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?

Markets of computing resources typically consist of a cluster (or a multi-cluster) and jobs that arrive over time and request computing resources in exchange for payment. In this paper we study a real system that is capable of preemptive process migration (i.e. moving jobs across nodes) and that uses a market-based resource allocation mechanism for job allocation. Specifically, we formalize our system into a market model and employ simulation-based analysis (performed on real data) to study the effects of users' behavior on performance and utility. Typically online settings are characterized by a large amount of uncertainty, therefore it is reasonable to assume that users will consider simple strategies to game the system. We thus suggest a novel approach to modeling users' behavior called the Small Risk-aggressive Group model. We show that under this model untruthful users experience degraded performance. The main result and the contribution of this paper is that using the k-th price payment scheme, which is a natural adaptation of the classical second-price scheme, discourages these users from attempting to game the market. The preemptive capability makes it possible not only to use the k-th price scheme, but also makes our scheduling algorithm superior to other non-preemptive algorithms. Finally, we design a simple one-shot game to model the interaction between the provider and the consumers. We then show (using the same simulation-based analysis) that market stability in the form of (symmetric) Nash-equilibrium is likely to be achieved in several cases.

Often, we wish to design incentive-compatible algorithms for settings in which the players' private information is drawn from discrete domains (e.g., integer values). Our main result is identifying discrete settings in which an algorithm can be made incentive-compatible iff the function it computes upholds a simple monotonicity constraint, known as weak-monotonicity. To the best of our knowledge, this is the first such characterization of incentive-compatibility in discrete domains (such characterizations were previously known only for inherently non-discrete domains, e.g., convex domains). We demonstrate the usefulness of this result by showing an application to the TCP-inspired congestion-control problem presented in [20].

Algorithmic Mechanism Design focuses on Dominant Strategy Implementations. The main positive results are the celebrated Vickrey-Clarke-Groves (VCG) mechanisms and computationally efficient mechanisms for severely restricted players ("single-parameter domains"). As it turns out, many natural social goals cannot be implemented using the dominant strategy concept [35, 32, 22, 20]. This suggests that the standard requirements must be relaxed in order to construct general-purpose mechanisms.We observe that in many common distributed environments computational entities can take advantage of the network structure to collect and distribute information. We thus suggest a notion of partially informed environments. Even if the information is recorded with some probability, this enables us to implement a wider range of social goals, using the concept of iterative elimination of weakly dominated strategies. As a result, cooperation is achieved independent of agents' belief. As a case study, we apply our methods to derive Peer-to-Peer network mechanism for file sharing.

This paper analyzes incentive compatible (truthful) mechanisms over restricted domains of preferences, the leading example being combinatorial auctions. Our work generalizes the characterization of Roberts (1979) who showed that truthful mechanisms over unrestricted domains with at least 3 possible outcomes must be "affine maximizers". We show that truthful mechanisms for combinatorial auctions (and related restricted domains) must be "almost affine maximizers" if they also satisfy an additional requirement of "independence of irrelevant alternatives". This requirement is without loss of generality for unrestricted domains as well as for auctions between two players where all goods must be allocated. This implies unconditional results for these cases, including a new proof of Roberts' theorem. The computational implications of this characterization are severe, as reasonable "almost affine maximizers" are shown to be as computationally hard as exact optimization. This implies the near-helplessness of such truthful polynomial-time auctions in all cases where exact optimization is computationally intractable.

Scheduling jobs on the IBM SP2 system and many other distributed-memory MPPs is usually done by giving each job a partition of the machine for its exclusive use. Allocating such partitions in the order in which the jobs arrive (FCFS scheduling) is fair and predictable, but suffers from severe fragmentation, leading to low utilization. This situation led to the development of the EASY scheduler which uses aggressive backfilling: Small jobs are moved ahead to fill in holes in the schedule, provided they do not delay the first job in the queue. We compare this approach with a more conservative approach in which small jobs move ahead only if they do not delay any job in the queue and show that the relative performance of the two schemes depends on the workload. For workloads typical on SP2 systems, the aggressive approach is indeed better, but, for other workloads, both algorithms are similar. In addition, we study the sensitivity of backfilling to the accuracy of the runtime estimates provided by the users and find a very surprising result. Backfilling actually works better when users overestimate the runtime by a substantial factor.