Priority Scheduling Policy Research Articles

Time Series-Based Edge Resource Prediction and Parallel Optimal Task Allocation in Mobile Edge Computing Environment

The offloading of computationally intensive tasks to edge servers is indispensable in the mobile edge computing (MEC) environment. Once the tasks are offloaded, the subsequent challenges lie in buffering them and assigning them to edge virtual machine (VM) resources to meet the multicriteria requirement. Furthermore, the edge resources’ availability is dynamic in nature and needs a joint prediction and optimal allocation for the efficient usage of resources and fulfillment of the tasks’ requirements. To this end, this work has three contributions. First, a delay sensitivity-based priority scheduling (DSPS) policy is presented to schedule the tasks as per their deadline. Secondly, based on exploratory data analysis and inferred seasonal patterns in the usage of edge CPU resources from the GWA-T-12 Bitbrains VM utilization dataset, the availability of VM resources is predicted by using a Holt–Winters-based univariate algorithm (HWVMR) and a vector autoregression-based multivariate algorithm (VARVMR). Finally, for optimal and fast task assignment, a parallel differential evolution-based task allocation (pDETA) strategy is proposed. The proposed algorithms are evaluated extensively with standard performance metrics, and the results show nearly 22%, 35%, and 69% improvements in cost and 41%, 52%, and 78% improvements in energy when compared with MTSS, DE, and min–min strategies, respectively.

Incentives for Shared Services: Multiserver Queueing Systems with Priorities

Problem definition: We study shared service whereby multiple independent service providers collaborate by pooling their resources into a shared service center (SSC). The SSC deploys an optimal priority scheduling policy for their customers collectively by accounting for their individual waiting costs and service-level requirements. We model the SSC as a multiclass [Formula: see text] queueing system subject to service-level constraints. Academic/practical relevance: Shared services are increasingly popular among firms for saving operational costs and improving service quality. One key issue in fostering collaboration is the allocation of costs among different firms. Methodology: To incentivize collaboration, we investigate cost allocation rules for the SSC by applying concepts from cooperative game theory. Results: To empower our analysis, we show that a cooperative game with polymatroid optimization can be analyzed via simple auxiliary games. By exploiting the polymatroidal structures of the multiclass queueing systems, we show when the games possess a core allocation. We explore the extent to which our results remain valid for some general cases. Managerial implications: We provide operational insights and guidelines on how to allocate costs for the SSC under the multiserver queueing context with priorities.

Energy-aware fixed priority scheduling with shared resources in standby-sparing systems

The problem of joint energy and reliability management for fixed priority periodic real-time tasks set with shared resources on a standby-sparing system is studied in this paper. A novel algorithm called FPMPSA is proposed. It considers frequency transition and processor state transition overheads imposed by dynamic voltage frequency scaling (DVFS) and dynamic power management (DPM). In addition, it applies the rate monotonic with dual priority scheduling policy (RM/DPP) to ensure that shared resources can be accessed in a mutual exclusive manner. Moreover, the mixed mapping method is applied to map the main task and backup task. Furthermore, the energy efficient speed of the main task SM is determined and the backup task executes with the maximum processor speed. For energy efficiency, the algorithm called DFPMPSA which is an extension of FPMPSA is proposed. It can use the dynamic slack time to reduce energy consumption. The experimental results show that DFPMPSA algorithm consumes average 8.15% less energy than that of FPMPSA.

A Realization of Two-Level Scheduling in Embedded Operating System

In embedded operating systems, how to ensure thread tasks execute in order of priority is an important issue. Considering that thread may have child threads, and child threads are scheduled by their parent thread, so they also need to be executed accroding to their priorities. For such two-level scheduling issue, this paper proposes a new scheduling policy: parent thread uses the table-driven scheduling policy, and child thread uses the fixed priority scheduling policy to ensure the orderly execution of both parent and child threads. In order to cooperate with the implementation of two-level scheduling, this paper also realizes the creation of child threads in an embedded operating system by imitating Linux thread replication, connects the parent and child threads through pointers, and realizes a priority queue of child threads in their parent thread. In addition, by saving the ID of current executing child thread into the stack of corresponding parent thread, this paper ensures that parent thread can recover its last executing child thread after several switches of parent threads. Several experiments on 2440 single core development board showed that method proposed by this paper could realize the creation of sub threads and two-level thread scheduling in embedded operating system.

Incentives for Shared Services: Multi-Server Queueing Systems with Priorities

Problem definition: We study shared service whereby multiple independent service providers collaborate by pooling their resources into a shared service center (SSC). The SSC deploys an optimal priority scheduling policy for their customers collectively by accounting for their individual waiting costs and service level requirements. We model the SSC as a multi-class M/M/c queueing system subject to service level constraints. Academic/Practical relevance: Shared services are increasingly popular among firms for saving operational costs and improving service quality. One key issue in fostering collaboration is the allocation of costs among different firms. Methodology: To incentivize collaboration, we investigate cost allocation rules for the SSC by applying concepts from cooperative game theory. Results: To empower our analysis, we show that a cooperative game with polymatroid optimization can be analyzed via simple auxiliary games. By exploiting the polymatroidal structures of the multiclass queueing systems, we show when the games possess a core allocation. We explore the extent to which our results remain valid for some general cases. Managerial implications: We provide operational insights and guidelines on how to allocate costs for SSC under the multi-server queueing context with priorities.

Transaction Issues in Mobile Distributed Real-Time Database Systems

In recent years, a large number of populations are dependent on mobile database technology and it is difficult for us to imagine our lifestyle in absence of database. Today’s portable handy mobile devices take part in emerging new technology for sharing distributed applications or/ and information between many users even on the move (from one network to another). To manage this resulting large Volume of data in a wireless environment with time constraints such as deadline making it the fertile land of research for researchers. Fast transaction processing in many industrial applications is needed efficient algorithm and protocols in the field of mobile distributed real-time database (MDRTDBS). Transaction execution in a mobile environment has various interesting research issues like low bandwidth, storage capacity, power backup, priority scheduling policy, and concurrency and commits protocols, security, check-pointing etc. At first in our paper, we address performance issues that are important to MDRTDBS and then survey the various researches that have been done so far. In fact, this paper provides ground knowledge for addressing the performance issues important for mobile distributed real-time database and somehow helping to find out the future area of research in the field of MDRTDBS.

Optimal Scheduling of Critically Loaded Multiclass GI/M/n+M Queues in an Alternating Renewal Environment

In this paper, we study optimal control problems for multiclass GI/M/n+M queues in an alternating renewal (up-down) random environment in the Halfin-Whitt regime. Assuming that the downtimes are asymptotically negligible and only the service processes are affected, we show that the limits of the diffusion-scaled state processes under non-anticipative, preemptive, work-conserving scheduling policies, are controlled jump diffusions driven by a compound Poisson jump process. We establish the asymptotic optimality of the infinite-horizon discounted and long-run average (ergodic) problems for the queueing dynamics. Since the process counting the number of customers in each class is not Markov, the usual martingale arguments for convergence of mean empirical measures cannot be applied. We surmount this obstacle by demonstrating the convergence of the generators of an augmented Markovian model which incorporates the age processes of the renewal interarrival times and downtimes. We also establish long-run average moment bounds of the diffusion-scaled queueing processes under some (modified) priority scheduling policies. This is accomplished via Foster-Lyapunov equations for the augmented Markovian model.

A Delay Optimal Multiclass Packet Scheduler for General M2M Uplink

The uplink data arriving at the Machine-to-Machine (M2M) Application Server (AS) via M2M Aggregators (MAs) is fairly heterogeneous along several dimensions such as maximum permissible packet delay, arrival rate, and payload size, thus necessitating the design of Quality-of-Service (QoS) aware packet schedulers. In this paper, we classify the M2M uplink data into multiple QoS classes and use sigmoidal function to map the delay requirements of each class onto utility functions. We propose a delay-optimal multiclass packet scheduler at AS obtained by iteratively searching for the optimal fraction of time-sharing between all preemptive priority scheduling policies, such that it maximizes a proportionally fair system utility metric. The iterative optimization process leads to reduced complexity, thus facilitating its online implementation. We then extend this work to determine a jointly optimal packet scheduler at the MAs and AS. We significantly reduce its computational complexity by iteratively solving a distributed optimization problem, independently at MAs and AS. Using Monte Carlo simulations, we show that the proposed joint packet scheduler outperforms other state-of-the-art packet schedulers and results in (desirable) near-minimal delay-jitter for delay-sensitive traffic at the expense of (tolerable) higher delay-jitter for delay-tolerant traffic.

Isolation Modeling and Analysis Based on Mobility

In a mobile system, mobility refers to a change in position of a mobile object with respect to time and its reference point, whereas isolation means the isolation relationship between mobile objects under some scheduling policies. Inspired by event-based formal models and the ambient calculus, we first propose the two types of special events, entering and exiting an ambient, as movement events to model and analyze mobility. Based on mobility, we then introduce the notion of the isolation of mobile objects for ambients. To ensure the isolation, a priority policy needs to be used to schedule the movement of mobile objects. However, traditional scheduling policies focus on task scheduling and depend on the strong hypothesis: The scheduled tasks are independent—that is, the scheduled tasks do not affect each other. In a practical mobile system, mobile objects and ambients interact with each other. It is difficult to separate a mobile system into independent tasks. We finally present an automatic approach for generating a priority scheduling policy without considering the preceding assumption. The approach can guarantee the isolation of the mobile objects for ambients in a mobile system. Experiments demonstrate these results.

Schedulability Analysis of Graph-Based Real-Time Task Model with Precedence Constraints

The timing requirements of real-time systems can be guaranteed by well-designed scheduling policies. The analysis of such scheduling uses an abstract task model of the system to diagnose the practicability of timing requirements. The task models have evolved from periodic models to more sophisticated graph-based ones, among which digraph real-time (DRT) task model is the most applicable because of its good expressiveness and analysis efficiency. However, the DRT model cannot support the commonly used precedence constraints within or between tasks. In this paper, we propose a new task model that extends the DRT model to support precedence constraints. Based on our model, we present two methods of uniprocessor schedulability analysis for static priority scheduling policy and earliest deadline first (EDF) scheduling policy. We also introduce an optimization technique to improve the efficiency of model analysis. Our experiments show that, despite a high computational complexity of the problem, our approach scales very well for large sets of tasks with precedence constraints.

Message response time analysis for automotive cyber–physicalsystems with uncertain delay: An M/PH/1 queue approach

The analysis problem of message response time is a key and challenging problem for in-vehicle networks that has gained much research attention in recent years. The arbitrated networked control system (ANCS), which is a special cyber–physicalsystem (CPS), was recently designed for scheduling or arbitrating networks in a control system. A dominant feature of this CPS is the arbitration of messages on a shared communication medium. The control applications in an ANCS are sensitive to the end-to-end delay of message responses from sensors to actuators. In this study, a multi-hierarchical flexible TDMA/fixed priority scheduling policy is first adopted to configure the bus in an ANCS, which is based on the event trigger protocol. Then, an M/PH/1 queue model with random-sized batch arrivals is used to model the control application in the ANCS, and to obtain the stationary probability for each control application. On the basis of the model, a constrained optimization problem is formulated to minimize the nonlinear cost function of the ANCS, and the probability density function of the uncertain response delay for each message is generated. The use of an optimal minislot algorithm that is based on the DFP variable-scale method and a parameter estimation algorithm of the pdfs of the uncertain response delays is proposed to obtain optimal values of the bus minislot and matrix parameters of PH distribution, respectively. Furthermore, the probability transformed GM (1,1) (PTGM (1,1)) with two parameters, which is a novel grey model (1,1) (GM (1,1)), is proposed for predicting the response delay for each message, in which a two-parameters optimization algorithm of PTGM (1,1) that is based on the DFP-method is provided to minimize the mean absolute percentage error and obtain the optimal values of the two parameters for each message. Extensive numerical results are provided to illustrate the usefulness of the proposed algorithms and show the accuracy of the predicted results.

Infinite-Horizon Average Optimality of the N-Network in the Halfin–Whitt Regime

We study the infinite-horizon optimal control problem for N-network queueing systems, which consists of two customer classes and two server pools, under average (ergodic) criteria in the Halfin–Whitt regime. We consider three control objectives: (1) minimizing the queueing (and idleness) cost, (2) minimizing the queueing cost while imposing a constraint on idleness at each server pool, and (3) minimizing the queueing cost while requiring fairness on idleness. The running costs can be any nonnegative convex functions having at most polynomial growth. For all three problems, we establish asymptotic optimality; namely, the convergence of the value functions of the diffusion-scaled state process to the corresponding values of the controlled diffusion limit. We also present a simple state-dependent priority scheduling policy under which the diffusion-scaled state process is geometrically ergodic in the Halfin–Whitt regime, and some results on convergence of mean empirical measures, which facilitate the proofs.

Coexistence of delay-sensitive MTC/HTC traffic in large scale networks

The support for mission critical machine-type-communication (cMTC) services is indispensable for the 5th generation (5G) mobile communication systems. As the cMTC and (part of) the conventional human-type-communication (HTC) services are broadband and delay-sensitive services, how to ensure their coexistence is a new and challenging problem. This paper investigates the problem of service-level resource allocation, which decides how cMTC and HTC traffic share a limited amount of radio resource. Considering a large-scale network, we put forth a system model that integrates queuing models and stochastic geometric models to characterize the delay performance in self-interfering scenarios. A service-level resource allocation scheme called load division is proposed. The delay and throughput performance of cMTC and HTC are derived under different resource allocation schemes and priority scheduling policies. We show that compared with the baseline scheme of frequency division, the proposed load division scheme can significantly improve the delay performance of cMTC service, at a cost of slightly degraded MTC and HTC service capacities.

Scheduling Algorithms of Flat Semi-Dormant Multicontrollers for a Cyber-Physical System

Recently, the modeling and design of distributed controllers in cyber-physical systems (CPSs), which suffer from messages lost, delay variation, and jitter, has gained lots of research attentions. A special CPS, arbitrated networked control system (ANCS), has been designed for scheduling or arbitrating networks in a control system. In this paper, we propose a novel ANCS with dual communication channels. The proposed ANCS uses a hierarchical flexible time-division multiple access (TDMA)/fixed priority scheduling policy that is based on the event trigger protocol. A flat semi-dormant multicontrollers (FSDMC) model is developed for the proposed ANCS. We then model the FSDMC as an N/(d,c)-M/M/c/K/SMWV queue, and obtain various performance indices. Based on the model, a multiobjective optimization problem is then formulated to minimize the nonlinear energy consumption function and the nominal delay function presented in this study. To resolve the multiobjective optimization problem, a scheduling algorithm based on the multiobjective particle swarm optimization algorithm is proposed to generate the Pareto front and the corresponding nondominated vector sets. An optimal stopping algorithm is also designed to obtain the optimal value of the number of semi-dormant controllers. The optimal values of various parameters of the control system are obtained by using the above nondominated vector sets, and are applied to the proposed ANCS. Extensive numerical results are provided to illustrate the usefulness of the proposed algorithms and the effects of the control system parameters on the optimal policy.

Low power fixed priority scheduling sporadic task with shared resources in hard real time systems

Dynamic voltage scaling (DVS) and dynamic power management (DPM) are two effective techniques in a real time system. In this paper, we address the problem of the canonical sporadic task scheduling based on a fixed-priority scheduling scheme and take a generalized power model into account. The sporadic tasks share a set serially reusable, single-unit resources. First, we present a rate monotonic with dual priority scheduling policy, called RM/DPP, to solve the sporadic tasks shared resources scheduling problem and discuss the feasibility of the RM/DPP algorithm. Second, a static fixed-priority sporadic tasks scheduling algorithm with shared resources, called SFPSASR, has been put forward, which considers the off-chip workload and assumes that each task executes with its worst case execution time. Third, for energy efficiency, a dynamic fixed-priority sporadic tasks scheduling algorithm with shared resources, called DFPSASR, has been put forward, which considers the speed transition overhead and combines the DVS and the DPM technology. The experimental results show that the proposed SFPSASR algorithm can reduce the energy consumption by 42.14%∼51.73% over the RM/DPP algorithm and the DFPSASR algorithm can reduce the energy consumption by 79.37%∼82.94% over the SFPSASR algorithm.

Schedulability Analysis of Task Sets with Upper- and Lower-Bound Temporal Constraints

Increasingly, real-time systems must handle the self-suspension of tasks (that is, lower-bound wait times between subtasks) in a timely and predictable manner. A fast schedulability test that does not significantly overestimate the temporal resources needed to execute self-suspending task sets would be of benefit to these modern computing systems. In this paper, a polynomial-time test is presented that is known to be the first to handle nonpreemptive self-suspending task sets with hard deadlines, where each task has any number of self-suspensions. To construct the test, a novel priority scheduling policy is leveraged, the th subtask first, which restricts the behavior of the self-suspending model to provide an analytical basis for an informative schedulability test. In general, the problem of sequencing according to both upper-bound and lower-bound temporal constraints requires an idling scheduling policy and is known to be nondeterministic polynomial-time hard. However, the tightness of the schedulability test and scheduling algorithm are empirically validated, and it is shown that the processor is able to effectively use up to 95% of the self-suspension time to execute tasks.

Handling Priorities in Optical Buffers

In this letter we focus on the construction of optical buffers exploiting a static priority scheduling policy. Optical Switches and fiber Delay Lines (SDLs) are used to build the proposed multiple input/output ports Optical Priority Multiplexers and Priority Switching Nodes, used for contention-resolution in Optical Packet Switched networks.

High performance dynamic voltage/frequency scaling algorithm for real-time dynamic load management

Modern cyber-physical systems assume a complex and dynamic interaction between the real world and the computing system in real-time. In this context, changes in the physical environment trigger changes in the computational load to execute. On the other hand, task migration services offered by networked control systems require also management of dynamic real-time computing load in nodes. In such systems it would be difficult, if not impossible, to analyse off-line all the possible combinations of processor loads. For this reason, it is worthwhile attempting to define new flexible architectures that enable computing systems to adapt to potential changes in the environment.We assume a system composed by three main components: the first one is responsible of the management of the requests arisen when new tasks require to be executed. This management component asks to the second component about the resources available to accept the new tasks. The second component performs a feasibility analysis to determine if the new tasks can be accepted coping with its real-time constraints. A new processor speed is also computed. A third component monitors the execution of tasks applying a fixed priority scheduling policy and additionally controlling the frequency of the processor.This paper focus on the second component providing a “correct” (a task never is accepted if it is not schedulable) and “near-exact” (a task is rarely rejected if it is schedulable) algorithm that can be applicable in practice because its low/medium and predictable computational cost. The algorithm analyses task admission in terms of processor frequency scaling. The paper presents the details of a novel algorithm to analyse tasks admission and processor frequency assignment. Additionally, we perform several simulations to evaluate the comparative performance of the proposed approach. This evaluation is made in terms of energy consumption, task rejection ratios, and real computing costs. The results of simulations show that from the cost, execution predictability, and task acceptance points of view, the proposed algorithm mostly outperforms other constant voltage scaling algorithms.

On deciding stability of multiclass queueing networks under buffer priority scheduling policies

One of the basic properties of a queueing network is stability. Roughly speaking, it is the property that the total number of jobs in the network remains bounded as a function of time. One of the key questions related to the stability issue is how to determine the exact conditions under which a given queueing network operating under a given scheduling policy remains stable. While there was much initial progress in addressing this question, most of the results obtained were partial at best and so the complete characterization of stable queueing networks is still lacking. In this paper, we resolve this open problem, albeit in a somewhat unexpected way. We show that characterizing stable queueing networks is an algorithmically undecidable problem for the case of nonpreemptive static buffer priority scheduling policies and deterministic interarrival and service times. Thus, no constructive characterization of stable queueing networks operating under this class of policies is possible. The result is established for queueing networks with finite and infinite buffer sizes and possibly zero service times, although we conjecture that it also holds in the case of models with only infinite buffers and nonzero service times. Our approach extends an earlier related work [Math. Oper. Res. 27 (2002) 272--293] and uses the so-called counter machine device as a reduction tool.

Optimal Scheduling and Incentive Compatible Pricing for a Service System with Quality of Service Guarantees

This paper proposes a resource allocation and pricing mechanism for a service system that serves multiple classes of jobs within an organization. Each class of service request is subject to a class-dependent quality of service (QoS) guarantee on the expected delay bound, which may be imposed by business rules in an organization or other application-specific technical constraints. We develop an extension of a resource allocation and pricing mechanism for an M/M/1 system. In contrast to the system without the QoS guarantee, where a fixed priority scheduling policy—known as the cμ rule—is optimal, we show that the system may need to adopt a more general randomized priority scheduling policy to maximize the overall system profit. We also develop a transfer pricing scheme that is both optimal and incentive compatible, allowing users to act in their self-interests while collectively achieving the system optimum. We show that the pricing scheme with the QoS guarantee depends on the scheduling policy implemented and has different characteristics from that without the QoS guarantee.