School of Computing FACULTY OF ENGINEERING Grids and QoS Grid Computing has emerged in the last two decades, initially as a model for large-scale, resource-intensive scientific applications. Grids can be viewed as a federation of many computing units distributed world-wide and shared among users communicating with them over the Internet. Todays Grids provide only a best effort service to the users [1]. Grid and Cloud markets introduced the notion of cost for computation. Our project, ISQoS, focuses on Quality of Service (QoS) provision for Grids. QoS metrics: Time (makespan), Cost. Typically, these two objectives antagonize each other. When scheduling a divisible workload [3] on a set of uniform machines with cost we can provide an exact solution in the following user scenarios Time vs CostCurrent Work Grid Scheduling faces multiple challenges due to the dynamic character of the computing environment. Speeds or costs of the processors may change over time while resources may leave or fail. Providing QoS to the users in such an environment requires more than offline heuristics. Online Algorithms and Rescheduling techniques may be considered. Bags-of-Tasks is not the only model for Grid applications. Applications may be a series of connected interdependent tasks (Workflows). In the Cloud computing paradigm users are not only charged for computation but also for data storage and movement. Therefore, for Data-intensive tasks, cost should be embodied in the stage-in, compute, stage-out model. More Quality of Service metrics may be considered. The goal of this project is to combine classical scheduling theory with models drawn from the paradigms of Grids and Clouds. Future WorkScheduler A scheduler is a part of a broker which plays the role of the intermediate between the user(s) and the resource owner(s). System-centric schedulers aim to optimize system metrics (e.g. makespan, throughput). while user-centric schedulers aim to optimize user metrics (e.g. cost, flowtime). The problem of minimizing the makespan is NP-hard even in the case of two processors. Moreover, the problem of minimizing the cost is NP-hard. Therefore, we propose heuristics which are quick and dirty algorithms that return feasible solutions which are not necessarily optimal [2]. For the problem of cost minimization with a deadline constraint we compare our algorithms to the ones used in Nimrod/G [4] which a cost-aware scheduler. Problem School of Computing Anastasia Grekioti Supervisors: N. Shakhlevich and K. Djemame Grid Scheduling algorithms References [1] Kokkinos P. and Varvarigos, E.A., A framework for providing hard delay guarantees and user fairness in Grid computing, Future Generation Computer Systems, vol. 25, no. 6, 2009, pp [2] Papadimitriou, C.H., Computational Complexity, Addison-Wesley, 1994 [3] Robertazzi, T.G., Networks and Grids: Technology and Theory,Springer, New York, 2007 [4] GRACE, The Grid Economy Project, h ttp:// ISQoS Algorithms and Complexity Group Collaborative Systems and Performance A Bag-of-Tasks (BoT) is a parallel application whose tasks are completely independent from each other, making it ideal for taking advantage of the computing power provided by Grids. Classic Grid Scheduling algorithms for BoT include Min-Min, Max-Min and Sufferage. We compare our proposed algorithms to them as well as the algorithms proposed in [4] (DBCs).