Universal scaling for the dilemma strength in evolutionary games.

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

The crucial role that networking plays in Cloud computing calls for a holistic vision that allows combined control, management, and optimization of both networking and computing resources in a Cloud environment, which leads to a convergence of networking and Cloud computing. Network virtualization is being adopted in both telecommunications and the Internet as a key attribute for the next generation networking. Virtualization, as a potential enabler of profound changes in both communications and computing domains, is expected to bridge the gap between these two fields. Service-Oriented Architecture (SOA), when applied in network virtualization, enables a Network-as-a-Service (NaaS) paradigm that may greatly facilitate the convergence of networking and Cloud computing. Recently the application of SOA in network virtualization has attracted extensive interest from both academia and industry. Although numerous relevant research works have been published, they are currently scattered across multiple fields in the literature, including telecommunications, computer networking, Web services, and Cloud computing. In this article we present a comprehensive survey on the latest developments in service-oriented network virtualization for supporting Cloud computing, particularly from a perspective of network and Cloud convergence through NaaS. Specifically, we first introduce the SOA principle and review recent research progress on applying SOA to support network virtualization in both telecommunications and the Internet. Then we present a framework of network-Cloud convergence based on service-oriented network virtualization and give a survey on key technologies for realizing NaaS, mainly focusing on state of the art of network service description, discovery, and composition. We also discuss the challenges brought in by network-Cloud convergence to these technologies and research opportunities available in these areas, with a hope to arouse the research community's interest in this emerging interdisciplinary field.

A Survey on Service-Oriented Network Virtualization Toward Convergence of Networking and Cloud Computing

Remote sensing data have become very widespread in recent years, and the exploitation of this technology has gone from developments mainly conducted by government intelligence agencies to those carried out by general users and companies. There is a great deal more to remote sensing data than meets the eye, and extracting that information turns out to be a major computational challenge. For this purpose, high performance computing (HPC) infrastructure such as clusters, distributed networks or specialized hardware devices provide important architectural developments to accelerate the computations related with information extraction in remote sensing. In this paper, we review recent advances in HPC applied to remote sensing problems; in particular, the HPC-based paradigms included in this review comprise multiprocessor systems, large-scale and heterogeneous networks of computers, grid and cloud computing environments, and hardware systems such as field programmable gate arrays (FPGAs) and graphics processing units (GPUs). Combined, these parts deliver a snapshot of the state-of-the-art and most recent developments in those areas, and offer a thoughtful perspective of the potential and emerging challenges of applying HPC paradigms to remote sensing problems.

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Recent Developments in High Performance Computing for Remote Sensing: A Review

Resource allocation is a complicated task in cloud computing environment because there are many alternative computers with varying capacities. The goal of this paper is to propose a model for task-oriented resource allocation in a cloud computing environment. Resource allocation task is ranked by the pairwise comparison matrix technique and the Analytic Hierarchy Process giving the available resources and user preferences. The computing resources can be allocated according to the rank of tasks. Furthermore, an induced bias matrix is further used to identify the inconsistent elements and improve the consistency ratio when conflicting weights in various tasks are assigned. Two illustrative examples are introduced to validate the proposed method.

The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment

As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.

Yao Zheng

Papers

A game-theoretic method of fair resource allocation for cloud computing services

PIVOT: An adaptive information discovery framework for computational grids