A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment

We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function

Allocation de ressources et ordonnancement multi-utilisateurs : une approche basée sur l'équité

Grid and Cloud computing make possible the sharing of computer system resources, such as storage or computation time, among a set of users, according to their requests, thereby creating an illusion of infinite resources. However, as soon as those resources are insufficient to meet users’s expectations, conflicts of interest arise. Therefore, unlimited access to limited resources may lead to inefficient usage which penalizes the whole set of users. In such environments, arbitration becomes necessary in order to settle those conflicts and ensure a fair allocation to all users. We present two classes of problems : multi-user resource allocation under uncertainty and multi-user periodic task scheduling. We tackle these problems from the point of view of fairness.Les grilles de calcul et le “cloud computing” permettent de distribuer un ensemble de ressources informatiques, telles que du stockage ou du temps de calcul, à un ensemble d’utilisateurs en fonction de leurs demandes en donnant l’illusion de ressources infinies. Cependant, lorsque l’ensemble de ces ressources est insuffisant pour satisfaire les exigences des utilisateurs, des conflits d’intérêts surgissent. Ainsi, un libre accès à des ressources limitées peut entraîner une utilisation inefficace qui pénalise l’ensemble des participants. Dans de tels environnements, il devient nécessaire d’établir des procédures d’arbitrage afin de résoudre ces conflits en garantissant une distribution équitable aux différents utilisateurs. Nous présentons une nouvelle classe de problèmes : celle des ordonnancements multi-utilisateurs. Cette thèse aborde la notion d’équité au travers de problèmes d’allocation de ressources sous incertitudes et d’ordonnancement de tâches périodiques

Cooperation in Multi-Organization Scheduling

Abstract. The distributed nature of the grid results in the problem of scheduling parallel jobs produced by several independent organizations that have partial control over the system. We consider systems composed of n identical clusters of m processors. We show that it is always possible to produce a collaborative solution that respects participant’s selfish goals, at the same time improving the global performance of the system. We propose algorithms with a guaranteed worst-case performance ratio on the global makespan: a 3-approximation algorithm if the last completed job requires at most m/2 processors, and a 4-approximation algorithm in the general case.