D-SPACE4Cloud: A Design Tool for Big Data Applications

The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools and techniques to support the design of the underlying hardware configuration backing such systems. In particular, the focus in this report is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying QoS constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method

Multi-objective scheduling of many tasks in cloud platforms.

h i g h l i g h t s • We propose an ordinal optimized method for multi-objective many-task scheduling. • We prove the suboptimality of the proposed method through mathematical analysis. • Our method significantly reduces scheduling overhead by introducing a rough model. • Our method delivers a set of semi-optimal good-enough scheduling solutions. • We demonstrate the effectiveness of the method on a real-life workload benchmark. a r t i c l e i n f o b s t r a c t The scheduling of a many-task workflow in a distributed computing platform is a well known NP-hard problem. The problem is even more complex and challenging when the virtualized clusters are used to execute a large number of tasks in a cloud computing platform. The difficulty lies in satisfying multiple objectives that may be of conflicting nature. For instance, it is difficult to minimize the makespan of many tasks, while reducing the resource cost and preserving the fault tolerance and/or the quality of service (QoS) at the same time. These conflicting requirements and goals are difficult to optimize due to the unknown runtime conditions, such as the availability of the resources and random workload distributions. Instead of taking a very long time to generate an optimal schedule, we propose a new method to generate suboptimal or sufficiently good schedules for smooth multitask workflows on cloud platforms. Our new multi-objective scheduling (MOS) scheme is specially tailored for clouds and based on the ordinal optimization (OO) method that was originally developed by the automation community for the design optimization of very complex dynamic systems. We extend the OO scheme to meet the special demands from cloud platforms that apply to virtual clusters of servers from multiple data centers. We prove the suboptimality through mathematical analysis. The major advantage of our MOS method lies in the significantly reduced scheduling overhead time and yet a close to optimal performance. Extensive experiments were carried out on virtual clusters with 16 to 128 virtual machines. The multitasking workflow is obtained from a real scientific LIGO workload for earth gravitational wave analysis. The experimental results show that our proposed algorithm rapidly and effectively generates a small set of semi-optimal scheduling solutions. On a 128-node virtual cluster, the method results in a thousand times of reduction in the search time for semi-optimal workflow schedules compared with the use of the Monte Carlo and the Blind Pick methods for the same purpose