RePro-Active: a reactive–proactive scheduling method based on simulation in cloud computing

Cloud computing is a scalable computing infrastructure in which the number of resources and requests change dynamically. There are usually a huge number of tasks and resources in cloud computing. A scheduler does allocating resources to tasks, which is an operation with a large number of parameters that is of NP-hard problems. Approaches such as metaheuristic, simulation-based optimization (SBO), predictive algorithms, etc. are applied to mitigate the complexity of scheduling. Reactive scheduling methods are able to adapt their behavior based on a feedback loop from runtime environment, while proactive scheduling methods try to predict future events to adapt their behavior as well. These algorithms suffer from two problems: (1) they require additional information, like the execution time of tasks that are not usually available in practice and (2) they use the history of past activities that is not easy to maintain and process in order to make future decisions. To address the problems, this paper presents a reactive/proactive scheduling framework, dubbed RePro-Active that presents an iterative reactive/proactive scheduling algorithm called RePro-Active.SB runs periodically. It includes the algorithm called ReactiveScheduling to support reactive behavior, and the algorithms called ProactiveSSLB/ProactiveSSELB to reinforce proactive actions in which the algorithm called Simulate predicts future by simulating possible prospective events. First, the presented scheduling algorithms have the least dependence on prior knowledge about tasks. They are extracted from the category of round-robin methods that are more realistic and do not need extra information about the tasks that is not available in practice. Second, they also begin from current conditions (rather than relying on the history of data) and use SBO techniques that try to simulate possible prospective events to make better decisions. In order to realize the idea, RePro-Active is used to improve both task scheduling and load balancing in the cloud-computing environment. In comparison to the base methods, the results indicate that the completion time of tasks decreased by 30%, and average resource utilization ratio increased by 20%; while, throughput increased by 19%.

[1]  Xiaohui Gu,et al.  CloudScale: elastic resource scaling for multi-tenant cloud systems , 2011, SoCC.

[2]  Anees Shaikh,et al.  A Cost-Aware Elasticity Provisioning System for the Cloud , 2011, 2011 31st International Conference on Distributed Computing Systems.

[3]  Thomas L. Casavant,et al.  A Taxonomy of Scheduling in General-Purpose Distributed Computing Systems , 1988, IEEE Trans. Software Eng..

[4]  Liang-Teh Lee,et al.  An Adaptive Task Scheduling System for Grid Computing , 2006, The Sixth IEEE International Conference on Computer and Information Technology (CIT'06).

[5]  Ciprian Dobre,et al.  Scheduling of Sporadic Tasks with Deadline Constrains in Cloud Environments , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[6]  Upendra Bhoi,et al.  Enhanced Load Balanced Min-min Algorithm for Static Meta Task Scheduling in Cloud Computing , 2015 .

[7]  Rajkumar Buyya,et al.  CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services , 2009, ArXiv.

[8]  Fatos Xhafa,et al.  Computational models and heuristic methods for Grid scheduling problems , 2010, Future Gener. Comput. Syst..

[9]  Daniel A. Menascé,et al.  A framework for resource allocation in grid computing , 2004, The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004. (MASCOTS 2004). Proceedings..

[10]  Michael A. Trick,et al.  Round robin scheduling - a survey , 2008, Eur. J. Oper. Res..

[11]  Aniruddha S. Gokhale,et al.  Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[12]  Albert Y. Zomaya,et al.  Evolutionary Scheduling of Dynamic Multitasking Workloads for Big-Data Analytics in Elastic Cloud , 2014, IEEE Transactions on Emerging Topics in Computing.

[13]  Christoph Meinel,et al.  Elastic VM for Cloud Resources Provisioning Optimization , 2011, ACC.

[14]  Chuang Lin,et al.  Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction , 2011, J. Netw. Comput. Appl..

[15]  Faramarz Safi Esfahani,et al.  An adaptive fuzzy threshold-based approach for energy and performance efficient consolidation of virtual machines , 2015, Computing.

[16]  H. Ali,et al.  Task Scheduling in Multiprocessing Systems , 1995, Computer.

[17]  Jan Broeckhove,et al.  Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds , 2013, Future Gener. Comput. Syst..

[18]  Rajkumar Buyya,et al.  A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[19]  N. Nagaveni,et al.  Design and Implementation of an Efficient Two-level Scheduler for Cloud Computing Environment , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

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

[21]  P. K. Suri,et al.  QPSMax-Min Min-Min: A QoS Based Predictive Max-Min, Min-Min Switcher Algorithm for Job Scheduling in a Grid , 2008 .

[22]  M A Nada,et al.  Ant Colony Optimization Algorithm , 2009 .

[23]  N. Metropolis,et al.  The Monte Carlo method. , 1949 .

[24]  Johann-Christoph Freytag,et al.  Adaptive workflow scheduling under resource allocation constraints and network dynamics , 2008, Proc. VLDB Endow..

[25]  Gregor von Laszewski,et al.  QoS guided Min-Min heuristic for grid task scheduling , 2003, Journal of Computer Science and Technology.

[26]  Faramarz Safi-Esfahani,et al.  An Adaptive and Fuzzy Resource Management Approach in Cloud Computing , 2019, IEEE Transactions on Cloud Computing.

[27]  Faramarz Safi Esfahani,et al.  Scientific Workflow Scheduling Based on Deadline Constraints in Cloud Environment , 2015 .

[28]  Isam Azawi Mohialdeen.  Comparative Study of Scheduling Al-gorithms in Cloud Computing Environment , 1980 .

[29]  Isam Azawi Mohialdeen.  Comparative Study of Scheduling al-Grotihms in Cloud Computing , 2013, J. Comput. Sci..

[30]  Debra A. Hensgen,et al.  The relative performance of various mapping algorithms is independent of sizable variances in run-time predictions , 1998, Proceedings Seventh Heterogeneous Computing Workshop (HCW'98).

[31]  T. Kokilavani,et al.  Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing , 2011 .

[32]  P. Muthulakshmi,et al.  An Overview of the Scheduling Policies and Algorithms in Grid Computing , 2011 .

[33]  Svetozar Miuÿ,et al.  DejaVu: Accelerating Resource Allocation in Virtualized Environments , 2012 .

[34]  Lin Zhang,et al.  A Dynamic Task Scheduling Method Based on Simulation in Cloud Manufacturing , 2016 .

[35]  Mehran Mohsenzadeh,et al.  ATSDS: adaptive two-stage deadline-constrained workflow scheduling considering run-time circumstances in cloud computing environments , 2017, The Journal of Supercomputing.

[36]  Sajal K. Das,et al.  A de-centralized scheduling and load balancing algorithm for heterogeneous grid environments , 2002, Proceedings. International Conference on Parallel Processing Workshop.

[37]  Andrew S. Tanenbaum,et al.  Distributed Systems , 2007 .

[38]  Rajkumar Buyya,et al.  Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments , 2011, 2011 International Conference on Parallel Processing.

[39]  Shing Chih Tsai,et al.  Genetic-algorithm-based simulation optimization considering a single stochastic constraint , 2014, Eur. J. Oper. Res..

[40]  Rajkumar Buyya,et al.  Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms , 2006, Sci. Program..

[41]  Faramarz Safi Esfahani,et al.  Knowledge-based adaptable scheduler for SaaS providers in cloud computing , 2015, Human-centric Computing and Information Sciences.

[42]  Zhenhuan Gong,et al.  PRESS: PRedictive Elastic ReSource Scaling for cloud systems , 2010, 2010 International Conference on Network and Service Management.

[43]  Paul Marshall,et al.  Elastic Site: Using Clouds to Elastically Extend Site Resources , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[44]  Marios D. Dikaiakos,et al.  Scheduling Workflows with Budget Constraints , 2007, Grid 2007.

[45]  Wei Tan,et al.  Self-Adaptive Learning PSO-Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud , 2014, IEEE Transactions on Automation Science and Engineering.

[46]  Shin-ichi Kuribayashi.  Optimal Joint Multiple Resource Allocation Method for Cloud Computing Environments , 2011, ArXiv.

[47]  O. M. Elzeki,et al.  Improved Max-Min Algorithm in Cloud Computing , 2012 .

[48]  Xuejie Zhang,et al.  An Approach to Optimized Resource Scheduling Algorithm for Open-Source Cloud Systems , 2010, 2010 Fifth Annual ChinaGrid Conference.

[49]  Jeffrey S. Chase,et al.  Automated control in cloud computing: challenges and opportunities , 2009, ACDC '09.

[50]  Sameer Singh Chauhan,et al.  QoS Guided Heuristic Algorithms for Grid Task Scheduling , 2010 .

[51]  Miren Karamta,et al.  Comparison of Virtual Machine Scheduling Algorithms in Cloud Computing , 2013 .

[52]  T. Kokilavani,et al.  Load Balanced MinMin Algorithm for Static MetaTask Scheduling in Grid Computing , 2011 .

[53]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[54]  R. F. Freund,et al.  Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet , 1998, Proceedings Seventh Heterogeneous Computing Workshop (HCW'98).

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

[56]  Huankai Chen,et al.  User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing , 2013, 2013 National Conference on Parallel Computing Technologies (PARCOMPTECH).

[57]  Rizos Sakellariou,et al.  A hybrid heuristic for DAG scheduling on heterogeneous systems , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[58]  Nguyen Hong Son,et al.  Load balancing algorithm based on estimating finish time of services in cloud computing , 2016, 2016 18th International Conference on Advanced Communication Technology (ICACT).

[59]  N CalheirosRodrigo,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011 .

[60]  Ajit Singh,et al.  An Optimized Round Robin Scheduling Algorithm for CPU Scheduling , 2010 .

[61]  Jagbeer Singh,et al.  An Algorithm to Reduce the Time Complexity of Earliest Deadline First Scheduling Algorithm in Real-Time System , 2010, ArXiv.

[62]  S. Gupta,et al.  Thermal-aware task scheduling for data centers through minimizing heat recirculation , 2007, 2007 IEEE International Conference on Cluster Computing.