Randomized Scheduling Algorithm Research Articles

A Dynamic Task Scheduling Algorithm for Airborne Device Clouds

The rapid development of mobile Internet has promoted the rapid rise of cloud computing technology. Mobile terminal devices have greatly expanded the service capacity of mobile terminals by migrating complex computing tasks to run in the cloud. However, in the process of data exchange between mobile terminals and cloud computing centers, on the one hand, it consumes the limited power of mobile terminals, and on the other hand, it results in longer communication time, which negatively affects user QoE. Mobile cloud can effectively improve user QoE by shortening the data transmission distance, reducing the power consumption, and shortening the communication time at the same time. In this paper, we utilize the property that genetic algorithm can perform global search seeking the global optimal solution and construct a dynamic task scheduling model by combining the device-cloud link. The task scheduling model based on genetic algorithm and random scheduling algorithm is compared through comparison experiments, which show that the assignment time of the task scheduling model based on genetic algorithm is shortened by 11.82% to 48.51% and the energy consumption is reduced by 22.28% to 47.52% under different load conditions.

Distributed Data Flow Scheduling Optimization in Industrial Internet of Things Based on Optimal Transport Theory

The development of industrial internet of things (IIoT) has completely changed the traditional manufacturing industry. The data exchange between controllers and actuators needs to achieve extremely low delay in IIoT. Due to the limited communication resources, it is necessary to reasonably schedule data flow to reduce delay. Although the studies of data flow scheduling exist in IIoT, they have not considered the impact of time-varying environmental factors and most of them adopted centralized scheduling schemes, which increase computation and communication cost rapidly in large-scale network scenarios. In this paper, the consensus-based distributed optimal transport (OT) algorithm is proposed to optimize data flow scheduling for IIoT networks. Specifically, a data flow scheduling optimization mechanism based on time-varying environmental factors is proposed and an online distributed data flow scheduling optimization algorithm is designed. Compared with the random data flow scheduling algorithm, numerical results show that the proposed algorithm can maximally reduce the average delay by 87%, increase the transmission rate and the spectral efficiency by 157% and 98%, respectively.

A reinforcement learning-based sleep scheduling algorithm for compressive data gathering in wireless sensor networks

Compressive data gathering (CDG) is an adequate method to reduce the amount of data transmission, thereby decreasing energy expenditure for wireless sensor networks (WSNs). Sleep scheduling integrated with CDG can further promote energy efficiency. Most of existing sleep scheduling methods for CDG were formulated as centralized optimization problems which introduced many extra control message exchanges. Meanwhile, a few distributed methods usually adopted stochastic decision which could not adapt to variance in residual energy of nodes. A part of nodes were prone to prematurely run out of energy. In this paper, a reinforcement learning-based sleep scheduling algorithm for CDG (RLSSA-CDG) is proposed. Active nodes selection is modeled as a finite Markov decision process. The mode-free Q learning algorithm is used to search optimal decision strategies. Residual energy of nodes and sampling uniformity are considered into the reward function of the Q learning algorithm for load balance of energy consumption and accurate data reconstruction. It is a distributed algorithm that avoids large amounts of control message exchanges. Each node takes part in one step of the decision process. Thus, computation overhead for sensor nodes is affordable. Simulation experiments are carried out on the MATLAB platform to validate the effectiveness of the proposed RLSSA-CDG against the distributed random sleep scheduling algorithm for CDG (DSSA-CDG) and the original sparse-CDG algorithm without sleep scheduling. The simulation results indicate that the proposed RLSSA-CDG outperforms the two contrast algorithms in terms of energy consumption, network lifetime, and data recovery accuracy. The proposed RLSSA-CDG reduces energy consumption by 4.64% and 42.42%, respectively, compared to the DSSA-CDG and the original sparse-CDG, prolongs life span by 57.3%, and promotes data recovery accuracy by 84.7% compared to the DSSA-CDG.

Research on private desktop cloud platform based on random scheduling algorithm and load balancing scheduling algorithm

Research on private desktop cloud platform based on random scheduling algorithm and load balancing scheduling algorithm

Research on private desktop cloud platform based on random scheduling algorithm and load balancing scheduling algorithm

Research on private desktop cloud platform based on random scheduling algorithm and load balancing scheduling algorithm

Data-Driven Optimal Scheduling Algorithm of Human Resources in Colleges and Universities

At present, the development process of human resource management in Colleges and universities in China has gone through a period of time. In the whole process, the mode of human resource management in colleges and universities is gradually maturing, but there are also some problems. In this paper, data-driven stochastic optimal scheduling algorithm and robust optimal scheduling algorithm are used to model and analyze the human resources. Then, the two models are applied to the human resource management of Nanjing University and Southeast University. The data optimization results show that the robust optimal scheduling algorithm is helpful to the management of new teachers, while the random optimal scheduling algorithm can improve the management of teachers who have been in service for a long time, but there are still some disadvantages. If the combination of two data-driven scheduling algorithms is adopted, it can manage the human resources of colleges and universities well. In short, this paper provides some theoretical and experimental support for the specific application of data-driven human resources optimal scheduling algorithm in colleges and universities.

Parallel random matrix particle swarm optimization scheduling algorithms with budget constraints on cloud computing systems

Nowadays, increasing number of Internet of Things and mobile Internet application services are migrated to cloud computing systems. One of the most important cloud challenges for this business is to optimize services cost. The efficient way to deal with this challenge is to improve the performance of resource management and task scheduling in cloud systems. However, this kind of service scheduling is a typical combinatorial optimization problem, and it is difficult to obtain the optimal solution. In this study, we first formalize this cloud services on virtual machines (VMs) with budget constraints scheduling problem. Then, we propose a random matrix particle swarm optimization scheduling algorithm (RMPSO), which uses the random integer matrix to represent its position and a feasible task scheduling scheme, to achieve the optimal total cost of cloud services. However, the drawback of this solution for large-scale systems is its high time complexity. Therefore, we propose two parallel RMPSO algorithms: CPU parallel algorithm (M-RMPSO) on multi-core system with shared memory and manycore GPU-accelerated strategy (G-RMPSO) to reduce its time complexity. Finally, the rigorous performance evaluation results clearly show that our proposed G-RMPSO outperforms M-RMPSO and existing FMPSO, HYBRID (MPSO+MCSO) in terms of cloud services total cost and algorithm execution time. Therefore, our proposed GPU-accelerated G-RMPSO algorithm is very suitable for cloud service scheduling.

Algorithm for CPU resource allocator case study and compering between ordinary and ML Algorithms

The problem of resource allocation of scarce is a very important task especially in dynamic and non-stable environments when the importance and priority of recourse are changing while the system is already working in our work firstly we simulate the problem by mathematical model than the essential part is to use intelligent algorithms like comparing random scheduling, hill climbing, simulating annealing and genetic algorithms, by using these tactics of such kind of algorithms we designed an algorithm that can handle with every type of multi-agent learning-based resource allocation for distributed systems that act place of most promising approaches to these kinds of allocating systems problems. The approach will be authorized by achieving on different types of large scale systems than correlated with the regular algorithms to compare the result and get the efficacy of new algorithm.

Massive MIMO system lower bound spectral efficiency analysis with precoding and perfect CSI

The analytical lower bound of Spectral Efficiency (SE) of downlink transmission of the Massive Multiple Input Multiple Output (Ma-MIMO) system is analyzed. In this paper, we derive some novel and approximate mathematical expressions for the lower bound of the SE of a Ma-MIMO with linear precoding schemes, i.e., Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF). For simulation analysis of the SE, we consider three joint users and antenna scheduling algorithms, namely, the semi-orthogonal, random, and distance-based user scheduling algorithms, whereas the antennas are selected based on Maximum Signal to Noise Ratio (MSNR) with scheduled users. The channel between the user and the transmitter is assumed to have characteristics of Small Scale Fading (SSF) and Large Scale Fading (LSF) with the Rayleigh fading model. We investigate the effect of the variation of transmitting SNR, the number of base station antennas (M), and the radius (R) of the cell area on the SE. We simulate the downlink transmission of Ma-MIMO and compare the simulation and analytical results. It is observed that the trends of variation of both results are similar to the variation of identical factors, and the difference between the simulated and analytical lower bounds of the SE is approximately 1–1.5 bits. The analytical lower bound is smaller than the simulation result.

System delay optimization for Mobile Edge Computing

Mobile edge computing (MEC) has emerged as an effective paradigm that delivers cloud services and functions to edge devices, with the objective to further enhance quality of service (QoS) of terminal users by offloading their computation-intensive tasks. In this article, a multi-user and multi-server MEC system is considered and each user can choose one MEC server to execute its computation task. We try to minimize the system delay (i.e., the maximum server delay). The problem is decomposed into task offloading problem and transmit power allocation problem which are solved by matching theory and a heuristic idea, respectively. The experimental results show that the proposed algorithm can not only obtain less delay, but also generate less energy consumption compared with the decomposed computation offloading and resource allocation algorithm, the shortest distance based scheduling algorithm and the random scheduling algorithm, especially when the data amount of tasks is the same but the workload is random.

Randomized testing of distributed systems with probabilistic guarantees

Several recently proposed randomized testing tools for concurrent and distributed systems come with theoretical guarantees on their success. The key to these guarantees is a notion of bug depth—the minimum length of a sequence of events sufficient to expose the bug—and a characterization of d -hitting families of schedules—a set of schedules guaranteed to cover every bug of given depth d . Previous results show that in certain cases the size of a d -hitting family can be significantly smaller than the total number of possible schedules. However, these results either assume shared-memory multithreading, or that the underlying partial ordering of events is known statically and has special structure. These assumptions are not met by distributed message-passing applications. In this paper, we present a randomized scheduling algorithm for testing distributed systems. In contrast to previous approaches, our algorithm works for arbitrary partially ordered sets of events revealed online as the program is being executed. We show that for partial orders of width at most w and size at most n (both statically unknown), our algorithm is guaranteed to sample from at most w 2 n d −1 schedules, for every fixed bug depth d . Thus, our algorithm discovers a bug of depth d with probability at least 1 / ( w 2 n d −1 ). As a special case, our algorithm recovers a previous randomized testing algorithm for multithreaded programs. Our algorithm is simple to implement, but the correctness arguments depend on difficult combinatorial results about online dimension and online chain partitioning of partially ordered sets. We have implemented our algorithm in a randomized testing tool for distributed message-passing programs. We show that our algorithm can find bugs in distributed systems such as Zookeeper and Cassandra, and empirically outperforms naive random exploration while providing theoretical guarantees.

Randomized Algorithms for Scheduling Multi-Resource Jobs in the Cloud

We consider the problem of scheduling jobs with multiple-resource requirements (CPU, memory, and disk) in a distributed server platform, motivated by data-parallel and cloud computing applications. Jobs arrive dynamically over time and require certain amount of multiple resources for the duration of their service. When a job arrives, it is queued and later served by one of the servers that has sufficient remaining resources to serve it. The scheduling of jobs is subject to two constraints: 1) packing constraints : multiple jobs can be served simultaneously by a single server if their cumulative resource requirement does not exceed the capacity of the server, and 2) non-preemption : to avoid costly preemptions, once a job is scheduled in a server, its service cannot be interrupted or migrated to another server. Prior scheduling algorithms rely on either bin packing heuristics which have low complexity but can have a poor throughput, or MaxWeight solutions that can achieve maximum throughput but repeatedly require to solve or approximate instances of a hard combinatorial problem (Knapsack) over time. In this paper, we propose a randomized scheduling algorithm for placing jobs in servers that can achieve maximum throughput with low complexity. The algorithm is naturally distributed and each queue and each server needs to perform only a constant number of operations per time unit. Extensive simulation results, using both synthetic and real traffic traces, are presented to evaluate the throughput and delay performance compared to prior algorithms.

Statistical Admission Control in Multi-Hop Cognitive Radio Networks

We address the problem of online admission control in multi-hop, multi-transceiver cognitive radio networks where the channel access is regulated by a bare-bones time-division multiple access protocol and the primary user activity is modeled as an ON/OFF process. We show that the problem of computing the available end-to-end bandwidth–necessary for admission control–is NP-Complete. Rather than working on an approximation algorithm and analyzing its worst-case performance, we relax the problem of online admission control by using a randomized scheduling algorithm and analyzing its average performance. Randomized scheduling is widely used because of its simplicity and efficiency. However, computing the resulting average throughput is challenging and remains an open problem. We solve this problem analytically and use the solution as vehicle for BRAND–a centralized heuristic for computing the average bandwidth available with randomized scheduling between a source destination pair in cognitive radio networks. Driven by practical considerations, we introduce a distributed version of BRAND and prove its correctness. An extensive numerical analysis demonstrates the accuracy of BRAND and its enabling value in performing admission control.

DEBTS: Delay Energy Balanced Task Scheduling in Homogeneous Fog Networks

Vehicular ad hoc networks, wireless sensor networks, Internet of Things, and mobile device-to-device communications can be modeled as different homogeneous fog networks, wherein similar terminals/things/devices/nodes are sharing their computation, communication, and storage resources in the neighborhood for achieving better system performance through effective collaborations. It is very desirable, but quite challenging, to simultaneously reduce service delay and energy consumption in such networks for delay-sensitive and energy-constraint applications, e.g., virtual reality and online 3-D gaming on mobile devices. In this paper, a cross-layer analytical framework is developed to formulate and study the balance between service delay and energy consumption. An effective control parameter ${V}$ is derived to characterize their tradeoff relationship during dynamic task scheduling processes in fog networks. Combining this analysis with Lyapunov optimization techniques, a novel delay energy balanced tasking scheduling (DEBTS) algorithm is proposed to minimize the overall energy consumption while reducing average service delay and delay jitter. It is proved that DEBTS can achieve the theoretical [ ${O(1/V), O(V)}$ ] tradeoff between these two performance metrics. Further, extensive simulation results show that DEBTS can offer much better delay-energy performance in task scheduling challenges. Specifically, for a typical ${V}$ value of ${4 \times 10^{4}}$ , DEBTS can save 26% and 29% more energy, and at the same time, reduce average service delay by 29% and 32%, than traditional random scheduling and least busy scheduling algorithms, respectively.

Associate multi-task scheduling algorithm based on self-adaptive inertia weight particle swarm optimization with disruption operator and chaos operator in cloud environment

Random search-based scheduling algorithms, such as particle swarm optimization (PSO), are often used to solve independent multi-task scheduling problems in cloud, but the quality of optimal solution of the algorithm often has greater deviation and poor stability when the tasks are associate. In this paper, we propose an algorithm called SADCPSO to solve this challenging problem, which improves the PSO algorithm by uniquely integrating the self-adaptive inertia weight, disruption operator and chaos operator. In particular, the self-adaptive inertia weight is adopted to adjust the convergence rate, the disruption operator is applied to prevent the loss of population diversity, and the chaos operator is introduced to prevent the solution from tending to jump into the local optimal. Furthermore, we also provide a scheme to apply the SADCPSO algorithm to solve the associate multi-task scheduling problem. In the simulation experiments, we initialize two associate multi-task scheduling examples and take the minimum execution time as our optimization objective. The simulation results demonstrate that the optimal solution of our proposed algorithm has better quality and stability than the baseline PSO algorithm.

Improved analytical model for performance evaluation of crosspoint‐queued switch under uniform traffic

In this study, a crosspoint-queued (CQ) switch that features a buffer size larger than one and employs the work-conserving random scheduling algorithm under uniform Bernoulli i.i.d. traffic is investigated through both theoretical analysis and simulation. The effect of the crosspoint buffer state on switch performance is modelled and quantified. A novel method for calculating the throughput and delay of a CQ switch with a given switch size, input load, and buffer size is proposed. Compared with existing models, the proposed calculation model offers a simpler and more accurate result, and can serve as a theoretical guide in designing high-speed switch fabrics.

Scheduling Storms and Streams in the Cloud

Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. Each graph is a job, where nodes represent compute tasks and edges indicate data flows between these compute tasks. Jobs (graphs) arrive randomly over time and, upon completion, leave the system. When a job arrives, the scheduler needs to partition the graph and distribute it over the servers to satisfy load balancing and cost considerations. Specifically, neighboring compute tasks in the graph that are mapped to different servers incur load on the network; thus a mapping of the jobs among the servers incurs a cost that is proportional to the number of “broken edges.” We propose a low-complexity randomized scheduling algorithm that, without service preemptions, stabilizes the system with graph arrivals/departures; more importantly, it allows a smooth tradeoff between minimizing average partitioning cost and average queue lengths. Interestingly, to avoid service preemptions, our approach does not rely on a Gibbs sampler; instead, we show that the corresponding limiting invariant measure has an interpretation stemming from a loss system.

Analyzing the impact of buffer capacity on crosspoint-queued switch performance

We use both theoretical analysis and simulations to study the impact of crosspoint-queued (CQ) buffer size on CQ switch throughput and delay performance under different traffic models, input loads, and scheduling algorithms. In this paper, we present the following. 1) We prove the stability of CQ switch using any work-conserving scheduling algorithm. 2) We present an exact closed-form formula for the CQ switch throughput and a nonclosed- form but convergent formula for its delay using static nonwork- conserving random scheduling algorithms with any given buffer size under independent Bernoulli traffic. 3) We show that the above results can serve as a conservative guide on deciding the required buffer size in pure CQ switches using work-conserving algorithms such as the random scheduling, under independent Bernoulli traffic. 4) Furthermore, our simulation results under real-trace traffic show that simple round-robin and random workconserving algorithms can achieve quite good throughput and delay performance with a feasible crosspoint buffer size. Our work reveals the impact of buffer size on the CQ switch performance and provides a theoretical guide on designing the buffer size in pure CQ switch, which is an important step toward building ultra-highspeed switch fabrics.

A Randomized Scheduling Algorithm for Multiprocessor Environments Using Local Search

The LOCAL(A, B) randomized task scheduling algorithm is proposed for fully connected multiprocessors. It combines two given task scheduling algorithms (A, and B) using local neighborhood search to give a hybrid of the two given algorithms. Objective is to show that such type of hybridization can give much better performance results in terms of parallel execution times. Two task scheduling algorithms are selected: DSC (Dominant Sequence Clustering as algorithm A), and CPPS (Cluster Pair Priority Scheduling as algorithm B) and a hybrid is created (the LOCAL(DSC, CPPS) or simply the LOCAL task scheduling algorithm). The LOCAL task scheduling algorithm has time complexity O(|V||E|(|V |+|E|)), where V is the set of vertices, and E is the set of edges in the task graph. The LOCAL task scheduling algorithm is compared with six other algorithms: CPPS, DCCL (Dynamic Computation Communication Load), DSC, EZ (Edge Zeroing), LC (Linear Clustering), and RDCC (Randomized Dynamic Computation Communication). Performance evaluation of the LOCAL task scheduling algorithm shows that it gives up to 80.47 % improvement of NSL (Normalized Schedule Length) over other algorithms.

Work-Competitive Scheduling on Task Dependency Graphs

A fundamental problem in distributed computing is the task of cooperatively executing a given set of [Formula: see text] tasks by [Formula: see text] asynchronous processors where the communication medium is dynamic and subject to failures. Also known as do-all, this problem been studied extensively in various distributed settings. In [2], the authors consider a partitionable network scenario and analyze the competitive performance of a randomized scheduling algorithm for the case where the tasks to be completed are independent of each other. In this paper, we study a natural extension of this problem where the tasks have dependencies among them. We present a simple randomized algorithm for [Formula: see text] processors cooperating to perform [Formula: see text] known tasks where the dependencies between them are defined by a [Formula: see text]-partite task dependency graph and additionally these processors are subject to a dynamic communication medium. By virtue of the problem setting, we pursue competitive analysis where the performance of our algorithm is measured against that of the omniscient offline algorithm which has complete knowledge of the dynamics of the communication medium. We show that the competitive ratio of our algorithm is tight and depends on the dynamics of the communication medium viz. the computational width defined in [2] and also on the number of partitions of the task dependency graph.