Organizational clusters are often shared among users who compete over resources, but the organization's goal is to increase the overall utility produced by the cluster. That is, to increase the aggregate benefit drawn from the cluster. To overcome this problem, we enhanced SLURM with an auctioning system. We evaluated our work on several real cluster traces. Our auctioning scheduler increases the responsiveness to highly valued jobs. It reduces the weight of the queued jobs (the sum of multiplication of jobs by their wait time by their number of required nodes and by their bid) by 3X--33X compared with backfilling.

Organizational clusters are often shared among users who compete over resources, but the organization's goal is to increase the overall utility gathered from the cluster. That is, to maximize the aggregate benefit drawn from the cluster. To overcome this problem, we enhanced SLURM with an auctioning system. We evaluated our work on several real cluster traces. Our auctioning scheduler reduces the weight of the queued jobs by 3X-33X compared with backfilling.

Alibaba Cloud was the second cloud provider to offer preemptible (spot) instances, and yet their price traces have never been analyzed. We analyzed thousands of price traces collected for over 3 years to find sharp and coordinated behavioral changes in the pricing.

Cloud computing handles a vast share of the world's computing, but it is not as efficient as it could be due to its lack of support for memory elasticity. An environment that supports memory elasticity can dynamically change the size of the application's memory while it's running, thereby optimizing the entire system's use of memory. However, this means at least some of the applications must be memory-elastic. A memory elastic application can deal with memory size changes enforced on it, making the most out of all of the memory it has available at any one time. The performance of an ideal memory-elastic application would not be hindered by frequent memory changes. Instead, it would depend on global values, such as the sum of memory it receives over time.

Suboptimal resource utilization among public and private cloud providers prevents them from maximizing their economic potential. Long-term allocated resources are often idle when they might have been subleased for a short period. Alternatively, arbitrary resource overcommitment may lead to unpredictable client performance.

No abstract available.

In the near future, cloud providers will sell their users virtual machines with CPU, memory, network, and storage resources whose prices constantly change according to market-driven supply and demand conditions. Running traditional operating systems in these virtual machines is a poor fit: traditional operating systems are not aware of changing resource prices and their sole aim is to maximize performance with no consideration of costs. Consequently, they yield low profits.

Physical memory is the scarcest resource in today's cloud computing platforms. Cloud providers would like to maximize their clients' satisfaction by renting precious physical memory to those clients who value it the most. But real-world cloud clients are selfish: they will only tell their providers the truth about how much they value memory when it is in their own best interest to do so. How can real-world cloud providers allocate memory efficiently to those (selfish) clients who value it the most?

Many scientists perform extensive computations by executing large bags of similar tasks (BoTs) in mixtures of computational environments, such as grids and clouds. Although the reliability and cost may vary considerably across these environments, no tool exists to assist scientists in the selection of environments that can both fulfill deadlines and fit budgets. To address this situation, we introduce the Expert BoT scheduling framework. Our framework systematically selects from a large search space the Pareto-efficient scheduling strategies, that is, the strategies that deliver the best results for both make span and cost. Expert chooses from them the best strategy according to a general, user-specified utility function. Through simulations and experiments in real production environments, we demonstrate that Expert can substantially reduce both make span and cost in comparison to common scheduling strategies. For bioinformatics BoTs executed in a real mixed grid + cloud environment, we show how the scheduling strategy selected by Expert reduces both make span and cost by 30%-70%, in comparison to commonly-used scheduling strategies.

Cloud providers possessing large quantities of spare capacity must either incentivize clients to purchase it or suffer losses. Amazon is the first cloud provider to address this challenge, by allowing clients to bid on spare capacity and by granting resources to bidders while their bids exceed a periodically changing spot price. Amazon publicizes the spot price but does not disclose how it is determined. By analyzing the spot price histories of Amazon's EC2 cloud, we reverse engineer how prices are set and construct a model that generates prices consistent with existing price traces. We find that prices are usually not market-driven as sometimes previously assumed. Rather, they are typically generated at random from within a tight price interval via a dynamic hidden reserve price. Our model could help clients make informed bids, cloud providers design profitable systems, and researchers design pricing algorithms.