Exact and Approximate Algorithms for Scheduling Nonidentical Processors
Journal of the ACM (JACM)
Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
Journal of the ACM (JACM)
Dynamic mapping of a class of independent tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Scheduling independent tasks to reduce mean finishing time
Communications of the ACM
Scheduling Resources in Multi-User, Heterogeneous, Computing Environments with SmartNet
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
A Comparison among Grid Scheduling Algorithms for Independent Coarse-Grained Tasks
SAINT-W '04 Proceedings of the 2004 Symposium on Applications and the Internet-Workshops (SAINT 2004 Workshops)
Job scheduling and processor allocation for grid computing on metacomputers
Journal of Parallel and Distributed Computing - Special issue: Design and performance of networks for super-, cluster-, and grid-computing: Part II
Practical Scheduling of Bag-of-Tasks Applications on Grids with Dynamic Resilience
IEEE Transactions on Computers
Scheduling data-intensive bags of tasks in P2P grids with bittorrent-enabled data distribution
Proceedings of the second workshop on Use of P2P, GRID and agents for the development of content networks
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Falkon: a Fast and Light-weight tasK executiON framework
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Heuristic for resources allocation on utility computing infrastructures
Proceedings of the 6th international workshop on Middleware for grid computing
GridBot: execution of bags of tasks in multiple grids
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Task profiling model for load profile prediction
Future Generation Computer Systems
Investigating Business-Driven Cloudburst Schedulers for E-Science Bag-of-Tasks Applications
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Bag-of-Tasks Scheduling under Budget Constraints
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
A novel multi-agent reinforcement learning approach for job scheduling in Grid computing
Future Generation Computer Systems
Cost optimized provisioning of elastic resources for application workflows
Future Generation Computer Systems
Improving job scheduling algorithms in a grid environment
Future Generation Computer Systems
A Family of Heuristics for Agent-Based Cloud Bag-of-Tasks Scheduling
CYBERC '11 Proceedings of the 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery
Adapting market-oriented scheduling policies for cloud computing
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka
Future Generation Computer Systems
Empirical prediction models for adaptive resource provisioning in the cloud
Future Generation Computer Systems
Toward fine-grained online task characteristics estimation in scientific workflows
WORKS '13 Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science
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The scheduling and execution of bag-of-tasks applications (BoTs) in Clouds is performed on sets of virtualized Cloud resources that start being exhausted right after their allocation disregarding whether tasks are being executed. In addition, BoTs may be executed in potentially heterogeneous sets of Cloud resources, which may be either previously allocated for a different and fixed number of hours or dynamically reallocated as needed. In this paper, a family of 14 scheduling heuristics for concurrently executing BoTs in Cloud environments is proposed. The Cloud scheduling heuristics are adapted to the resource allocation settings (e.g., 1-hour time slots) of Clouds by focusing on maximizing Cloud resource utilization based on the remaining allocation times of Cloud resources. Cloud scheduling heuristics supported by information about BoT tasks (e.g., task size) and/or Cloud resource performances are proposed. Additionally, scheduling heuristics that require no information of either Cloud resources or tasks are also proposed. The Cloud scheduling heuristics support the dynamic inclusion of new Cloud resources while scheduling and executing a given BoT without rescheduling. Furthermore, an elastic Cloud resource allocation mechanism that autonomously and dynamically reallocates Cloud resources on demand to BoT executions is proposed. Moreover, an agent-based Cloud BoT scheduling approach that supports concurrent and parallel scheduling and execution of BoTs, and concurrent and parallel dynamic selection and composition of Cloud resources (by making use of the well-known contract net protocol) from multiple and distributed Cloud providers is designed and implemented. Empirical results show that BoTs can be (i) efficiently executed by attaining similar (in some cases shorter) makespans to commonly used benchmark heuristics (e.g., Max-min), (ii) effectively executed by achieving a 100% success execution rate even with high BoT execution request rates and executing BoTs in a concurrent and parallel manner, and that (iii) BoTs are economically executed by elastically reallocating Cloud resources on demand.