Deadline-sensitive workflow orchestration without explicit resource control

Abstract

Deadline-sensitive workflows require careful coordination of user constraints with resource availability. Current distributed resource access models provide varying degrees of resource control: from limited or none in grid batch systems to explicit in cloud systems. Additionally applications experience variability due to competing user loads, performance variations, failures, etc. These variations impact the quality of service(QoS) that goes unaccounted for in planning strategies. In this paper we propose Workflow ORchestrator for Distributed Systems (WORDS ) architecture based on a least common denominator resource model that abstracts the differences and captures the QoS properties provided by grid and cloud systems. We investigate algorithms for effective orchestration (i.e., resource procurement and task mapping) for deadline sensitive workflows atop the resource abstraction provided in WORDS . Our evaluation compares orchestration methodologies over TeraGrid and Amazon EC2 systems. Experimental results show that WORDS enables effective orchestration possible at reasonable costs on batch queue grid and cloud systems with or without explicit resource control.

Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities

“…Note that these aspects are created based on the workflow reference model proposed by the Workflow Management Coalition (WfMC) [50] (Figure 4). The cloud system model contains three levels [3,31,[50][51][52][53]: (i) Workflow application level represents the development and management called Software as a Service (SaaS). The National Institute of Standards and Technology (NIST) has defined SaaS as a capability provided to the service consumer to use the utility providers applications which are running on a cloud infrastructure [54].…”

“…business model) [3]. The market-oriented perspective facilitates the resource manager in meeting the user specified QoS constraints (i.e.…”

“…Meta-heuristics Workflow Orchestrator for Distributed Systems Architecture (WORDS) [3] The disparities are extracted and the QoS features offered by the cloud and grid structure are encapsulated.…”

“…Researchers focus on WFS due to its ability to handle complicated applications (e.g. drug discovery and weather modeling [3]) with the whole or part of complex task dependencies. WFS in cloud computing allocates each task to a relevant cloud service by ordering the execution of various resources to obtain satisfactory Quality of Service (QoS).…”

Comparing FutureGrid, Amazon EC2, and Open Science Grid for Scientific Workflows

“…For example, many researchers have investigated ways to take advantage of cloud elasticity to develop dynamic provisioning algorithms for workflows [16,18,22,28,31]. Other researchers have developed new cloud workflow scheduling algorithms that optimize for cost, performance, and other QoS metrics [2,26,30,35]. Finally, many researchers have been investigating techniques for deploying data-intensive workflows in the cloud using unique architectures that are difficult to deploy on traditional platforms such as grids [3,[38][39][40].…”