Technical report, GRIDS-TR-2009-1, Grid Computing and Distributed Systems Laboratory, The University of Melbourne (2009)īuyya, R., Ranjan, R., Rodrigo, N.: Calheiros, Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: challenges and opportunities. 14, 13–15 (2002)Ĭalheiros, R.N, Ranjan, R., De Rose, C.A.F, Buyya, R.: CloudSim: a novel framework for modeling and simulation of cloud computing infrastructures and services. 7(3), 547–553 (2012)īuyya, R., Murshed, M.: GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Guo, L., Zhao, S., Shen, S., Jiang, C.: Task scheduling optimization in cloud computing based on heuristic algorithm. In: The 3rd International Conference on Bioinformatics and Biomedical Engineering, pp. Theory 67, 171–190 (2016)Ĭao, Q., Wei, Z., Gong, W.: An optimized algorithm for task scheduling based on activity based costing in cloud computing. 85, 1–17 (2017)Ĭhongdarakul, W., Sophatsathit, P., Lursinsap, C.: Efficient task scheduling based on theoretical scheduling pattern constrained on single I/O port collision avoidance. Khorandi, S.M., Sharifi, M.: Scheduling of online compute-intensive synchronized jobs on high performance virtual clusters. Hao, Y., Wang, L., Zheng, M.: An adaptive algorithm for scheduling parallel jobs in meteorological Cloud. 341–346 (2011)Īlhakami, H., Aldabbas, H., Alwada, T.: Comparison between cloud and grid computing: review paper. In: Tenth International Conference on Networks, pp. Theory 76, 67–81 (2017)ĭouglas, O., Balen, C.B.W., Westphall, C.M: Experimental assessment of routing for grid and cloud. Jakóbik, A., Grzonk, D., Palmieri, F.: Non-deterministic security driven meta scheduler for distributed cloud organizations. CJS method can reduce the response time of submitted jobs, which may consist of data-intensive and computing -intensive jobs. Finally, we conducted simulations using CloudSim toolkit and compared CJS with other existing algorithms, like FUGE, Berger, MQS, and HPSO algorithms. The CJS algorithm uses data, processing power and network characteristics in job allocation process. To discuss the problem of simultaneously taking into account both job types, Cost-based job scheduling (CJS) algorithm is proposed in this paper. But, job scheduling based on one job type does not appropriate in the viewpoint of all environments, and sometimes may lead to wasting of resources on the other side. Most available scheduling techniques in cloud only concentrate on one job type that can be data-intensive or computation-intensive. With increasing number of users and jobs, optimal job scheduling becomes a strenuous process. The different characteristics of cloud such as on-demand capabilities, measured service, virtualization and rapid elasticity make the cloud more interesting in scientific organizations. Apollo is robust, with means to cope with unexpected system dynamics, and can take advantage of idle system resources gracefully while supplying guaranteed resources when needed.Cloud computing is one of the important approach for business actions in nowadays industry. Each scheduling decision considers future resource availability and optimizes various performance and system factors together in a single unified model. The framework performs scheduling decisions in a distributed manner, utilizing global cluster information via a loosely coordinated mechanism. This paper presents Apollo, a highly scalable and coordinated scheduling framework, which has been deployed on production clusters at Microsoft to schedule thousands of computations with millions of tasks efficiently and effectively on tens of thousands of machines daily. It is becoming even more challenging with growing cluster sizes and more complex workloads with diverse characteristics. Efficiently scheduling data-parallel computation jobs over cloud-scale computing clusters is critical for job performance, system throughput, and resource utilization.
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