presence-aware optimum resource allocation for virtual collaboration web 3.0 environments

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  • 8/3/2019 Presence-Aware Optimum Resource Allocation For Virtual Collaboration Web 3.0 Environments


    Presence-aware Optimum Resource Allocation for

    Virtual Collaboration Web 3.0 Environments

    Michael G. Kallitsis , Robert D. Callaway , Michael Devetsikiotis and George Michailidis

    Department of Electrical and Computer Engineering, NC State University, Raleigh, North Carolina 27695 IBM Datapower SOA Appliances, IBM, Research Triangle Park, North Carolina 27709

    Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109,,,

    AbstractIn this paper, we present a system that optimallyand dynamically allocates the available computing resources tovirtual machines that support virtual collaboration environments.Such environments are emerging fast via on-line social networks,virtual worlds, and the Web 3.0 or collaborative Webparadigm. We use a utility-based framework to differentiate theapplications hosted by the virtual machines based on both theirrelative profitability and their social distance information. Wedefine social distance as the importance of social interaction

    between the participants of a virtual environment that utilize aparticular application and this interaction is a function of their

    presence status. Presence is affected by the physical distance, theunderlying network and the social or business relationship ofthe participants. We use graph theory tools to represent usersconnectivity and extract the social distance information. Ouroptimization model involves a variation of the 2D Knapsackproblem and a nonlinear programming one.


    Combined advances in high speed networking, application

    sharing, virtual world technologies and large scale event

    processing are converging to create a new world of ubiquitous

    presence of users, which offers tremendous potential for

    social interaction. The communication networking and com-puting requirements of this converged human-centric environ-

    ment are also increasing at an accelerated pace. In this new

    environment, it is imperative that the much-needed networking

    and computing resources align closely with the needs and

    patterns dictated by the applications, social networks, and

    indeed, by the upper-most human layer. It is believed that the

    success of such socio-technical systems will hinge on the way

    they capture and interact with human presence and location,

    in all of its physical and virtual aspects.

    We anticipate a cloud computing trend of hosting multi-

    ple virtual machines responsible for several Online Social

    Networks (OSN), as well as virtual worlds for education

    or business collaboration purposes. We elaborate more usingthe example of a popular virtual world platform, the Qwaq

    Forums. The suggested system architecture presents the Qwaq

    server [3] hosted on a virtual machine which can be hosted on

    a cloud (e.g., Amazon EC2 [1]). The cloud can support several

    virtual machines that correspond to different virtual worlds

    (i.e., Qwaq virtual rooms). Figure 1 shows a typical scenario: a

    cloud is responsible for three different virtual rooms of NCSU

    that serve different purposes and are visited by different groups

    of people. Since resources are scarce, we need to answer

    Fig. 1. Running multiple virtual worlds in the same cloud.

    questions like which virtual machine to place for execution and

    how to allocate the excess resources to those virtual machines.Other educational worlds that can share resources of a cloud

    include the Second Classroom [4] world in Second Life, and

    the Virtual Northstar project [2] based on Suns Wonderland.

    In order to move towards presence-aware networks and

    applications, we formulate and quantify key measures of socio-

    technical presence and distance and their necessary interplay

    with the control of the computing infrastructure. We introduce

    the metric of social distance that represents the social inter-

    action between the participants of a virtual environment that

    utilize a particular application. We build a utility-based frame-

    work that considers the social distance and the price of each

    application, and optimally allocates the available computing

    resources. Social distance is a function of the presence statusof each user; presence is influenced by the physical distance

    (captured as round trip delay in our model), the underlying

    network and the business distance of the participants. As an

    example, consider the scenario in Figure 2. Assume that A is

    the supervisor of B, they are communicating from somewhere

    in the same city and they reside on a high-speed optical

    network. On the other hand, C is just a regular colleague of D,

    they have an interstate communication and D is connected via

    a wireless network. Therefore, the business relationship of the

    978-1-4244-5626-0/09/$26.00 2009 IEEE

  • 8/3/2019 Presence-Aware Optimum Resource Allocation For Virtual Collaboration Web 3.0 Environments


    Fig. 2. Allocation of resources based on our social distance awareness model.

    first couple is stronger, their round trip delay is smaller and

    their network connection faster. Hence, their social interaction

    is higher than the one of the other couple and this implies a

    smaller social distance (see definition in Section II). Based on

    our optimization framework, the application that users A and

    B are working with would be assigned more resources than the

    one of users C and D because their social distance is smaller.

    The aforementioned framework is realized by our measure-

    ment based optimal resource allocation (MBORA) system,

    which is responsible for utilizing the information collected

    from the social environment and allocating the available re-

    sources. The employed system is comprised of three com-

    ponents: (i) the measurement module, (ii) the optimization

    module and (iii) the resource orchestrator module. The mod-ules interact amongst themselves in the following way: the

    measurement module gathers information about the social

    environment (see Section II). The optimization module re-

    ceives this information and calculates the optimal allocation of

    resources by solving various optimization problems discussed

    in Section III. Finally, the calculated optimal solution is fed

    to the resource orchestrator.

    The concept of MBORA has been introduced in our ear-

    lier work [7], where we studied the optimization issues of

    network elements in service oriented networks (SON) and

    suggested a distributed algorithm for optimal allocation of

    network resources in directed acyclic network topologies.

    In the current work, MBORA represents a hypervisor thatmanages the computing resources (CPU, memory) of virtual

    machines. Related work on resource management and quality-

    of-service (QoS) regarding virtual machines can be found

    in [10]. Optimization problems similar to the ones we study in

    this work appear in [8], [9]. In [8], the authors study algorithms

    for dynamic placement of Web applications that maximize the

    satisfied demand by solving a variant of the Class Constrained

    Multiple-Knapsack Problem. In [9], another Knapsack-type

    problem arises involving services composition under end-to-

    end QoS constraints and distributed optimization algorithms

    are proposed.

    Resource allocation for virtual world environments is con-

    sidered in [6], [12]. In [6], the authors propose the introduction

    of users computers to support the computational demands

    as the virtual worlds population rises. In [12], an economic

    based resource allocation is approached, in which surplus

    resources can be exchanged between collaborative peer-to-

    peer environments. Optimization techniques for enhancing the

    quality of experience in a virtual environment are addressed

    in [5], [11].

    The organization of the paper is as follows: in Section II,

    we start by describing construction and use of the connectivity

    graph that yields the required social distance information. In

    Section III, we discuss our optimization problem formulation

    and in Section IV we present a case-study and some numerical

    results. Finally, we conclude and discuss some future work in

    Section V.


    We describe next a framework that extends the moretraditional network utility maximization to include presence

    and distance-awareness in virtual collaboration environments

    (VCEs) and similar social settings and goes beyond our

    previous work on SONs. In this context, the presence aware

    connectivity graph from which the users social distance

    information will be obtained plays a crucial role.

    Let G = (V, E) denote a weighted graph with node andedge set V and E, respectively, where the nodes correspondto the users (e.g., users i, j) and the edges to their connec-tivity in social space (see Figure 3(a)). The edge weights,

    wij > 0, are calculated so as to reflect the following factors:application used, bandwidth capabilities, physical and busi-

    ness/interpersonal distance. We elaborate next on these factors:(i) Application factor: vertices of users accessing the same

    application (or, more generally, the same virtual space) are

    connected; i.e. eij E. (ii) Bandwidth availability of eachuser: the faster the network connection of a participating user


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