Scheduling Of Independent Tasks Research Articles

Cloud Environment Task Scheduling Optimization of Modified Genetic Algorithm

From the availability of resources to the accomplishment of tasks, cloud computing is a development of supercomputing. One of the most trustworthy paradigms in computing technology is built on internet-based parallel and distributed computing models. Optimization algorithms can be used to distribute user workloads to provided logical resources termed 'Virtual Machines' in the cloud computing system, which is a major aspect of resource management (VM). A fundamental challenge in cloud computing is the dynamic heterogeneity of resources and workloads, which necessitates efficient task scheduling and distribution. It is possible that task scheduling in distributed environments will improve our understanding of workflow scheduling, independent task scheduling that takes into account security and execution time for applications, trust between various system entities, and improved system utilisation and energy efficiency, among other things. The goal of this research is to contribute to these advancements in these areas: An independent task scheduling system based on genetics is presented to obtain the best outcomes in terms of time and resource consumption while allocating tasks to resources in accordance with the task's security needs. Various meta-heuristic algorithms, such as Genetic Algorithm, are currently being used to solve task scheduling difficulties.

Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

Cloud computing technology is favored by users because of its strong computing power and convenient services. At the same time, scheduling performance has an extremely efficient impact on promoting carbon neutrality. Currently, scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak hours. Therefore, the scheduling problem has promising application prospects under the multi-cloud environment. This paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between tasks. This paper reviews the concepts, types, objectives, advantages, challenges, and research status of task scheduling in the multi-cloud environment. Task scheduling strategies proposed in the existing related references are analyzed, discussed, and summarized, including research motivation, optimization algorithm, and related objectives. Finally, the research status of the two kinds of task scheduling is compared, and several future important research directions of multi-cloud task scheduling are proposed.

List and shelf schedules for independent parallel tasks to minimize the energy consumption with discrete or continuous speeds

Scheduling independent tasks on a parallel platform is a widely-studied problem, in particular when the goal is to minimize the total execution time, or makespan (P||Cmax problem in Graham's notations). Also, many applications do not consist of sequential tasks, but rather parallel tasks, either rigid, with a fixed degree of parallelism, or moldable, with a variable degree of parallelism (i.e., for which we can decide at the execution on how many processors they are executed). Furthermore, since the energy consumption of data centers is a growing concern, both from an environmental and economical point of view, minimizing the energy consumption of a schedule is a main challenge to be addressed. One can then decide, for each task, on how many processors it is executed, and at which speed the processors are operated, with the goal to minimize the total energy consumption. We further focus on co-schedules, where tasks are partitioned into shelves, and we prove that the problem of minimizing the energy consumption remains NP-complete when static energy is consumed during the whole duration of the application. We are however able to provide an optimal algorithm for the schedule within one shelf, i.e., for a set of tasks that start at the same time. Several approximation results are derived, both with discrete and continuous speed models, and extensive simulations are performed to show the performance of the proposed algorithms.

Energy Aware Genetic Algorithm for Independent Task Scheduling in Heterogeneous Multi-Cloud Environment

Cloud datacentres contain a vast number of processors. The rapid expansion of cloud computing is resulting in massive energy usage and carbon emissions which has reported a substantial increase day by day. Consequently, the cloud service providers are looking for eco-friendly solutions. The energy consumption can be evaluated with an energy model, which identifies that, server energy consumption scales linearly with resource (cloud) utilization. This research provides an alternate solution to task scheduling problem which designs an optimized task schedule to minimize the makespan and energy consumptions in cloud datacenters. The proposed method is based on the principle of Genetic Algorithm (GA). In the context of task-scheduling using GA, chromosomal representation is considered as a schedule of set of independent tasks mapped with available cloud or machine in the proposed methodology. A fitness function is taken to optimize the overall execution time or makespan. Energy consumption is evaluated based on minimum makespan value. The proposed technique also tested upon synthesized and benchmark dataset which outperforms the conventional cloud task scheduling algorithms like Min-Min, Max-Min, and suffrage heuristics in heterogeneous multi-cloud system.

Research on Optimization Strategy of Task Scheduling Software Based on Genetic Algorithm in Cloud Computing Environment

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.

Firefly technique with courtship training optimized for load balancing independent parallel computer task scheduling in cloud computing

Load Balancing strategies is essential provisioning towards cloud environments is a hard challenge due of the situation dynamic nature and varying additional actions selves. Load balancing approaches are spoken at a data center levels in just this investigation. A much more effective source management technique for virtualizing data centers that raises the quantity of computers to satisfy the necessities of changing assignments requiring relocation is suggested in this study. The anticipated method, termed First-ahead load balancing VM Allocation (FALB), incorporates the optimization unbiased, which constructs a accurate formula of UPMSP with sequential setup weeks back basic feasible. In particular, an enhanced firefly algorithm along with engagement learning is planned. Ultimately, in order to offer numerical solutions inside an appropriate timescale, the suggested algorithm is utilized to calculate the UPMSP with set of images process time. FALB is contrasted the with best solution availability of Cloud centered on VM migration dubbed Local Regression-Minimum Migration Time (LR-MMT) (LR-MMT).

Statistical-Based Heuristic for Scheduling of Independent Tasks in Cloud Computing

Cloud computing is an emerging and innovative technology that is used for solving large-scale complex problems. It considers as an extension to distributed and parallel computing. Additionally, it enables sharing, organizing and aggregation of computational machines to satisfy the user demands. One of the main goals of the task scheduling is to minimize the makespan (i.e. the overall processing time) and maximize the machine utilization. This paper addresses the problem of how to schedule many independent tasks when using different machines. It introduces two batch mode heuristics algorithms for scheduling independent task in the computational cloud environment, high mean absolute deviation first heuristic and QoS Guided Sufferage-HMADF heuristic. Besides, the paper presented other existing batch mode heuristics such as, Min-Min, Max-Min and Sufferage. The four heuristic modes are simulated and the experimental results are discussed using two performance measures, makespan and machine resource utilization.

Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning

Workload balancing in cloud computing is still challenging problem, especially in Infrastructure as a Service (IaaS) in the cloud model. A problem that should not occur during cloud access is a host or server being overloaded or underloaded, which may affect the processing time or may result in a system crash. Therefore, to prevent these problems, an appropriate schedule of access should be considered so that the system can distribute tasks across all available resources, which is called load balancing. The load balancing technique should ensure that all Virtual Machines (VMs) are used appropriately. In this paper, an independent task scheduling approach in cloud computing is proposed using a Multi-objective task scheduling optimization based on the Artificial Bee Colony Algorithm (ABC) with a Q-learning algorithm,which is a reinforcement learning technique that helps the ABC algorithm work faster, called the MOABCQ method. The proposed method aims to optimize scheduling and resource utilization, maximize VM throughput, and create load balancing between VMs based on makespan, cost, and resource utilization, which are limitations of concurrent considerations. Performance analysis of the proposed method was compared using CloudSim with the existing load balancing and scheduling algorithms: Max-Min, FCFS, HABC_LJF, Q-learning, MOPSO, and MOCS algorithms in three datasets: Random, Google Cloud Jobs (GoCJ), and Synthetic workload. The experimental results indicated that the algorithms used MOABCQ approach outperformed the other algorithms in terms of reducing makespan, reducing cost, reducing degree of imbalance, increasing throughput and average resource utilization.

Novel hybrid ANN and clustering inspired load balancing algorithm in cloud environment

Load balancing in the cloud environment is a NP-hard problem. To address this problem, a new hybridized soft computing inspired technique named Clustering based Artificial Neural Network for Dynamic Load Balancing (CANN-DLB) is introduced. Artificial Neural Network aims to achieve an optimized load of VMs by training the cloud environment. Back-propagation ANN is performed to calculate the optimized Virtual Machine (VM) load in cloud systems for improving QoS. The CANN-DLB technique uses K-means clustering algorithm over calculated VM loads and clusters them into under-loaded and over-loaded VMs. The scheduling of homogeneous independent non-preemptive tasks is performed using Particle Swarm Optimization (PSO) technique. CloudSim (Cloud Simulator) tool has been used to implement the proposed algorithm. The performance of the CANN-DLB technique has been achieved for Space Shared and Time Shared VM task scheduler methods. The simulation results of the CANN-DLB algorithm are compared with existing load balancing and scheduling algorithms with the objectives to improve load balance fairness. Results of CANN-DLB have achieved 69.5%, 96.9%, 96.0% and 97.4% less processing cost and 87.12%, 87.12%, 87.57% and 81.78% less degree of imbalance compared to CM-eFCFS, Static RR, MinMin and MaxMin method respectively. The system fairness with respect to system load is calculated to analyze CANN-DLB algorithm performance on heavy load. The proposed algorithm achieved 12% higher load fairness than the existing Two-level method and proves that the introduced model works better in dynamic scenarios. Plotted graphs show that the proposed idea is innovative for load balancing in a dynamic cloud environment. This hybridization of the ANN and K-means clustering method produces remarkable results as compared to existing algorithms for different cases.

Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments.

This work establishes a set of methodologies to evaluate the performance of any task scheduling policy in heterogeneous computing contexts. We formally state a scheduling model for hybrid edge–cloud computing ecosystems and conduct simulation-based experiments on large workloads. In addition to the conventional cloud datacenters, we consider edge datacenters comprising smartphone and Raspberry Pi edge devices, which are battery powered. We define realistic capacities of the computational resources. Once a schedule is found, the various task demands can or cannot be fulfilled by the resource capacities. We build a scheduling and evaluation framework and measure typical scheduling metrics such as mean waiting time, mean turnaround time, makespan, throughput on the Round-Robin, Shortest Job First, Min-Min and Max-Min scheduling schemes. Our analysis and results show that the state-of-the-art independent task scheduling algorithms suffer from performance degradation in terms of significant task failures and nonoptimal resource utilization of datacenters in heterogeneous edge–cloud mediums in comparison to cloud-only mediums. In particular, for large sets of tasks, due to low battery or limited memory, more than 25% of tasks fail to execute for each scheduling scheme.

Dynamic scheduling of independent tasks in cloud computing applying a new hybrid metaheuristic algorithm including Gabor filter, opposition-based learning, multi-verse optimizer, and multi-tracker optimization algorithms

Dynamic scheduling of independent tasks in cloud computing applying a new hybrid metaheuristic algorithm including Gabor filter, opposition-based learning, multi-verse optimizer, and multi-tracker optimization algorithms

An EPTAS for scheduling fork-join graphs with communication delay

This paper presents an EPTAS for scheduling fork-join task graphs with communication delay on homogeneous processors, denoted as P|fork-join,cij|Cmax in the α|β|γ-notation. The fork-join structure is a basic structure found in many parallel computations. The algorithm uses an integer program as the feasibility test and searches for a solution which would be guaranteed to be within a 1+ϵ factor of the optimum. It is shown that this runs in time exponential in terms of 1/ϵ and polynomial in terms of the input size. Communication costs are dealt with effectively for this fork-join graph structure. The EPTAS is also adapted to scheduling independent tasks with release times and deadlines, which is denoted as P|rj|Lmax.

Independent Task Scheduling in Cloud Computing using Meta-Heuristic HC-CSO Algorithm

Cloud computing is a vital paradigm of emerging technologies. It provides hardware, software, and development platforms to end-users as per their demand. Task scheduling is an exciting job in the cloud computing environment. Tasks can be divided into two categories dependent and independent. Independent tasks are not connected to any type of parent-child concept. Various meta-heuristic algorithms have come into force to schedule the independent tasks. In this, paper a hybrid HC-CSO algorithm has been simulated using independent tasks. This hybrid algorithm has been designed by using the HEFT algorithm, Self-Motivated Inertia Weight factor, and standard Cat Swarm Optimization algorithm. The Crow Search algorithm has been applied to overcome the problem of premature convergence and to avoid the H-CSO algorithm getting stuck in the local fragment. The simulation was carried out using 500-1300 random lengths independent tasks and it was found that the H-CSO algorithm has beaten PSO, ACO, and CSO algorithms whereas the hybrid algorithm HC-CSO is working fine despite Cat Swarm Optimization, Particle Swarm Optimization, and H-CSO algorithm in the name of processing cost and makespan. For all scenarios, the HC-CSO algorithm is found overall 4.15% and 7.18% efficient than the H-CSO and standard CSO respectively in comparison to the makespan and in case of computation cost minimization, 9.60% and 14.59% than the H-CSO and the CSO, respectively.

A dynamic load scheduling in IaaS cloud using binary JAYA algorithm

Datacenters receive dynamic workloads with disparate specifications to be scheduled on virtual machines (VMs). These unpredictable, alarmingly growing workloads with varying resource specifications may bring down the servers of datacenters into an imbalanced state. Thus, resulting in low resource utilization and high energy consumption among the servers. To cater to the need of fluctuating on-demand resource provisioning, it is essential to scale up the ability and capacityof existing infrastructure through virtualization. Moreover, due to the involvement of conflicting scheduling constraints, load scheduling in cloud computing fall under NP-hard problem. An effective scheduling mechanism in amalgamation with a load balancing strategy based on binary JAYA is implemented to alleviate the challenges as mentioned above. This technique not only improves resource utilization but also brings down the degree of energy consumption and makespan while keeping the whole system balanced. At first, it focuses on evoking a load balancing procedure to uniformly disperse the loads among VMs based on the compatibility between the tasks and VMs and secondly, JAYA algorithm is executed to find the best possible mapping of tasks onto VMs. In order to appraise the efficacy of the proposed algorithm, the scheduling of independent and non-preemptive tasks is simulated in CloudSim using a benchmark parallel workload by NASA-iPSC. Experiments are conducted in both homogenous and heterogeneous environments. The proposed algorithm is statistically validated using the Friedman test, followed by a Holm’s test. The proposed approach is compared over other algorithms such as Round Robin (RR), binary Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The simulation results show a notable amelioration over other mentioned algorithms by an increase of resource utilization with 18.47% (GA), 12.65% (BPSO) and 4.18% (GA), 2.51% (BPSO) and a reduction of makespan by 6.47% (GA), 4.35% (BPSO) and 4.17% (GA), 2.20% (BPSO) with an increasing number of tasks and VMs in two different test cases respectively.

A hybrid heuristic of Variable Neighbourhood Descent and Great Deluge algorithm for efficient task scheduling in Grid computing

To achieve the ultimate success of global collaborative resource sharing in Grid computing, an effective and efficient Grid resource management system is necessary and it is only possible if its core component, the scheduler, can perform scheduling in an efficient manner. Scheduling tasks to resources in Grid computing is a challenging task and known as a NP hard problem. In this paper, we propose a novel hybrid heuristic-based algorithm, which synergised the excellent diversification capability of Great Deluge (GD) algorithm with the powerful systematic multi-neighbourhood search strategy captured in Variable Neighbourhood Descent (VND) algorithm, to efficiently schedule independent tasks in Grid computing environment with an objective of minimising the makespan. Simulation experiments have been conducted to examine the impact of hybridising GD and VND. In addition, the performance of the proposed algorithm has been evaluated and compared with some other recent meta-heuristics in the literature. The experimental simulation results show that our proposed algorithm outperforms the other algorithms in the literature and the performance improvement achieved by this hybrid strategy is effective and efficient with respect to makespan and computational time as it can obtain good quality (makespan) of solutions while obviating the drawback of requiring high computational cost from the VND.

Weakly-hard Real-time Guarantees for Earliest Deadline First Scheduling of Independent Tasks

The current trend in modeling and analyzing real-time systems is toward tighter yet safe timing constraints. Many practical real-time systems can de facto sustain a bounded number of deadline-misses, i.e., they have Weakly-Hard Real-Time (WHRT) constraints rather than hard real-time constraints. Therefore, we strive to provide tight Deadline Miss Models (DMMs) in complement to tight response time bounds for such systems. In this work, we bound the distribution of deadline-misses for task sets running on uniprocessors using the Earliest Deadline First (EDF) scheduling policy. We assume tasks miss their deadlines due to transient overload resulting from sporadic jobs, e.g., interrupt service routines. We use Typical Worst-Case Analysis (TWCA) to tackle the problem in this context. Also, we address the sources of pessimism in computing DMMs, and we discuss the limitations of the proposed analysis. This work is motivated by and validated on a realistic case study inspired by industrial practice (satellite on-board software) and on a set of synthetic test cases. The synthetic experiment is dedicated to extensively study the impact of EDF on DMMs by presenting a comparison between DMMs computed under EDF and Rate Monotonic (RM). The results show the usefulness of this approach for temporarily overloaded systems when EDF scheduling is considered. They also show that EDF is especially useful for WHRT tasks.

Independent Task Scheduling in Heterogeneous System

Recently, the rapid development in processing speeds, fast storage devices and better network connectivity, hasaccelerated the popularization of cloud computing. Cloud computing is an on-demand-servicewhich provides users with high end servers,storage and processing capabilities where the user need not be concerned with its infrastructure.Although, there are abundant resources in the cloud infrastructure, for the efficient working and execution of tasks, task scheduling plays a crucial role. Task scheduling results in better performance (throughput) of the system along with better resource utilization which ultimately results inreduced energy consumption. At any given time, a processor should never be in idle state, as it still consumes some amount of energy. In this paper, the use of Quantum Genetic Algorithm has led to the reduction in energy consumption. The objective is to find a scheduling sequencewhich can be implemented ina cloud computing environment. Along with minimizing energy consumption, the algorithm helps reduce makespan time of a processor as well.The results show a decrease in energy consumption by 10-15% under different test scenarios involving a variable number of tasks, processors, and the number of iterations (generations) for which the algorithm was run. The algorithm converges to the desired result within 10-15 iterations, as can be seen from the results published in this paper.

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Over two decades, Heterogeneous Computing Systems (HCS) are offering large amount of federated computing resources, spanning across different administrative domains, to compute-intensive user applications. Efficient job schedulers are required to allocate HCS resources to user applications to satisfy system provider and user requirements. Offline scheduling is most popular kind of job scheduling in heterogeneous system, in which jobs are collected in batch and scheduled together. Job scheduling in HCS has become NP-hard problem due to system scale, federated structure and high resource as well as job heterogeneity. Simple queuing and deterministic heuristics have failed to provide optimal solution to NP-hard job scheduling problem. Due to NP-hard nature of job scheduling problem, there is always a scope to propose new scheduling solutions using meta-heuristics. Offline scheduling in HCS has been focused more on scheduling independent sequential tasks viz. Bag-of-tasks or Many-tasks. Offline scheduling of parallel jobs (composed of collaborating tasks with no precedence) in HCS has not gained much attention. In this paper, a novel hybrid multi-objective meta-heuristic known as HCSPSO, which combines the qualities of Cuckoo search (CS) and Particle Swarm Optimization (PSO), has been proposed to schedule batch of parallel jobs in multi-cluster HCS platform. Proposed HCSPSO policy is extensively compared with different heuristics and metaheuristics using different resource configurations and real supercomputing workload logs. Comparative results have showed the dominance of the proposed hybrid scheduling algorithm over other algorithms.

Heuristic Algorithm for Independent Task Scheduling in Grid Computing

Grid enables the integration of large number of geographically distributed heterogeneous resources owned by different organizations for resource sharing and collaboration in solving advanced sciences and engineering applications. In a distributed heterogeneous computing grid environment, scheduling independent tasks on the grid resources is more complicated and is an NP-Complete problem. Scheduling is the process of mapping the tasks to the available resources. In order to utilize the essence of grid efficiently, this paper presents a heuristic technique for scheduling/mapping the tasks to the resources. The efficiency of the proposed algorithm (WSSLVA) in terms of reduced makespan as well as better resource utilization is achieved. The experimental results indicate that the proposed WSSLVA algorithm is a promising algorithm than the Min-min heuristic scheduling algorithm in terms of makespan and resource utilization.

Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing

Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing