An Advance Algorithm for Task Management On Activity Based Costing in Cloud Computing By : Ashutosh Ingole Sumit Chavan Rajesh Singh 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Contents Introduction Cloud Computing Architecture Problem Statement Problems Overall Scenario Flow of Algorithm Cost calculation and Scheduling Feedback Mechanism Advantages Conclusion and References 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Introduction What is Cloud Computing? • Shared computing resources • As opposed to local servers and devices • Made up of Grid Infrastructure • Scalable • Virtualization • Web applications • Specialized raw computing services Technical Key Points • User interaction interface:How users of cloud interface with the cloud • Services catalog:Services a user can request • System management: Manages the resources available • Provisioning tool: Carves out the systems from the cloud to deliver on the requested service • Monitoring and metering:Tracks the usage of the cloud (optional) • Servers:Virtual or physical servers managed by System management 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Cloud Computing Architecture 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Problem Statement • To reduce the total time required for task scheduling in Cloud Computing using Activity Based Costing Algorithm 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Keywords • Cloud Computing • Task Management • Activity Based Costing • Advance Algorithm 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Overall Scenario Dynamic Priority Allocator Cost Calculation Decide Priority According To Cost Virtual Machines $4 Queue 1 $1 Queue 2 $2 Queue 3 Scheduling Server 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Flow of Algorithm 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Dependencies and DAG • Get the Activity dependencies form Task divider module. • Calculate the DAG from the given information. • According to the DAG fill up the Task queue. • An Activity only starts after all its immediate predecessors finish. • Activities with no immediate predecessor are entry-activities, and activities without immediate successors are exit-activities. T1 T2 T1 T3 T2 T4 T3 T4 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Cost calculation of Task Parameters considered • Resource cost • Arrival Time • Predicted Execution Time • Dependency (DAG) 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Continued… (Formulae) 1)Resource Cost Evaluation 2)Estimated Execution Time Where, Tij: estimated execution time for ith activity of jth task Tj: estimated execution time for complete jth task Kj: total number of activities in jth task 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Continued… (Formulae) ∑ (∑ Resource cost for a single activity) * execution time Total cost of Activity = ∑ (Activity Cost) Total cost of Task = 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Concept of Waiting Queue • In case of dependent activities, if a certain activity say A11 needs server1 and its dependent activity say A12 needs to be on server2, then without complete execution of A11, we cant forward A12 from ready queue to server2. • In such a case, we also cant keep a2 in ready queue for a long time and hence we use waiting queue to store these kind of dependent activities Waiting Queue A62 A22 A34 A52 …. A12 A31 A33 A51 Ready Queue A32 A61 …. 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Feedback and Update • When we get feedback from server that a particular activity is completed then we can schedule its dependent activity from waiting queue to its destination server. • After that each resource table and universal resource table gets updated. Waiting Queue A62 A22 A34 A52 …. Sinhgad Institute of Technology ,LONAVALA 19th Apr 2012
Failure Cases • Simulation parameters • Average bounded slowdown and average per-processor slowdown • Average waiting time & average weighted waiting time • Network Error • Computational Error • Power Failure • Queue Overflow 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Conclusion • An ABC-system of low complexity, such as a system with a small number of cost drivers, is not only easier to handle but also easier to understand. • It seems to be an interesting next step to analyze how ABC decision rules perform when several approximations apply. • Thus ABC reduces the scheduling cost and reduces delays. 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
References Sandeep Tayal, University School of Information Technology, Guru Gobind Singh, Indraprastha University, Delhi- 10006, India “Tasks Scheduling optimization for the Cloud Computing System2011, IJAEST,2011 Archana Ganapathi, Yanpei Chen, Armando Fox, Randy Katz, David Patterson Computer Science Division, University of California at Berkeley,” Statistics-Driven Workload Modeling for the Cloud”, IEEE 2011 QI CAO, ZHI-BO WEI, WEN-MAO GONG International School of Software Wuhan University Wuhan, China, “An Optimized Algorithm for Task Scheduling Based On Activity Based Costing in Cloud Computing”, IEEE 2009 Jinhua Hu, Jianhua Gu, Guofei Sun, Tianhai Zhao, NPU HPC Center Xi‟an, China “A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment”, IEEE 2010 Jiahui Jin, Junzhou Luo, Aibo Song, Fang Dong and Runqun Xiong School of Computer Science and Engineering, Southeast University Nanjing, P.R. China “BAR: An Efficient Data Locality Driven Task Scheduling Algorithm for Cloud Computing” , IEEE 2011 GAN Guo-ning, HUANG Ting-Iei, GAO Shuai School of Computer science and engineering Guilin University of Electronic Technology Guilin, China “Genetic Simulated Annealing Algorithm for Task Scheduling based on Cloud Computing Environment”, IEEE 2010 [1] [2] [3] [4] [5] [6] 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Any Questions…??? 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
Thank You…!!! 19th Apr 2012 Sinhgad Institute of Technology ,LONAVALA
