TAILIEUCHUNG - Scheduling algorithm for user requirements on cloud computing base on deadline and budget constraints

The goal of the SaaS provider is the most profitable; the user’s goal is to meet requirements as quickly as possible but still within budget and deadline. In this paper, a heuristic ACO (Ant Colony Optimization) is used to propose an algorithm to admission control, then building a scheduling algorithm based on the overlapping time between requests. | Journal of Computer Science and Cybernetics, , (2015), 231–243 DOI: SCHEDULING ALGORITHM FOR USER REQUIREMENTS ON CLOUD COMPUTING BASE ON DEADLINE AND BUDGET CONSTRAINTS NGUYEN HOANG HA† AND NGUYEN THANH BINH‡ Hue University of Sciences, Vietnam, † nhha76@, ‡ Abstract. The goal of the SaaS provider is the most profitable; the user’s goal is to meet requirements as quickly as possible but still within budget and deadline. In this paper, a heuristic ACO (Ant Colony Optimization) is used to propose an algorithm to admission control, then building a scheduling algorithm based on the overlapping time between requests. The goal of both algorithms is to minimize the total execution time of the system, satisfying QoS constraints for all requirements and provide the greatest returned profit for SaaS providers. These two algorithms are set up and run a complete test on CloudSim, the experimental results are compared with sequential and EDF (Earliest Deadline First) algorithms. Keywords. Admission control, scheduling algorithms, constraint QoS, resource allocation 1. INTRODUCTION Cloud computing is a distributed computing model for large scale; it provides services to users by employing resources (hardware, software, storage resources, etc.) via internet. Users may employ the various resources through their requirements and pay as they use. When users send requests together with the constraints as to arrival time, deadline, budget, workload, etc. to SaaS vendors, SaaS providers use PaaS to admission control, then conduct scheduling requirements as Figure 1. PaaS provider searches for suitable resources on IaaS to logical mapping to user requirements. Generally, the admission control and scheduling request with parameters such as arrival time, deadline, budget, workload, and penalty rate, etc. is an NP-complete problem [1]. Therefore, to give an optimal solution one must often do exhaustive search while .

• Kỹ thuật lập trình
• Scheduling algorithm
• Budget constraints
• Cloud computing base
• Admission control
• Scheduling algorithms
• Constraint QoS
• Resource allocation
• International Journal of Computer Science & Communication Networks
• ERR broadcast scheduling algorithm
• Broadcast scheduling algorithm
• The average flow time
• Longest Wait First For Broadcast Scheduling
• Uninterrupted flow shop scheduling
• Meta heuristics algorithm
• Two machine no wait flow shop scheduling problem
• Effects of learning
• No wait flow shop scheduling mode
• Hybrid algorithm for unrelated parallel machines scheduling
• Unrelated parallel machines scheduling
• Multi objective genetic algorithm
• Precedence relations among jobs
• Sscheduling unrelated parallel machines
• A genetic algorithm for preemptive scheduling of a single machine
• Preemptive scheduling of a single machine
• Large scale problems
• A genetic algorithm
• Simulation optimization based ant colony algorithm
• The uncertain quay crane scheduling problem
• The optimization to find solutions with good quality
• Te optimization algorithm
• Optimization and swarm intelligence
• Evolutionary algorithm
• Resourceconstrained project scheduling problem
• Cuckoo search algorithm
• Optimization algorithm
• Discrete Jaya algorithm
• Permutation flow shop scheduling problem
• Permutation flow shop scheduling
• Largest Order Value
• Deterministic polynomial time
• Modified genetic algorithm for time
• Cost optimization of an additive manufacturing single machine scheduling
• Additive manufacturing single machine scheduling
• Direct Metal Laser Sintering
• 3D model data
• Hybrid algorithm for the multi depot vehicle scheduling problem arising
• The multi depot vehicle scheduling problem arising
• Set Partitioning model
• Real life instance
• Well known MDVSP model
• Workflow scheduling Cloud computing
• Branch and Bound Algorithm
• Process and deliver data
• The Branch and Bound Algorithm
• The network technology
• Grid computing
• Heuristic algorithm
• Resource utilization
• Sort Mid tasks scheduling algorithm
• Bi objective optimization
• Integrated forward/reverse logistic network
• Non dominated sorting genetic algorithm II
• Bi objective freight scheduling optimization
• Facilities planning and design
• Supply Chain Scheduling
• Closed loop supply chain systems
• Scheduling manufacturing workshops
• Simulated annealing algorithm
• Two agent scheduling problem
• Two machine flowshop
• The tabu search algorithm show
• The multi agent scheduling
• International Journal of Computer Networks and Communications Security
• Optimal scheduling in cloud computing environment
• The bee algorithm
• Cloud computing environment
• Honeybee colony algorithm
• Evolutionary algorithm with feasibility rule
• Constraint handling method
• Intelligent production scheduling
• Constrained handling
• Minimizing makespan of a resource constrained scheduling problem
• A hybrid greedy and genetic algorithms
• Resource constrained scheduling problem
• Resource Constrained Project Scheduling Problem
• Backward forward search method
• Two stage genetic algorithm
• Scheduling algorithm with controllable train speeds
• Departure times to decrease the total train tardiness
• The total train tardiness
• The DR algorithm
• The pre schedule
• Algorithm design
• Lecture Algorithm design
• Algorithm analysis
• Dynamic programming
• Weighted interval scheduling
• Knapsack problem
• Multi objective optimization
• Genetic algorithm
• Optimization method
• Machine scheduling problem
• Evolutionary algorithm methods
• Efficient cloud computing scheduling
• Comparing classic algorithms with generic algorithm
• Comparing classic algorithms
• Multi processor structure
• Efficient scheduling in cloud networks
• Chakoos evolutionary algorithm
• DSC and MCP algorithms
• The graphs involving communication cost
• Modified tabu search algorithm
• The single machine scheduling problem
• Additive manufacturing technology
• New meta heursitic
• The tabu search algorithms
• Heuristic algorithm for scheduling
• Flow shop environment to minimize makespan
• Flow shop environment
• Three well known heuristics
• The reduced weightage