HEART: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and meta‐heuristic algorithms

Abstract

Summary Cloud computing is becoming a profitable technology because of it offers cost‐effective IT solutions globally. A well‐designed task scheduling algorithm ensures the optimal utilization of clouds resources and reducing execution time dynamically. This research article deals with the task scheduling of inter‐dependent subtasks on unrelated parallel computing machines in a cloud computing environment. This article considers two variants of the problem‐based on two different objective function values. The first variant considers the minimization of the total completion time objective function while the second variant considers the minimization of the makespan objective function. Heuristic and meta‐heuristic (HEART) based algorithms are proposed to solve the task scheduling problems. These algorithms utilize the property of list scheduling algorithm of unrelated parallel machine scheduling problem. A mixed integer linear programming (MILP) formulation has been provided for the two variants of the problem. The optimal solution is obtained by solving MILP formulation using A Mathematical Programming Language (AMPL) software. Extensive numerical experiments have been performed to evaluate the performance of proposed algorithms. The solutions obtained by the proposed algorithms are found to out‐perform the existing algorithms. The proposed algorithms can be used by cloud computing service providers (CCSPs) for enhancing their resources utilization to reduce their operating cost.

ESPS: Energy Saving Power Spectrum-Aware Scheduling to Leverage Differences in Power Ratings of Physical Hosts in Datacenters

“…meta-heuristic task allocation strategies [32] that not only focus on energy but also other parameters such as makespan, conform to SLA constraints, etc.…”

Nature-Inspired Metaheuristics for Two-Agent Scheduling with Due Date and Release Time

“…Constraint (6) ensures that any job will not be scheduled at any position before the previous job is completed. Constraint (7) states that any job will not be scheduled at any position before its release date. Constraints ( 8) and ( 9) ensure that the job's starting time is the same as the previous job's completion time.…”

“…(1) / * Initial Solution * / (2) Create a partial sequence σ A and σ B (3) Create a solution σ ⟵ σ A ∪ σ B ) //combine sequences σ A and σ B to construct σ (4) Calculate U ⟵ f B (σ) //Calculate the weighted number of tardy job for sequence σ (5) / * Feasibility phase * / (6) for k � n 1 to 1 do (7) Select job J � σ(k) (8) for l � k+1 to n − (k − n 1 ) do (9) σ temp ⟵ Create new sequence from σ by reinserting job J at position l (10)…”

“…e usual scheduling problem involves finding a sequence of jobs in a machine, flow of jobs through different machines, or assigning jobs to different machines. e machine-scheduling problem involves the allocation of resources to process different jobs under different machine settings such as single-machine setting [4], flowshop machine setting [5], job shop machine setting [6], and parallel machine setting [7]. e classical machine-scheduling involves the processing of different jobs for a single customer/agent.…”

“…e research on the multiagent scheduling models started approximately fourteen years ago. An application of multiple agents scheduling problem can be encountered in many industries such as aircraft landing problem [8], railroad track allowance problem [9], networks [10], and cloud computing services [7]. Two-agent scheduling problem is a special case of a multiagent scheduling problem.…”