A Multi-Commodity Flow Approach to Maximising Utility in Linked Market-Based Grids

In this paper we consider the problem of maximising utility in linked market-driven distributed and Grid systems. In such systems, users submit jobs through brokers who can virtualise and make available the resources of multiple service providers, achieving greater economies of scale, improving throughput and potentially reducing cost. Customers compete against each other by assigning a utility value or function to the successful processing of their jobs in an effort to have them prioritised in the face of contested and constrained resources. Brokers and service providers also attempt to maximise the utility they gain, choosing to process jobs that will earn them the highest profit with respect to the resources required. For this to be effective over many linked computing marketplaces highly distributed resource allocation is needed, where each participant can operate independently using only local information, and ideally reach a global state where all participants are satisfied. We model such a system by adapting the classical multi-commodity flow problem to the market-based, utility driven distributed systems, where all participants selfishly attempt to maximise their own gain. We then obtain a utility-aware distributed algorithm that generates increased utility for participants in such systems, especially under scenarios of high contention

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