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This paper investigates scheduling loosely coupled task-bundles in highly heterogeneous distributed systems. Two allocation quality metrics are used in pay-per-service distributed applications: efficiency in terms of social welfare, and fairness in terms of envy-freeness. The first contribution of this work is that we build a unified hypergraph scheduling model under which efficiency and fairness are compatible with each other. Second, in the scenario of budget-unawareness, we formulate a strategic algorithm design for distributed negotiations among autonomous self-interested computing peers and prove its convergence to complete local efficiency and envy-freeness. Third, we add budget limitation to the allocation problem and propose a class of hill-climbing heuristics in favor of different performance metrics. Finally we conduct extensive simulations to validate the performance of all the proposed algorithms. The results show that the decentralized hypergraph scheduling method is scalable, and yields desired allocation performance in various scenarios.