How to Price Shared Optimizations in the Cloud

Data-management-as-a-service systems are increasingly being used in collaborative settings, where multiple users access common datasets. Cloud providers have the choice to implement various optimizations, such as indexing or materialized views, to accelerate queries over these datasets. Each optimization carries a cost and may benefit multiple users. This creates a major challenge: how to select which optimizations to perform and how to share their cost among users. The problem is especially challenging when users are selfish and will only report their true values for different optimizations if doing so maximizes their utility. In this paper, we present a new approach for selecting and pricing shared optimizations by using Mechanism Design. We first show how to apply the Shapley Value Mechanism to the simple case of selecting and pricing additive optimizations, assuming an offline game where all users access the service for the same time-period. Second, we extend the approach to online scenarios where users come and go. Finally, we consider the case of substitutive optimizations. We show analytically that our mechanisms induce truth- fulness and recover the optimization costs. We also show experimentally that our mechanisms yield higher utility than the state-of-the-art approach based on regret accumulation.Comment: VLDB201

Dynamic Pricing Strategy for Maximizing Cloud Revenue

The unexpected growth, flexibility and dynamism of information technology (IT) over the last decade has radically altered the civilization lifestyle and this boom continues as yet. Many nations have been competing to be forefront of this technological revolution, quite embracing the opportunities created by the advancements in this field in order to boost economy growth and to increase the accomplishments of everyday’s life. Cloud computing is one of the most promising achievement of these advancements. However, it faces many challenges and barriers like any new industry. Managing and maximizing such a very complex system business revenue is of paramount importance. The wealth of the cloud protfolio comes from the proceeds of three main services: Infrastructure as a service (IaaS), Software as a service (SaaS), and Platform as a service (PaaS). The Infrastructure as a Service (IaaS) cloud industry that relies on leasing virtual machines (VMs) has a significant portion of business values. Therefore many enterprises show frantic effort to capture the largest portion through the introducing of many different pricing models to satisfy not merely customers’ demands but essentially providers’ requirements. Indeed, one of the most challenging requirements is finding the dynamic equilibrium between two conflicting phenomena: underutilization and surging congestion. Spot instance has been presented as an elegant solution to overcome these situations aiming to gain more profits. However, previous studies on recent spot pricing schemes reveal an artificial pricing policy that does not comply with the dynamic nature of these phenomena. In this thesis, we investigate dynamic pricing of stagnant resources so as to maximize cloud revenue. To achieve this task, we reveal the necessities and objectives that underlie the importance of adopting cloud providers to dynamic price model, analyze adopted dynamic pricing strategy for real cloud enterprises and create dynamic pricing model which could be a strategic pricing model for IaaS cloud providers to increase the marginal profit and also to overcome technical barriers simultaneously. First, we formulate the maximum expected reward under discrete finite-horizon Markovian decisions and characterize model properties under optimum controlling conditions. The initial approach manages one class but multiple fares of virtual machines. For this purpose, the proposed approach leverages Markov decision processes, a number of properties under optimum controlling conditions that characterize a model’s behaviour, and approximate stochastic dynamic programming using linear programming to create a practical model. Second, our seminal work directs us to explore the most sensitive factors that drive price dynamism and to mitigate the high dimensionality of such a large-scale problem through conducting column generation. More specifically we employ a decomposition approach. Third, we observe that most previous work tackled one class of virtual machines merely. Therefore, we extend our study to cover multiple classes of virtual machines. Intuitively, dynamic price of multiple classes model is much more efficient from one side but practically is more challenging from another side. Consequently, our approach of dynamic pricing can scale up or down the price efficiently and effectively according to stagnant resources and load threshold aims to maximize the IaaS cloud revenue

Scientific Workflow Scheduling for Cloud Computing Environments

The scheduling of workflow applications consists of assigning their tasks to computer resources to fulfill a final goal such as minimizing total workflow execution time. For this reason, workflow scheduling plays a crucial role in efficiently running experiments. Workflows often have many discrete tasks and the number of different task distributions possible and consequent time required to evaluate each configuration quickly becomes prohibitively large. A proper solution to the scheduling problem requires the analysis of tasks and resources, production of an accurate environment model and, most importantly, the adaptation of optimization techniques. This study is a major step toward solving the scheduling problem by not only addressing these issues but also optimizing the runtime and reducing monetary cost, two of the most important variables. This study proposes three scheduling algorithms capable of answering key issues to solve the scheduling problem. Firstly, it unveils BaRRS, a scheduling solution that exploits parallelism and optimizes runtime and monetary cost. Secondly, it proposes GA-ETI, a scheduler capable of returning the number of resources that a given workflow requires for execution. Finally, it describes PSO-DS, a scheduler based on particle swarm optimization to efficiently schedule large workflows. To test the algorithms, five well-known benchmarks are selected that represent different scientific applications. The experiments found the novel algorithms solutions substantially improve efficiency, reducing makespan by 11% to 78%. The proposed frameworks open a path for building a complete system that encompasses the capabilities of a workflow manager, scheduler, and a cloud resource broker in order to offer scientists a single tool to run computationally intensive applications

Robust Methods for Influencing Strategic Behavior

Today's world contains many examples of engineered systems that are tightly coupled with their users and customers. In these settings, the strategic and economic behavior of users and customers can have a significant impact on the performance of the overall system, and it may be desirable for an engineer to devise appropriate methods of incentivizing human behavior to improve system performance. This work seeks to understand the fundamental tradeoffs involved in designing behavior-influencing mechanisms for complex interconnected sociotechnical systems. We study several examples and pose them as problems of game design: a planner chooses appropriate ways to select or modify the utility functions of individual agents in order to promote desired behavior. In social systems these modifications take the form of monetary or other incentives, whereas in multiagent engineered systems the modifications may be algorithmic. Here, we ask questions of sensitivity and robustness: for example, if the quality of information available to the planner changes, how can we quantify the impact of this change on the planner's ability to influence behavior? We propose a simple overarching framework for studying this, and then apply it to three distinct domains: incentives for network routing, distributed control design for multiagent engineered systems, and impersonation attacks in networked systems. We ask the following questions:- What features of a behavior-influencing mechanism directly confer robustness?We show weaknesses of several existing methodologies which use pricing for congestion control in transportation networks. In response to these issues, we propose a universal taxation mechanism which can incentivize optimal routing in transportation networks, requiring no information about network structure or user sensitivities, provided that it can charge sufficiently large prices. This suggests that large prices have more robustness than small ones. We also directly compare flow-varying tolls to fixed tolls, and show that a great deal of robustness can be gained by using a flow-varying approach.- How much information does a planner need to be confident that an incentive mechanism will not inadvertently induce pathological behavior?We show that for simple enough transportation networks (symmetric parallel networks are sufficient), a planner can provably avoid perverse incentives by applying a generalized marginal-cost taxation approach. On the other hand, we show that on general networks, perverse incentives are always a risk unless the incentive mechanism is given some information about network structure.- How can robust games be designed for multiagent coordination?We investigate a setting of multiagent coordination in which autonomous agents may suffer from unplanned communication loss events; the planner's task is to program agents with a policy (analogous to an incentive mechanism) for updating their utility functions in response to such events. We show that even when the nominal game is well-behaved and the communication loss is between weakly-coupled agents, there exists no utility update policy which can prevent arbitrarily-poor states from emerging. We also investigate a setting in which an adversary attempts to influence a distributed system in a robust way; here, by understanding susceptibility to adversarial influence, we hope to inform the design of more robust network systems