A comparison of resource allocation process in grid and cloud technologies

Grid Computing and Cloud Computing are two different technologies that have emerged to validate the long-held dream of computing as utilities which led to an important revolution in IT industry. These technologies came with several challenges in terms of middleware, programming model, resources management and business models. These challenges are seriously considered by Distributed System research. Resources allocation is a key challenge in both technologies as it causes the possible resource wastage and service degradation. This paper is addressing a comprehensive study of the resources allocation processes in both technologies. It provides the researchers with an in-depth understanding of all resources allocation related aspects and associative challenges, including: load balancing, performance, energy consumption, scheduling algorithms, resources consolidation and migration. The comparison also contributes an informal definition of the Cloud resource allocation process. Resources in the Cloud are being shared by all users in a time and space sharing manner, in contrast to dedicated resources that governed by a queuing system in Grid resource management. Cloud Resource allocation suffers from extra challenges abbreviated by achieving good load balancing and making right consolidation decision.

[1]  Syed Saadat Bokhari,et al.  Scalable Grid Resource Discovery through Distributed Search , 2011, ArXiv.

[2]  Miron Livny,et al.  Condor-a hunter of idle workstations , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[3]  Hua Zou,et al.  A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast , 2011, 2011 IEEE International Conference on Cloud Computing and Intelligence Systems.

[4]  Rajkumar Buyya,et al.  GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing , 2002, Concurr. Comput. Pract. Exp..

[5]  Asgarali Bouyer,et al.  Maximizing job submission rate in market-oriented grids , 2013 .

[6]  Robert L. Henderson,et al.  Job Scheduling Under the Portable Batch System , 1995, JSSPP.

[7]  Nordin Zakaria,et al.  Grid Resource Allocation: A Review , 2012 .

[8]  Peter R. Pietzuch,et al.  Resource allocation across multiple cloud data centres , 2010, MGC '10.

[9]  Parisa Ghodous,et al.  Enhanced First-Fit Decreasing Algorithm for Energy-Aware Job Scheduling in Cloud , 2014, 2014 International Conference on Computational Science and Computational Intelligence.

[10]  Sheng Di,et al.  Characterization and Comparison of Cloud versus Grid Workloads , 2012, 2012 IEEE International Conference on Cluster Computing.

[11]  Calton Pu,et al.  A Cost-Sensitive Adaptation Engine for Server Consolidation of Multitier Applications , 2009, Middleware.

[12]  S. Santhanalakshmi,et al.  Allocation of Resources Dynamically In Cloud Systems , 2014 .

[13]  Valentin Cristea,et al.  Modelling Requirements for Enabling Meta-scheduling in Inter-Clouds and Inter-Enterprises , 2011, 2011 Third International Conference on Intelligent Networking and Collaborative Systems.

[14]  Marek Kisiel-Dorohinicki,et al.  Future Generation Computer Systems ( ) – Future Generation Computer Systems Security, Energy, and Performance-aware Resource Allocation Mechanisms for Computational Grids , 2022 .

[15]  Abdul Hanan Abdullah,et al.  A Taxonomy of Grid Resource Selection Mechanisms , 2011 .

[16]  Naidila Sadashiv,et al.  Cluster, grid and cloud computing: A detailed comparison , 2011, 2011 6th International Conference on Computer Science & Education (ICCSE).

[17]  Maunika M Ramani,et al.  Energy Aware Load Balancing In Cloud Computing Using Virtual Machines , 2015 .

[18]  Kobra Etminani,et al.  A Min-Min Max-Min Selective Algorithm for Grid Task Scheduling , 2007, 2007 3rd IEEE/IFIP International Conference in Central Asia on Internet.

[19]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[20]  Marc Frîncu,et al.  Scheduling highly available applications on cloud environments , 2014, Future Gener. Comput. Syst..

[21]  Sunilkumar S. Manvi,et al.  Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey , 2014, J. Netw. Comput. Appl..

[22]  Wei Wang,et al.  A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing , 2014, EURASIP Journal on Wireless Communications and Networking.

[23]  Schahram Dustdar,et al.  Grid vs Cloud — A Technology Comparison , 2011, it Inf. Technol..

[24]  Zahir Tari,et al.  A distributed aggregation and fast fractal clustering approach for SOAP traffic , 2014, J. Netw. Comput. Appl..

[25]  Sherali Zeadally,et al.  A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems , 2016, Computing.

[26]  M K Jha,et al.  Computing infrastructure for ATLAS data analysis in the Italian Grid cloud , 2011 .

[27]  Chen Jin,et al.  LIF: A Dynamic Scheduling Algorithm for Cloud Data Centers Considering Multi-dimensional Resources ⋆ , 2013 .

[28]  Chuliang Weng,et al.  Heuristic scheduling for bag-of-tasks applications in combination with QoS in the computational grid , 2005, Future Gener. Comput. Syst..

[29]  Chien-Hung Chen,et al.  Integrating QoS awareness with virtualization in cloud computing systems for delay-sensitive applications , 2014, Future Gener. Comput. Syst..

[30]  Apurva Shah,et al.  Adaptive Scheduling for Real-Time Distributed Systems , 2014 .

[31]  C.T.Sivakumar,et al.  Hydrogeological Studies At Jalakandapuram Sub – Basin Of Sarabanga Minor Basin, Salem District,Tamil Nadu. , 2014 .

[32]  A. Taleb-Bendiab,et al.  A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing , 2010, 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops.

[33]  Bharadwaj Veeravalli,et al.  Requirement-Aware Scheduling of Bag-of-Tasks Applications on Grids with Dynamic Resilience , 2013, IEEE Transactions on Computers.

[34]  Abdul Hanan Abdullah,et al.  A taxonomy of grid resource selection mechanism , 2011 .

[35]  K. Chandrasekaran,et al.  Load Balancing of Virtual Machine Resources in Cloud Using Genetic Algorithm , 2013 .

[36]  Azween B. Abdullah,et al.  A new grid resource discovery framework , 2011, Int. Arab J. Inf. Technol..

[37]  Xavier Lorca,et al.  Entropy: a consolidation manager for clusters , 2009, VEE '09.

[38]  Kuo-Qin Yan,et al.  Towards a Load Balancing in a three-level cloud computing network , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[39]  Zoltán Ádám Mann,et al.  A taxonomy for the virtual machine allocation problem∗ , 2015 .

[40]  Jameela Al-Jaroodi,et al.  DDFTP: Dual-Direction FTP , 2011, 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[41]  Uwe Schwiegelshohn,et al.  Online Bi-Objective Scheduling for IaaS Clouds Ensuring Quality of Service , 2016, Journal of Grid Computing.

[42]  Zoltán Ádám Mann,et al.  Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms , 2015, ACM Comput. Surv..

[43]  Shriram Raghunathan,et al.  Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds , 2016, J. Comput. Syst. Sci..

[44]  Rajkumar Buyya,et al.  Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic , 2014, Euro-Par.

[45]  Ehsan Amiri,et al.  Resource Allocation in Grid: A Review , 2014 .