Optimization in distributed information systems

Abstract In past decades, intensive attention has been drawn towards the optimization problem, such as task scheduling, resource allocation and network traffic management, in distributed information systems. However, the rapid increases in the scale of physical devices and types of applications in distributed information systems make the system more difficult to be operated and managed. Optimizing the system performance is an ongoing challenging and important issue. This special issue reports twelve high-quality contributions on solving the challenging optimization problems in distributed information systems.

[1]  Jakub Gasior,et al.  A Sandpile cellular automata-based scheduler and load balancer , 2017, J. Comput. Sci..

[2]  Deo Prakash Vidyarthi,et al.  An Energy Aware Cost Effective Scheduling Framework for Heterogeneous Cluster System , 2017, Future Gener. Comput. Syst..

[3]  Xiaoliang Xu,et al.  AQP++: a hybrid approximate query processing framework for generalized aggregation queries , 2018, J. Comput. Sci..

[4]  Albert Y. Zomaya,et al.  Cost efficient scheduling of MapReduce applications on public clouds , 2017, J. Comput. Sci..

[5]  Fang Dong,et al.  Fast multi-resource allocation with patterns in large scale cloud data center , 2018, J. Comput. Sci..

[6]  Syed Abdul Rahman Al-Haddad,et al.  An effective approach for managing power consumption in cloud computing infrastructure , 2017, J. Comput. Sci..

[7]  Helen D. Karatza,et al.  High performance system based on Cloud and beyond: Jungle Computing , 2017, J. Comput. Sci..

[8]  Albert Y. Zomaya,et al.  GA-ETI: An enhanced genetic algorithm for the scheduling of scientific workflows in cloud environments , 2016, J. Comput. Sci..

[9]  Radhya Sahal,et al.  Exploiting coarse-grained reused-based opportunities in Big Data multi-query optimization , 2018, J. Comput. Sci..

[10]  Wilfried N. Gansterer,et al.  Scalable and fault tolerant orthogonalization based on randomized distributed data aggregation , 2013, J. Comput. Sci..

[11]  Kenli Li,et al.  A DAG task scheduling scheme on heterogeneous cluster systems using discrete IWO algorithm , 2016, J. Comput. Sci..

[12]  Yijun Cheng,et al.  A hybrid game method for interference management with energy constraint in 5G ultra-dense HetNets , 2018, J. Comput. Sci..

[13]  Zhao Liu,et al.  Optimizing cost for geo-distributed storage systems in online social networks , 2017, J. Comput. Sci..

[14]  Min Chen,et al.  Cost-aware optimal data allocations for multiple dimensional heterogeneous memories using dynamic programming in big data , 2018, J. Comput. Sci..

[15]  Xiaomin Zhu,et al.  Scheduling for Workflows with Security-Sensitive Intermediate Data by Selective Tasks Duplication in Clouds , 2017, IEEE Transactions on Parallel and Distributed Systems.

[16]  Wei Zhou,et al.  Improving big data storage performance in hybrid environment , 2017, J. Comput. Sci..

[17]  Lin Wu,et al.  Synthesizing distributed pipelining systems with timing constraints via optimal functional unit assignment and communication selection , 2017, J. Comput. Sci..

[18]  Mehdi Kargahi,et al.  Reliability-driven scheduling of time/cost-constrained grid workflows , 2016, Future Gener. Comput. Syst..

[19]  Keqin Li,et al.  Maximizing reliability of energy constrained parallel applications on heterogeneous distributed systems , 2017, J. Comput. Sci..

[20]  Jinjun Chen,et al.  Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds , 2017, Future Gener. Comput. Syst..

[21]  Chuang Liu,et al.  Online resource matching for heterogeneous grid environments , 2005, CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005..