Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing

The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the difference in service attributes,the solution efficiency of a single strategy is low for such problems.In this paper,we presents a hyper-heuristic algorithm based on reinforcement learning(HHRL)to optimize the completion time of the task sequence.Firstly,In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively deter-mine the task scheduling and the selection of low-level heuristic strategies.Secondly,a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities.Besides,we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process.Compared with HHSA,ACO,GA,F-PSO,etc,HHRL can quickly obtain task complexity,select appropriate heuristic strategies for task scheduling,search for the the best makspan and have stronger disturbance detection ability for population diversity.

AMTS:Adaptive Multi-Objective Task Scheduling Strategy in Cloud Computing

Task scheduling in cloud computing environments is a multi-objective optimization problem, which is NP hard. It is also a challenging problem to find an appropriate trade-off among resource utilization, energy consumption and Quality of Service(QoS) requirements under the changing environment and diverse tasks. Considering both processing time and transmission time, a PSO-based Adaptive Multi-objective Task Scheduling(AMTS) Strategy is proposed in this paper. First, the task scheduling problem is formulated. Then, a task scheduling policy is advanced to get the optimal resource utilization, task completion time, average cost and average energy consumption. In order to maintain the particle diversity, the adaptive acceleration coefficient is adopted. Experimental results show that the improved PSO algorithm can obtain quasi-optimal solutions for the cloud task scheduling problem.

An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing

Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution,and an inefficient task scheduling algorithm results in under-or over-utilization of the resources,which in turn leads to degradation of the services.Therefore,in the proposed work,load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.In this paper,we propose an adaptive genetic algorithm-based load balancing(GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan,performance improvement ratio,and degree of imbalance.The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination.CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.

Particle Swarm Optimization Embedded in Variable Neighborhood Search for Task Scheduling in Cloud Computing

In cloud computing system,it is a hot and hard issue to find the optimal task scheduling method that makes the processing cost and the running time minimum. In order to deal with the task assignment,a task interaction graph was used to analyze the task scheduling; a modeling for task assignment was formulated and a particle swarm optimization (PSO)algorithm embedded in the variable neighborhood search (VNS) to optimize the task scheduling was proposed. The experimental results show that the method is more effective than the PSO in processing cost,transferring cost, and running time. When the task is more complex,the effect is much better. So,the algorithm can resolve the task scheduling in cloud computing and it is feasible,valid,and efficient.

A PSO Improved with Imbalanced Mutation and Task Rescheduling for Task Offloading in End-Edge-Cloud Computing

To serve various tasks requested by various end devices with different requirements,end-edge-cloud(E2C)has attracted more and more attention from specialists in both academia and industry,by combining both benefits of edge and cloud computing.But nowadays,E2C still suffers from low service quality and resource efficiency,due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility.To address these issues,this paper focuses on task offloading,which makes decisions that which resources are allocated to tasks for their processing.This paper first formulates the problem into binary non-linear programming and then proposes a particle swarm optimization(PSO)-based algorithm to solve the problem.The proposed algorithm exploits an imbalance mutation operator and a task rescheduling approach to improve the performance of PSO.The proposed algorithm concerns the resource heterogeneity by correlating the probability that a computing node is decided to process a task with its capacity,by the imbalance mutation.The task rescheduling approach improves the acceptance ratio for a task offloading solution,by reassigning rejected tasks to computing nodes with available resources.Extensive simulated experiments are conducted.And the results show that the proposed offloading algorithm has an 8.93%–37.0%higher acceptance ratio than ten of the classical and up-to-date algorithms,and verify the effectiveness of the imbalanced mutation and the task rescheduling.

Extended Balanced Scheduler with Clustering and Replication for Data Intensive Scientific Workflow Applications in Cloud Computing

Cloud computing is an advance computing model using which several applications,data and countless IT services are provided over the Internet.Task scheduling plays a crucial role in cloud computing systems.The issue of task scheduling can be viewed as the finding or searching an optimal mapping/assignment of set of subtasks of different tasks over the available set of resources so that we can achieve the desired goals for tasks.With the enlargement of users of cloud the tasks need to be scheduled.Cloud’s performance depends on the task scheduling algorithms used.Numerous algorithms have been submitted in the past to solve the task scheduling problem for heterogeneous network of computers.The existing research work proposes different methods for data intensive applications which are energy and deadline aware task scheduling method.As scientific workflow is combination of fine grain and coarse grain task.Every task scheduled to VM has system overhead.If multiple fine grain task are executing in scientific workflow,it increase the scheduling overhead.To overcome the scheduling overhead,multiple small tasks has been combined to large task,which decrease the scheduling overhead and improve the execution time of the workflow.Horizontal clustering has been used to cluster the fine grained task further replication technique has been combined.The proposed scheduling algorithm improves the performance metrics such as execution time and cost.Further this research can be extended with improved clustering technique and replication methods.

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 themulti-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.

Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Owing to massive technological developments in Internet of Things(IoT)and cloud environment,cloud computing(CC)offers a highly flexible heterogeneous resource pool over the network,and clients could exploit various resources on demand.Since IoT-enabled models are restricted to resources and require crisp response,minimum latency,and maximum bandwidth,which are outside the capabilities.CC was handled as a resource-rich solution to aforementioned challenge.As high delay reduces the performance of the IoT enabled cloud platform,efficient utilization of task scheduling(TS)reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user job.Therefore,this article concentration on the design of an oppositional red fox optimization based task scheduling scheme(ORFOTSS)for IoT enabled cloud environment.The presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud platform.It achieves the makespan by performing optimum TS procedures with various aspects of incoming task.The designing of ORFO-TSS method includes the idea of oppositional based learning(OBL)as to traditional RFO approach in enhancing their efficiency.A wide-ranging experimental analysis was applied on the CloudSim platform.The experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.

Task scheduling and virtual machine allocation policy in cloud computing environment

Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.

Task Scheduling Optimization in Cloud Computing Based on Genetic Algorithms

Task scheduling is the main problem in cloud computing that reduces system performance;it is an important way to arrange user needs and perform multiple goals.Cloud computing is the most popular technology nowadays and has many research potential in various areas like resource allocation,task scheduling,security,privacy,etc.To improve system performance,an efficient task-scheduling algorithm is required.Existing task-scheduling algorithms focus on task-resource requirements,CPU memory,execution time,and execution cost.In this paper,a task scheduling algorithm based on a Genetic Algorithm(GA)has been presented for assigning and executing different tasks.The proposed algorithm aims to minimize both the completion time and execution cost of tasks and maximize resource utilization.We evaluate our algorithm’s performance by applying it to two examples with a different number of tasks and processors.The first example contains ten tasks and four processors;the computation costs are generated randomly.The last example has eight processors,and the number of tasks ranges from twenty to seventy;the computation cost of each task on different processors is generated randomly.The achieved results show that the proposed approach significantly succeeded in finding the optimal solutions for the three objectives;completion time,execution cost,and resource utilization.

Task Scheduling Optimization in Cloud Computing by Rao Algorithm

Cloud computing is currently dominated within the space of highperformance distributed computing and it provides resource polling and ondemand services through the web.So,task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user’s services demand modification dynamically.The main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence restrictions.In heterogeneous multiprocessor systems,task assignments and schedules have a significant impact on system operation.Within the heuristic-based task scheduling algorithm,the different processes will lead to a different task execution time(makespan)on a heterogeneous computing system.Thus,a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce(makespan).In this paper,we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing problem.The basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best solution.We evaluate our algorithm’s performance by applying it to three examples with a different number of tasks and processors.The experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.

An Effective Cloud Workflow Scheduling Approach Combining PSO and Idle Time Slot-Aware Rules

Workflow scheduling is a key issue and remains a challenging problem in cloud computing.Faced with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriate VM type for each task.Multiple task scheduling sequences exist in a workflow application.Different task scheduling sequences have a significant impact on the scheduling performance.It is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling sequence.Besides,the idle time slots on VM instances should be used fully to increase resources'utilization and save the execution cost of a workflow.This paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization(PSO)and idle time slot-aware rules,to minimize the execution cost of a workflow application under a deadline constraint.A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of tasks.An idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling solution.To handle tasks'invalid priorities caused by the randomness of PSO,a repair method is used to repair those priorities to produce valid task scheduling sequences.The proposed approach is compared with state-of-the-art cloud workflow scheduling algorithms.Experiments show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.

Low-power task scheduling algorithm for large-scale cloud data centers

How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data centers. The winner tree is introduced to make the data nodes as the leaf nodes of the tree and the final winner on the purpose of reducing energy consumption is selected. The complexity of large-scale cloud data centers is fully consider, and the task comparson coefficient is defined to make task scheduling strategy more reasonable. Experiments and performance analysis show that the proposed algorithm can effectively improve the node utilization, and reduce the overall power consumption of the cloud data center.

An Energy-Saving Task Scheduling Strategy Based on Vacation Queuing Theory in Cloud Computing

High energy consumption is one of the key issues of cloud computing systems. Incoming jobs in cloud computing environments have the nature of randomness, and compute nodes have to be powered on all the time to await incoming tasks. This results in a great waste of energy. An energy-saving task scheduling algorithm based on the vacation queuing model for cloud computing systems is proposed in this paper. First, we use the vacation queuing model with exhaustive service to model the task schedule of a heterogeneous cloud computing system.Next, based on the busy period and busy cycle under steady state, we analyze the expectations of task sojourn time and energy consumption of compute nodes in the heterogeneous cloud computing system. Subsequently, we propose a task scheduling algorithm based on similar tasks to reduce the energy consumption. Simulation results show that the proposed algorithm can reduce the energy consumption of the cloud computing system effectively while meeting the task performance.

Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics.

Trust Based Meta-Heuristics Workflow Scheduling in Cloud Service Environment

Cloud computing has emerged as a new style of computing in distributed environment. An efficient and dependable Workflow Scheduling is crucial for achieving high performance and incorporating with enterprise systems. As an effective security services aggregation methodology, Trust Work-flow Technology (TWT) has been used to construct composite services. However, in cloud environment, the existing closed network services are maintained and functioned by third-party organizations or enterprises. Therefore service-oriented trust strategies must be considered in workflow scheduling. TWFS related algorithms consist of trust policies and strategies to overcome the threats of the application with heuristic workflow scheduling. As a significance of this work, trust based Meta heuristic workflow scheduling (TMWS) is proposed. The TMWS algorithm will improve the efficiency and reliability of the operation in the cloud system and the results show that the TMWS approach is effective and feasible.

Multi-objective workflow scheduling in cloud system based on cooperative multi-swarm optimization algorithm

In order to improve the performance of multi-objective workflow scheduling in cloud system, a multi-swarm multiobjective optimization algorithm(MSMOOA) is proposed to satisfy multiple conflicting objectives. Inspired by division of the same species into multiple swarms for different objectives and information sharing among these swarms in nature, each physical machine in the data center is considered a swarm and employs improved multi-objective particle swarm optimization to find out non-dominated solutions with one objective in MSMOOA. The particles in each swarm are divided into two classes and adopt different strategies to evolve cooperatively. One class of particles can communicate with several swarms simultaneously to promote the information sharing among swarms and the other class of particles can only exchange information with the particles located in the same swarm. Furthermore, in order to avoid the influence by the elastic available resources, a manager server is adopted in the cloud data center to collect the available resources for scheduling. The quality of the proposed method with other related approaches is evaluated by using hybrid and parallel workflow applications. The experiment results highlight the better performance of the MSMOOA than that of compared algorithms.

Hypergraph-Based Data Reduced Scheduling Policy for Data-Intensive Workflow in Clouds

Data-intensive computing is expected to be the next-generation IT computing paradigm. Data-intensive workflows in clouds are becoming more and more popular. How to schedule data-intensive workflow efficiently has become the key issue. In this paper, first, we build a directed hypergraph model for data-intensive workflow, since Hypergraphs can more accurately model communication volume and better represent asymmetric problems, and the cut metric of hypergraphs is well suited for minimizing the total volume of communication.Second, we propose a concept data supportive ability to help the presentation of data-intensive workflow application and provide the merge operation details considering the data supportive ability. Third, we present an optimized hypergraph multi-level partitioning algorithm. Finally we bring a data reduced scheduling policy HEFT-P for data-intensive workflow. Through simulation,we compare HEFT-P with three typical workflow scheduling policies.The results indicate that HEFT-P could obtain reduced data scheduling and reduce the makespan of executing data-intensive

Cloud Computing-System Implementation for Business Applications

Nowadays, companies are faced with the task of processing huge quantum of data. As the traditional database systems cannot handle this task in a cost-efficient manner, companies have built customized data processing frameworks. Cloud computing has emerged as a promising approach to rent a large IT infrastructure on a short-term pay-per-usage basis. This paper attempts to schedule tasks on compute nodes so that data sent from one node to the other has to traverse as few network switches as possible. The challenges and opportunities for efficient parallel data processing in cloud environments have been demonstrated and Nephele, the first data processing framework, has been presented to exploit the dynamic resource provisioning offered by the IaaS clouds. The overall utilisation of resources has been improved by assigning specific virtual machine types to specific tasks of a processing job and by automatically allocating or deallocating virtual machines in the course of a job execution. This has led to substantial reduction in the cost of parallel data processing.