SiCoGrid: A Complete Grid Simulator for Scheduling and Algorithmical
Research, with Emergent Artificial Intelligence data algorithms.
Technical Report RR-06-11. DIIS. UNIZAR.
Vı́ctor Méndez
Computer Science Departament, Computer Architecture area.
Universidad de Zaragoza. Centro Politécnico Superior,
Edificio Ada Byron, Marı́a de Luna, 1. 50018 Zaragoza, Spain.
E-mail: vmendez@unizar.es, eureka@nodo50.org
Felix Garcı́a
Computer Science Departament, ARCOS group.
Universidad Carlos III de Madrid. Escuela Politécnica Superior.
Av. Universidad 30 - 28911 Leganes (Madrid) Spain.
E-mail: fgcarbal@inf.uc3m.es
Abstract
Grids are become popular paradigms for parallel and
distributed computing. Dynamically aggregation of resources on heterogeneous infrastructures allow to solve
large scale problems in science, engineering, and ecommerce. The numerous parameters that have to be taken
in account, becomes difficult to use analytical models. In
this paper we propose a completed Grid simulator, called
SiCoGrid (from the Spanish Simulator Completo Grid),
where every Grid entity is modelled. We focus on event
driven low level Grid simulation, regard the less of the Grid
application running on the top level, and the upper architecture abstractions. The aim is to provide an easy scaling tool for Grid researches, where implementing different
core algorithms and policies, integrating new approaches
on the existing SiCoGrid code, and thus analyse performances in terms of response rate and resources use. Actually SiCoGrid have implemented canonical data algorithms
and job schedulers, and also an economic data algorithm,
and two Emergent Artificial Intelligence algorithms proposed by authors on previous works: the Grid flavours of
Particle Swarm Optimization(PSO) and Ant Colony Optimization(ACO).
1 Introduction
It has been achieved improvements on the interconnection technologies, first within single system, second among
nodes on a cluster or over multiple cluster, and more recently over Grids that enhance the paradigms for heterogeneous platforms with a common Grid interface. GT[1],
Condor branch for Grid called Condor-G[2], and other specific implementations like EGEE[3], have contributed to
core Grid middleware services that are available as the basis for further application development. However, less effort has been made to optimise the use of Grid resources.
One of the reasons of this drawback for theoretical work on
Grid scheduling algorithms and data Grid management algorithms, is the difficult of the evaluation methodology. It
is almost impossible to obtain any analytical result, due to
the heterogeneous, complex and dynamic parameters of the
Grid infrastructures. The most simple is to compare the efficacy of algorithms on real applications with real resources.
But real applications run for to long time to get enough reliable statistical repetitions, on all factors and levels. Furthermore, variations in resource load over time, make it difficult
to obtain repeat results, so it is very difficult to validate the
experiments. Simulation is then the most viable approach
to effectively evaluate Grid algorithms.
It is not possible to Grid simulate with general purpose
simulation tools[4][5][6], because they does not fit with
Grid paradigm, that is a much more complex issue. Thus
there is necessary to use specific Grid simulators as the better way to evaluate the different approaches.
On previous works we identified the drawback of a
toolkit for a rouge evaluation methodology. SiCoGrid was
create to fill this requirements and may be used on reliable
experiment evaluations.
On the next section we summarise some Grid simulation
background, to face our simulation aproach, and to fill some
researches and computing engineers requirements. Third
section describe SiCoGrid design, and some implementation details. We have develop SiCoGrid with the meaningful Grid entities involved on the paradigm, mainly computing, storage and network. This split on parts may be taken
as a hold, where the interrelations and dependencies are on
a very complex level. So it is not possible to have reliable
simulations if we do not shape all of them in our design.
SiCoGrid is focus on the Grid core components simulation
level, those where are develop all the theoretical algorithms
for data management and scheduling policies for Computing and Data Grid.
All Grid clients performs jobs. Every job is composed
of many sequential requests of block files or data objects.
When the request is runing on the local Grid site, then we
are in a pure Data Grid. If the computing task can be executed on remote Grid resources, then it is Computing Grid,
that is also combined with Data Grid.
Data Grid performances depends on the replica location,
selection, optimisation and deletion algorithms. Computing
Grid performances also depends on scheduling algorithms.
On SiCoGrid both of them are easy to implement on modular code, in a very similar way as computer engineers may
do on a real Grid packet. SiCoGrid gives to the researches
the possibilities to obtain information about job response
time, disk, network an computation usage; for evaluating
different approaches, from different points of view.
At the end of the third section some simulation performances are given.
The fourth section is a brief summary of our EAI algorithm implementation for data replication services.
We finish with some conclusions and future work of
SiCoGrid project.
2 Grid simulation background
Hight performance computing environments, are to complex for any simulation tools. The implementations cannot
address all the Grid features of the executions like network
errors, batch resources behaviour, complex network topologies, virtual organisations interactions, etc. There are few
Grid simulators, and every one is focus on different aspects
of studies, and varying degrees of Grid components modelled.
Bricks Grid[7] is focus on simulating scheduling policies over the Computational Grid. It is a multiple clients
and multiple servers scenarios simulator that returns aver-
age overall service rates. The network is shaped as stand
alone simulating entity between a Grid Computing Client
and a Grid Server, without complex topology implementation. There is also an enhanced Bricks Grid simulator[8],
for Data Grid purpose, with local disks I/O overheads and
a disk management mechanism, but the mayor lack of the
network an other Grid components simplifications still the
same as the original simulator.
Microgrid [9] is a virtualization layer that intercepts calls
from the Globus application to emulate them on real resources of an homogeneous cluster. The code is executed
on the virtual Grid over the local cluster, so it is not possible to run large number of experiments and repetitions, because on the most of the cases Microgrid spend much more
time than the equivalent real execution on the Grid infrastructure. On the other hand the results of the emulations
are more precise than the obtained with simulations, due to
much more aspects of the Grid that are modelled, like authentication cost or software overhead.
GridSim [10] is a toolkit for analyse and compare the
performance of resource scheduling algorithms of the grid.
There is not Data Grid simulation, only processing is implemented on variants of the Nimrod-G[11] resource broker based on the market based economic model. GridSim is
shape of heterogeneous, multi-tasking Grid resources, calendar based on deadline and budget based constraints. It
also allow to evaluate various scheduling policies in a single
simulation. The implementation of each grid task as a separate thread in the Java Virtual Machine, limits the scalability
of the simulation on tackling enterprise level requirements.
SimGrid [12] is focus on scheduling to model the grid
network topology, and simulate the data flow over the available network bandwidth. The mayor drawback of this tool
is that is not modelling job decomposition and task parallelisation characteristics, or resource availability is also not
modelled.
Katitha and Foster propose ChiSim[13], a tool for job
scheduling and data replication algorithms simulations. The
main contrib is the holistic point of view of both computing
and data grid, more than a splited analysis of them. This fit
well on Grid paradigm as a hold of a wide range of parameters. The major drawback of this simulator is not considerer
the network topology, assumes that sites are connected to
the Grid via a limited bandwidth network. The bandwidth
between any two sites is constant without real simulation of
remote connections between sites.
OptorSim [14] has explicitly accounts for both dynamic
provisioning and resource scheduling policies, obtaining resulting performance. Dynamic provisioning is performed in
the context of replication in data Grid. Various economic
market model replication strategies can be evaluated with
the canonical reference. The storage simulation is only
taken in an implicit way, through the storage cost estima-
tions, but not any IO simulation is done.
GangSim [15] have been developed to support studies of
scheduling strategies in Grid environments, with a particular focus on interactions of local and global resource allocation policies. It is derived as part of the Ganglia monitoring
framework, an implementation that mix simulated and real
Grid components. It is evaluated the impact of different Virtual Organizations(VO)-level task assignment strategies and
site usage policies on achieved performance. It is the first
simulator that model not only sites but also VO users and
planners, and its ability to model usage policies at both the
site an VO levels. There are algorithms for job selection,
job assignment, and data file replication. There are algorithms for considering costs associated with various operations. All the algorithms are grouped in a single module,
and invoked every time a decision is going to be taken.
Sugato Bagchi simulator [16] is focus in analysing the
performance of Grid computing workloads. It is a toolkit
for IT consultants to design schedulers workloads for optimal resource capacity, and to estimate the adecuation of
the Grid infrastructures design. Resource scheduling has
few typical policies such as load levelling, greedy(the task
is sent to the resource that can complete it the faster), round
robin scheduling, or threshold-based (tasks are sent to a preferred resource until a certain performance parameter level
is breached). Other interesting feature is that it has a nonprogrammer user interface, it is a graphical modelling environment easy to use for designers and consultants.
We have seen a state of the art on Grid scheduling and
replication evaluation methods using both simulating and
emulating. We stand out the extremely complexity of the
issue, that makes the existing tools only valid for specific
studies, and exposes the uncovered necessity of a completed
simulation tool for generic Grid evaluation methodology.
• Latency.
Figure 1 presents the SiCoGrid UML design, with all
the modelled components and some of the main attributes.
Figure 1. SiCoGrid UML design
The complete toolkit includes a workload generating
program, represented on the figure 1 as the Access Pattern. The workload application create the log for the given
input arguments: access pattern, random seed, number of
Grid clients by node or site, number of jobs by Grid client.
The access pattern may choose full file, sequential block
access, random, unitary random walk, or gaussian random
walk. The random seed is for statistical experiment repetitions. Number of Grid Clients in a node or site, is measurement of the workload weigh. The number of jobs by grid
client is a component for lenght of the experiment.
3 SiCoGrid Design and Implementation
The meaningful components of the Grid simulator are
• Computing resources.
• Storage resources.
• Networking resources.
Our goal is to provide an evaluation tool of the scheduling
and replication approaches, that correspon with low level
layer of Grid infrastructure. On this context, we realize on
the significance of the attributes allocation. Asociated to the
main Grid components are the following attributes:
• Computing power.
• IO throughput.
• Bandwidth.
Figure 2. SiCoGrid node scheme
Each job will request many file blocks. The workload
application return for each file request an Active Time and a
Passive Time. Those times are empirical model of Web document arrivals at access link[17]. After a job get a file block
response, it spend an Active Time for process the block part
of the job, this time is calculated based on Computer Elements featured specifications on network configuration file.
Passive Time is the time that the user hold between one job
and another. For this parameter we use a Pareto distribution
with k=1 and alpha = 0.9 with infinite mean and variance,
that is a characteristic Web Service users distribution[18].
The Grid Site may inherit in a Router, and Grid Node
with assigned Computing and Storage Elements. The Grid
node specification shown on Figure 2 is used on GT
scheme, and therefore in many others Grid middleware like
EGEE. All Grid site components are connected to a Local
Network, usually token media access with a hight bandwidth if we compare with Remote Networks.
The node and RB are services runing on the same machine, and the rest of the local networks conections are show
on the figure 2.
Following with the Figure 1, we conect sites and routers
each others with remote networks. Each Remote Network
has aggregate various Socket instantiations that implements
partial bandwidth of the total assigned for the Remote Network. SiCoGrid response as real systems: when the available bandwith is close to the top, more bandwith is asigned
for a network transaction, when is close zero, then less
bandwidth is asigned.
Figure 3. Computing on the node comunication protocol
Figure 3 is a very representative issue. We can see a
communications protocol between Grid site components,
with computing on the node scheduling policy and pure
Data Grid. (a): The client read the request from the log
file. (b): The client launch a request on the site or node,
through the Local Network to the Resource Broker, that will
manage the request in order to return to the client the appropriate data and computing results. (c): If the requested
file is not on the site, then the RB pass the request to the
node that depending on the replication and scheduling algorithms, it route to the appropriate Remote Network instantiation. (d): Asynchronous data replies from remote sites
are received on the node, that send it to the apropiate SE.
(e): If the requested file is on the site, the block file is send
to CE. (e)ACK: Partial computing results are send to Node.
(b)ACK: Partial computing results are send to Client. This
is the case that the client has to know the results of the job
request, to obtain the next block file object to request. The
process is the same for N request of a job, and after the last
job request, the Grid client user will spend a Pasive Time.
When the Grid Client receives results, from local or remote sites, then submit a new request, if the request was
the last one of the job, the Grid Client holds a Passive Time
before a new job starts.
The drawback of the presented SiCoGrid is that suppose
unlimited buffering resources for scheduling and replica algorithms. This is a small lack comparing with the much
more significant assumptions of others Grid simulators,
anyway it will fix on future releases.
We have implemented SiCoGrid, developed in
Parsec[19] that is a combination of C and an event driven
simulator definition for ANSI C automatic code generation.
We also use DiskSim[5] for the storage disks simulation
subsystem. The Parsec specification is an enhancement C
language for a general purpose simulation language, that
is design as data abstract types, moreover than a object
oriented language. A class definition is implemented as
an entity, and there also is an entity communication using
message passing scheme. One of the Parsec meaningful
features is that it launch a thread for any entity instantiation,
that in our environment makes SiCoGrid to be scalable,
taking advance over other simulators toolkits, that uses one
thread for each task(task as a part of a job) in a way that
avoid uses for large scale simulations.
Nowadays we have a beta release that implements on
the Node layer some replica algorithms functions, that are
invoked both on Grid Node and RB. Thus the Grid researches can define the algorithms on the Grid Node class,
make some code update on the Resource Broker, and evaluate them comparing with the implemented canonical algorithms and with others algorithms of our beta release.
The SiCoGrid scheduling policies are canonical FIFO algorithms with three variants:
• Computing on the Grid client, that is equivalent of pure
Data Grid. The specific data are reply to the client that
holds the computing resources for an Active Time.
4 Emergent Artificial Intelligence algorithms
on SiCoGrid
• Computing on the node: The Active Time is spend on
the Computing Elements of the site, like on Figure 3.
EAI is an Artificial Intelligence branch that inspired in
the natural social behaviour is used for optimization. Bees
swarm, birds flocks searching food[24], or ant colony[25]
Traditional PSO algorithm was created by Dr. Eberhart. The PSO-Grid algorithm was proposed on our previous work[21]. On Grid environments we introduce some
tactic modifications, based on the strategy ”follow the closer
bird from the food chunk” as social PSO flavour. A bird
flock is in a random search for food in an area. For each
bird there is only one valid kind of food. The bird does not
known where is the food chunk, but its known how long is
from the d ifferent areas and it know how many birds are
finding they food chunk on this areas, this i s called food
chirp. This is the social component of our approach, thus
the distance to the food chunk is calculated for each bird
flock, not for individual birds. The strategy is to follow the
closer bird flock with best success food search.
The PSO-Grid uses a performance metric for a file replication between two nodes i, j , defined in equation 1.
• Computing on the Grid: Agents are launch to the appropriate remote Grid site with the required data.
On the GT middleware and family, data services or components can be classified as replica selection, location, optimisation and deletion. SiCoGrid file deletion mechanism could be Least Frecuent Used(LFU) and Least Recent Used(LRU), but the most of the times LRU will performs better. It also is implemented deletion based on market model. For replica selection, location and optimisation
the required functionalities are within each of the four implemented approaches for beta release:
• Unconditional replication: This is the canonical that
always take the replica source from the Grid site file
producer.
• Market Model: This is the OptorSim scheme[14][20]
that uses the economic market model for replica optimisation.
• Particle Swarm Optimisation approach, as we propose
on a previous paper[21]
• Ant Colony Optimisation[22].
SiCoGrid also offers additional functionalities in alpha
development stage: IO Kind with two options, the beta
tested local IO, and the advanced remote IO, allowing to
read a block of a remote file without replication on local
node. Routing scheme option parameter is available, also
with two options, one in beta released that is the traditional static routing, and the second in alpha release that
is a pseudo-adaptive routing, based on Duato approach[23].
SiCoGrid may implements different simulation Grid infrastructures, using a header file where is defined a specific Grid topology and others simulation parameters, for
compiling a specific simulation program to the specific
parametrised environment.
We are using our beta SiCoGrid for some Grid research
works. The simulations on Intel Copermine 1600MHz single processor and 512Mb memory size machine, performs
same magnitude simulation time than real Grid time, for
a small Grid infrastructure of 8 Grid sites. Actually this
model does not fit with our experimentation design requirements, that include many factors, levels and repetitions. A
compromise solution is scale Grid resources, and scale file
size, obtaining scaled job response times. This is not as
reliable as the original dimension of the environments, but
allow us to complete our proposed algorithm evaluations.
pi,j = (ej ∗ ci,j ) + ((1 − eb ) ∗ ci,b )
(1)
The external hit ration, e is calculated based on N lasts external success request ratio on node j. We use b as the identifier of the node with the best performance metric asociated
to i, from the evaluated j nodes. Initially b is the producer
node of the replica, and in the pseudo-code below is the get
producer function.
Considering network access cost, c, we propose the following equeation 2:
c(i, j) = lti,j ∗ c1 + (M AXBW − bwi,j ) ∗ c2
(2)
For our case c1 = 1 and c2 = 0.2. On the equation 2 c1
and c2 are coefficients that balance the relative relevance between latency and bandwidth, they also should fit with the
bandwidth and latency values of the specific Grid infrastructure, and also fit with their measure relationship (ms.
and MB/s.). At the end of the day latency is more important
than bandwidth, because latency is always constant, and
bandwidth has a variable behaviour depending on sockets
allocations and number of network request in a specific moment. M AXBW is the highest bandwidth of all the Grid
infrastructure.
The performance function is balancing the probability of
find a replica in a node j w ith the probability of not finding on j, where we have to reply from the node with be st
metricb, initially the producer.
The core pseudo-code is the function getPSOBest that
return the best performance node from node-Id to file
referenced on f. The get PSO metric function calculate the
performance PSO metric described in the equation above
1. The 2 equation is implemented on the get network cost
function.
NodeIdType getPSOBest(NodeIdType i, FileIdType f)
bestIdNode = getproducer(f)
bestPSOmetric = getnetworkcost(i,bestIdNode)
τ (r, s) = (1 − α)τ (r, s) + Σ∆τk (r, s)
• Every Grid request is an ant, when it find its file object,
the ant died.
• The Grid replies routing is done with traditional methods.
The ACO-Grid algorithm was proposed on our previous work[22]. For computational purposes is relevant the
way of finding paths between food sources and anthill.
While walking ants places on the ground some amount of
pheromone. Ants smell pheromone and when choosing
their way, they tend in probability to the paths marked with
stronger pheromone concentrations. When the time pass
the pheromone concentration decrease. Repeating same behaviour they compose optimised trails that are dynamically
defining, and they use to find food sources and the nests.
The historic algorithm was enunciate by Dr.Dorigo for
salesman traveller[26]. This environment is very similar to
the Grid, and can be used in a very direct way following the
algorithm:
Initialise
Loop /* An iteration */
Each ant is positioned on a starting node.
Loop /* A step */
Each ant applies a state transition rule to incrementally
build a solution and a local pheromone updating rule
Until all ants have built a complete solution
A global pheromone updating rule is applied
Until End condition.
• ACO-Grid does not use global updating. Every time a
request is processed on a Grid site, tau is update for all
the site conections .
• The Grid distance or cost is defined on equation 5 as a
function of netwo rk latency lt and bandwidth bw.
δ(r, s) = ltr,s ∗ c1 + (M AXBW − bwr,s ) ∗ c2
(5)
On the equation 5 is calculated the same as 2, explanined on
PSO above. Advancing routing features may change network cost used for file reply routing, but Delta is constant
for every for file request routing.
5 Conclusions and Future Work
Each edge between node (r, s) has a distance or cost associate δ(r, s) and a pheromone concentration τ (r, s). The
equation 3 is the state transition rule, that is a probabilistic
function for each node u, that has not been visited by each
ant on node r.
[τ (r, s)][η(r, s)]β
Σ[τ (r, u)][η(r, u)]β
(4)
Where α is the pheromone evaporation factor between 0 and
1. And ∆τk (r, s) is the reverse of the distance or cost done
by ant k, if (r,s) is its path and is 0 if it is not in the path.
The ACO-Grid flavour modified from the original ACO
is as follow:
For each j from the Grid node set repeat
if ( i != j )
if ( get-PSO-metric(i,j) less bestPSOmetric )
bestPSOmetric = getPSOmetric(i,j)
bestIdNode = j
End if
End if
Do Repeat
return(bestIdNode)
Pk (r, s) =
The parameter β determine the relevance of the
pheromone concentration compared with the distance. The
pheromone concentration on equation 4 is applied in each
edge of the systems, for a global pheromone updating rule.
(3)
We also have there the η(r, s) that is the reverse tau function.
We have investigated the problem of an universal Grid
evaluation method. This issue is not resolved with analytical studies or real experiments, and simulation is the usual
approach. But no generic simulation tool was available
for researchers and computing engineers for both replication and scheduling. We have presented SiCoGrid, a Grid
simulation toolkit, that enhance features over existing Grid
simulators, and present a more complete Grid components
shaping that presents a more reliable results. We have identify the components of disk, network, and computing resources and some attributes that should be shaped in a grid
simulator. A wide variety of that replication algorithms can
be compared with SiCoGrid, and some canonical scheduling policies. We also offer an extensible open source in
a modular way, that allow easy integration of others algorithms. Advanced Grid functionalities are in alpha development stage, including remote IO and adaptive routing that
are considerer in our approach to the evaluation method.
In future work we will present a gparsec, a GNU-GPL
version of the parsec, that will achieve our goal of a production release for SiCoGrid, that will be published for the research community as the best guarantee for collective contribution scheme. We also plan to introduce parallelisation
features to the gparser in order to run real scale simulations,
using a cluster or Grid environment with some of the more
used techniques like OpenMP or MPI. We also like to validate our simulation results on real Grids, such as EGEE.
Another interesting aspect is to introduce real state of the art
scheduling policies, furthermore than presented canonicals,
and also Emergent Artificial Intelligent algorithms following our Data Grid studies. For this purpose we are looking
partners from the Grid scheduling community.
Our ultimate goal is make SiCoGrid an universal toolkit
for evaluating Grid scheduling and replication algorithms,
with GNU-GPL license for all the toolkit code, including
gparsec, that will allow full control and scalable development paradigm, and a parallelised simulations of realistic
Grid scenarios and high intensive experimentations designs.
6 Acknowledgments
This work has been supported by the Spanish Ministry of
Education and Science under the TIN 2004-02156 contract.
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