Cloud providers provision their various resources such as CPUs, memory, and storage in the form of virtual machine (VM) instances which are then allocated to the users. The users are charged based on a pay-as-you-go model, and their payments should be determined by considering both their incentives and the incentives of the cloud providers. Auction markets can capture such incentives, where users name their own prices for their requested VMs. We design an auction-based online mechanism for VM provisioning, allocation, and pricing in clouds that considers several types of resources. Our proposed online mechanism makes no assumptions about future demand of VMs, which is the case in real cloud settings. The proposed online mechanism is invoked as soon as a user places a request or some of the allocated resources are released and become available. The mechanism allocates VM instances to selected users for the period they are requested for, and ensures that the users will continue using their VM instances for the entire requested period. In addition, the mechanism determines the payment the users have to pay for using the allocated resources. We prove that the mechanism is incentive-compatible, that is, it gives incentives to the users to reveal their actual requests. We investigate the performance of our proposed mechanism through extensive experiments.

The amount of computing resources required by current and future data-intensive applications is expected to increase dramatically, creating high demands for cloud resources. The cloud providers' available resources may not be sufficient enough to cope with such demands. Therefore, the cloud providers need to reshape their business structures and seek to improve their dynamic resource scaling capabilities. Federated clouds offer a practical platform for addressing this service management issue. We introduce a cloud federation formation game that considers the cooperation of the cloud providers in offering cloud IaaS services. Based on the proposed federation formation game, we design a cloud federation formation mechanism that enables the cloud providers to dynamically form a cloud federation maximizing their profit. In addition, the proposed mechanism produces a stable cloud federation structure, that is, the participating cloud providers in the federation do not have incentives to break away from the federation. We analyze the performance of the proposed mechanism by performing extensive experiments. The results of the experiments show that the cloud federation obtained by our proposed mechanism is stable, yielding high profit for the participating cloud providers.

We address the problem of physical machine resource management in clouds considering multiple types of physical machines and resources. We formulate this problem in an auction-based setting and design optimal and approximate strategy-proof mechanisms that solve it. Our proposed mechanisms consist of a winner determination algorithm that selects the users, provisions the virtual machines (VMs) to physical machines (PMs), and allocates them to the selected users; and a payment function that determines the amount that each selected user needs to pay to the cloud provider. We prove that our proposed approximate winner determination algorithm satisfies the loser-independent property, making the approximate mechanism robust against strategic users who try to manipulate the system by changing other users' allocations. We show that our proposed mechanisms are strategy-proof, that is, the users do not have incentives to lie about their requested bundles of VM instances and their valuations. In addition, our proposed mechanisms are in alignment with green cloud computing strategies in which physical machines can be powered on or off to save energy. Our theoretical analysis shows that the proposed approximation mechanism has an approximation ratio of 3. We perform extensive experiments in order to investigate the performance of our proposed approximation mechanism compared to that of the optimal mechanism.

Cloud providers provision their heterogeneous resources such as CPUs, memory, and storage in the form of virtual machine (VM) instances which are then allocated to the users. One of the major challenges faced by the cloud providers is to allocate and provision these resources such that their profit is maximized, and the resources are utilized efficiently. Recently, cloud providers have introduced auction-based models which allow users to submit bids for their requested VMs. We address the problem of autonomic VM provisioning and allocation for the auction-based model considering multiple types of resources by designing an approximation mechanism. In addition, the mechanism determines the payment the users have to pay for using the allocated resources. This problem is computationally intractable, and our proposed mechanism is by far the strongest approximation result that can be achieved for this problem. We show that the proposed approximation mechanism is a polynomial-time approximation scheme (PTAS). Furthermore, our proposed mechanism drives the system into an equilibrium in which the users do not have incentives to manipulate the system by untruthfully reporting their VM bundle requests and valuations. We perform extensive experiments using real workload traces in order to investigate the performance of the proposed mechanism.

The majority of large-scale data intensive applications executed by data centers are based on MapReduce or its open-source implementation, Hadoop. Such applications are executed on large clusters requiring large amounts of energy, making the energy costs a considerable fraction of the data center's overall costs. Therefore minimizing the energy consumption when executing each MapReduce job is a critical concern for data centers. In this paper, we propose a framework for improving the energy efficiency of MapReduce applications, while satisfying the service level agreement (SLA). We first model the problem of energy-aware scheduling of a single MapReduce job as an Integer Program. We then propose two heuristic algorithms, called energy-aware MapReduce scheduling algorithms (EMRSA-I and EMRSA-II), that find the assignments of map and reduce tasks to the machine slots in orderto minimize the energy consumed when executing the application. We perform extensive experiments on a Hadoop cluster to determine the energy consumption and execution time for several workloads from the HiBench benchmark suite including TeraSort, PageRank, and K-means clustering, and then use this data in an extensive simulation study to analyze the performance of the proposed algorithms. The results show that EMRSA-I and EMRSA-II are able to find near optimal job schedules consuming approximately 40 percent less energy on average than the schedules obtained by a common practice scheduler that minimizes the makespan.

A major challenging problem for cloud providers is designing efficient mechanisms for virtual machine (VM) provisioning and allocation. Such mechanisms enable the cloud providers to effectively utilize their available resources and obtain higher profits. Recently, cloud providers have introduced auction-based models for VM provisioning and allocation which allow users to submit bids for their requested VMs. We formulate the dynamic VM provisioning and allocation problem for the auction-based model as an integer program considering multiple types of resources. We then design truthful greedy and optimal mechanisms for the problem such that the cloud provider provisions VMs based on the requests of the winning users and determines their payments. We show that the proposed mechanisms are truthful, that is, the users do not have incentives to manipulate the system by lying about their requested bundles of VM instances and their valuations. We perform extensive experiments using real workload traces in order to investigate the performance of the proposed mechanisms. Our proposed mechanisms achieve promising results in terms of revenue for the cloud provider.

Cloud providers provision their various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances which are then allocated to the users. We design online mechanisms for VM provisioning and allocation in clouds that consider several types of available resources. Our proposed online mechanisms make no assumptions about future demand of VMs, which is the case in real cloud settings. The proposed mechanisms are invoked as soon as a user places a request or some of the allocated resources are released and become available. The mechanisms allocate VM instances to selected users for the period they are requested for, and ensure that the users will continue using their VM instances for the entire requested period. In addition, the mechanisms determine the payment the users have to pay for using the allocated resources. We prove that the mechanisms are incentive-compatible, that is, they give incentives to the users to reveal their true valuations for their requested bundles of VM instances. We investigate the performance of our proposed mechanisms through extensive experiments.

The big data trend is generating compute-intensive and data-intensive applications requiring unique services that are different from conventional computing services. Therefore, there is a need to fundamentally address such requirements by developing market mechanisms for managing, trading, and pricing big data computing services. The cloud computing platforms have a great potential to meet the economic requirements of market mechanisms for big data applications due to their technological advances, cost benefit ratios, and easy to use services. We design a two-sided mechanism for trading computing resources for big data applications. Our proposed mechanism is universally strategy-proof, providing incentives for both cloud providers and users to voluntarily reveal their true private information. We perform extensive experiments to evaluate our proposed mechanism.

One of the benefits of cloud computing is that a cloud provider can dynamically scale-up its resource capabilities by forming a cloud federation with other cloud providers. Forming cloud federations requires taking the data privacy and security concerns into account, which is critical in satisfying the Service Level Agreements (SLAs). The nature of privacy and security challenges in clouds requires that cloud providers design data protection mechanisms that work together with their resource management systems. In this paper, we consider the privacy requirements when outsourcing data and computation within a federation of clouds, and propose a framework for minimizing the cost of outsourcing while considering two key data protection restrictions, the trust and disclosure restrictions. We model these restrictions as conflict graphs, and formulate the problem as an integer program. In the absence of computationally tractable optimal algorithms for solving this problem, we design a fast heuristic algorithm. We analyze the performance of our proposed algorithm through extensive experiments.

Designing efficient mechanisms for Virtual Machine (VM) provisioning and allocation is a major challenging problem that needs to be solved by cloud providers. We formulate the VM provisioning and allocation problem in clouds as an integer program and design truthful greedy mechanisms that solve it. We show that the proposed mechanisms are truthful, that is, the users do not have incentives to lie about their requested bundles of VM instances and their valuations. We perform extensive experiments in order to investigate the performance of the proposed mechanisms.