Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud.

Due to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling. Among these purposes, Cost and Makespan are probably the most two primitive objectives. Another critical factor in a large-scale scientific workflow is tremendous amount of data during execution. Therefore, this work also includes Data Movement as an additional objective as it has a major impact on network utilization and energy consumption in network equipment in cloud data center. In considering these three objectives, this work proposes a framework for scheduling solutions which combines a new nodes clustering technique in Directed Acyclic Graph (DAG) model known as Multilevel Dependent Node Clustering (MDNC) and the multiobjective optimization, Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III). E-NSGAIII is the recent extension of Nondominated Sorting Genetic Algorithm (NSGA-III). Five well-known scientific workflows, CyberShake, Epigenomics, LIGO, Montage, and SIPHT are selected as testbeds, while the commonly known Hypervolume is chosen as the performance metric. In this work, MDNC is also experimented with both NSGA-III. Comparison among three approaches, E-NAGA-III alone, E-NAGA-III with Peer-to-Peer clustering and E-NAGA-III with MDNC are carried out. The superiority of the proposed framework among them and its limitation are discussed

A Study of Token Traversal Strategies on Tree-Based Backbones for Mobile Ad Hoc - Delay Tolerant Networks

International audienceTree-based backbone establishment and maintenance in Mobile ad hoc Delay Tolerant Networks is often operated throught the use of traversing tokens. A study and framework are proposed here for various token traversal strategies on tree-based backbones. The proposed strategies execute in a distributed and purely decentralized manner, and require only 1-hop knowledge. Aiming at providing the highest robustness and quality of services, these token-traversal strategies are studied in particular with an algorithm for merging and maintaining the different trees based on the quality of the nodes. For the robustness aspect, the use of a trust-based evaluation framework is assumed and the weights of the different nodes are based on their quality of cooperation. Three cost functions are implemented in order to evaluate the trust based framework proposed, including another function for evaluating tree-convergence time. Results and comparison charts are provided to illustrate the trade-off between the various strategies in terms of performances, cost (memory and communication) and robustness