Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds

A comprehensive review and evaluation of permutation flowshop heuristics

Grid computing is increasingly considered as a promising next-generation computational platform that supports wide-area parallel and distributed computing. In grid environments, applications are always regarded as workflows. The problem of scheduling workflows in terms of certain quality of service (QoS) requirements is challenging and it significantly influences the performance of grids. By now, there have been some algorithms for grid workflow scheduling, but most of them can only tackle the problems with a single QoS parameter or with small-scale workflows. In this frame, this paper aims at proposing an ant colony optimization (ACO) algorithm to schedule large-scale workflows with various QoS parameters. This algorithm enables users to specify their QoS preferences as well as define the minimum QoS thresholds for a certain application. The objective of this algorithm is to find a solution that meets all QoS constraints and optimizes the user-preferred QoS parameter. Based on the characteristics of workflow scheduling, we design seven new heuristics for the ACO approach and propose an adaptive scheme that allows artificial ants to select heuristics based on pheromone values. Experiments are done in ten workflow applications with at most 120 tasks, and the results demonstrate the effectiveness of the proposed algorithm.

An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements

A hybrid decision support system for sustainable office building renovation and energy performance improvement

In this study, the authors focus on theoretical modelling of the fog computing
 architecture and compare its performance with the traditional cloud computing model. Existing research works on fog computing have primarily focused on the principles and concepts of fog computing and its significance in the context of internet of things
 (
 IoT
). This work, one of the first attempts in its domain, proposes a mathematical formulation for this new computational paradigm by defining its individual components and presents a comparative study with cloud computing in terms of service latency and energy consumption. From the performance analysis, the work establishes fog computing, in collaboration with the traditional cloud computing platform, as an efficient green computing platform to support the demands of the next generation IoT applications. Results show that for a scenario where 25% of the IoT applications demand real-time, low-latency services, the mean energy expenditure in fog computing is 40.48% less than the conventional cloud computing model.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-net.2015.0034

Theoretical modelling of fog computing: a green computing paradigm to support IoT applications

Hybrid genetic algorithm for permutation flowshop scheduling problems with total flowtime minimization

Deadline division-based heuristic for cost optimization in workflow scheduling

In this paper, permutation flow shops with total flowtime minimization are considered. General flowtime computing (GFC) is presented to accelerate flowtime computation. A newly generated schedule is divided into an unchanged subsequence and a changed part. GFC computes total flowtime of a schedule by inheriting temporal parameters from its parent in the unchanged part and computes only those of the changed part. Iterative methods and LR (developed by Liu J, Reeves, CR. Constructive and composite heuristic solutions to the P ∥ Σ C i scheduling problem, European Journal of Operational Research 2001; 132:439–52) are evaluated and compared as solution improvement phase and index development phase. Three composite heuristics are proposed in this paper by integrating forward pair-wise exchange-restart (FPE-R) and FPE with an effective iterative method. Computational results show that the proposed three outperform the best existing three composite heuristics in effectiveness and two of them are much faster than the existing ones.

Efficient composite heuristics for total flowtime minimization in permutation flow shops

No-wait or constrained flow shops with makespan minimization are considered in this paper. An Objective Increment method is introduced to judge whether or not a new schedule is better than its parent. The judgment is made just by the makespan increment rather than by the traditional method to compute the whole makespan, and the time complexity of a heuristic can be decreased by one order. As a new schedule is usually generated by insertion or pair wise exchanging, objective increments are analysed for the fundamental operations. A composite heuristic is proposed for the problems considered. Experimental results show that the proposed approach outperforms the best existing algorithms with the least CPU time.

Qian Wang

Papers

Hybrid genetic algorithm for permutation flowshop scheduling problems with total flowtime minimization

Deadline division-based heuristic for cost optimization in workflow scheduling

Efficient composite heuristics for total flowtime minimization in permutation flow shops

Heuristic for no-wait flow shops with makespan minimization

Complete local search with limited memory algorithm for no-wait job shops to minimize makespan