Scheduling Different Types of Applications in a SaaS Cloud

Georgios L. Stavrinides and Helen D. Karatza

Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

{gstavrin, karatza}@csd.auth.gr

Keywords: Scheduling, Complex Workload, SaaS Cloud, Performance, Modeling, Simulation.

Abstract: As Software as a Service (SaaS) cloud computing gains momentum, the efficient scheduling of different

types of applications in such platforms is of great importance, in order to achieve good performance. In

SaaS clouds the workload is usually complex and comprises applications with various degrees of

parallelism and priority. Therefore, one of the major challenges is to cope with the case where high-priority

real-time single-task applications arrive and have to interrupt other non-real-time parallel applications in

order to meet their deadlines. In this case, it is required to effectively deal with the real-time applications, at

the smallest resulting degradation of parallel performance. In this paper, we investigate by simulation the

performance of strategies for the scheduling of complex workloads in a SaaS cloud. The examined

workload consists of non-real-time applications featuring fine-grained parallelism (gangs) and periodic

high-priority soft real-time single-task applications that can tolerate deadline misses by bounded amounts.

We examine the impact of gang service time variability on the performance of the scheduling algorithms, by

considering service demands that follow a hyper-exponential distribution. The simulation results reveal that

the relative performance of the employed scheduling strategies depends on the type of the workload.

1 INTRODUCTION

Software as a Service (SaaS) cloud computing has

become prevalent in recent years, replacing the

traditional software delivery model, which required

the installation of the software on the client’s

servers. According to this emerging software

distribution approach, which may be considered as

the evolution of the Service-Oriented Architecture

(SOA) model, the software is hosted by the vendor

and made available to the end-users over the

Internet, as a cloud service (Cusumano, 2010) and

(Beloglazov et al., 2012).

Some of the main benefits of the SaaS cloud

computing model are:

 The software is always up to date.

 The software can be accessed via various devices

and from different locations.

 It provides overall easier administration and

maintenance, as the SaaS clients do not have to

acquire, maintain and monitor expensive

hardware and software infrastructure.

The clients use the software services, without any

control on the host environment, either on a pay-as-

you-go basis or a subscription based pricing model

(Bittencourt et al., 2012) and (Dillon et al., 2010).

1.1 Motivation

SaaS cloud computing usually relies on a multi-

tenant model, where applications of different users

run on the same virtual machines (VMs). Due to the

tremendous increase of users and the number of

different applications sharing the underlying

virtualized resources, the performance of SaaS clouds

has become a crucial area of research. Therefore, the

efficient scheduling of different types of applications

in such platforms is of great importance, in order to

effectively utilize the underlying multi-tenant

infrastructure and achieve good performance, while

maintaining a certain level of Quality of Service

(QoS) (Hofer and Karagiannis, 2011), (Rimal et al.,

2009) and (Stavrinides and Karatza, 2015).

In SaaS clouds the workload is usually complex

and comprises applications with various degrees of

parallelism and priority. Consequently, one of the

major challenges is to cope with the case where

high-priority real-time single-task applications arrive

and have to interrupt other non-real-time parallel

applications in order to meet their deadlines. In this

case, it is required to effectively deal with the real-

time applications, at the smallest resulting

degradation of parallel performance. Moreover, part

of the workload may consist of fine-grained parallel

144

Stavrinides G. and Karatza E.

Scheduling Different Types of Applications in a SaaS Cloud.

DOI: 10.5220/0006223101440151

In Proceedings of the Sixth International Symposium on Business Modeling and Software Design (BMSD 2016), pages 144-151

ISBN: 978-989-758-190-8

applications that present high variability in their

service times. In this case, the scheduling algorithm

should cope with bursts of very small service times

and a few but very large ones, compared to the

average service time of applications.

1.2 Contribution

In this paper, we focus on the performance of

strategies for the scheduling of complex workloads

in a SaaS cloud with multi-tenant VMs. The

examined workload consists of non-real-time

applications featuring fine-grained parallelism and

periodic high-priority soft real-time single-task

applications that can tolerate deadline misses by

bounded amounts.

We examine the impact of service time

variability of parallel applications on the

performance of the scheduling algorithms, by

considering service demands that follow a hyper-

exponential distribution. To our knowledge,

scheduling complex workloads, including fine-

grained parallel applications and periodic soft real-

time single-task jobs, in such a framework, does not

appear elsewhere in the research literature.

1.3 Background and Related Work

One type of workload submitted to SaaS clouds is

bag-of-tasks (BoT) applications. Each BoT is a

collection of independent tasks that do not

communicate with each other and can run in any

order (Karatza, 2004), (Kim et al., 2007), (Moschakis

and Karatza, 2015), (Papazachos and Karatza, 2015)

and (Terzopoulos and Karatza, 2016). Another type

of workload is applications that consist of

interdependent tasks, which often have precedence

constraints among them and thus form a directed

acyclic graph (DAG) (Stavrinides and Karatza,

2010), (Stavrinides and Karatza, 2011), (Stavrinides

and Karatza, 2012) and (Stavrinides and Karatza,

2014).

However, most often the workload in SaaS clouds

consists of fine-grained parallel applications. They

consist of frequently communicating tasks, which are

scheduled to run simultaneously on different VMs, as

gangs. Gang scheduling is an efficient resource

management technique in the case where parallel

applications feature fine-grained parallelism.

Since with gang scheduling a gang’s task can

start execution only if all of the other sibling tasks

can also start processing, some VMs may remain

idle even when there are tasks waiting in their

queues. This problem becomes more complex when

the workload also includes real-time applications

which have higher priority than gangs.

Gang scheduling algorithms have been proposed

and studied by many authors, each differing in the

way resources are shared among the jobs

(Stavrinides and Karatza, 2008), (Stavrinides and

Karatza, 2009), (Streit, 2005) and (Zhang et al.,

2003).

In (Karatza, 2006), the performance of two well-

known gang scheduling methods is studied, the

Adapted-First-Come-First-Served (AFCFS) and the

Largest-Job-First-Served (LJFS). It has been shown

that in many cases LJFS performs better than

AFCFS. However, in this case no real-time jobs are

considered and the overall performance is expressed

by the average response time of gangs.

In this paper, we study gang scheduling in a

queueing network model of a SaaS cloud. The

performance of two policies that are variations of

AFCFS and LJFS, is studied under various work-

loads, which include gangs, as well as periodic real-

time single-task jobs that have higher priority than

gangs. The periodic single-task jobs are considered

as soft-real time applications, in the sense that they

can tolerate deadline misses by bounded amounts.

Related research includes (Karatza, 2007) and

(Karatza, 2008). These papers examine critical

sporadic jobs that need to start execution upon

arrival and therefore they interrupt gangs. In this

paper, we examine periodic soft real-time jobs that

can tolerate some delay called slack time. This

happens in the case when gangs in service require

only a small amount of time to finish their execution

that is less than or equal to the slack time.

Scheduling workloads consisting of soft-real

time jobs and gangs in distributed systems has also

been studied in (Karatza, 2014). However, the

workload considered in this case does not include

parallel applications with highly variable service

demands.

Tardiness bounds for sporadic real-time task

systems have been studied in (Devi and Anderson,

2006) and (Leontyev and Anderson, 2010).

However, these papers do not consider gangs in their

workloads.

In this paper, we examine the impact of gang

service time variability on the performance of the

scheduling algorithms, by considering service

demands that follow a hyper-exponential

distribution. A high variability in task service

demand implies that there is a proportionately large

number of service demands that are very small

compared to the mean service time and a

comparatively small number of service demands that

Scheduling Different Types of Applications in a SaaS Cloud

145

are very large.

When a gang with a long service demand starts

execution, it occupies its assigned VMs for a long

time interval, and depending on the scheduling

policy that is employed, it may introduce inordinate

queueing delays for other tasks waiting for service.

The performance evaluation of complex

distributed systems such as clouds is often possible

only by simulation rather than by analytical

techniques, due to the complexity of the systems.

Simulation can provide important insights into the

efficiency and tradeoffs of scheduling in such

environments. Therefore, due to the complexity of

the system under study, we use discrete event

simulation to evaluate the performance of the

scheduling algorithms.

The remainder of this paper is organized as

follows: Section 2 introduces the model and the

methodology employed, describing the system and

workload models. Sections 3 and 4 describe the

routing and gang scheduling policies respectively,

whereas Section 5 presents the performance metrics.

The model implementation and its input parameters,

as well as the simulation results, are presented and

analysed in Section 6. Finally, Section 7 summarizes

the paper and provides future research directions.

2 MODEL AND METHODOLOGY

2.1 System and Workload Models

This paper uses a simulation model to address

performance issues. The target SaaS cloud is

considered to consist of a set V of p virtual machines

V = {VM

, VM

, …, VM

}, that are fully connected

by a virtual network (Figure 1).

It is assumed that the communication between the

virtual machines is contention-free. Each virtual

machine VM

serves its own queue of tasks and has

mean execution rate μ. The VMs in the cloud are

multi-tenant, that is each VM processes tasks of

applications submitted by different users.

Each parallel job is submitted for execution in the

cloud by a user. Users share the virtual machines to

run concurrently their applications. Since the parallel

applications are submitted dynamically by multiple

users, it is assumed that they arrive in the cloud in a

Poisson stream with mean arrival rate λ.

The number of tasks in a parallel job x is the

job’s degree of parallelism and it is represented as

t(x). In Figure 1 virtual machines are allocated to a

parallel job x, which has j parallel tasks. VM

assigned to a periodic single-task job.

Periodic real-time

single-task jobs

Gang jobs

Periodic real-time

single-task jobs

Gang jobs

task-1of Gang x

task-2 of Gang x

task-j of Gang x

Periodic job y

Figure 1: The queueing network model.

If vm(x) represents the number of VMs required

by job x, then the following relationship holds: 1 

t(x) = vm(x)  p. We call size of job x the number of

its parallel tasks. A job is small (large) if it consists

of a small (large) number of tasks. Soft real-time

jobs are periodic single-task jobs.

Each task of a gang x is routed to a different VM.

Gang x starts to execute only if all of the vm(x) VMs

assigned to its tasks are available. Otherwise, all of

the x gang tasks wait in their assigned queues. When

a gang terminates execution, all VMs assigned to it

are released.

An important issue that arises is the need to serve

a real-time single-task job on a virtual machine

executing a gang task. Therefore, a mechanism is

needed to deal with the fact that resources allocated

to a gang are changing. The scheduler must give

priority to real-time applications, but at the same

time provide good performance for parallel

applications that compete for resources.

For each soft real-time application there is a

slack time during which it can wait for a gang that

needs a small amount of time to finish execution.

The slack time is a constant tardiness that is

independent of elapsed time. If the time until the

completion of a gang is larger than the slack time,

then the job scheduler interrupts the gang upon the

arrival of a soft real-time application, so that it can

occupy a VM. The remaining virtual machines that

are assigned to the interrupted gang can serve tasks

of other gangs that are waiting at their queues. All of

the work that was performed on all tasks associated

with the interrupted gang must be redone. The tasks

of an interrupted parallel application are rescheduled

for execution at the head of their assigned queues.

A technique used to evaluate the performance of

the scheduling disciplines, is experimentation using

a synthetic workload. The workload considered here

is characterized by the following parameters:

 The distribution of the number of tasks of gangs.

Sixth International Symposium on Business Modeling and Software Design

146

 The distribution of gang tasks service demand.

 The distribution of soft real-time jobs service

demand.

 The mean inter-arrival time of gangs.

 The period of soft real- time jobs.

2.2 The Distribution of Gang Sizes

We assume that the number of gang tasks is

uniformly distributed in the range [1, p]. Therefore,

the mean number of tasks per parallel application is

equal to m = (1+p)/2.

2.3 Service Time Distribution

For the service demands of parallel applications and

real-time jobs we assume that:

 Service demands of gang tasks follow a hyper-

exponential distribution with a coefficient of

variation CV and a mean 1/μ. CV determines the

degree of variability. This is the ratio of the

standard deviation of task execution time to its

mean. CV >1 implies a large variability,

compared to the variability of the exponential

distribution.

 Real-time jobs service demands are

exponentially distributed with a mean of 1/μ.

2.4 Distribution of Job Inter-arrival

Times

We consider two arrival streams, one for gangs and

one for real-time jobs:

 The inter-arrival times of gangs are exponential

random variables with a mean of 1/λ

 The inter-arrival times of soft real-time jobs are

constant time intervals equal to 1/λ

(i.e. the real-

time jobs are periodic).

3 ROUTING POLICIES

Job routing in SaaS clouds is an important aspect for

good performance. In this paper, our workload

model consists of two different types of jobs.

Therefore, we examine two different routing

policies, one for each job type.

The first job type concerns the parallel

applications, which are gangs. Hence, in this case

routing occurs at the task level. The second job type

concerns the single task real-time applications.

Consequently, in this case routing occurs at the job

level.

3.1 Parallel Applications Routing

A variation of the join the shortest queue policy is

used. That is, the t(x) tasks of a gang x are assigned

to the shortest t(x) of the p queues, every task to a

different VM queue.

3.2 Real-time Applications Routing

The routing policy of real-time jobs is join the

shortest queue. In the experiments that we have

conducted for this research, appropriate values of the

period have been chosen, so that there are no further

real-time job arrivals in the case where all of the

VMs serve real-time jobs.

4 PARALLEL APPLICATIONS

SCHEDULING POLICIES

It is assumed that the scheduler knows the exact

number of VMs required by each gang. The

following two scheduling strategies are employed in

our simulations.

4.1 Adapted-First-Come-First-Served

with Execution Interruption based

on Slack Time (AFCFS-ST)

This strategy schedules a gang job whenever VMs

assigned to its tasks are available. When there are

not enough VMs available for a large job whose

tasks are waiting in the front of the queues, then the

AFCFS-ST policy schedules tasks of smaller jobs in

the queues. A problem with this scheduling policy is

that it tends to favor small gangs at the expense of

larger gangs and thus it may increase system

fragmentation. A gang job may be interrupted upon

arrival of a real-time single-task job.

4.2 Largest-Job-First-Served with

Execution Interruption based on

Slack Time (LJFS-ST)

With this strategy tasks that belong to larger gangs

are placed at the head of queues. All gang tasks in

queues are searched in order, and the first jobs

whose assigned VMs are available begin execution.

This scheduling method favors large, highly parallel

gangs at the expense of smaller gangs, but in many

cases this treatment of large gangs is acceptable. For

example, supercomputers are often used to

particularly run highly parallel jobs for fast

Scheduling Different Types of Applications in a SaaS Cloud

147

execution. However, LJFS-ST involves an overhead

because the VM queues need to be re-arranged each

time a new gang is added. Similarly to the AFCFS-

ST case, a gang job may be interrupted upon arrival

of a real-time job.

In both cases of AFCFS-ST and LJFS-ST, when

a real-time job terminates, the interrupted gang may

not resume execution immediately, as some of the

VMs assigned to its tasks may be running other jobs.

Those jobs will not terminate at the same time so the

interrupted gang will not use the assigned VMs

efficiently. It is worth noting that when a real-time

job is assigned to a virtual machine, it is not only the

gang tasks of the particular VM queue that are

delayed, but also gang tasks in other queues that

have a sibling task waiting in the particular queue.

5 PERFORMANCE METRICS

The response time rt

of a gang i is the time interval

from the dispatching of its tasks to different VM

queues, to the service completion of the gang − this

is the time spent in VM queues plus the time spent in

service.

The average response time RT of n gangs is

defined as:





(1)

Additionally, we weight each gang’s response time

with its size (Streit, 2005). Consequently, it is

avoided that gangs with the same execution time, but

with different number of tasks, have the same impact

on performance. The average weighted response

time WRT of n gangs is defined as:









xvm

rtxvm

WRT

)(

(2)

The parameters used in the simulation experiments

are shown in Table 1.

Table 1: System parameters and performance metrics.

ST Slack time of real-time jobs

p Number of VMs

m Average number of tasks per parallel job

μ Mean execution rate of a VM

1/μ Mean execution time of a VM

CV Coefficient of variation of service demands

Mean arrival rate of gangs

1/λ

Period of real-time jobs

U Average VM utilization

RT Average response time of gangs

Relative (%) decrease in RT when LJFS-ST method

is employed instead of the AFCFS-ST policy

WRT Average weighted response time of gangs

WRT

Relative (%) decrease in WRT when LJFS-ST

method is employed instead of the AFCFS-ST

policy

6 SIMULATION RESULTS AND

DISCUSSION

6.1 Model Implementation and Input

Parameters

The queueing network model described in Section 2

is implemented via discrete event simulation. Due to

the complexity of the system and the workload

model under study, we implemented our own

discrete event simulation program in C, tailored to

the requirements of the specific case study.

In order to derive the mean values of the

performance parameters we used the independent

replications method. For each set of workload

parameters we run 30 replications of the simulation

with different seeds of random numbers and for

32,000 served jobs in each replication. We

considered this simulation length long enough to

derive results, as we found by experimentation that

longer runs did not affect simulation output

significantly. The use of sufficiently

long simulation

runs, is one of the ways to reduce the effect of initial

bias on simulation results.

For every mean value of the performance

parameters, a 95% confidence interval was

evaluated. The half-widths of all confidence

intervals are less than 5% of their respective mean

values evaluated. For our experiments, we used the

simulation input parameters shown in Table 2.

In our workload model there are on average m =

(p+1)/2 = 8.5 tasks per parallel job. Therefore, if we

do not consider any real-time jobs and all VMs are

busy serving gangs, then an average of p / m =

1.88235 parallel jobs can be served per each unit of

time. This implies that we should choose a λ

such

that the condition λ

< 1.88235 holds, so that the VM

queues will not be saturated. However, due to the

real-time job arrivals, the number of VMs that are

Sixth International Symposium on Business Modeling and Software Design

148

available for gang service is [p – (λ

/μ)]. Therefore,

we have to choose a value of λ

for which the

following relationship holds:

< [p – (λ

/μ)] / m (3)

However, due to gang scheduling, there are often

idle VMs, despite the fact that there are gang tasks

waiting in the respective queues. Therefore, the VM

queues get very easily saturated when the mean

inter-arrival time of gangs is close to [p – (λ

/μ)] / m.

After experimental runs with various values of 1/λ

we chose 0.78 as the smallest mean inter-arrival time

of gangs for the experiments, for all cases of 1/λ

The input parameter values which were used for the

experiments are presented in Table 2.

Table 2: Input parameters.

ST 0.1

1/μ 1

1/λ

0.84, 0.78

1/λ

20, 30, 40

CV 2

6.2 Performance Analysis

The simulation results that are presented next

describe the relative performance of the two gang

scheduling policies.

6.2.1 Performance with Regard to Parallel

Applications Service

Figure 2 presents D

versus 1/λ

in the cases of

period 1/λ

= 20, 30 and 40. Figure 3 presents D

WRT

versus 1/λ

in the cases of period 1/λ

= 20, 30 and

40.

Figures 2 and 3 show that for both arrival rates

of gangs and for all cases of real-time jobs period,

the LJFS-ST method yields lower mean response

time than AFCFS-ST.

In each real-time job period case, the superiority

of LJFS-ST over AFCFS-ST is more significant in

the case of high load (1/λ

= 0.78). This is due to the

fact that the advantages of the LJFS-ST case are

better exploited when there is a sufficient number of

gangs in the queues, so that they can be selected

according to the LJFS-ST criteria.

The simulation results also reveal that the D

increase with increasing load is more significant in

the larger periods. This may be explained by the fact

that the smaller the period of real-time jobs, the

larger is the possibility that fewer virtual machines

are available for gang service. Therefore, the

potential of the LJFS-ST policy is not completely

exploited, as large applications cannot find enough

VMs available to serve them.

0.84 0.78

PER-20

PER-30

PER-40

1/λ

Figure 2: D

ratio versus 1/λ

, soft real-time jobs with

period 20, 30 and 40.

0.84 0.78

WRT

PER-20

PER-30

PER-40

1/λ

Figure 3: D

WRT

ratio versus 1/λ

, soft real-time jobs with

period 20, 30 and 40.

In each case of the arrival rate of gangs, D

decreases with increasing period size. This is

because when real-time jobs arrive less frequently,

then a smaller number of gangs are interrupted. The

decrease with increasing period size is smaller

in the case of heavier load (1/λ

= 0.78). This is due

to the fact that when a real-time job blocks a gang,

then it does not only affect the performance of the

blocked gang, but it also affects the performance of

a number of subsequent gangs.

Figure 3 shows that the observations that hold for

the relative performance of the scheduling policies

in terms of the average response time of parallel

applications, also generally hold in terms of the

average weighted response time. In all cases D

WRT

larger than D

. Therefore, the superiority of LJFS-

ST over AFCFS-ST appears more significant when

the job response time is weighted by the degree of

parallelism of the jobs.

Scheduling Different Types of Applications in a SaaS Cloud

149

6.2.2 Performance with Regard to VM

Utilization

Tables 3 and 4 depict the mean virtual machine

utilization U versus 1/λ

, for period size 1/λ

= 20, 30

and 40 respectively. In most cases, the mean VM

utilization is either equal for both of the scheduling

policies or it is very slightly larger in the LJFS-ST

case. This is because the LJFS-ST policy schedules

large gangs first and consequently, it schedules jobs

on the available VMs more efficiently than the

AFCFS-ST policy.

Table 3: U for AFCFS-ST.

1/λ

0.84 0.78

PER-20 0.711 0.740

PER-30 0.672 0.716

PER-40 0.636 0.690

Table 4: U for LJFS-ST.

1/λ

0.84 0.78

PER-20 0.712 0.768

PER-30 0.672 0.718

PER-40 0.637 0.695

However, with both scheduling strategies, part of

the virtual machines utilization is comprised of

repeated gang work due to the real-time job service.

The amount of the repeated work depends on the

number of gang tasks, the service demand of each

task and the work that has already be done by the

gang at the moment of interruption. It is possible for

a gang to be interrupted several times during its

execution. This would be caused by multiple real-

time job arrivals at different virtual machines

serving tasks of the same gang.

For each λ

, the utilization slightly decreases

with increasing period size. This is due to the fact

that it is more possible to have to restart the parallel

applications execution when the period of real-time

applications is small, than when it is large. However,

due to the variability in gang service demand in our

workload model, only the gangs that have very large

service demands may experience multiple service

interruptions.

7 CONCLUSIONS AND FURTHER

RESEARCH

This paper examines the performance of two gang

scheduling policies, the LJFS-ST and the AFCFS-

ST, in a SaaS cloud in the presence of periodic soft

real-time single-task jobs. The objective is to

enhance the performance of parallel applications,

assuming that soft real-time jobs can tolerate a

delay.

Simulation results show that the relative

performance of the two scheduling methods depends

on the workload. With regard to the performance

metrics considered in this paper, the LJFS-ST

method performs better than the AFCFS-ST, in all

cases of workloads, providing promising results.

Furthermore, the superiority of LJFS-ST over

AFCFS-ST appears more significant when the

response time of parallel applications is weighted by

their degree of parallelism.

Our future research plans include the

examination of cases where along with gangs there

are also workflow applications. Moreover, we plan

to consider additional distributions for service

demands and investigate their impact on the

monetary cost charged to the end-users of the SaaS

cloud.

REFERENCES

Cusumano, M., 2010. Cloud computing and SaaS as new

computing platforms. Communications of the ACM.

ACM, 53(4), 27-29.

Beloglazov, A., Abawajy, J. and Buyya, R., 2012. Energy-

aware resource allocation heuristics for efficient

management of data centers for cloud computing.

Future Generation Computer Systems. Elsevier, 28(5),

755-768.

Bittencourt, L. F., Madeira, E. R. M. and Da Fonseca, N.

L. S., 2012. Scheduling in hybrid clouds.

Communications Magazine. IEEE, 50(9), 42-47.

Devi, U. C. and Anderson, J. H., 2006. Flexible tardiness

bounds for sporadic real-time task systems on

multiprocessors. In IPDPS’06, 20th IEEE

International Parallel and Distributed Processing

Symposium. IEEE, Rhodes Island, Greece.

Dillon, T., Wu, C. and Chang, E., 2010. Cloud computing:

issues and challenges. In AINA’10, 24th IEEE

International Conference on Advanced Information

Networking and Applications. IEEE, Perth, Australia,

pp. 27-33.

Hofer, C. N. and Karagiannis, G., 2011. Cloud computing

services: taxonomy and comparison. Journal of

Internet Services and Applications. Springer, 2(2), 81-

94.

Karatza, H. D., 2004. Simulation study of multitasking in

distributed server systems with variable workload.

Simulation Modelling Practice and Theory. Elsevier,

12(7), 591-608.

Karatza, H. D., 2006. Scheduling gangs in a distributed

system. International Journal of Simulation: Systems,

Science Technology. UK Simulation Society, 7(1), 15-

Sixth International Symposium on Business Modeling and Software Design

150

22.

Karatza, H. D., 2007. Performance of gang scheduling

policies in the presence of critical sporadic jobs in

distributed systems. In SPECTS’07, 2007

International Symposium on Performance Evaluation

of Computer and Telecommunication Systems. SCS,

San Diego, CA, pp. 547-554.

Karatza, H. D., 2008. The impact of critical sporadic jobs

on gang scheduling performance in distributed

systems. Simulation: Transactions of the Society for

Modeling and Simulation International. Sage

Publications, 84(2-3), 89-102.

Karatza, H. D., 2014. Scheduling Jobs with different

characteristics in distributed systems. In CITS’14,

2014 International Conference on Computer,

Information and Telecommunication Systems. IEEE,

Jeju Island, South Korea, pp. 1-5.

Kim, K. H., Buyya, R. and Kim, J., 2007. Power aware

scheduling of bag-of-tasks applications with deadline

constraints on DVS-enabled clusters. In CCGRID’07,

7th IEEE International Symposium on Cluster

Computing and the Grid. IEEE, Rio de Janeiro, Brazil,

pp. 541-548.

Leontyev, H. and Anderson, J. H., 2010. Generalized

tardiness bounds for global multiprocessor scheduling.

Real-Time Systems. Springer, 44(1-3), 26-71.

Moschakis, I. A. and Karatza, H. D., 2015. A meta-

heuristic optimization approach to the scheduling of

Bag-of-Tasks applications on heterogeneous Clouds

with multi-level arrivals and critical jobs. Simulation

Modelling Practice and Theory. Elsevier, 57, 1-25.

Papazachos, Z. C. and Karatza, H. D., 2015. Scheduling

bags of tasks and gangs in a distributed system. In

CITS’15, 2015 International Conference on Computer,

Information and Telecommunication Systems. IEEE,

Gijón, Spain, pp. 1-5.

Rimal, B. P., Choi, E. and Lumb, I., 2009. A taxonomy

and survey of cloud computing systems. In NCM’09,

5th International Joint Conference on INC, IMS and

IDC. IEEE, Seoul, Korea, pp. 44-51.

Stavrinides, G. L. and Karatza, H. D., 2008. In

SPECTS’08, 2008 International Symposium on

Performance Evaluation of Computer and

Telecommunication Systems. IEEE, Edinburgh, UK,

pp. 1-7.

Stavrinides, G. L. and Karatza, H. D., 2009. Fault-tolerant

gang scheduling in distributed real-time systems

utilizing imprecise computations. Simulation:

Transactions of the Society for Modeling and

Simulation International. Sage Publications, 85(8),

525-536.

Stavrinides, G. L. and Karatza, H. D., 2010. Scheduling

multiple task graphs with end-to-end deadlines in

distributed real-time systems utilizing imprecise

computations. Journal of Systems and Software.

Elsevier, 83(6), 1004-1014.

Stavrinides, G. L. and Karatza, H. D., 2011. Scheduling

multiple task graphs in heterogeneous distributed real-

time systems by exploiting schedule holes with bin

packing techniques. Simulation Modelling Practice

and Theory. Elsevier, 19(1), 540-552.

Stavrinides, G. L. and Karatza, H. D., 2012. Scheduling

real-time DAGs in heterogeneous clusters by

combining imprecise computations and bin packing

techniques for the exploitation of schedule holes.

Future Generation Computer Systems. Elsevier, 28(7),

977-988.

Stavrinides, G. L. and Karatza, H. D, 2014. The impact of

resource heterogeneity on the timeliness of hard real-

time complex jobs. In PETRA’14, 7th International

Conference on Pervasive Technologies Related to

Assistive Environments. ACM, Rhodes Island, Greece,

pp. 65:1-65:8.

Stavrinides, G. L. and Karatza, H. D., 2015. A cost-

effective and QoS-aware approach to scheduling real-

time workflow applications in PaaS and SaaS clouds.

In FiCloud'15, 3rd International Conference on

Future Internet of Things and Cloud. IEEE, Rome,

Italy, pp. 231-239.

Streit, A., 2005. Enhancements to the decision process of

the self-tuning dynP scheduler. In JSSPP’05, 11th

Workshop on Job Scheduling Strategies for Parallel

Processing. Springer, Cambridge, MA, pp. 63-80.

Terzopoulos, G. and Karatza, H. D., 2016. Bag-of-Tasks

load balancing on power-aware clusters. In PDP’16,

24th Euromicro International Conference on Parallel,

Distributed and Network-Based Processing. IEEE,

Heraklion, Crete.

Zhang, Y., Franke, H., Moreira, J. and Sivasubramaniam,

A., 2003. An integrated approach to parallel

scheduling using gang-scheduling, backfilling and

migration. IEEE Transactions on Parallel and

Distributed Systems. IEEE, 14(3), 236-247.

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