Cloud Resource Scheduling Research Articles

Bee and Frog Co-Evolution Algorithm and its application

In order to obtain better generalization abilities and mitigate the impacts of the best and worst individuals during the process of optimization, this paper suggests Bee and Frog Co-Evolution Algorithm (abbreviation for BFCEA), which combines Mnemonic Shuffled Frog Leaping algorithm With Cooperation and Mutation (abbreviation for MSFLACM) with improved Artificial Bee Colony (abbreviation for ABC). The contrast experimental study about different iteratively updating strategies was acted in BFCEA, including strategy of integrating with ABC, regeneration of the worst frog and its leaping step. The key techniques focus on the first 10 and the last 10 frogs evolving ABC in BFCEA, namely, the synchronous renewal strategy for those winner and loser should be applied, after certain G times’ MSFLACM-running, so as to avoid trapping local optimum in later stage. The ABC evolution process will be called between all memes’ completing inner iteration and all frogs’ outer shuffling, the crossover operation is removed from MSFLACM for its little effect on time-consuming and convergence in this novel algorithm. Besides, in ABC, the scout bee is generated by Cauchy mutating instead at random. The performance of proposed approach is examined by well-known 16 numerical benchmark functions, and obtained results are compared with basic Shuffled Frog Leaping algorithm (abbreviation for SFLA), ABC and four other variants. The experimental results and related application in cloud resource scheduling show that the proposed algorithm is effective and outperforms other variants, in terms of solution quality and convergence, and the improved variants can obtain a lower degree of unbalanced load and relatively stable scheduling strategy of resources in complicated cloud computing environment.

A Multi-Objective Hybrid Cloud Resource Scheduling Method Based on Deadline and Cost Constraints

We propose a task-oriented multi-objective scheduling method based on ant colony optimization (MOSACO) to optimize the finite pool of public and private computing resources in a hybrid cloud computing environment according to deadline and cost constraints. MOSACO is employed to minimize task completion times and costs using time-first and cost-first single-objective optimization strategies, respectively, and to maximize user quality of service and the profit of resource providers using an entropy optimization model. The effectiveness of the MOSACO algorithm based on multiple considerations of task completion time, cost, number of deadline violations, and degree of private resource utilization is verified using simulation and three application examples. Comparisons with similar scheduling methods demonstrate that MOSACO provides the highest optimality, and that the time-first and cost-first strategies provide definite advantages for minimizing completion time and cost, respectively.

Resource scheduling in a private cloud environment: an efficiency priority perspective

Purpose Resource scheduling is the study of how to effectively measure, evaluate, analyze, and dispatch resources in order to meet the demands of corresponding tasks. Aiming at the problem of resource scheduling in the private cloud environment, the purpose of this paper is to propose a resource scheduling approach from an efficiency priority point of view. Design/methodology/approach To measure the computational efficiencies for the resource nodes in a private cloud environment, the data envelopment analysis (DEA) approach is incorporated and a suitable DEA model is proposed. Then, based on the efficiency scores calculated by the proposed DEA model for the resource nodes, the 0-1 programming technique is introduced to build a simple resource scheduling model. Findings The proposed DEA model not only has the ability of ranking all the decision-making units into different positions but also can handle non-discretionary inputs and undesirable outputs when evaluating the resource nodes. Furthermore, the resource scheduling model can generate for the calculation tasks an optimal resource scheduling scheme that has the highest total computational efficiency. Research limitations/implications The proposed method may also be used in studies of resource scheduling studies in the environments of public clouds and hybrid clouds. Practical implications The proposed approach can achieve the goal of resource scheduling in private cloud computing platforms by attaining the highest total computational efficiency, which is very significant in practice. Originality/value This paper uses an efficiency priority point of view to solve the problem of resource scheduling in private cloud environments.

SOCCER: Self-Optimization of Energy-efficient Cloud Resources

Cloud data centers often schedule heterogeneous workloads without considering energy consumption and carbon emission aspects. Tremendous amount of energy consumption leads to high operational costs and reduces return on investment and contributes towards carbon footprints to the environment. Therefore, there is need of energy-aware cloud based system which schedules computing resources automatically by considering energy consumption as an important parameter. In this paper, energy efficient autonomic cloud system [Self-Optimization of Cloud Computing Energy-efficient Resources (SOCCER)] is proposed for energy efficient scheduling of cloud resources in data centers. The proposed work considers energy as a Quality of Service (QoS) parameter and automatically optimizes the efficiency of cloud resources by reducing energy consumption. The performance of the proposed system has been evaluated in real cloud environment and the experimental results show that the proposed system performs better in terms of energy consumption of cloud resources and utilizes these resources optimally.

Ant colony Algorithm based on Three Constraint Conditions for Cloud Resource Scheduling

An improved ant colony algorithm based on the three constraint conditions that aiming at the problem of resource scheduling in cloud computing is proposed in this paper. this method is divided into three steps: Firstly, we describe the state transition probability by using the information heuristic factor and the expected heuristic factor. Secondly, the pheromone update strategy is used to design the scheduling process. Finally, the optimal path is based on the expected execution time, network delay and network bandwidth of three constraints. Experimental results show that the proposed method has faster execution speed than the traditional ant colony algorithm, and the load balancing of the results is more satisfactory.

Efficient service selection approach for mobile devices in mobile cloud

The paper proposes optimal efficient cloud service selection scheme for mobile device through local and public cloud in hybrid cloud. The proposed model consists of non-dedicated public cloud resources and local cloud resources. At different locations, local cloud service providers maintain finite dedicated resources that can be used by mobile device users. In the proposed model, the system middleware first examines if any of the local cloud servers have sufficient idle capacity to satisfy the desired requirement of mobile application. If the mobile users' requirement cannot be handled locally, the proposed architecture must choose mobile users' jobs to burst to the remote public cloud. The decision of when to trigger a cloud burst involves monitoring the system context and workload. A cloud-based service selection optimization is proposed, which can optimize the resource utilization, cloud supplier's benefit and mobile user's QoS. The proposed optimization problem is partitioned by local cloud service selection and public cloud resource scheduling, which is implemented by the mobile device, mobile cloud service broker and cloud supplier. A cloud service selection algorithm for mobile device is proposed, which is composed of two sub-algorithms: cloud service selection optimization and cloud resource scheduling optimization. The efficiency of the cloud service selection algorithm for mobile device is tested by experiments.

EARTH: Energy-aware autonomic resource scheduling in cloud computing

In Cloud computing, data centers gain popularity as an effective platform for scheduling of resources and hosting cloud applications. However, tremendous amount of energy is consumed by these data centers which leads to high operational costs and contributes towards carbon footprints to the environment. Therefore, there is need of energy aware cloud based framework which schedules computing resources automatically by considering energy consumption as a QoS parameter itself. In this paper, we present fuzzy logic based energy-aware autonomic resource scheduling framework for cloud for energy efficient scheduling of cloud computing resources in data centers. We have evaluated the proposed framework in CloudSim based simulation environment and real cloud environment. The experimental results show that the proposed framework performs better in terms of resource utilization and energy consumption along with other QoS parameters.

A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.

Exploring Non-Homogeneity and Dynamicity of High Scale Cloud through Hive and Pig

Cloud environment is usually associated with non-homogeneity and dynamicity in terms of resource usage and access at all levels. The study of this heterogeneous and non-uniform behavior is therefore an important problem. Google cluster trace which is a production trace released by Google in November 2014 serves as an example of a high scale Cloud environment. This paper deals with statistical analysis of this cluster trace. Since the size of production trace is very huge therefore, Hive which is a HadoopDistributed File System (HDFS) based platform for querying and analysis of big data, has been used. Hive was accessed through its Beeswax interface. The data was imported into HDFS through HCatalog. Apart from Hive, Pig which is a scripting language and provides abstraction on top of Hadoop was used. The method adopted deals with clustering and studying the distribution of arrival time of jobs, distribution of resource usage and also study of distribution of process runtime. To the best of our knowledge the analytical method adopted by us is novel. The findings revealed that jobs in a production trace can be classified into major, mediocre and minor resource usage types. Furthermore, it can be concluded from our study that arrival time of jobs followed weibull distribution. Usage of resources such as CPU and memory was observed to be following a zipf like distribution while study of process runtime shows that some jobs had very small values of runtime while others had very large values hence they followed heavy tailed distribution. Our analysis will help researchers in properly understanding the nonhomogenous and dynamic behavior synonymous with cloud environment. It will also help them in developing new algorithms for resource allocation and scheduling in Cloud.

An optimal resources scheduling strategy on multimedia cloud computing under multi- device constraint

In view of the problem of inaccurate scheduling by using traditional resource scheduling method, because the method is mainly based on extracting and classifying the resource features to make scheduling, ignoring the effect of the feature relevance between the resources on the scheduling results. This paper presents a model for multimedia cloud resource scheduling based on multi- device constraint. In this method the objective function is no longer constrained only by the CPU computing capacity and the minimized completion time, but to achieve a minimum time-consuming of CPU, memory and other peripherals operation are considered as the scheduling objectives. Then the utilization of solving constrained jointly is employed to obtain the mapping relationship of the optimal virtual and physical machine. Moreover, a regressive dimensionality reduction algorithm is designed for this scheduling model to solve the high dimensional problems aroused by multi-device constraints. Simulation results show that the improved algorithm has a better performance than the traditional algorithm, has a good efficiency and has a certain convergence.

Cloud-Integrated Cyber-Physical Systems for Complex Industrial Applications

Recently, with many advances in wireless sensor networks, big data, mobile and cloud computing, Cyber-Physical Systems (CPS) can tightly couple cyber space with the physical world better than ever before. Also, the cloud-based systems can provide massive storage resources and low-cost computing as well as the flexibility of customizing the operating environment to Complex Industrial Applications (CIA). In our view, Cloud-integrated CPS (CCPS) will open the door to allow previously unachievable application scenarios to be built, deployed, managed and controlled effectively. In this paper, we propose a novel architecture of CCPS (termed CCPSA) and outline the enabling technologies for CIA. Then, we dissect three potential challenges and provide solutions from the perspective of CIA, including virtualized resource management techniques, the scheduling of cloud resources, and life cycle management. We hope this paper can provide insight and a roadmap for future research efforts in the emerging field of CCPS.

User-oriented cloud resource scheduling with feedback integration

Resource scheduling has been one of the key challenges facing both academia and industry ever since the inauguration of cloud computing. Most of the existing research and practices have been focused on the maximization of the profits of cloud providers, whereas attention to the real needs of cloud users has largely been neglected. In this research, we propose a resource scheduling mechanism empowered with a relevance feedback network, which can be employed by a cloud provider to better meet a user's resource needs. Our approach is a continuous refinement process that involves three stages: resource matching, resource selection, and feedback integration, where the feedback integration stage allows the resource scheduling history of a user to be considered to update the user's resource demands and preference. The feedback information integrated in one cycle will effectively adjust the resource matching and selection in the next cycle. Incrementally, this mechanism will produce resource selections that are closer and closer to the user's real needs. Simulation results indicated that this relevance feedback scheduling mechanism is very effective in satisfying users' diverse requirements, and it also performs well in terms of the resource utilization rate from the cloud provider's perspective.

Research on Cloud Computing Resource Scheduling Based on PSO-MC Algorithm

The reasonable resource scheduling under the cloud computing environment has become the current research focus. Based on the disadvantages of the traditional particle swarm optimization, this paper introduces a membrane computing theory, and proposes a particle swarm optimization based on the membrane computing (PSO-MC). The paper first analyzes the problems of cloud resource scheduling, takes the membrane system as a framework, conducts the particle’s local optimization for intramembrane and the particle swarm’s global search and optimization for intermembrane, finds the optimal solution of cloud resource scheduling, and finally performs the algorithm simulation experiment via the CloudSim platform. According to the simulation experiment, the PSO-MC algorithm could reduce the average time to complete the task, improve the efficiency of task processing, and enable the cloud computing resource scheduling to be more reasonable.

Cloud Resource Scheduling Using Semantic Search Engine Based on Improved Parallel Genetic Algorithm

Cloud Resource Scheduling Using Semantic Search Engine Based on Improved Parallel Genetic Algorithm

Research on the Resource Scheduling of the Improved SFLA in Cloud Computing

It has always been a key topic in the current research how to make rational resource scheduling in the cloud computing environment. In this paper, the status of cloud computing resources is first analyzed, to point out the existing problems, and then combined with the characteristics of resource scheduling in cloud computing, the Shuffled Frog Leaping Algorithm is introduced. First, in its stage of subgroups classification, the chaos strategy is introduced and in the internal search the positive learning strategy is introduced, which makes the improved frog leaping algorithm gain good convergence, reduces the time of global search and optimization. Through the CloudSim platform, it shows that this algorithm can improve the efficiency of task processing and make the resource scheduling in cloud computing rational and effective.

QRSF: QoS-aware resource scheduling framework in cloud computing

Cloud computing harmonizes and delivers the ability of resource sharing over different geographical sites. Cloud resource scheduling is a tedious task due to the problem of finding the best match of resource-workload pair. The efficient management of dynamic nature of resource can be done with the help of cloud workloads. Till cloud workload is deliberated as a central capability, the resources cannot be utilized in an effective way. In literature, very few efficient resource scheduling policies for energy, cost and time constraint cloud workloads are reported. This paper presents an efficient cloud workload management framework in which cloud workloads have been identified, analyzed and clustered through K-means on the basis of weights assigned and their QoS requirements. Further scheduling has been done based on different scheduling policies and their corresponding algorithms. The performance of the proposed algorithms has been evaluated with existing scheduling policies through CloudSim toolkit. The experimental results show that the proposed framework gives better results in terms of energy consumption, execution cost and time of different cloud workloads as compared to existing algorithms.

Research on Resource Scheduling in Cloud Computing: Issues and Solutions

.Resource scheduling is the core technology to provide efficient and reliable services in cloud computing, and it is the basis of cloud computing to implement quick deploy and rapid response and save money. This article firstly introduces the research status of the resource scheduling in cloud computing including resource scheduling policies, replica technology and metadata management. Next we analyze the issues of Hadoop platform in resource scheduling including high latency, small files I/O, single point of failure and hot data. On the basis of these, the effective resource scheduling and management mechanisms are given including dynamic replica management, metadata management and horizontal scalability.

Research on Resource Scheduling Algorithm in Cloud Computing Data Center

In recent years, with the rapid development of Internet and virtualization technology, cloud computing, which providing users with on-demand services, has become a research hotspot. Under the environment of cloud computing, the datacenter, consisted by hardware and software, is a loosely coupled resource sharing architecture. The existing cloud computing's inadequacies are as following three aspects: 1. For lacking of real adequate and effective transaction of global bidirectional-way selection, the revenue of most of cloud resource provider is too low. 2. Since not fully considering the scheduling of multi-dimensional cloud resources, existing cloud computing's utilization for multi-dimensional cloud resource is too low. 3. Because existing cloud datacenter does not fully consider the energy consumption of communication between the cloud tasks, its energy consumption is too high. Resource scheduling is a major research direction of cloud computing. First, we make a in-depth investigation and analysis of the research status of cloud computing resource scheduling, and then focus on resource scheduling method to reduce the energy consumption of cloud computing data center. Finally we set an important future research direction of cloud computing resource management research in order to provide a useful reference for cloud computing research.

Resource Scheduling in Cloud using Bacterial Foraging Optimization Algorithm

Cloud computing is a form of parallel and distributed computing that consists of interconnected and virtualized computers. Resource scheduling in cloud computing is considered to be a complex task since multiple copies of the same tasks are assigned to different computers. Bacterial foraging optimization algorithm is a global optimization algorithm for the distributed optimization and control. In this paper a bacterial foraging optimization algorithm is used for the scheduling of the resources in the cloud environment. The performance of the algorithm is measured by the use of Cloud Sim. The experimental result shows that the proposed algorithms can reduce the cost, make span and also improve the reliability. General Terms Cloud Computing, Scheduling Algorithm

Prediction based proactive thermal virtual machine scheduling in green clouds.

Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated.