A Survey of Data-Intensive Scientific Workflow Management <s> Scheduling Optimization Algorithms <s> With the advent of next-generation scientific applications, the workflow approach that integrates various computing and networking technologies has provided a viable solution to managing and optimizing large-scale distributed data transfer, processing, and analysis. This paper investigates a problem of mapping distributed scientific workflows for maximum throughput in faulty networks where nodes and links are subject to probabilistic failures. We formulate this problem as a bi-objective optimization problem to maximize both throughput and reliability. By adapting and modifying a centralized fault-free workflow mapping scheme, we propose a new mapping algorithm to achieve high throughput for smooth data flow in a distributed manner while satisfying a pre-specified bound of the overall failure rate for a guaranteed level of reliability. The performance superiority of the proposed solution is illustrated by both extensive simulation-based comparisons with existing algorithms and experimental results from a real-life scientific workflow deployed in wide-area networks. <s> BIB001 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Scheduling Optimization Algorithms <s> Scheduling multiple large-scale parallel workflow applications on heterogeneous computing systems like hybrid clouds is a fundamental NP-complete problem that is critical to meeting various types of QoS (Quality of Service) requirements. This paper addresses the scheduling problem of large-scale applications inspired from real-world, characterized by a huge number of homogeneous and concurrent bags-of-tasks that are the main sources of bottlenecks but open great potential for optimization. The scheduling problem is formulated as a new sequential cooperative game and propose a communication and storage-aware multi-objective algorithm that optimizes two user objectives (execution time and economic cost) while fulfilling two constraints (network bandwidth and storage requirements). We present comprehensive experiments using both simulation and real-world applications that demonstrate the efficiency and effectiveness of our approach in terms of algorithm complexity, makespan, cost, system-level efficiency, fairness, and other aspects compared with other related algorithms. <s> BIB002 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Scheduling Optimization Algorithms <s> Executing large-scale applications in distributed computing infrastructures (DCI), for example modern Cloud environments, involves optimization of several conflicting objectives such as makespan, reliability, energy, or economic cost. Despite this trend, scheduling in heterogeneous DCIs has been traditionally approached as a single or bi-criteria optimization problem. In this paper, we propose a generic multi-objective optimization framework supported by a list scheduling heuristic for scientific workflows in heterogeneous DCIs. The algorithm approximates the optimal solution by considering user-specified constraints on objectives in a dual strategy: maximizing the distance to the user's constraints for dominant solutions and minimizing it otherwise. We instantiate the framework and algorithm for a four-objective case study comprising makespan, economic cost, energy consumption, and reliability as optimization goals. We implemented our method as part of the ASKALON environment (Fahringer et al., 2007) for Grid and Cloud computing and demonstrate through extensive real and synthetic simulation experiments that our algorithm outperforms related bi-criteria heuristics while meeting the user constraints most of the time. <s> BIB003
Since there are many criteria to measure the scientific workflow execution, SWfMS users may have multiple objectives for workflow execution, such as reducing execution time, minimizing execution cost etc. Therefore, SPs should also be optimized to attain multiple objective in a given context (cluster, grid, cloud). Unlike query optimization in database, however, this optimization phase is often not explicit and mixed with the scheduling method. Though there are some existing scheduling optimization algorithms  BIB002 BIB003 BIB001 , they do not consider a multisite environment with distributed data at each site. We present BIB001 and BIB003 as examples. Gu et al. BIB001 address the scheduling optimization problem of mapping distributed scientific workflows to maximize the throughput in unstable networks where nodes and links are subject to probabilistic failures. And they propose a mapping algorithm to maximize both throughput and reliability for workflow scheduling. They consider a network where the failure occurrences follow a Poisson distribution with a constant parameter. The mapping algorithm includes three algorithms: disLDP-F, Greedy disLDP-F and decentralized Greedy disLDP-F. The disLDP-F algorithm schedules the tasks by identifying and minimizing the global bottleneck time based on the rank of computational requirements of tasks. Greedy disLDP-F reduces the search complexity of disLDP-F by selecting the best node for each type of requirement of the current task and then generates a best computing node for the current task. The decentralized Greedy disLDP-F algorithm decentralizes the disLDP-F algorithm by storing all the parameters of each individual node locally and selecting the node through the communication between individual nodes instead of centralized control. Fard et al. BIB003 propose a multi-objective scheduling method of SWf execution in distributed computing infrastructures. Their approach generates a best scheduling strategy, which is a Pareto optimality (no other strategy can achieve a result of a better component while ensuring the other components of the result as well as this strategy), to achieve 4 objectives, i.e. execution time, financial cost, energy consumption and reliability. Nevertheless, this approach does not consider data distribution in a multisite environment.