Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm

We investigate that resource provisioning and scheduling is a prominent problem due to heterogeneity as well as dispersion of cloud resources. Cloud service providers are building more and more datacenters due to demand of high computational power which is a serious threat to environment in terms of energy requirement. To overcome these issues, we need an efficient meta-heuristic technique that allocates applications among the virtual machines fairly and optimizes the quality of services (QoS) parameters to meet the end user objectives. Binary particle swarm optimization (BPSO) is used to solve real-world discrete optimization problems but simple BPSO does not provide optimal solution due to improper behavior of transfer function. To overcome this problem, we have modified transfer function of binary PSO that provides exploration and exploitation capability in better way and optimize various QoS parameters such as makespan time, energy consumption, and execution cost. The computational results demonstrate that modified transfer function-based BPSO algorithm is more efficient and outperform in comparison with other baseline algorithm over various synthetic datasets.

     

Fair scheduling of bag-of-tasks applications using distributed Lagrangian optimization

Large scale distributed systems typically comprise hundreds to millions of entities (applications, users, companies, universities) that have only a partial view of resources (computers, communication links). How to fairly and efficiently share such resources between entities in a distributed way has thus become a critical question.     

A meta-heuristic optimization approach to the scheduling of bag-of-tasks applications on heterogeneous clouds with multi-level arrivals and critical jobs

As cloud computing evolves, it is becoming more and more apparent that the future of this industry lies in interconnected cloud systems where resources will be provided by multiple “Cloud” providers instead of just one. In this way, the hosts of services that are cloud-based will have access to even larger resource pools while at the same time increasing their scalability and availability by diversifying both their computing resources and the geographical locations where those resources operate from. Furthermore the increased competition between the cloud providers in conjunction with the commoditization of hardware has already led to large decreases in the cost of cloud computing and this trend is bound to continue in the future. Scientific focus in cloud computing is also headed this way with more studies on the efficient allocation of resources and effective distribution of computing tasks between those resources. This study evaluates the use of meta-heuristic optimization algorithms in the scheduling of bag-of-tasks applications in a heterogeneous cloud of clouds. The study of both local and globally arriving jobs has been considered along with the introduction of sporadically arriving critical jobs. Simulation results show that the use of these meta-heuristics can provide significant benefits in costs and performance.     

A hybrid meta-heuristic algorithm for optimization of crew scheduling

Crew scheduling problem is the problem of assigning crew members to the flights so that total cost is minimized while regulatory and legal restrictions are satisfied. The crew scheduling is an NP-hard constrained combinatorial optimization problem and hence, it cannot be exactly solved in a reasonable computational time. This paper presents a particle swarm optimization (PSO) algorithm synchronized with a local search heuristic for solving the crew scheduling problem. Recent studies use genetic algorithm (GA) or ant colony optimization (ACO) to solve large scale crew scheduling problems. Furthermore, two other hybrid algorithms based on GA and ACO algorithms have been developed to solve the problem. Computational results show the effectiveness and superiority of the proposed hybrid PSO algorithm over other algorithms.     

A multitime-steps-ahead prediction approach for scheduling live migration in cloud data centers

One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime-ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.     

Multiple cross-docks scheduling using two meta-heuristic algorithms

This paper considers a truck scheduling problem in a multiple cross docks while there is temporary storage in front of the shipping docks. Receiving and shipping trucks can intermittently move in and out of the docks during the time intervals between their task execution, in which trucks can enter to any of the cross docks. Thus, a mixed-integer programming (MIP) model for multiple cross docks scheduling is developed inspired by models in the body of the respective literature. Its objective is to minimize the total operation time or maximize the throughput of the cross-docking system. Moreover, additional concepts considered in the new method is multiple cross docks with a limited capacity. In this study, there are two types of delay times. The first type occurs when there is a shipping truck change and the second one occurs when the current shipping truck does not load any product from a certain receiving truck or temporary storage and waits until its needed products arrive at the shipping docks. To solve the developed model, two meta-heuristics, namely simulated annealing (SA) and firefly algorithms (FA), are proposed. In addition, a procedure for trucks scheduling in a state of a constant discrete firefly algorithm for the discrete adaptation has been proposed. The experimental design is carried out to tune the parameters of algorithms. Finally, the solutions obtained by the proposed SA and FA are compared.     

Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing

The Journal of Supercomputing - Scheduling in cloud computing is the assignment of tasks to resources with maximum performance, which is a multi-purpose problem. The scheduling is of NP-Hard issues...     

A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop

In this paper we address a hybrid flow shop scheduling problem considering the minimization of the sum of the total earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, we propose an ant colony optimization method to deal with this problem. Our proposed method has several features, including some heuristics that specifically take into account both earliness and tardiness penalties to compute the heuristic information values. The performance of our algorithm is tested by numerical experiments on a large number of randomly generated problems. A comparison with solutions performance obtained by some constructive heuristics is presented. The results show that the proposed approach performs well for this problem.     

A novel hybrid meta-heuristic algorithm for solving multi objective flexible job shop scheduling

Finding feasible scheduling that optimize all objective functions for flexible job shop scheduling problem (FJSP) is considered by many researchers. In this paper, the novel hybrid genetic algorithm and simulated annealing (NHGASA) is introduced to solve FJSP. The NHGASA is a combination of genetic algorithm and simulated annealing to propose the algorithm that is more efficient than others. The three objective functions in this paper are: minimize the maximum completion time of all the operations (makespan), minimize the workload of the most loaded machine and minimize the total workload of all machines. Pareto optimal solution approach is used in NHGASA for solving FJSP. Contrary to the other methods that assign weights to all objective functions to reduce them to one objective function, in the NHGASA and during all steps, problems are solved by three objectives. Experimental results prove that the NHGASA that uses Pareto optimal solutions for solving multi-objective FJSP overcome previous methods for solving the same benchmarks in the shorter computational time and higher quality.     

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

Cloud 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.     

Block transportation scheduling under delivery restriction in shipyard using meta-heuristic algorithms

Special vehicles called transporters are used to deliver heavy blocks from one plant to another in shipyards. Because of the limitation on the number of transporters, the scheduling of transporters is important for maintaining the overall production schedule of the blocks. This paper considers a scheduling problem of block transportation under a delivery restriction to determine when and by which transporter each block is delivered from its source plant to its destination plant. The objective of the problem is to minimize the penalty times that can cause delays in the overall block production schedule. A mathematical model for the optimal solution is derived, and two meta-heuristic algorithms based on a genetic algorithm (GA) and a self-evolution algorithm (SEA) are proposed. The performance of the algorithms is evaluated with several randomly generated experimental examples.     

Dynamic scheduling of bags-of-tasks with sensitive input data and end-to-end deadlines in a hybrid cloud

Multimedia Tools and Applications - As organizations with existing on-premise infrastructure investments shift to the hybrid cloud computing paradigm, it is imperative to address the various...     

A dual encoding-based meta-heuristic algorithm for solving a constrained hybrid flow shop scheduling problem

Though scheduling problems have been largely investigated by literature over the last 50 years, this topic still influences the research activity of many experts and practitioners, especially due to a series of studies which recently emphasized the closeness between theory and industrial practice. In this paper the scheduling problem of a hybrid flow shop with m stages, inspired to a truly observed micro-electronics manufacturing environment, has been investigated. Overlap between jobs of the same type, waiting time limit of jobs within inter-stage buffers as well as machine unavailability time intervals represent just a part of the constraints which characterize the problem here investigated. A mixed integer linear programming model of the problem in hand has been developed with the aim to validate the performance concerning the proposed optimization technique, based on a two-phase metaheuristics (MEs). In the first phase the proposed ME algorithm evolves similarly to a genetic algorithm equipped with a regular permutation encoding. Subsequently, since the permutation encoding is not able to investigate the overall space of solutions, a random search algorithm equipped with an m-stage permutation encoding is launched for improving the algorithm strength in terms of both exploration and exploitation. Extensive numerical studies on a benchmark of problems, along with a properly arranged ANOVA analysis, demonstrate the statistical outperformance of the proposed approach with respect to the traditional optimization approach based on a single encoding. Finally, a comprehensive comparative analysis involving the proposed algorithm and several metaheuristics developed by literature demonstrated the effectiveness of the dual encoding based approach for solving HFS scheduling problems.     

Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm

Three-dimension path planning of uninhabited combat air vehicle (UCAV) is a complicated optimal problem, which mainly focuses on optimizing the flight route considering the different types of constrains under complicated combating environments. A new hybrid meta-heuristic ant colony optimization (ACO) and differential evolution (DE) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the pheromone trail of the improved ACO model during the process of ant pheromone updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the three-dimensional mesh while avoiding the threats area and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic ACO. The realization procedure for this hybrid meta-heuristic approach is also presented in detail. In order to make the optimized UCAV path more feasible, the к-trajectory is adopted for smoothing the path. Finally, series experimental comparison results demonstrate that this proposed hybrid meta-heuristic method is more effective and feasible in UCAV three-dimension path planning than the basic ACO model.     

Simulation of power consumption of cloud data centers

Energy efficiency of cloud data centers received significant attention recently as data centers often consume significant resources in operation. Most of the existing energy-saving algorithms focus on resource consolidation for energy efficiency. This paper proposes a simulation-driven methodology with the accurate energy model to verify its performance, and introduces a new resource scheduling algorithm Best-Fit-Decreasing-Power (BFDP) to improve the energy efficiency without degrading the QoS of the system. Both the model and the resource algorithm have been extensively simulated and validated, and results showed that they are effective. In fact, the proposed model and algorithm outperforms the existing resource scheduling algorithms especially under light workloads.     

GPU-aware resource management in heterogeneous cloud data centers

The Journal of Supercomputing - The power of rapid scalability and easy maintainability of cloud services is driving many high-performance computing applications from company server racks into...     

Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

The use of High Performance Computing (HPC) in commercial and consumer IT applications is becoming popular. HPC users need the ability to gain rapid and scalable access to high-end computing capabilities. Cloud computing promises to deliver such a computing infrastructure using data centers so that HPC users can access applications and data from a Cloud anywhere in the world on demand and pay based on what they use. However, the growing demand drastically increases the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high energy cost which will reduce the profit margin of Cloud providers, but also high carbon emissions which are not environmentally sustainable. Hence, there is an urgent need for energy-efficient solutions that can address the high increase in the energy consumption from the perspective of not only the Cloud provider, but also from the environment. To address this issue, we propose near-optimal scheduling policies that exploit heterogeneity across multiple data centers for a Cloud provider. We consider a number of energy efficiency factors (such as energy cost, carbon emission rate, workload, and CPU power efficiency) which change across different data centers depending on their location, architectural design, and management system. Our carbon/energy based scheduling policies are able to achieve on average up to 25% of energy savings in comparison to profit based scheduling policies leading to higher profit and less carbon emissions.     

云数据中心资源调度机制研究

云数据中心包含大量计算机,运作成本很高。有效整合资源、提高资源利用率、节约能源、降低运行成本是云数据中心关注的热点。云数据中心通过虚拟化技术将计算资源、存储资源和网络资源构建成动态的虚拟资源池;使用虚拟资源管理技术实现云计算资源自动部署、动态扩展、按需分配;用户采用按需和即付即用的方式获取资源。因此,数据中心对提高资源利用率的迫切需求,促使人们寻求新的方式以建设下一代数据中心。     

A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud

In big data applications, data privacy is one of the most concerned issues because processing large-scale privacy-sensitive data sets often requires computation resources provisioned by public cloud services. Sub-tree data anonymization is a widely adopted scheme to anonymize data sets for privacy preservation. Top–Down Specialization (TDS) and Bottom–Up Generalization (BUG) are two ways to fulfill sub-tree anonymization. However, existing approaches for sub-tree anonymization fall short of parallelization capability, thereby lacking scalability in handling big data in cloud. Still, either TDS or BUG individually suffers from poor performance for certain valuing of k-anonymity parameter. In this paper, we propose a hybrid approach that combines TDS and BUG together for efficient sub-tree anonymization over big data. Further, we design MapReduce algorithms for the two components (TDS and BUG) to gain high scalability. Experiment evaluation demonstrates that the hybrid approach significantly improves the scalability and efficiency of sub-tree anonymization scheme over existing approaches.