutual exclusion, is proposed as a solution to guarantee the correctness of read/written information from/in the shared resource or critical sections (CS).Wireless Network is an unstable network formed dynamically by a connection of wireless mobile nodes without the use of an existing network substructure. Since energy consumption during communication is a major depletion parameter the number of communication must be reduced as much as possible to achieve extended battery life. Since battery technology does not grow as rapidly as CPU or memory does so there is a strong need for the presence of protocols which are as energy efficient as effective.In this paper we present a new fully distributed token–based mutual-exclusion algorithm for clustered Mobile ad-hoc network with message count reduction purpose which eventuates to power optimization. Therefore we use Raymond's algorithm (which is well-known in stepping down message complexity) within the clusters and perpetual Mobility of token (that is so efficient in high load network such as Manet's) between the clusters.

The mathematical study known as queueing theory has recently become a major point of interest for many government agencies and private companies for increasing efficiency. One such application is vehicle queueing at an international port-of-entry (POE). When queueing, fumes from idling vehicles negatively affect the overall health and well-being of the community, especially the U.S. Customs and Border Protection (CBP) agents that work at the POEs. As such, there is a need to analyze and optimize the border crossing queuing operations to minimize wait times and number of vehicles in the queue and, thus, reduce the vehicle emissions. For this research, the U.S.–Mexico POE located at The Gateway International Bridge in Brownsville, Texas, is used as a case study. Due to data privacy concerns, the hourly wait times for vehicles arriving at the border had to be extracted manually each day using a live wait time tracker online. The data extraction was performed for the month of March 2022. Using these wait times, a queueing simulation software, SIMIO, was used to develop an interactive simulation model and calibrate the service rates. The output from the SIMIO model was then used to develop an artificial neural network (ANN) to predict hourly particulate matter content with an R2 of 0.402. From the ANN, a predictive equation has been developed, which may be used by CBP to make operational decisions and improve the overall efficiency of this POE. Thus, lowering the average wait times and the emissions from idling vehicles in the queue.

Mutual exclusion between process and interrupt-driven execution is normally confined by the designer to passages within the kernel of an operating system and has been implemented traditionally by the judicious use of the interrupt inhibit instruction. This paper illustrates an alternative approach to the problem through the implementation of the conflicting software and hardware-driven kernel primitives as a limited set of concurrent subprocesses sharing a singular kernel domain. Manipulations of kernel data are rendered mutually exclusive through the use of P and V operations on simple semaphores associated with the data objects themselves. The kernel programmer is thereby relieved of real-time considerations in guarding his critical sections and the implementation of the kernel acquires a formalism which can greatly aid its verification, testability and maintainability.

Time/Contention Trade-Offs for Multiprocessor Synchronization