Problem Of Task Scheduling Research Articles

Resource allocation optimization in cloud computing using the whale optimization algorithm

Cloud computing is a massively distributed system in which existing resources interact with user-requested tasks to meet their requests. In such a system, the problem of optimizing Resource Allocation and Scheduling (RAS) is vital, because recourse allocation and scheduling deals with the mapping between recourses and user requests and also is responsible for optimal allocating of tasks to available resources. In the cloud environment, a user may face hundreds of computational resources to do his work. Therefore, manually recourse allocation and scheduling are impossible, and having a schedule between user requests and available recourses seems logical. In this paper, we used Whale Optimization Algorithm (WOA) to solve resource allocation and task scheduling problem in cloud computing to have optimal resource allocation and reduce the total runtime of requested services by users. The proposed algorithm is compared with the other existed algorithms. Results indicate the proper performance of the proposed algorithm than other ones.

A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata

With the rapid development of cloud computing and container technology, more and more applications are deployed to the cloud, and the scale of cloud platform is expanding. Due to the large number of container instances running in the platform, complex dependency relationship, fast version iteration and other characteristics, the update of business can often cause the change of the whole cloud resource environment, which triggers the repetitive scheduling problem of related tasks and affects stability of the business. In this paper, we propose a self-adapting task scheduling algorithm (ADATSA) using learning automata to solve these problems. Firstly, we design a learning automata model and objective function for the system on task scheduling problem. Then, we realize an effective reward-penalty mechanism for scheduling actions in combination with the idle state of resources and the running state of tasks in the current environment. Meanwhile, the environment is modeled by cluster, node and task, and the probability of action selected is optimized by scheduling execution, thus enhancing the adaptability to the cloud environment of the scheduling and accelerating convergence. Finally, we construct a framework of task load monitoring with buffer queue to achieve dynamic scheduling based on priority. The experimental part verifies the effectiveness of proposed algorithm with different angles such as resource imbalance degree, resource residual degree and QoS. Compared with other learning automata scheduling models such as LAEAS, non-automata technology based algorithms such as PSOS and K8S scheduling engine, ADATSA shows the better performance of environment adaptability, resource optimization efficiency and QoS in dynamic scheduling. The theoretical analysis was consistent with the experimental results.

Multi-objective Dynamic Scheduling of Circuit Repair Based on Improved NSGA-II

To deal with the problem of tight wartime repair time, heavy repair task, and complicated constraints during wartime, the multi-objective dynamic scheduling study of circuit repair equipment maintenance task scheduling was carried out. The military problem of equipment maintenance task scheduling for circuit repair under complex constraints was proposed. Considering the uncertainty of the repair status of the equipment to be repaired, the constraints such as repair time window, non-traversal, and variety of repair capability were introduced on the basis of the repair capability and the repair time limit. A multi-objective dynamic scheduling model that takes the total number of repaired equipment, the sum of equipment importance degree, and the total time of secondary operations as the scheduling target.is established. An improved multi-objective genetic algorithm based on NSGA-II is designed to solve the model, and the rationality and effectiveness of the model and algorithm are verified through example simulation and analysis.

MOGATS: a multi-objective genetic algorithm-based task scheduling for heterogeneous embedded systems

Multi-objective optimisation is an unavoidable requirement in different steps of embedded systems design, including task mapping and scheduling. In this paper, a new multi-objective genetic algorithm-based task mapping and scheduling (abbreviated as MOGATS) is presented for heterogeneous embedded system design. In MOGATS, the architecture of the hardware platform and the set of tasks in the form of a task graph are assumed to be given as the inputs. Task mapping and scheduling problems are modelled as a genetic algorithm-based optimisation approach. In summary, our task scheduling tool is the first multi-objective task scheduling in the design stage of embedded systems to help the designer to figure out which set of scheduling would provide their desired outcome. Additionally, in comparison to EGA-TS, the state-of-art task scheduling algorithm, in terms of speedup and SLR, we have gain 27.8 and 28.6 immediate improvements respectfully.

A Constrained Static Scheduling Strategy in Edge Computing for Industrial Cloud Systems

With the development of industrial internet, attention has been paid for edge computing due to the low latency. However, some problems remain about the task scheduling and resource management. In this paper, an edge computing supported industrial cloud system is investigated. According to the system, a constrained static scheduling strategy is proposed to over the deficiency of dynamic scheduling. The strategy is divided into the following steps. Firstly, the queue theory is introduced to calculate the expectations of task completion time. Thereupon, the task scheduling and resource management problems are formulated and turned into an integer non-linear programming (INLP) problem. Then, tasks that can be scheduled statically are selected based on the expectation of task completion and constrains of various aspects of task. Finally, a multi-elites-based co-evolutionary genetic algorithm (MEB-CGA) is proposed to solve the INLP problem. Simulation result shows that the MEB-CGA significantly outperforms the scheduling quality of greedy algorithm.

Task scheduling for mobile edge computing enabled crowd sensing applications

Crowd sensing effectively solves the dilemma of massive data collection faced by most data-driven applications. Recently, mobile edge computing (MEC) is proposed to extend the frontier of cloud to the network edge so that it is quite suitable to integrate MEC with current crowd sensing systems. In this paper, we focus on the basic problem of task scheduling in such systems. The problem discussed here has some unique challenges, e.g., edge devices are not dedicated to perform sensing tasks, task scheduling on edge devices and edge servers are highly coupled, and it is hard to achieve long-term objectives. To this end, we first present a workflow framework that captures the unique execution logic of sensing tasks. Then we propose a staged scheme to decouple the original scheduling problem. Moreover, we leverage Lyapunov optimisation technique to achieve long-term objective. The experiment results verify the effectiveness and efficiency of our proposed algorithm.

Task Scheduling in Cloud Using Deep Reinforcement Learning

Cloud computing is an emerging technology used in many applications such as data analysis, storage, and Internet of Things (IoT). Due to the increasing number of users in the cloud and the IoT devices that are being integrated with the cloud, the amount of data generated by these users and these devices is increasing ceaselessly. Managing this data over the cloud is no longer an easy task. It is almost impossible to move all data to the cloud datacenters, and this will lead to excessive bandwidth usage, latency, cost, and energy consumption. This makes it evident that allocating resources to users’ tasks is an essential quality feature in cloud computing. This is because it provides the customers or the users with high Quality of Service (QoS) with the best response time, and it also respects the established Service Level Agreement. Therefore, there is a great importance of efficient utilization of computing resources for which an optimal strategy for task scheduling is required. This paper focuses on the problem of task scheduling of cloud-based applications and aims to minimize the computational cost under resource and deadline constraints. Towards this end, we propose a clipped double deep Q-learning algorithm utilizing the target network and experience relay techniques, as we as using the reinforcement learning approach.

An upgraded fruit fly optimisation algorithm for solving task scheduling and resource management problem in cloud infrastructure

In this manuscript, an upgraded Fruit Fly Optimization Algorithm (FFOA) is proposed for optimising task scheduling and resource management processes. The proposed FOA algorithm is utilized to address the issues. In the proposed algorithm, every solution is represented by fruit fly. Every fruit fly upgrades their status through well-organized smell search process. First, a basic approach is put forward to allocate every task for numerous resources and execution time is measured for every task. Second, the overloaded virtual machines (VMs) are identified and the load is balanced to obtain optimal system resource utilisation. The ability of Fly Task Scheduling Algorithm is to schedule the VMs execution time of tasks is minimal. The results of the Fruit Fly-based algorithms such as Fruit Fly Task Scheduling Algorithm, Modified Fruit Fly Task Scheduling Algorithm, Improved Fruit Fly Task Scheduling Algorithm and Multi-Swarm Fruit Fly Task Scheduling Algorithm (MSFFTSA) are proposed and analysed. The upgraded Fruit Fly Optimization algorithm of MSFFTSA is compared with different Fruit Fly algorithms. Finally, the proposed algorithm is compared with other algorithms and the experimental results shows that the proposed MSFFTSA technique is better than the other algorithms of the fruit fly algorithms.

A Fuzzy Enabled Genetic Algorithm for Task Scheduling Problem in Cloud Computing

Background & Objective: Cloud computing emerges out as a new way of computing which enables the users to fulfill their computation need using the underlying computing resources like software, memory, computing nodes or machines without owning them purely on the basis of pay-per-use that too round the clock and from anywhere. People defined this as the extension of the existing technologies like parallel computing, distributed computing or grid computing. Lots of research have been conducted in the field of cloud computing but the task scheduling is considered to be the most fundamental problem which is still in infancy and requires a lot of attention and a proper mechanism for the optimal utilization of the underlying computing resources. Task scheduling in cloud computing environment lies into the category of NP-hard problem and many heuristics and Meta heuristics strategies have been applied to solve the problem. Methods: In this work, Fuzzy Enabled Genetic Algorithm (FEGA) is proposed to solve the problem of task scheduling in cloud computing environment as classical roulette wheel selection method has certain limitations to solve complex optimization problem. Results & Discussion: In this work, an efficient fuzzy enabled genetic algorithm based task scheduling mechanism has been designed, implemented and investigated. The efficiency of the proposed FEGA algorithm is tested using various randomly generated data sets in different situations and compared with the other meta-heuristics. Conclusion: The authors suggest that the proposed Fuzzy Enabled Genetic Algorithm (FEGA) to solve the task scheduling problem helps in minimizing the total execution time or makespan and on comparing with other Meta-heuristic like genetic algorithm and greedy based strategy found that FEGA outperforms the both in different set of experiments.

Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems

Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

An Automated Task Scheduling Model Using Non-Dominated Sorting Genetic Algorithm II for Fog-Cloud Systems

Processing data from Internet of Things (IoT) applications at the cloud centers has known limitations relating to latency, task scheduling, and load balancing. Hence, there have been a shift towards adopting fog computing as a complementary paradigm to cloud systems. In this article, we first propose a multi-objective task-scheduling optimization problem that minimizes both the makespans and total costs in a fog-cloud environment. Then, we suggest an optimization model based on a Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II) to deal with the discrete multi-objective task-scheduling problem and to automatically allocate tasks that should be executed either on fog or cloud nodes. The NSGA-II algorithm is adapted to discretize crossover and mutation evolutionary operators, rather than using continuous operators that require high computational resources and not able to allocate proper computing nodes. In our model, the communications between the fog and cloud tiers are formulated as a multi-objective function to optimize the execution of tasks. The proposed model allocates computing resources that would effectively run on either the fog or cloud nodes. Moreover, it efficiently organizes the distribution of workloads through various computing resources at the fog. Several experiments are conducted to determine the performance of the proposed model compared with a continuous NSGA-II (CNSGA-II) algorithm and four peer mechanisms. The outcomes demonstrate that the model is capable of achieving dynamic task scheduling with minimizing the total execution times (i.e., makespans) and costs in fog-cloud environments.

Reinforcement Learning in Dynamic Task Scheduling: A Review

Scheduling is assigning shared resources over time to efficiently complete the tasks over a given period of time. The term is applied separately for tasks and resources correspondingly in task scheduling and resource allocation. Scheduling is a popular topic in operational management and computer science. Effective schedules ensure system efficiency, effective decision making, minimize resource wastage and cost, and enhance overall productivity. It is generally a tedious task to choose the most accurate resources in performing work items and schedules in both computing and business process execution. Especially in real-world dynamic systems where multiple agents involve in scheduling various dynamic tasks is a challenging issue. Reinforcement Learning is an emergent technology which has been able to solve the problem of the optimal task and resource scheduling dynamically. This review paper is about a research study that focused on Reinforcement Learning techniques that have been used for dynamic task scheduling. The paper addresses the results of the study by means of the state-of-the-art on Reinforcement learning techniques used in dynamic task scheduling and a comparative review of those techniques.

HEART: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and meta‐heuristic algorithms

SummaryCloud computing is becoming a profitable technology because of it offers cost‐effective IT solutions globally. A well‐designed task scheduling algorithm ensures the optimal utilization of clouds resources and reducing execution time dynamically. This research article deals with the task scheduling of inter‐dependent subtasks on unrelated parallel computing machines in a cloud computing environment. This article considers two variants of the problem‐based on two different objective function values. The first variant considers the minimization of the total completion time objective function while the second variant considers the minimization of the makespan objective function. Heuristic and meta‐heuristic (HEART) based algorithms are proposed to solve the task scheduling problems. These algorithms utilize the property of list scheduling algorithm of unrelated parallel machine scheduling problem. A mixed integer linear programming (MILP) formulation has been provided for the two variants of the problem. The optimal solution is obtained by solving MILP formulation using A Mathematical Programming Language (AMPL) software. Extensive numerical experiments have been performed to evaluate the performance of proposed algorithms. The solutions obtained by the proposed algorithms are found to out‐perform the existing algorithms. The proposed algorithms can be used by cloud computing service providers (CCSPs) for enhancing their resources utilization to reduce their operating cost.

PolyGraph: a data flow model with frequency arithmetic

Data flow formalisms are commonly used to model systems in order to solve problems of buffer sizing and task scheduling. A prerequisite for static analysis of a modeled system is the existence of a periodic schedule in which the sizes of communication channels can be bounded for an unbounded execution (consistency), and that communication dependencies do not introduce a deadlock in such an execution (liveness). In the context of Cyber-Physical Systems, components are often interfaced with the physical world and have frequency constraints. The existing data flow formalisms lack expressiveness to fully cover the expected behavior of these components. We propose an extension to static data flow paradigms, called PolyGraph, that includes frequency constraints and adjustable communication rates. We show that with these extensions, the conditions for a model to be consistent and live are no longer sufficient, and we extend the corresponding theorems with necessary and sufficient conditions to preserve these properties. We illustrate how PolyGraph can be used in practice on a realistic Advanced Driver Assistance System, and present a framework to check PolyGraph properties in the tool DIVERSITY, along with experiments on realistic and random models.

Multi-Objective Task and Workflow Scheduling Approaches in Cloud Computing: a Comprehensive Review

Efficient task and workflow scheduling are very important for improving resource management and reducing power consumption in cloud computing data centers (DCs). However, regarding numerous tasks, virtual machines, and several objectives which should be taken into account, scheduling is considered to be an NP-Hard problem. Multi-objective optimization is an interesting technique to deal with multiple conflicting goals which have been utilized by various schemes to solve the task and workflow scheduling problems. This paper focuses on the metaheuristic multi-objective optimization context and presents a comprehensive survey and overview of the multi-objective scheduling approaches designed for various cloud computing environments. It classifies the scheduling schemes regarding their applied multi-objective optimization algorithms and describes how they have adapted the optimization algorithms to solve scheduling problems. Furthermore, a comparison of the multi-objective scheduling schemes is provided, which illuminates future research directions, and finally concluding remarks are presented.

Load balancing strategy for medical big data based on low delay cloud network

Since the cloud servers are far away from the medical detection terminal and user terminal, the communication overhead such as the delay caused by data transmission is large. At the same time, a large number of medical terminals and user terminals access the cloud servers, which makes the cloud servers overloaded, the overall robustness of the network is poor, and the network is prone to failure, which may lead to the work efficiency of doctors cannot be guaranteed, and the waiting time of patients will also increase. To solve the above problems, according to the characteristics of dynamic resource allocation in the medical big data environment, a new cloud network architecture is proposed. To solve the resource scheduling problem, a chaotic algorithm is introduced into the artificial firefly algorithm, and a load balancing optimisation strategy based on a chaotic firefly algorithm is proposed. The simulation results show that the convergence rate of the proposed algorithm is accelerated by adding chaos factor, to avoid the algorithm falling into the local optimal solution. Compared with other load balancing algorithms, the proposed algorithm is more suitable for solving the resource scheduling problem of large-scale tasks in cloud-fog networks.

Multi-criteria HPC task scheduling on IaaS cloud infrastructures using meta-heuristics

With the rapid increase in the use of cloud computing systems, an efficient task scheduling policy, which deals with the assignment of tasks to resources, is required to obtain maximum performance. Cloud task scheduling (CTS) is an established NP-Hard optimization problem that can be effectively tackled with meta-heuristic algorithms. The cuckoo search (CS) algorithm is a powerful swarm-intelligence meta-heuristic that has been successfully applied over a wide-range of real-life optimization problems, including task scheduling problems. Besides its strong exploration ability, the CS algorithm suffers from insufficient local search, lack of solution diversity towards the end, and slow convergence problem. These drawbacks produce inefficient cloud task schedules resulting in sub-optimal performance. In this manuscript, an improved CS-based scheduling algorithm called CSDEO is introduced, which combines the features of the Opposition-based learning (OBL) method, Cuckoo search, and Differential evolution (DE) algorithms to optimize workload makespan and energy consumption of the cloud resources. Our CSDEO algorithm firstly uses the OBL method to produce an optimal initial population by providing solutions across the entire solution space. Then, the CSDEO uses an effective way of switching between the CS exploration phase and the DE exploitation phase, depending on each solution's fitness. Experiments are conducted on the CloudSim simulator by using the CEA-Curie and HPC2N supercomputing workloads. The observations show that in the case of CEA-Curie workloads, the proposed CSDEO algorithm achieves makespan improvement in the range of 6.29–29.76% and energy consumption improvement in the range of 3.76–201.98% over well-known scheduling algorithms. In the case of HPC2N workloads, the improvement ranges of the CSDEO approach for the makespan and energy consumption metrics are 9.86–281.69% and 6.12–233.3%, respectively compared to the tested scheduling algorithms.

Optimal task scheduling benefits from a duplicate-free state-space

The NP-hard problem of task scheduling with communication delays (P|prec,cij|Cmax) is often tackled using approximate methods, but guarantees on the quality of these heuristic solutions are hard to come by. Optimal schedules are therefore invaluable for properly evaluating these heuristics, as well as being very useful for applications in time critical systems. Optimal solving using branch-and-bound algorithms like A* has been shown to be promising in the past, with a state-space model we refer to as exhaustive list scheduling (ELS). The obvious weakness of this model is that it leads to the production of large numbers of duplicate states during a search, requiring special techniques to mitigate this which cost additional time and memory. In this paper we define a new state-space model (AO) in which we divide the problem into two distinct sub-problems: first we decide the allocations of all tasks to processors, and then we order the tasks on their allocated processors in order to produce a complete schedule. This two-phase state-space model offers no potential for the production of duplicates. We also describe how the pruning techniques and optimisations developed for the ELS model were adapted or made obsolete by the AO model. An experimental evaluation shows that the use of this new state-space model leads to a significant increase in the number of task graphs able to be scheduled within a feasible time-frame, particularly for task graphs with a high communication-to-computation ratio. Finally, some advanced lower bound heuristics are proposed for the AO model, and evaluation demonstrates that significant gains can be achieved from the consideration of necessary idle time.

Adaptive task scheduling in IoT using reinforcement learning

PurposeThe intelligence in the Internet of Things (IoT) can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment. The computational latency incurred by the cloud-only solution can be significantly brought down by the fog computing layer, which offers a computing infrastructure to minimize the latency in service delivery and execution. For this purpose, a task scheduling policy based on reinforcement learning (RL) is developed that can achieve the optimal resource utilization as well as minimum time to execute tasks and significantly reduce the communication costs during distributed execution.Design/methodology/approachTo realize this, the authors proposed a two-level neural network (NN)-based task scheduling system, where the first-level NN (feed-forward neural network/convolutional neural network [FFNN/CNN]) determines whether the data stream could be analyzed (executed) in the resource-constrained environment (edge/fog) or be directly forwarded to the cloud. The second-level NN ( RL module) schedules all the tasks sent by level 1 NN to fog layer, among the available fog devices. This real-time task assignment policy is used to minimize the total computational latency (makespan) as well as communication costs.FindingsExperimental results indicated that the RL technique works better than the computationally infeasible greedy approach for task scheduling and the combination of RL and task clustering algorithm reduces the communication costs significantly.Originality/valueThe proposed algorithm fundamentally solves the problem of task scheduling in real-time fog-based IoT with best resource utilization, minimum makespan and minimum communication cost between the tasks.

Application of discrete artificial bee colony algorithm for cloud task optimization scheduling

The performance of task scheduling algorithm in cloud computing determines the performance of the cloud system. This study mainly analyzed the application of the artificial bee colony (ABC) algorithm in the cloud task scheduling. In order to solve the problem of cloud task scheduling, the ABC algorithm was discretized to get the discrete artificial bee colony (DABC) algorithm. Then the mathematical model of cloud task scheduling was established and solved by the DABC algorithm. Finally, the simulation experiment was carried out, and the performance of first-come-first-served (FCFS), MIN–MIN, ABC and DABC algorithms under different cloud tasks was compared to verify the performance of the proposed algorithm. The results showed that the user waiting time of the DABC algorithm was 1210s, the load balance degree was 0.01, and the user payment fee was 1688 yuan when the number of cloud tasks was 500; compared with other algorithms, the user waiting time of the DABC algorithm was shorter, the resource load balance degree was higher, and the overall performance was better. The research results verify the effectiveness of the DABC algorithm in solving the problem of cloud task optimal scheduling, and it can be further extended and applied in practice.