Adaptive Resource Management and Scheduling for Cloud Computing

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, ARMS-CC 2015, held in Conjunction with ACM Symposium on Principles of Distributed Computing, PODC 2015, in Donostia-San Sebastián, Spain, in July 2015.

The 12 revised full papers, including 1 invited paper, were carefully reviewed and selected from 24 submissions. The papers have identified several important aspects of the problem addressed by ARMS-CC: self-* and autonomous cloud systems, cloud quality management and service level agreement (SLA), scalable computing, mobile cloud computing, cloud computing techniques for big data, high performance cloud computing, resource management in big data platforms, scheduling algorithms for big data processing, cloud composition, federation, bridging, and bursting, cloud resource virtualization and composition, load-balancing and co-allocation, fault tolerance, reliability, and availability of cloud systems.

Inhalt

Competitive Analysis of Task Scheduling Algorithms on a Fault-Prone Machine and the Impact of Resource Augmentation.- Using Performance Forecasting to Accelerate Elasticity.- Parametric Analysis of Mobile Cloud Computing Frameworks using Simulation Modeling.- Bandwidth Aware Resource Optimization for SMT Processors.- User-guided provisioning in federated clouds for distributed calculations.- Compute on the go: A case of mobile-cloud collaborative computing under mobility.- Impact of Virtual Machines Heterogeneity on Datacenter Power Consumption in Data-Intensive Applications.- Implementing the Cloud Software to Data approach for OpenStack environments.- Is Cloud Self-organization Feasible.- Cloud Services composition through Cloud Patterns.- An Eye on the Elephant in the Wild: A Performance Evaluation of Hadoop's Schedulers Under Failures.- Partitioning graph databases by using access patterns.- Cloud Search Based Applications for Big Data - Challenges and Methodologies for Acceleration.