Scholarly article on topic 'Identifying Information Requirement for Scheduling Kepler Workflow in the Cloud'

Identifying Information Requirement for Scheduling Kepler Workflow in the Cloud Academic research paper on "Computer and information sciences"

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Procedia Computer Science
OECD Field of science
{Kepler / "cloud workflow" / "workflow scheduling" / "scheduling information"}

Abstract of research paper on Computer and information sciences, author of scientific article — Sucha Smanchat, Kanchana Viriyapant

Abstract Kepler scientific workflow system has been used to support scientists to automatically perform experiments of various domains in distributed computing systems. An execution of a workflow in Kepler is controlled by a director assigned in the workflow. However, users still need to specify compute resources on which the tasks in the workflow are executed. To further ease the technical effort required by scientists, a workflow scheduler that is able to assign workflow tasks to resources for execution is necessary. To this end, we identify from a review of several cloud workflow scheduling techniques the information that should be made available in order for a scheduler to schedule Kepler workflow in the cloud computing context. To justify the usefulness, we discuss each type of information regarding workflow tasks, cloud resources, and cloud providers based on their benefit on workflow scheduling.

Academic research paper on topic "Identifying Information Requirement for Scheduling Kepler Workflow in the Cloud"

^Pl^ Procedia Computer Science

CrossMark Volume 29, 2014, Pages 1762-1769 ICCS 2014. 14th International Conference on Computational Science

Identifying Information Requirement for Scheduling Kepler Workflow in the Cloud

Sucha Smanchat and Kanchana Viriyapant

King Mongkut's University of Technology North Bangkok, Bangkok, Thailand {,},


Kepler scientific workflow system has been used to support scientists to automatically perform experiments of various domains in distributed computing systems. An execution of a workflow in Kepler is controlled by a director assigned in the workflow. However, users still need to specify compute resources on which the tasks in the workflow are executed. To further ease the technical effort required by scientists, a workflow scheduler that is able to assign workflow tasks to resources for execution is necessary. To this end, we identify from a review of several cloud workflow scheduling techniques the information that should be made available in order for a scheduler to schedule Kepler workflow in the cloud computing context. To justify the usefulness, we discuss each type of information regarding workflow tasks, cloud resources, and cloud providers based on their benefit on workflow scheduling.

Keywords: Kepler, cloud workflow, workflow scheduling, scheduling information

1 Introduction

Scientific workflow management systems, such as Kepler [1], have been used for facilitating eScience by providing tools to orchestrate scientific computations to be executed automatically. These systems can execute workflows on local resources, or on distributed environments such as the grid, which provides supercomputing power necessitated by intensive computations in scientific applications [2]. As the cloud computing emerges as a new computing paradigm, it has become possible to execute scientific workflows on cloud resources [3], which are allocated on demand reducing hardware investment. Because of several benefits offered by the cloud, more research effort has been directed toward the execution of workflows in cloud computing environment.

An essential mechanism for executing a workflow efficiently in the cloud is a scheduler, which decides on the mapping of the tasks (or actors in Kepler) in the workflow to cloud resources. To control Kepler workflow execution, several "directors" are available for users to choose for their workflows [4]. However, the directors in Kepler do not perform scheduling of actors to compute resources; users have to specify the resources on which the actors are to be executed. In order for

1762 Selection and peer-review under responsibility of the Scientific Programme Committee of ICCS 2014

(gi The Authors. Published by Elsevier B.V.

Kepler to relieve scientists from technical complication, a scheduler may be necessary. Toward a development of this scheduler, in this paper we identify the types of information that should be supplied by the Kepler environment to its scheduler based on existing cloud workflow scheduling techniques.

Unlike scheduling in the grid, workflow scheduling in the cloud context needs to address new issues specific to cloud computing. For example, instead of trying to finish a workflow execution as soon as possible, cloud workflow schedulers also have to control the cost incurred by the execution in the cloud. The resources in the cloud, being virtual machines in this case, are more homogeneous and are dependent upon the request from users (or from the schedulers) whereas grid resources are more heterogeneous and are not controlled by users [3]. Such issues shift the way in which scheduling techniques are designed, which therefore require additional information for scheduling.

This paper explores a number of cloud workflow scheduling techniques that utilize information specific to cloud computing environment in their scheduling processes. However, as cloud computing and cloud providers evolve, some techniques (and its required information) may no longer be as useful. Thus, we also discuss and justify each of the information identified whether it is of important in the current cloud computing context.

The structure of this paper is as follows. Section 2 presents a review of cloud workflow scheduling techniques. Section 3 discusses and summarizes the use of such information to address issues specific to cloud environment. Section 4 then concludes the paper and points toward our future work.

2 Cloud Workflow Scheduling Techniques

Before the emergence of cloud computing, scientific workflows had usually been executed in the grid and extensive effort had been put into grid workflow scheduling resulting in several techniques over many years. These techniques, however, may not be applicable to scheduling workflows in the cloud because of the differences between the grid and the cloud context. Thus, researchers have diverted their effort to cloud workflow scheduling.

Scheduling techniques have been developed in response to the evolving cloud technology and applications that runs in the cloud. More information regarding workflows and cloud resources has become necessary in a scheduling process. Workflow scheduling in the grid usually relies on three common metrics, which are the execution time of tasks, the wait time on grid resources, and the time required for file staging or data transfer time [5]. Additional metrics or information may be utilized by some techniques to address specific issues such as resource competition [6], fault tolerance [7], and reliability [8]. With cloud computing, compute resources or virtual machines can be requested at runtime while users are charged for the use of such resources. Therefore, the three common metrics are no longer sufficient as the total execution cost has become a more important metric.

This section explores several cloud workflow scheduling techniques to identify necessary scheduling information specific to cloud computing. We present these techniques in chronological order so that it may be possible to illustrate the evolution of the researches in this area.

Although many grid workflow scheduling techniques focus on minimizing makespan, a few techniques also address execution cost in utility grid. For example, Yu et al. [9] proposed a technique that partitions a workflow into branches based on synchronization tasks and distributed the global deadline among such partitions. The cost is then minimized for each partition while trying to preserve the deadline of the partition. This early technique considers the usage cost of grid resources and network cost for transferring data.

In 2009, Liu [10] proposed, among a number of algorithms in his thesis, the "CTC" algorithm. This algorithm addresses two objectives that are to minimize execution cost within user-defined deadline and to minimize makespan within user-defined budget. As one of earlier techniques, this algorithm considers execution time, data transfer time, and compute cost to allocate cheaper cloud

resources to workflow tasks. The estimated total cost and makespan are available for user to make any adjustment of deadline and budget during execution and the algorithm reschedules as necessary.

Pandey et al. [11] employ particle swarm optimization (PSO) to schedule workflows on cloud compute resources. PSO is used to generate a schedule for tasks that are ready to execute in each scheduling round. The optimization of the PSO considers the compute cost and the data communication cost to minimize total execution cost. The technique tries to avoid communication cost when data files are larger, and is able to balance the load on all resources based on their cost. The literature does not explicitly consider task execution time and data transfer time, and thus does not address time constraint. However, the aspect of time is expressed as the inverse proportion to the compute cost.

Another technique that employed a meta-heuristic was proposed by Barrett et al. [12]. This technique uses genetic algorithm to generate multiple schedules based on makespan, compute cost, and data transfer cost. Then among these schedules, a Markov Decision Process chooses the solution based on resource loads and the hour of the day.

An algorithm for scheduling tasks in hybrid cloud, called "RC2" was proposed by Lee and Zomaya [13] to achieve reliable completion. An initial schedule is first calculated based on private cloud (or locally owned resources) to minimize cloud resource usage. If a delay occurs at a local resource, tasks that have been scheduled to that resource may be rescheduled to cloud resources to compensate the delay. The RC2 algorithm considers task execution time and data transfer time to calculate makespan in its scheduling process. However, this algorithm does not explicitly consider computed cost.

In 2011, Bittencourt and Madeira [14] proposed the "HCOC" algorithm to schedule cloud workflows within deadline while minimizing compute cost. The algorithm assumes hybrid cloud model consisting of a private cloud of heterogeneous resources and a public cloud of (unlimited) resources. The scheduling process starts by using the grid workflow scheduling algorithm called "PCH" [15] to generate an initial plan for the private cloud based on execution time and data transfer. If the initial schedule is expected to violate the deadline, rescheduling is triggered to select and reassign tasks to public cloud resources. The selection of public cloud resources are based on cost, performance and number of cores on cloud resources. The authors also suggested later that it is necessary to consider an overhead cost incurred by a cloud resource being idle while waiting for input data to be transferred [16]. However, this had not been included in the HCOC algorithm.

In addition to task execution time, data transfer time, and compute cost that are used in the techniques described so far, the "PBTS" algorithm proposed by Byun et al. [17] begins to consider other aspects in the cloud. To addresses tasks implementing the MapReduce [18] programming model, the algorithm assumes that a task may requires varying numbers of virtual machines. In order to minimize the cost of cloud resources while maintaining deadline, this algorithm, as the extension of the authors' earlier algorithm called "BTS" [19], divides a workflow schedule into partitions based on the charge period of cloud resource. For example, users are charged based on hourly rates for using Amazon EC2 services [20]. A workflow is scheduled so that the virtual machine hours are minimized by trying to fit tasks in the charge periods. This consideration is necessary because a virtual machine may be utilized for only a short time and then left idle while users have to pay the price for the whole period, resulting in a higher total cost [17]. It is also mentioned in this work that the overhead for starting virtual machines should be considered. However, this issue is not addressed by the PBTS algorithm.

The most recent technique in our review is proposed by Abrishami et al. [21], appearing in 2013. Similar to the PBTS [17], this technique, consisting of the "IC-PCP" and the "IC-PCPD2" algorithms, also considers usage charge period in its scheduling process and tries to utilize the remaining time of each period as much as possible. The IC-PCP algorithm iteratively determines a critical path and its deadline then assigns all tasks in the path to a machine that can execute them within the deadline. The IC-PCPD2 algorithm, instead, distributes the workflow deadline to each task and assigns each task to a virtual machine that can execute the task within its deadline. This technique assumes that the

execution of a workflow takes place in a single cloud availability zone thus ignoring data transfer cost between virtual machines.

From the cloud workflow scheduling techniques described so far, the information that has been considered by each technique can be summarized in Table 1 with the cloud execution model specified below each technique. The three most essential metrics, which are used in most of the techniques described in this paper, are task execution time, compute cost, and data transfer time because they are required for estimating cost and makespan. In the next section, we discuss the applicability of the other five in the current cloud computing context.

Technique / Literature Exec time Data transfer time Compute cost Data transfer cost Usage charge period VM-per-task VM cores VM overhead cost/time Remark*

Yu et al. [9] (Utility Grid)

CTC [10] (Hybrid)

Pandey et al.[11] (Public) * Time as the inverse of cost

Barrett et al.[12] (Public)

RC2 [13] (Hybrid) * Not clearly addressed

HCOC [14] (Hybrid) * Mentioned only

PBTS [17] (Public) * Mentioned only

IC-PCP [21] (Public) * Single zone execution

Table 1: Summary of the information utilized by cloud workflow scheduling techniques

3 Discussion on Information Utilized in Workflow Scheduling

The inclusion of the cost for transferring data in and out of a cloud has received less attention than the three essential metrics. The inclusion of this information may depend on the assumption made by each technique. Infrastructure-as-a-Service cloud providers such as Windows Azure [22] and Amazon EC2 [20] charge minimal cost for transferring data to and within their cloud centers. If it is assumed that a workflow execution takes place only in a single cloud, this cost will be incurred mostly by retrieving output data out of a cloud center at the end of the execution. If a workflow is executed in a hybrid cloud, then the cost is also dependent on the data dependencies between tasks because data may need to be transferred from cloud center to private resources during execution and vice versa. In a scenario of "intercloud" [23], where a workflow is executed using resources from multiple cloud providers, the cost incurred by transferring data across providers become more complicated due to different pricings. Nevertheless, this cost is also subject to the size of data being transferred throughout an execution. Thus for a system supporting mainly data-intensive workflows, it is necessary to consider data transfer cost in the scheduling mechanism.

The more advanced techniques described in this paper (PBTS and IC-PCP) consider usage charge period of virtual machines. This consideration is significant if a workflow is composed mostly of tasks whose execution times are shorter than the charge period. Otherwise, the allocated virtual machines would be underutilized and users would have to pay higher for idle time [17, 21]. However, as of

2013, Windows Azure and Google Compute Engine charge their virtual machines per minute. The shorter charge period renders the consideration of charge period less beneficial [21], which may not justify the complexity of the scheduling algorithms and implementations. Nonetheless, some cloud providers such as Amazon EC2 still charges their service based on hourly rate. The consideration of charge period may still be useful until most cloud providers adjust to shorter charge period.

A shorter charge period makes it easier to allocate a virtual machine for a short period of time for a smaller task at reduced cost. However, doing so may lead to undesirable overhead. As mentioned in [3, 16, 17], the overhead of starting up a virtual machine should be considered. A virtual machine, though assuming its image is stored in the cloud, takes time before it is ready to execute a task (such as booting OS) [3]. Also, the machine may have to idly wait for input data to be transferred before the execution can start [16]. These issues introduce additional cost and time in an execution. A scheduler needs to consider this overhead to decide whether to allocate a new virtual machine (to meet a deadline) and/or to terminate one (to reduce cost when it is no longer required).

As various instance types of virtual machine are made available by cloud providers, a scheduler using algorithm such as HCOC can select an instance type based on number of cores to execute tasks in different workflow paths on the same virtual machine. This helps minimizing data transfers between tasks and reducing cost per core when a workflow has a high level of parallelism [14]. As for tasks implementing MapReduce model, the consideration shifts from the number of cores to the number of virtual machines. In most of the existing cloud and grid workflow scheduling techniques, a task is usually assumed to be executed by only a single resource to reduce scheduling complexity. Since MapReduce applications are usually executed on multiple worker machines in parallel, neglecting this virtual machine requirement may lead to degraded performance. This information is thus necessary, as shown in the PBTS algorithm [17], which allows a task to specify either static or adjustable number of virtual machines.

4 Information Requirement for Cloud Workflow Scheduler

In summary, for an implementation of a workflow scheduler in the Kepler system, at least the three essential metrics namely execution time, data transfer time, and compute cost need to be provided in order to estimate cost and makespan. The execution time can also be expressed as virtual machine performance (e.g. MIPS) and the size of task (e.g. millions of instructions) [16]. Alternatively, the scheduler can maintain a record of the execution time of each task on each virtual machine within itself (or as a part of provenance) to later determine expected execution times. The data transfer time can be expressed as the size of data to be transferred and the bandwidth of network links (which, in the simplest manner, can be recorded within the scheduler to estimate the expected transfer time) [6].

The data transfer costs of transferring data to and from each cloud provider in each availability zone (or region) should also be supplied to support scheduling of data-extensive workflows. The number of cores specified in each virtual machine instance type should be exposed to the scheduler to exploit workflow parallelism. The number of virtual machines required by each task is required to support distributed programming paradigm. This number may be specified (by users) as a parameter attached to the actor representing the task. The overhead for starting virtual machine of different types, although has not been clearly addressed in any work presented here, should be tracked to justify an allocation and a termination [3]. This tracking could be implemented inside the scheduler itself. Lastly, for the usage charge period, it is our opinion that it may become unnecessary. As more cloud providers shorten their charge periods, its usefulness diminishes and thus may not justify the overhead of complex scheduling process and implementation.

5 Conclusion and Future Work

In this paper, we explore several cloud workflow scheduling techniques and extract from them the information that the Kepler system environment should supply for an implementation of a workflow scheduler. Each type of information is discussed on its benefit in the current cloud computing context.

From our experience in the development of a prototype scheduler in the tool "Nimrod/K" [6, 24, 25], which is built on top of the Kepler system, "Nimrod Director" [26] invokes the scheduler to handle the scheduling of actors in a workflow onto compute resources. This could be an option to an implementation of a cloud workflow scheduler in the Kepler system. However, the prototype scheduler was developed for grid context and thus did not consider the new aspect of cloud computing. In addition, some of the information identified in this paper including compute cost, data transfer cost, and the number of cores in each virtual machine may not be obtained directly from the Kepler system. An additional component may be required to gather such information in the actual implementation.

Apart from the information and concerns described in this paper, several techniques assume other aspects of workflow and cloud execution contexts such as task requirement and user intervention [10]. As an extension to this work, we are comprehensively exploring literature in this area to form a taxonomy, which should serve as a starting point for further development of cloud workflow scheduling techniques and schedulers.


This research is funded by A New Researcher Scholarship of CSTS, MOST by the Coordinating Center for Thai Government Science and Technology Scholarship Students (CSTS) of the National Science and Technology Development Agency.


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