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We investigate a hierarchically organized cloud infrastructure and compare distributed hierarchical control based on resource monitoring with market mechanisms for resource management. The latter do not require a model of the system, incur a low overhead, are robust, and satisfy several other desiderates of autonomic computing. We introduce several performance measures and report on simulation studies which show that a straightforward bidding scheme supports an effective admission control mechanism, while reducing the communication complexity by several orders of magnitude and also increasing the acceptance rate compared to hierarchical control and monitoring mechanisms. Resource management based on market-based mechanisms can be seen as an intermediate step towards cloud self-organization, an ideal alternative to current mechanisms for cloud resource management., Comment: 13 pages, 4 figures

In this paper we propose a two-stage protocol for resource management in a hierarchically organized cloud. The first stage exploits spatial locality for the formation of coalitions of supply agents; the second stage, a combinatorial auction, is based on a modified proxy-based clock algorithm and has two phases, a clock phase and a proxy phase. The clock phase supports price discovery; in the second phase a proxy conducts multiple rounds of a combinatorial auction for the package of services requested by each client. The protocol strikes a balance between low-cost services for cloud clients and a decent profit for the service providers. We also report the results of an empirical investigation of the combinatorial auction stage of the protocol., Comment: 14 pages

The energy consumption of computer and communication systems does not scale linearly with the workload. A system uses a significant amount of energy even when idle or lightly loaded. A widely reported solution to resource management in large data centers is to concentrate the load on a subset of servers and, whenever possible, switch the rest of the servers to one of the possible sleep states. We propose a reformulation of the traditional concept of load balancing aiming to optimize the energy consumption of a large-scale system: {\it distribute the workload evenly to the smallest set of servers operating at an optimal energy level, while observing QoS constraints, such as the response time.} Our model applies to clustered systems; the model also requires that the demand for system resources to increase at a bounded rate in each reallocation interval. In this paper we report the VM migration costs for application scaling., Comment: 10 Pages

Bid-centric service descriptions have the potential to offer a new cloud service provisioning model that promotes portability, diversity of choice and differentiation between providers. A bid matching model based on requirements and capabilities is presented that provides the basis for such an approach. In order to facilitate the bidding process, tenders should be specified as abstractly as possible so that the solution space is not needlessly restricted. To this end, we describe how partial TOSCA service descriptions allow for a range of diverse solutions to be proposed by multiple providers in response to tenders. Rather than adopting a lowest common denominator approach, true portability should allow for the relative strengths and differentiating features of cloud service providers to be applied to bids. With this in mind, we describe how TOSCA service descriptions could be augmented with additional information in order to facilitate heterogeneity in proposed solutions, such as the use of coprocessors and provider-specific services.

The three traditional cloud delivery models -- IaaS, PaaS, and SaaS -- constrain access to cloud resources by hiding their raw functionality and forcing us to use them indirectly via a restricted set of actions. Can we introduce a new delivery model, and, at the same time, support improved security, a higher degree of assurance, find relatively simple solutions to the hard cloud resource management problems, eliminate some of the inefficiencies related to resource virtualization, allow the assembly of clouds of clouds, and, last but not least, minimize the number of interoperability standards? We sketch a self-organizing architecture for very large compute clouds composed of many-core processors and heterogeneous coprocessors. We discuss how self-organization will address each of the challenges described above. The approach is {\em bid-centric}. The system of heterogeneous cloud resources is dynamically, and autonomically, configured to bid to meet the needs identified in a high-level task or service specification. When the task is completed, or the service is retired, the resources are released for subsequent reuse. Our approach mimics the process followed by individual researchers who, in response to a call for proposals released by a funding agency, organize themselves in groups of various sizes and specialities. If the bid is successful, then the group carries out the proposed work and releases the results. After the work is completed, individual researchers in the group disperse, possibly joining other groups or submitting individual bids in response to other proposals. Similar protocols are common to other human activities such as procurement management., Comment: 17 pages

Cloud elasticity - the ability to use as much resources as needed at any given time - and low cost - a user pays only for the resources it consumes - represent solid incentives for many organizations to migrate some of their computational activities to a public cloud. As the interest in cloud computing grows, so does the size of the cloud computing centers and their energy footprint. The realization that power consumption of cloud computing centers is significant and it is expected to increase substantially in the future motivates our interest in scheduling and scaling algorithms which minimize power consumption. We propose energy-aware application scaling and resource management algorithms. Though targeting primarily the Infrastructure as a Service (IaaS), the system models and the algorithms we propose can be applied to the other cloud delivery models and to private clouds., Comment: 18 pages

In this paper we introduce algorithms for the construction of scale-free networks and for clustering around the nerve centers, nodes with a high connectivity in a scale-free networks. We argue that such overlay networks could support self-organization in a complex system like a cloud computing infrastructure and allow the implementation of optimal resource management policies., Comment: 14 pages, 8 Figurs, Journal

The complexity of the error correction circuitry forces us to design quantum error correction codes capable of correcting a single error per error correction cycle. Yet, time-correlated error are common for physical implementations of quantum systems; an error corrected during the previous cycle may reoccur later due to physical processes specific for each physical implementation of the qubits. In this paper we study quantum error correction for a restricted class of time-correlated errors in a spin-boson model. The algorithm we propose allows the correction of two errors per error correction cycle, provided that one of them is time-correlated. The algorithm can be applied to any stabilizer code when the two logical qubits $\mid 0_L>$ and $\mid 1_L>$ are entangled states of $2^{n}$ basis states in $\mathcal{H}_{2^n}$., Comment: 14 pages, 3 figures

Grover's search algorithm can be applied to a wide range of problems; even problems not generally regarded as searching problems, can be reformulated to take advantage of quantum parallelism and entanglement, and lead to algorithms which show a square root speedup over their classical counterparts. In this paper, we discuss a systematic way to formulate such problems and give as an example a quantum scheduling algorithm for an $R||C_{max}$ problem. $R||C_{max}$ is representative for a class of scheduling problems whose goal is to find a schedule with the shortest completion time in an unrelated parallel machine environment. Given a deadline, or a range of deadlines, the algorithm presented in this paper allows us to determine if a solution to an $R||C_{max}$ problem with $N$ jobs and $M$ machines exists, and if so, it provides the schedule. The time complexity of the quantum scheduling algorithm is $\mathcal{O}(\sqrt{M^N})$ while the complexity of its classical counterpart is $\mathcal{O}(M^N)$., Comment: 15 pages