Resource preprocessing and optimal task scheduling in cloud computing environments

Cloud computing came into being and is currently an essential infrastructure of many commerce facilities. To achieve the promising potentials of cloud computing, effective and efficient scheduling algorithms are fundamentally important. However, conventional scheduling methodology encounters a number of challenges. During the tasks scheduling in cloud systems, how to make full use of resources and how to effectively select resources are also important factors. At the same time, communication delay also plays an important role in cloud scheduling, which not only leads to waiting between tasks but also results in much idle interval time between processing units. In this paper, a fuzzy clustering method is used to effectively preprocess the cloud resources. Combining the list scheduling with the task duplication scheduling scheme, a new directed acyclic graph based scheduling algorithm called earliest finish time duplication algorithm for heterogeneous cloud systems is presented. Earliest finish time duplication attempts to insert suitable immediate parent nodes of the current selected node in order to reduce its waiting time on the processor. The case study and experimental results illustrate that the algorithm proposed in this paper is better than the popular heterogeneous earliest finish time algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

[1]  Rajkumar Buyya,et al.  Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers , 2012, Concurr. Comput. Pract. Exp..

[2]  Manjot Bhatia.  RR based grid scheduling algorithm , 2011, ACAI '11.

[3]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[4]  Ewa Deelman,et al.  Scientific workflows and clouds , 2010, ACM Crossroads.

[5]  Schahram Dustdar,et al.  Weighted fuzzy clustering for capability-driven service aggregation , 2010, IEEE International Conference on Service-Oriented Computing and Applications.

[6]  Nelson Luis Saldanha da Fonseca,et al.  Scheduling in hybrid clouds , 2012, IEEE Communications Magazine.

[7]  Xiaojun Chen,et al.  Resource management framework for collaborative computing systems over multiple virtual machines , 2011, Service Oriented Computing and Applications.

[8]  Amandeep Verma,et al.  An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment , 2012 .

[9]  Hai Jin,et al.  DAGMap: efficient and dependable scheduling of DAG workflow job in Grid , 2010, The Journal of Supercomputing.

[10]  Kwangsik Shin,et al.  Task scheduling algorithm using minimized duplications in homogeneous systems , 2008, J. Parallel Distributed Comput..

[11]  Tao Qin,et al.  DAG Cluster Scheduling Algorithm for Grid Computing , 2011, 2011 14th IEEE International Conference on Computational Science and Engineering.

[12]  Michael Devetsikiotis,et al.  Average delay SLAs in Cloud computing , 2012, 2012 IEEE International Conference on Communications (ICC).

[13]  Fred Douglis.  Information Overload, 140 Characters at a Time , 2009, IEEE Internet Comput..

[14]  Deger Cenk Erdil,et al.  Dynamic grid load sharing with adaptive dissemination protocols , 2010, The Journal of Supercomputing.

[15]  Bharadwaj Veeravalli,et al.  Dynamic Load Balancing and Pricing in Grid Computing with Communication Delay , 2008, Journal of Grid Computing.

[16]  Y.-K. Kwok,et al.  Static scheduling algorithms for allocating directed task graphs to multiprocessors , 1999, CSUR.

[17]  Keqiu Li,et al.  Multimedia Object Placement for Transparent Data Replication , 2007, IEEE Transactions on Parallel and Distributed Systems.

[18]  Jong Hyuk Park,et al.  An Efficient Cloud Storage Model for Cloud Computing Environment , 2012, GPC.

[19]  María Blanca Caminero,et al.  A Strategy to Improve Resource Utilization in Grids Based on Network-Aware Meta-scheduling in Advance , 2011, 2011 IEEE/ACM 12th International Conference on Grid Computing.

[20]  Keqiu Li,et al.  Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks , 2011, Comput. Commun..

[21]  Gudula Rünger,et al.  SEParAT: scheduling support environment for parallel application task graphs , 2012, Cluster Computing.

[22]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[23]  Michael Devetsikiotis,et al.  Aggregated-DAG Scheduling for Job Flow Maximization in Heterogeneous Cloud Computing , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[24]  Shalini Ramanathan,et al.  Linear Scheduling Strategy for Resource Allocation in Cloud Environment , 2012, CloudCom 2012.

[25]  Borja Sotomayor,et al.  Virtual Infrastructure Management in Private and Hybrid Clouds , 2009, IEEE Internet Computing.

[26]  R. Srikant,et al.  Stochastic models of load balancing and scheduling in cloud computing clusters , 2012, 2012 Proceedings IEEE INFOCOM.

[27]  Rizos Sakellariou,et al.  DAG Scheduling Using a Lookahead Variant of the Heterogeneous Earliest Finish Time Algorithm , 2010, 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing.

[28]  Ruay-Shiung Chang,et al.  Job scheduling and data replication on data grids , 2007, Future Gener. Comput. Syst..

[29]  Keqiu Li,et al.  Modeling and Analysis of Communication Networks in Multicluster Systems under Spatio-Temporal Bursty Traffic , 2012, IEEE Transactions on Parallel and Distributed Systems.

[30]  Keqiu Li,et al.  Optimal methods for coordinated enroute web caching for tree networks , 2005, TOIT.

[31]  Shiyong Lu,et al.  Scheduling Scientific Workflows Elastically for Cloud Computing , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[32]  Keqin Li,et al.  Exchanged Crossed Cube: A Novel Interconnection Network for Parallel Computation , 2013, IEEE Transactions on Parallel and Distributed Systems.

[33]  Helen D. Karatza,et al.  Evaluation of gang scheduling performance and cost in a cloud computing system , 2010, The Journal of Supercomputing.

[34]  Rajkumar Buyya,et al.  Adapting Market-Oriented Scheduling Policies for Cloud Computing , 2010, ICA3PP.