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

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

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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

[email protected], [email protected], [email protected], [email protected]

Abstract—In 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 Web”paradigm. 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 of the participants. We use graph theory tools to represent users’connectivity and extract the social distance information. Ouroptimization model involves a variation of the 2D Knapsackproblem and a nonlinear programming one.

I. INTRODUCTION

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 Sun’s 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



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 user’s computers to support the computational demands

as the virtual world’s 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.

I I . SOCIAL DISTANCE AND THE CONNECTIVITY GRAPH

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 and

edge set V and E , respectively, where the nodes correspond

to 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 each

user: the faster the network connection of a participating user,

the larger the allocation they should get. Assuming that user-ihas a connection of C i Mbps and user- j one of C j Mbps,

then the corresponding edge weight should be proportional

to the minimum of those values, i.e.: wij ∝ min−1(C i, C j).

The intuition is that it is pointless to allocate large amounts of

resources to connections with poor bandwidth capabilities. (iii)

Round trip delay (imitates real world distance), i.e.: wij ∝ dij.

The reasoning is that we do not want to allocate more than the

absolutely necessary resources to a session of users that are farapart, since such connections usually experience larger end-

to-end delays (e.g., propagation, queueing delays) and thus

MBORA should prefer to allocate more resources to users that

are closer to each other. (iv) Business/interpersonal distance:

we want the social distance to be smaller when we deal with an

employee-manager communication, rather than an employee-

employee communication, i.e., wij ∝ r−1ij where rij takes

discrete values that represent the relationship between user-

i and user- j. For example, rij ∈ {α, β, γ } with α < β < γ



i

j

wij

(a) The connectivity graph used torepresent each virtual world. Notethat the graph should be complete;for visual brevity, here it is not.

(b) Passing the presence informa-tion of the graph. The databasepushes information to MBORAonly when social distance changes.

Fig. 3. Connectivity graph and data mining.

should depict someone’s relationship with an unknown person,

a friend and her/his manager correspondingly. Given the above

factors, the proposed edge weights are given by

wij = ξdij + L/ min(C i, C j)

rij(1)

where ξ is a normalization constant and L is the size of a

dummy package. Other parameters (like the in-world popu-

lation or in-world distance) and other formulas for the edge

weights could also be considered and this constitutes ongoing

research.

The social distance between two nodes in the social net-

working environment S is dS(i, j) = wij . This holds only in

the case of two users collaborating in the same application. In a

multi-user, multi-application scenario we can use the diameter

of the graph, defined as the average shortest distance between

two vertices. Alternative metrics (e.g., clustering coefficient or

network flow) could also be used, if appropriate.

Given a database that contains the user connectivity patterns,the MBORA system needs to acquire that information through

a publisher-subscriber model (Figure 3(b)). We propose to use

a push model, so that MBORA will not have to update the

database. Since the distances amongst users would change

fairly frequently, the database needs to monitor them and

push updated information to MBORA only when significant

changes occur, so as to avoid excessive updates. We propose

to use exponential weighted moving average (EWMA) control

charts for this monitoring purpose. Such charts have proved

useful in previous work [7] for monitoring significant changes

in traffic parameters.

Suppose that time is divided into sequential intervals

(tn, tn+1), n = 0, 1, 2, . . . of duration Δ. The samplingmodule has to sample distance information every inter-

val in order to obtain the sequence of distance variations

{ρi(1), ρi(2), . . .}. The EWMA statistic is ρi(n + 1) =θρi(n) + ( 1 − θ)ρi(n), where ρi(n + 1) is the one-step-ahead

predictor of ρi(n + 1) and 0 < θ ≤ 1 represents the weight

that the most recent estimation is assigned. The lower/upper

control limits are given as LCL/UCL = ρi±cσi

θ

2−θ where

parameter c > 0 is a tuning variable that combined also with

parameter θ can set the sensitivity level that the provider needs

to accomplish. An out-of-control signal is generated at time

slot n when ρi(n) > UCL or ρi(n) < LCL. At that moment,

an update of distance information will be pushed to MBORA.

III. OPTIMUM ALLOCATION OF MULTIPLE RESOURCES

In principle, multiple computing and communication re-

sources might have to be allocated, including CPU cyclesand memory to the virtual machines (VM) running the re-

quired applications (or, more generally, the virtual machine

that hosts the virtual environment that users belong to). Our

proposed optimization framework consists of the following

two components: (i) A variation of the 2D Knapsack opti-

mization problem that decides which VMs would be placed

for execution according to the available resources and their

pricing parameters, (ii) a nonlinear programming problem that

allocates the excess resources to the VMs placed for execution.

We discuss next the first problem. We are given a set of

virtual machines, K , with each machine possessing Ψ amount

of memory and Ω amount of CPU power available. With

each virtual machine, we associate its demand values, ψ0k

and ω0k, which correspond to the machine’s memory and CPU

requirements, respectively. With each virtual machine we also

relate its social pricing parameter, pk, which is a function of

the application’s price, χk (either a real price or just a priority

indication), and the social distance, dkS . In summary, we have

pk = pk(χk, dkS) = χk/dkS .

The placement problem is to find a vector I , where I k is 1

if the virtual machine is running on that host and 0 otherwise,

such that (a) allocation requirements are met, (b) capacity

limits are observed and (c) the social profit is maximized. The

problem can be formally stated as follows:

maxk

I k pk subject to (2)

k

I kψ0k ≤ Ψ and

k

I kω0k ≤ Ω, I k binary, ∀k

This is an integer programming problem and the solution

can be found using branch and bound type algorithms. Once

we have placed the subset of virtual machines on the host

system, we can solve a nonlinear optimization problem, so

as to optimally allocate the excess resources of memory and

CPU power to the running virtual machines. Our objective

function is chosen to be a logarithmic one due to (a) the

concavity property of the function, and (b) our intentions for a

proportional fairness allocation scheme [7]. The optimization

problem formulation is:

maxω

k

pklog(ωk) and maxψ

k

pklog(ψk) (3)

subject tok

ωk ≤ Ω andk

ψk ≤ Ψ,

ωk ≥ ω0k, ∀k ∈ K and ψk ≥ ψ0

k, ∀k ∈ K



IV. PERFORMANCE EVALUATION

In this section, we present the results of a numerical study

for our framework based on a simple case study. The scenario

includes three different sets of users. We assume that each

member of the set is collaborating on the same application

via a virtual world that is hosted by a particular virtual

machine. The goal of MBORA is to optimally allocate the

resources (CPU, memory) to the three aforementioned virtualmachines (K = 3). In our scenario, each set consists of three

collaborators that cooperate from various geographic locations

and may have different bandwidth capabilities and/or business

relationships. We examine MBORA’s behavior under diverse:

(a) resource demands, (b) social graph characteristics and (c)

pricing of the required applications.

Our connectivity graphs consist of three different 3-node,

complete graphs. In the first scenario, we assume that the

connectivity database contains the information depicted in

Table I. These data are needed for calculating the social di-

ameter (named as δkS henceforth) of each graph. For example,

regarding application-α, the graph weights wij of each edge

are equal to wij = ξ 1+1/105 = 2.2 (in this case study we set

ξ = 100, L = 1) and the diameter (defined as the average

shortest path length (SP) between all nodes of our graph) can

be found by δαS = 2

{i,j∈V,i=j,i<j} SP ij

|V |×|V −1| = SP 12+SP 13+SP 233

=2.2+2.2+2.2

3= 2.2.

Table II illustrates the optimal allocation as provided by

MBORA. From the first 5 rows we see that resources are

allocated to all three virtual machines; this is because the

available resources are able to accommodate the minimum

requirements of each machine. However, in the last two rows,

we see that resources are allocated only to VM-α and VM-

β ; MBORA could not place all VMs for execution. Note that

the choice of VM-α and VM-β is arbitrary; VM-γ could havebeen selected as well due to the fact that the social price pkof all applications is identical. Finally, notice that in all cases,

the excess resources are optimally allocated so as to maximize

our objective functions (3).

In the second scenario, we evaluate our model by chang-

ing parameters of the social graph that affect its diameter.

Specifically, we alter the delay between user-1 and user-2 of

application-α. Initially, dkij = 5, ∀i, j ∈ {1, 2, 3}, i = j and

k ∈ {α, β, γ }. The remaining parameters are described in the

second row of Table I and the computing demands are depicted

in Table III. Figure 4 consolidates our results and should

be read as follows: the upper-left chart shows the values of

delay (and diameter) for our scenario. We ran our optimizationalgorithm for a sequence of 7 different events. The resource

demand for each event is found by inquiring Table III. The

remaining three charts depict the optimal allocation that our

algorithm would assign to each VM. We observe that as the

delay, dα12, decreases, the diameter of graph-α decreases too

(upper-left panel). This means that VM-α should be allocated

more resources. Also, note the different placement of VMs

between sequences 6 and 7 of the corresponding plots (upper-

right and bottom ones). This occurs because the diameter of

1 2 3 4 5 6 70

1

2

3

4

5

6

7

8

Sequence #

D e l a y / D i a m e t e r

1 2 3 4 5 6 7

0.3

0.35

0.4

0.45

0.5

Sequence #

C P U / M e m a

l l o c a t i o n

VM−α allocation

1 2 3 4 5 6 7

0.3

0.35

0.4

0.45

0.5

Sequence #

C P U / M e m a

l l o c a t i o n

VM−β allocation

1 2 3 4 5 6 7

0.3

0.35

0.4

0.45

0.5

Sequence #

C P U / M e m a

l l o c a t i o n

VM−γ allocation

Delay d12

α

Diameter δS

α CPU ω 1

*

Mem ψ 1

*

CPU ω 2

*

Mem ψ 2

*

CPU ω 3

*

Mem ψ 3

*

Fig. 4. Sensitivity on the delay (physical distance).

graph-α becomes larger than the diameter of the other two

graphs and, thus, the social price for application-α is less than

the social price of the competing applications. In other words,

VM-α gets no resources because the round trip delay has been

increased.

TABLE IIIDEMAND FOR SCENARIOS 2, 3

Sequence # (ω01, ψ0

1) (ω0

2, ψ0

2)=(ω0

3, ψ0

3)

1 (0.2, 0.2) (0.2, 0.2)2 (0.2, 0.2) (0.2, 0.2)3 (0.2, 0.2) (0.2, 0.2)4 (0.2, 0.4) (0.2, 0.2)5 (0.4, 0.4) (0.4, 0.4)6 (0.4, 0.4) (0.4, 0.4)7 (0.4, 0.4) (0.4, 0.4)

The last scenario involves changes in the pricing parameter,

χα, of application-α which affects social price pα and the

ideal/optimal allocations are presented in Figure 5. The re-

maining modeling parameters are depicted in Table I. Notice

that because of the inferior bandwidth capacity of user-1 of

application-α (see last row, Table I), the diameter of graph-

α is larger than the diameter of the combating graphs (i.e.,

δαS = 7, δβS = δγS = 1). Therefore, the initial social price, pα, is lesser (i.e., pα = 1/7, pβ = pγ = 1). This plays a

role in the placement of virtual machines as well as in the

allocation of the excess resources. For example, in Figure 5,

we perceive how the allocation changes as price χα gets larger

(first 4 sequences) combined with the fact that δαS = 7. The

reader should also recognise the distinction between cases 6

and 7; VM-α gets placed since its social price has surpassed

the social price of VM-β and VM-γ due to the considerable

increase of the pricing parameter χα.



TABLE ICONNECTIVITY GRAPH PARAMETERS FOR APPLICATION-k, k ∈ {α,β,γ }

Delay Bandwidth Business distance Price(distance units) (Mbps)

Scenario 1 dk12

= dk23

= dk13

= 1 C k1

= C k2

= C k3

= 10 rk12

= rk23

= rk13

= 5 χk = 1Scenario 2 dk

12= dk

23= dk

13= 5 C k

1= C k

2= C k

3= 10 rk

12= rk

23= rk

13= 10 χk = 1

Scenario 3 dk12

= dk23

= dk13

= 1 C α1

= 1 else C kj = 10 rk12

= rk23

= rk13

= 10 χk = 1

TABLE IISENSITIVITY ON RESOURCE DEMAND (ω0i , ψ

0

i )

(ω01, ψ0

1) (ω0

2, ψ0

2) (ω0

3, ψ0

3) Virtual Machine-α Virtual Machine-β Virtual Machine-γ

(ω∗1, ψ∗

1) (ω∗

2, ψ∗

2) (ω∗

3, ψ∗

3)

(0.1, 0.1) (0.1, 0.1) (0.1, 0.1) (0.33, 0.33) (0.33, 0.33) (0.33, 0.33)(0.2, 0.1) (0.1, 0.1) (0.1, 0.2) (0.33, 0.33) (0.33, 0.33) (0.33, 0.33)(0.3, 0.1) (0.1, 0.1) (0.1, 0.3) (0.33, 0.33) (0.33, 0.33) (0.33, 0.33)(0.4, 0.1) (0.2, 0.2) (0.1, 0.4) (0.4, 0.3) (0.3, 0.3) (0.3, 0.4)(0.4, 0.2) (0.35, 0.35) (0.2, 0.4) (0.4, 0.25) (0.35, 0.35) (0.25, 0.4)(0.4, 0.4) (0.4, 0.4) (0.4, 0.4) (0.5, 0.5) (0.5, 0.5) ( -, -)(0.2, 0.4) (0.2, 0.4) (0.2, 0.4) (0.5, 0.5) (0.5, 0.5) ( -, -)

V. CONCLUSIONS

In this paper, we presented a presence-aware optimization

framework that ensures optimal resource allocation of comput-

ing resources to virtual machines that host virtual collaboration

environments. We introduced the idea of social distance that

reflects the social proximity of collaborators; smaller social

distance implies a higher allocation of resources and vice

versa. The premise of this paper is that quantitative modeling

and optimization is essential to designing such presence-

aware communication systems. Therefore, we model presence-

aware networking interactions including the upper, social

layers of communication. Such presence-aware cross layering

is necessary in order to transform communication networks

into integrated socio-technical systems for interaction andcollaboration. We expect to see large connectivity graphs,

which would make the analysis of the current fully connected

graph infeasible. Hence, future work includes the investigation

of: (a) sparsification techniques of connectivity graphs that

would allow efficient calculation of the social distance in

the case of large collaboration groups; (b) more scalable and

efficient heuristics for the solution of the placement problem.

ACKNOWLEDGMENTS

The work of GM is supported by NSF grant DMS-0806094.

MK and MD are supported in part by IBM and CACC.

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1 2 3 4 5 6 70

0.25

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Sequence #

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

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