Scheduling Scientific Workflow Applications with Deadline and Budget Constraints Using Genetic Algorithms

Abstract

Grid technologies have progressed towards a service-oriented paradigm that enables a new way of service provisioning based on utility computing models, which are capable of supporting diverse computing services. It facilitates scientific applications to take advantage of computing resources distributed world wide to enhance the capability and performance. Many scientific applications in areas such as bioinformatics and astronomy require workflow processing in which tasks are executed based on their control or data dependencies. Scheduling such interdependent tasks on utility Grid environments need to consider users' QoS requirements. In this paper, we present a genetic algorithm approach to address scheduling optimization problems in workflow applications, based on two QoS constraints, deadline and budget.

Distributed Workflow Scheduling Under Throughput and Budget Constraints in Grid Environments

“…Such a guarantee of service is hard to provide in a Grid environment due to its shared, heterogeneous and distributed resources owned by different organizations with their own policies and pricing mechanisms [4]. Many algorithms for deadline and budget constrained scheduling have been proposed [21][22][23][24][25][26][27]. In [25], Sakellariou et al proposed two different approaches, namely the LOSS approach and the GAIN approach to find the schedule for a given DAGstructured workflow and a given set of resources without exceeding the budget and is still optimized for overall execution time.…”

Deadline-sensitive workflow orchestration without explicit resource control

“…Various resource protocols in grid and cloud systems [13,26,32] and workflow orchestration algorithms are being investigated for specific applications [16,25,31]. However, the resource abstraction and associated QoS model in WORDS provides a uniform knowledge of resource properties that was previously not available to higher-level tools.…”

“…Various DAG scheduling algorithms have been proposed for grid environments for optimizing makespan, meeting deadline and/or budget constraints or dealing with uncertainty [16,25,31]. The underlying assumption of all these algorithms is that resources are guaranteed to be available at a given time, whereas resource availability is highly variable.…”

Multi-objective planning for workflow execution on Grids

“…Recently, a number of cost-aware workflow scheduling heuristics [9] [10] have been proposed and evaluated. Even though multiple criteria have been considered, their aim was to optimize a single objective.…”