Options and Commodity Markets for Computing Resources

Marinescu, Dan C.; Morrison, John P.; Siegel, Howard Jay

doi:10.1002/9780470455432.ch5

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…Economic models. Economic models such as the one discussed in [32], cost-utility models [4], macroeconomic models [4], are an intriguing alternative and have been the focus of research in recent years [8,24].…”

Section: Cloud Resource Managementmentioning

confidence: 99%

“…The application of market-oriented policies [4,24,32] and their advantages over the other basic mechanisms implementing resource management policies in large-scale systems have been analyzed in the literature. Control theory [17,20] and utility-based methods require a detailed model of the system and are not scalable.…”

Section: B Computer Clouds Exhibit the Essential Aspects Of Complexity;mentioning

confidence: 99%

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Distributed Hierarchical Control versus an Economic Model for Cloud Resource Management

Marinescu¹,

Paya²,

Morrison³

et al. 2015

Preprint

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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.

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Section: Cloud Resource Managementmentioning

confidence: 99%

Section: B Computer Clouds Exhibit the Essential Aspects Of Complexity;mentioning

confidence: 99%

Distributed Hierarchical Control versus an Economic Model for Cloud Resource Management

Marinescu¹,

Paya²,

Morrison³

et al. 2015

Preprint

View full text Add to dashboard Cite

show abstract

“…At this time, Amazon Web Services (AWS) support the so called spot instances, based on a market-oriented pricing strategy. The application of market-oriented mechanisms [51,52,70] and their advantages over the other basic mechanisms implementing resource management policies in large-scale systems have been analyzed in the literature. Control theory [38,41], machine learning [39,71], and utility-based methods require a detailed model of the system, are not scalable, and typically support.…”

Section: A Cloud Architecture Based On Auctions and Self-managementmentioning

confidence: 99%

An Auction-driven Self-organizing Cloud Delivery Model

Marinescu¹,

Paya²,

Morrison³

et al. 2013

Preprint

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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 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.

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“…The on-going effort to apply concepts from financial markets to large-scale distributed computing, and to grid computing in particular, has generated a number of research ideas [6], [7], [8], [9]. However, Financial markets concepts have not been applied in practice, though it is agreed that computing and communication resources such as CPU cycles and communication bandwidth are perishable commodities that can be traded.…”

mentioning

confidence: 99%

A Self-Organization Model for Complex Computing and Communication Systems

Marinescu

Morrison

Chen

et al. 2008

2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems

Self Cite

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We introduce a model of self-organizing systems inspired by biological and Physics metaphors. The motivation for the development of this model is twofold, it enables us to identify critical properties of a system and to understand the important metrics characterizing its qualitative and the quantitative behavior. It also enables us to develop a methodology to investigate these systems and to carry out comparative studies of alternative policies that govern system behavior. We illustrate the application of the model with a preliminary study of a system designed to encourage green computing and we present some preliminary simulation results of the model. I. MOTIVATIONEngineering modern computer and communication systems is increasingly more challenging because the systems are more complex and more difficult to manage and to use. The complexity of computer and communication systems is caused by several interrelated factors summarized in Figure 1. New applications are developed to satisfy the more diverse needs of an increasingly larger population of users. New devices such as sensors find a wide range of applications, and, in turn, trigger the development of new applications and attract new users. At the same time, the individual components of a system are more complex, for example, 2-4 core processors are ubiquitous now and will be replaced by multi-core systems with tens to hundreds of cores in the next few years. Connectivity and mobility, required by virtually all users of the systems, increase the complexity of the applications and of the computer and communication infrastructure. At the same time, physical limitations, such as heat removal, bandwidth availability, the capacity to store energy, and other factors place additional constrains upon system design. Last, but not least, we have finite resources and the optimization of resource consumption increases system complexity. . Larger segment of population using the systems New and more complex components Optimization of resource consumption New and more complex applications Complexity of computing and communication systems Interconnectivity + mobility Physical constraints Fig. 1. Some of the factors contributing to the complexity of modern computer and communication systems.While we have an intuitive notion of the system complexity, a rigorous definition that allows us to quantify and measure the complexity of a system is not universally accepted. The thermodynamic, von Neumann, and Shannon entropy are related to the number of states of a system, thus they reflect to some extent the system complexity. Kolmogorov complexity [1], [2] of an object is a measure of computational resources needed to specify the object. Using a Markovian assumption and a Kologorov complexity model, we argued that scheduling and resource management are more complex on a computational grid than on a service grid due to the finer granularity of resource allocation [3].We propose to uses the complexity of a program that simulates the system as a measure of complexity of the system; this...

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Options and Commodity Markets for Computing Resources

Cited by 12 publications

References 17 publications

Distributed Hierarchical Control versus an Economic Model for Cloud Resource Management

Distributed Hierarchical Control versus an Economic Model for Cloud Resource Management

An Auction-driven Self-organizing Cloud Delivery Model

A Self-Organization Model for Complex Computing and Communication Systems

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