Design Of Scheduling Algorithms Research Articles

Design and analysis of key scheduling algorithm for symmetric cipher security

Design and analysis of key scheduling algorithm for symmetric cipher security

Ordered scheduling in control-flow distributed transactional memory

Consider the control-flow model of transaction execution in a distributed system modeled as a communication graph where shared objects positioned at nodes of the graph are immobile but the transactions accessing the objects send requests to the nodes where objects are located to read/write those objects. The control-flow model offers benefits to applications in which the movement of shared objects is costly due to their sizes and security purposes. In this paper, we study the ordered scheduling problem of committing dependent transactions according to their predefined priorities in this model. The considered problem naturally arises in areas, such as loop parallelization and state-machine-based computing, where producing executions equivalent to a priority order is needed to satisfy certain properties. Specifically, we study ordered scheduling considering two performance metrics fundamental to any distributed system: (i) execution time - total time to commit all the transactions and (ii) communication cost - the total distance traversed in accessing required shared objects. We design scheduling algorithms that are individually or simultaneously efficient for both the metrics and rigorously evaluate them through several benchmarks on random and grid graphs, validating their efficiency. To our best knowledge, this is the first study of ordered scheduling in the control-flow model of distributed transaction execution.

A Reassessment on Applying Protocol Interference Model Under Rayleigh Fading: From Perspective of Link Scheduling

Link scheduling plays a pivotal role in accommodating stringent reliability and latency requirements. In this paper, we focus on the availability and effectiveness of applying protocol interference model (PIM) under Rayleigh fading model to solve the problem. The motivation is that PIM caters to distributed link scheduling algorithm design, but usually lead to irrationality due to its localization behavior. While Rayleigh fading model can accurately describe the inherent characteristic of wireless signal propagation, but the features of global interference and channel fading make algorithm design more challenging. To be specific, we first remove the effect of channel fading on algorithmic design by establishing the relationship between Rayleigh fading model and non-fading model. We then propose a centralized once link elimination (OLE) algorithm by utilizing local nature of PIM, and achieve its distributed implementation based on the message delivery with time complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\Delta_{\max}\ln\Delta_{\max})$</tex-math> </inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta_{\max}$</tex-math> </inline-formula> is the maximum number of nodes around a given node inside some range. Furthermore, based on random contention resolution, we design another distributed algorithm to schedule all the links within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\Delta^{3}_{\max}\ln\Delta_{\max})$</tex-math> </inline-formula> rounds. Simulations show that the PIM is of great confidence as same as Rayleigh fading model, and the proposed algorithms outperform three popular link scheduling algorithms.

Non-clairvoyant Scheduling with Predictions

In the single-machine non-clairvoyant scheduling problem, the goal is to minimize the total completion time of jobs whose processing times are unknown a priori . We revisit this well-studied problem and consider the question of how to effectively use (possibly erroneous) predictions of the processing times. We study this question from ground zero by first asking what constitutes a good prediction; we then propose a new measure to gauge prediction quality and design scheduling algorithms with strong guarantees under this measure. Our approach to derive a prediction error measure based on natural desiderata could find applications for other online problems.

A survey on energy‐efficient workflow scheduling algorithms in cloud computing

AbstractThe advancements in computing and storage capabilities of machines and their fusion with new technologies like the Internet of Thing (IoT), 5G networks, and artificial intelligence, to name a few, has resulted in a paradigm shift in the way computing is done in a cloud environment. In addition, the ever‐increasing user demand for cloud services and resources has resulted in cloud service providers (CSPs) expanding the scale of their data center facilities. This has increased energy consumption leading to more carbon dioxide emission levels. Hence, it becomes all the more important to design scheduling algorithms that optimize the use of cloud resources with minimum energy consumption. This paper surveys state‐of‐the‐art algorithms for scheduling workflow tasks to cloud resources with a focus on reducing energy consumption. For this, we categorize different workflow scheduling algorithms based on the scheduling approaches used and provide an analytical discussion of the algorithms covered in the paper. Further, we provide a detailed classification of different energy‐efficient strategies used by CSPs for energy saving in data centers. Finally, we describe some of the popular real‐world workflow applications as well as highlight important emerging trends and open issues in cloud computing for future research directions.

A First Look at Wi-Fi 6 in Action: Throughput, Latency, Energy Efficiency, and Security

This paper presents a first-of-its-kind performance measurement of Wi-Fi 6 (IEEE 802.11ax) using real experiments. Our experiments focus on multi-client scenarios. The results reveal the impact of the new channel access mechanisms (i.e., OFDMA and TWT) on the spectrum efficiency, energy consumption, latency, and network security. (i) A comparison with the legacy CSMA/CA scheme shows that the commodity Wi-Fi 6 achieves 3× overall throughput and dramatically reduce the latency (5×) when coexisting with legacy Wi-Fi network. (ii) However, the current OFDMA implementation significantly increases the power consumption (6×), implying a design tradeoff between throughput and latency gain versus the cost of energy consumption. (iii) Finally, TWT negotiating procedure is vulnerable to various malicious attacks. We believe that our findings provide critical insights for the scheduling algorithm design, power optimization, and security protection of the next-generation WLANs.

Optimal Latency-Oriented Coding and Scheduling in Parallel Queuing Systems

The evolution of 5G and Beyond networks has enabled new applications with stringent end-to-end latency requirements, but providing reliable low-latency service with high throughput over public wireless networks is still a significant challenge. One of the possible ways to solve this is to exploit path diversity, encoding the information flow over multiple streams across parallel links. The challenge presented by this approach is the design of joint coding and scheduling algorithms that adapt to the state of links to take full advantage of path diversity. In this paper, we address this problem for a synchronous traffic source that generates data blocks at regular time intervals (e.g., a video with constant frame rate) and needs to deliver each block within a predetermined deadline. We first develop a closed-form performance analysis in the simple case of two parallel servers without any buffering and single-packet blocks, and propose a model for the general problem based on a Markov Decision Process (MDP). We apply policy iteration to obtain the coding and scheduling policy that maximizes the fraction of source blocks delivered within the deadline: our simulations show the drawbacks of different commonly applied heuristic solutions, drawing general design insights on the optimal policy.

KubeSC‐RTP: Smart scheduler for Kubernetes platform on CPU‐GPU heterogeneous systems

SummaryHeterogeneous systems composed of multiple CPUs and GPUs are progressively attractive as platforms for high performance computing because of their higher performance. Especially with the use of containers which are rapidly replacing virtual machines as the compute instance of choice in cloud‐based deployments such in Kubernetes clusters. The task scheduling in a heterogeneous environment became one of the most important issues considered by the platform providers. The ability to choose the appropriate device, CPU or GPU, has a direct impact on the performance of a particular system. It reduces total processing time and increases customer satisfaction. In heterogeneous systems, optimizing resource consumption is a critical aspect for cloud service providers. Adequate scheduling of an application implies optimization of its execution time, which results in resource consumption for the service provider. The development of algorithms for scheduling applications in heterogeneous computing systems has received a significant amount of attention in recent years. A variety of efforts are dedicated to the design of such scheduling algorithms. This article is one of those efforts. We present in this work, KubeSC‐RTP, a scheduler for Kubernetes environment using machine learning based on runtime prediction of the applications in order to better select the appropriate device, CPU or GPU.

Hierarchical scheduling algorithm design of active distribution network based on multi-microgrid system

Hierarchical scheduling algorithm design of active distribution network based on multi-microgrid system

An efficient interval many-objective evolutionary algorithm for cloud task scheduling problem under uncertainty

Task scheduling is an important research direction in cloud computing. The current research on task scheduling considers mainly the design of scheduling strategies and algorithms and rarely gives attention to the influences of uncertain factors, such as the network bandwidth and millions of instructions per second (MIPS), on the scheduling process. The network bandwidth and MIPS directly affect the performance of a virtual machine (VM), which further influences the scheduling performance. In this paper, uncertain factors are transformed into interval parameters. The make-span, scheduling cost, load balance, and task completion rate are simultaneously considered in the scheduling process. Then, an interval many-objective cloud task scheduling optimization (I-MCTSO) model is designed to simulate real cloud computing task scheduling. To implement this model, an interval many-objective evolutionary algorithm (InMaOEA) is proposed. An interval credibility strategy is employed to improve the convergence performance. The hyper-volume and degree of overlap based on the interval congestion distance strategy are used to increase the population diversity. Simulation results demonstrate the effectiveness and superior performance of InMaOEA in comparision with other algorithms. The proposed approaches can provide decision-makers with an efficient allocation plan for cloud task scheduling.

Empowering the configuration-IP: new PTAS results for scheduling with setup times

Integer linear programs of configurations, or configuration IPs, are a classical tool in the design of algorithms for scheduling and packing problems where a set of items has to be placed in multiple target locations. Herein, a configuration describes a possible placement on one of the target locations, and the IP is used to choose suitable configurations covering the items. We give an augmented IP formulation, which we call the module configuration IP. It can be described within the framework of n-fold integer programming and, therefore, be solved efficiently. As an application, we consider scheduling problems with setup times in which a set of jobs has to be scheduled on a set of identical machines with the objective of minimizing the makespan. For instance, we investigate the case that jobs can be split and scheduled on multiple machines. However, before a part of a job can be processed, an uninterrupted setup depending on the job has to be paid. For both of the variants that jobs can be executed in parallel or not, we obtain an efficient polynomial time approximation scheme (EPTAS) of running time f(1/varepsilon )cdot mathrm {poly}(|I|). Previously, only constant factor approximations of 5/3 and 4/3 + varepsilon , respectively, were known. Furthermore, we present an EPTAS for a problem where classes of (non-splittable) jobs are given, and a setup has to be paid for each class of jobs being executed on one machine.

Scheduling of Traffic Entities Under Reduced Traffic Flow by Means of Fuzzy Logic Control

This paper presents the design of a fuzzy logic-based traffic scheduling algorithm aimed at reducing traffic congestion for the case of partial obstruction of a bidirectional traffic lane. Such a problem is typically encountered in rail traffic and personal rapid transportation systems with predefined and fixed traffic corridors. The proposed proportional-derivative (PD) fuzzy control algorithm, serving as a traffic control automaton, alternately assigns adaptive green light periods to traffic coming from each direction. The proposed fuzzy logic-based traffic controller has been compared with the conventional traffic control automaton featuring fixed-durations of green light intervals. The comparison has been carried out within a simulation environment for four different probability distributions of stochastic traffic flows at each end of the considered traffic corridor. Results have shown that the proposed fuzzy logic-based traffic controller performance is far superior to that of the conventional traffic control law in terms of achieving shorter vehicle queue lengths and less disparity in queue lengths for all considered simulation scenarios.

A Whittle Index Approach to Minimizing Age of Multi-Packet Information in IoT Network

Age of information (AoI) captures the freshness of information and has been used broadly as an important performance metric in big data analytics in the Internet of Things (IoT). We consider a general scenario where a meaningful piece of information consists of multiple packets and the information is not complete until all related packets have been correctly received. Minimizing AoI in this general scenario is challenging in both scheduling algorithm design and theoretical analysis, because we need to track the history of received packets before a complete piece of information can be updated. We first analyse the necessary condition for optimal scheduling, based on which we present an optimal scheduling method. The optimal solution, however, has high time complexity. To address the problem, we investigate the problem with a special type of learning, i.e., learning in restless multi-armed bandits (RMAB), and propose a Whittle index-based scheduling method. We also propose a new transmission strategy based on erasure codes to improve the performance of scheduling policies in lossy networks. Performance evaluation results demonstrate that our solution outperforms other baseline policies such as greedy policy and naive Whittle index policy in both lossless and lossy networks.

Design and implementation of regional online ride-hailing scheduling algorithm

The network car has solved some problems of traditional taxis from the perspective of safety and convenience, but there are still gaps in the inter-city field. This paper proposes a scheduling method to solve the inter-city travel field from the regional perspective. Firstly, the platform of the system is built from the perspective of software development, and then the traditional scheduling algorithm for dispatching orders in the city is studied. Finally, the intercity scheduling algorithm is mainly designed. This paper fills the gap in the field of inter-city travel between vehicles, and the scheduling algorithm performs well, with good order taking rate and travel efficiency. The establishment of the regional network car platform meets the needs of the public for inter-city travel. The platform itself has practical significance, and the study of scheduling algorithm also provides ideas and reference for the field of vehicle scheduling.

Artificial Fuzzy-PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms.

Mobile robots are promising devices which are dedicated to human comfort in all areas. However, the control algorithm of the wheels of mobile robot is entirely challenging due to the nonlinearity. Recently, the classical PID (proportional-integral-derivative) controllers are frequently used in robotics for their high accuracy and the smooth determination of their parameters. A robust approach called fuzzy control which is based on the conversion of linguistic inference sets in a suitable control value is a widely used method in industrial system control in our days. A new challenging method to solve the problem of intelligent navigation of nonholonomic mobile robot is suggested. In this work, the presented methodology is based on three hybrid fuzzy logic PID controllers which are adapted to guarantee target achievement and trajectory tracking. A fuzzy-PID control algorithm is designed with 2 inputs and 3 outputs. By the information given by the system response, error and error derivate can be used to extract and adopt the PID controller parameters: proportional, integral, and derivative gains. Besides, a tuning value A is introduced to improve the resulted response in terms of speeding up and reducing error, overshoot, and oscillation, as well as reducing ISE and IAE values. A modelization of a differential drive mobile robot is presented. The developed algorithm is tested and implemented to this mobile robot model via Simulink/MATLAB.

A novel task scheduling algorithm with improved make span based on prediction of tasks computation time algorithm for cloud computing

In this paper new scheduling algorithm called Prediction of Tasks Computation Time algorithm (PTCT) to estimate minimum task execution time/Makespan time for cloud computing environment. Now-a-days all cloud service providers providing all resources to end users at very cheap rate and at the same time by designing scheduling algorithms cloud service providers are ensuring that all users can get response data in quick time. Various scheduling algorithms are implemented in cloud environment such MINMIN, MAXMIN, QOS GUIDE etc. MINMIN algorithm will schedule all task with less execution time first and then schedule remaining task. In simple terms MINMIN algorithm give priority to less execution time task. MAXMIN algorithm will schedule all task with more execution time and then schedule small execution time task. In simple terms MAXMIN give priority to high execution time first. Many more scheduling algorithms are there but above two algorithms are very much popular. This two algorithms will not look for resources which can take minimum execution time and propose PTCT algorithm will look for all resources/processors/machines and then form a matrix which contains estimated execution time for all jobs and then by applying PCA (Principal Component Analysis) algorithm it will predict or choose resource which took minimum execution time and then assign new task to that selected minimum execution time resource. Here resource could be computer or processor or Virtual Machine. In propose PTCT algorithm we build an array with all task and processors as Directed Acyclic Graph (DAG) and then build a matrix with all processors and task. A matrix will contain estimated execution task time on each processor and all rows of a matrix will filled with all processor’s execution time for all tasks. On generated matrix we will apply PCA algorithm to choose processor which take less execution time for selected task. This process continues till all task assigned to all processors. By applying PTCT algorithm we can further decrease computation and communication cost at cloud side. To implement this paper, we design 3 algorithms in the form of simulation and then compare execution/Make span time between them. In all 3 algorithms PTCT algorithms took less execution time for all tasks.

Minimizing Age of Information With Power Constraints: Multi-User Opportunistic Scheduling in Multi-State Time-Varying Channels

This work is motivated by the need of collecting fresh data from power-constrained sensors in the industrial Internet of Things (IIoT) network. A recently proposed metric, the Age of Information (AoI) is adopted to measure data freshness from the perspective of the central controller in the IIoT network. We wonder what is the minimum average AoI the network can achieve and how to design scheduling algorithms to approach it. To answer these questions when the channel states of the network are time-varying and scheduling decisions are restricted to both bandwidth and power consumption constraint, we first decouple the multi-sensor scheduling problem into a single-sensor constrained Markov decision process (CMDP) by relaxing the hard bandwidth constraint. Next we exploit the threshold structure of the optimal policy for the decoupled single sensor CMDP and obtain the optimum solution through linear programming (LP). Finally, an asymptotically optimal truncated policy that can satisfy the hard bandwidth constraint is built upon the optimal solution to each of the decoupled single-sensor. Our investigation shows that to obtain a small average AoI over the network: (1) The scheduler exploits good channels to schedule sensors supported by limited power; (2) Sensors equipped with enough transmission power are updated in a timely manner such that the bandwidth constraint can be satisfied.

Research and Design of Hybrid Scheduling Algorithm Based on LIN Bus

In the LIN(Local Interconnect Network) bus communication transmission, there is a problem of data transmission delay in the process of sending messages by the master-slave node ECU(Electronic Control Unit).Aiming at this kind of delay problem, this paper analyzes the characteristics of static scheduling and dynamic scheduling algorithm of LIN bus, optimizes the scheduling strategy of LIN bus message, and proposes a hybrid scheduling algorithm based on time slice scheduling. At the same time, the simulation model of LIN bus master-slave node message transmission is established, and the simulation experiment verification platform of LIN bus hardware-in-the-loop is built. It is verified that the algorithm reduces the delay by about 30%.

Channel-Dependent Scheduling in Wireless Energy Transfer for Mobile Devices

Resonant Beam Charging (RBC) is the Wireless Power Transfer (WPT) technology, which can provide high-power, long-distance, mobile, and safe wireless charging for Internet of Things (IoT) devices. Supporting multiple IoT devices charging simultaneously is a significant feature of the RBC system. To optimize the multi-user charging performance, the transmitting power should be scheduled for charging all IoT devices simultaneously. In order to keep all IoT devices working as long as possible for fairness, we propose the First Access First Charge (FAFC) scheduling algorithm. Then, we formulate the scheduling parameters quantitatively for algorithm implementation. Finally, we analyze the performance of FAFC scheduling algorithm considering the impacts of the receiver number, the transmitting power and the charging time. Based on the analysis, we summarize the methods of improving the WPT performance for multiple IoT devices, which include limiting the receiver number, increasing the transmitting power, prolonging the charging time and improving the single-user's charging efficiency. The FAFC scheduling algorithm design and analysis provide a fair WPT solution for the multi-user RBC system.

Median Access Control Protocols for Sensor Data Collection: A Review

Median Access Control (MAC) protocols are designed to mitigate collisions and enhance the energy efficiency for sensor data collection. This paper reviews two basic categories of MAC protocols. The first class is the contention-based protocols, where nodes randomly compete for channel access. The second class is the schedule-based MAC protocols, in which nodes access the channel on the basis of the predetermined schedules. We focus on discussing the Time Division Multiple Access (TDMA) protocols and classify different TDMA schedulings into three categories according to the communication patterns in the network, i.e., the link scheduling, aggregate scheduling and non-aggregate scheduling. Link scheduling deals with the peer to peer communication pattern, where there is no central node in the network. In comparison, both the aggregate and non-aggregate schedulings handle the convergecast communication pattern, in which all traffic are destined to the sink. This survey provides a comprehensive overview of these three categories and provided a detailed briefing on how the TDMA schedules handle the network traffic dynamics in the network. Compared with other surveys in this domain, this review does not confine itself to deal with collecting a particular form of data, but provides a unified framework to integrate the data semantics in a broader sense into the design of TDMA scheduling algorithms.