Trust aware workflow scheduling in Scalable Cloud Environment

Trust aware workflow scheduling in Scalable Cloud Environment

1

Punit Gupta,

Ankit Mundra,

Navaditya Gaur,

Mayank Kumar Goyal,

Rohit Kumar Gupta

1,2,3,5

Manipal University Jaipur, India

4

Sharda University, India

Abstract

Cloud computing is a virtualized shared pool of hardware. It is playing a prominent role in service-oriented computing paradigm. Service-oriented computing is a subset of utility computing. There are various features of utility-based computing. Utility computing paradigm includes cloud computing and grid computing. It provides hardware and application software as a service. Efficient resource management is one of the claiming concerns in scalable shared pool of blade servers. The shared pool of servers’ places in different time zone. In scalable cloud aura, trust-based dependent tasks scheduling is the prominent affair. Specifically, we focus on dependent tasks provisioning. The performance metrics include execution time, failure probability, and reliability. The performance metrics are measured using the trust aware Max-Min and trust aware Min- Min scheduling. The proposed trust aware Min-Min scheduling and trust aware Max-Min techniques outperform the Min-Min, and Max-Min approaches. The performance metrics are measured using various types of tasks. The failure probability, Reliability, and execution time measure the level of trust of the TMax-Min and TMin-Min techniques.

Keywords: Max-Min, Min-Min, MIPS, PE, Scheduling, TMax-Min, TMin-Min, VM.

1. Introduction

Cloud computing is the virtualized shared pool of resources. It has become a milestone and deals with service-oriented computing across the globe using high- speed Internet. Its primary objectives are reliability and scalability. The cloud computing technology provides the hardware at datacenter. The primary objective includes efficiency improvement. Cloud service providers provide resources, which are allocated to the client, dynamically in an on -demand way. Cloud user‟s requirement and the economy are the driving factors which decide the services of infrastructure, platform, and software as service. The significant concerns are related to the efficiency of the IaaS. In the scalable cloud aura, resource provisioning is a way of distributing the data center configurations among the SaaS modeler. The resource mapping (allocation of cloudlets on virtual machine) achieves the performance metrics of the SaaS modeler. The performance metrics cover the requirement of developers, client, and datacenter owner. The dat a center owner assures the quality of service using service level agreement. Efficient resource utilization manages using virtualization technologies.

.Primarily, scheduling focuses on performance improvement of hardware configuration at datacenter. Scheduling is essential for various category of users across the globe [1]. The challenging concerns are monitored by efficient provisioning techniques [2]. Dependent resource provisioning has attained much concentration from the research groups.

* Corresponding Author: Ankit Mundra ([email protected]), Manipal University Jaipur

The prominent provisioning method is required that is optimally applicable in scalable data center configurations. The survey papers focused on a specific domain which covers the mobility feature in scalable cloud aura [3], energy efficiency in data centers [4], load balancing [4][5], resource management [6][7], resource provisioning [8], and resource scheduling [9][10][11].

The various resource scheduling schemes are presented in scalable cloud aura, and also focused on detailed literature review on open issues in cloud computing aura. We highlight the performance metrics execution time, failure probability, and reliability. The scientific application supports workflow scheduling in a scalable cloud aura. Various class of users demands massive resources from various computing infrastructures.

Automatic provisioning of an application on real cloud platform is challenging. The scheduling approaches may not be reliable and fault-tolerant in real dynamic environment.In general, multi-tasks workflow scheduling in distributed surrounding is an NP-hard problem [12]. It includes the scalable cloud computing aura. The key challenge of the workflow scheduling on virtual machines lies in how to reduce the scheduling overhead and adapt the dynamic workload [13]. In past work, the optimal methods are applied for multi-objective provisioning in cloud platforms. We reduce the solution space significantly and minimize scheduler responsibility. In this paper, a new trust aware scheduling algorithm is proposed.The trust aware workflow scheduling applies the Min- Min and Max-Min scheduling in scalable cloud infrastructure at datacenter node.

Adaptive schedules are executed on datacenter nodes. "We present a TMax-Min and TMin-Min resource provisioning schemes for workflow scheduling application on a scalable cloud platform. A novel simulation-based approach is introduced for handling the tasks allocation in scalable cloud aura. The Max-Min and Min-Min scheduling methods are extended for handling workflow scheduling scenarios. Results exhibit that the trust aware method achieves the performance metrics, low failure probability, high reliability, and minimum execution time. The sub-sections of the article are constructed as follows:

Section 2, covers related works, and review of resource scheduling in cloud computing.

The sub-section three exhibits the basic idea about resource allocation. Section 4 focuses on the trust aware workflow scheduling. Section 4 also incorporates the trust aware Max- Min and trust aware Min-Min techniques, which improves execution time, failure probability, and reliability. The proposed trust aware scheduling metrics are used to evaluate the trust aware model and existing techniques. Section 5 covers the results and discussion of the output generated using trust aware techniques. In section 6 presents the conclusion and recommendations.

2. Related Work

The efficient scheduling of cloudlets on virtual machines is the priority in a scalable cloud. The authors Venters et al. explained the requirements in detail. The cloud computing technology provides multi-dimension performance improvement of traditional computing architecture. The challenging issues in cloud computing research are efficiency and reliability. The efficiency measures using trust level measurement of the virtual machine allocation technique. The knowledge and the trust level is considered between clients and resource manager. The authors Tsai et al. focused on literature of evolution based provisioning techniques for scalable cloud aura. Moreover, researchers prefer evolution based nature-inspired techniques than traditional scheduling technique in cloud computing [10]. The nature-inspired techniques give better results than existing traditional approaches.

The author Rawat et al. focused on nature-inspired techniques for efficient scheduling of the tasks on virtual machines. The researchers claimed that the nature-inspired meta- heuristic techniques, which give better results than traditional resource provisioning

techniques. Further, Kalra et al. have delivered a survey and comparative analysis of various nature-inspired, service level agreement aware, and load balancing aware resource provisioning techniques for cloud and grid environments. Specifically, authors focused on makespan, flow time, and economic costs. Optimization metrics, open concern, and challenges for further researches in cloud computing are also presented [9].

Madni et al. presented an appraisal of various types of resource allocation nature-inspired algorithms that have been utilized in IaaS cloud. The comparative criterion and the experimental tools are used for validation of the static, dynamic and nature-inspired evolution techniques [7]. The literature review and classification serve as a foundation for further enhancement in trust aware scheduling in innovative cloud environment.

Similarly, Zhang et al. delivered an extensive conclusion of the modern resource arrangement algorithms. It addressed objectives of the new proposed trust aware workflow scheduling [8]. The authors Mastelic et al. presented the terminologies for energy efficiency using a systematic review of cloud resource provisioning [14].

Dasgupta K et al. focused oncloudlets allocation schemes which follow the features of the bio-inspired technique [16]. The literature survey outcomes exhibit the performance metrics, energy savings, cost, execution time, an average finish time, and flow time of user requests. Kansal and Chana presented a systematic review of existing load balancing based scheduling techniques [5]. The review determines that all the existing techniques emphasized balancing the workload, minimize the response time, and enhancing hardware efficiency of datacenter.

Gabi et al. found that the existing task scheduling techniques failed to address reliability and failure probability of the cloudlets submitted on virtual machines [4]. Some of the recent techniques give better results only for homogenous environment. However, none of the existing techniques works effectively for scientific workflow scheduling.

Dinh et al. focused on mobile cloud computing, which includes architecture, applications, concerns, and existing solutions. Mobile cloud computing is one of the emerging trends that provide the benefits of both mobility and remote computing [3]. The article covers trust aware workflow scheduling in a scalable cloud datacenter.

Resource scheduling plays a prominent role in cloud computing, predominantly to improve the execution efficiency and utilization of resources, energy-saving, users QoS satisfaction. Moreover, the resource provisioning techniques (for the deployment of the dependent tasks on virtual machines) directly influence the cost and time.Ma et al.

discussed five important issues of resource allocation in cloud [15]. Additionally, a discussion and comprehensive analysis are performed on various resource allocation and scheduling policies. However, Zhang et al. reviewed the general resource scheduling procedures [11]. Task scheduling is considered as a research object in the cloud environment. The principle objective is performance improvement, enhance throughput and response time.

3.

This section describes the basic idea of resource provisioning. The primarily we focused on trust aware techniques for the cloudlets execution on virtual machines.

3.1. Resource provisioning Technique

Primarily, virtual provisioning on host and cloudlets provisioning on virtual machine is a way of defining which event is performed. The event includes the execution of virtual machines and execution of cloudlets. It is a requirement of host configuration for an applicable consignment of cloudlets on virtual machines. Resources are accessible for hardware allocation at datacenter. There is an efficient utilization of resources and optimal global cost. Scheduling techniques are used to decrease the execution cost and time (average start time, average finish time, simulation time, and makespan, average flow time). It handles the problem of host configuration requirement (number processing cores)

for the execution of received cloudlets. Efficient scheduling algorithms should consider the total execution time of the user requests on infrastructure and platform, failure probability, and reliability. Scheduling in scalable cloud infrastructure covers cost-aware, efficiency aware, energy-aware, quality of service-aware, and utilization aware resource provisioning techniques. Our trust aware proposed work is the cornerstone for quality of service-aware resource organization at hardware and platform level. The quality of service-aware scheduling measures the performance using performance benchmark (availability, reliability, fault-tolerant, SLA, throughput, and recovery time). The proposed techniques TMin-Min and TMax-Min workflow scheduling focus on reliability, failure probability, and efficiency measurement of scalable cloud aura.

4.

In this section, a trust aware workflow scheduling is proposed. Existing techniques of the dependent tasks (workflow) and independent tasks scheduling consider the cost and deadline of the cloudlets execution. This limit the decision-making process of the scheduler to make the correct decision for achieving the quality of service requirement of the client, developers, and service providers.The proposed trust aware algorithm is designed, which considers fault-tolerant behavior and reliability. The trust evaluation of the resources is divided in to direct trust and reputation\ relative trust, which is dynamic in nature and changes dynamically.Theproposed trust-based scheduling algorithm depends on the trust value of the cloud entity (virtual machines). Trust refines the reliability of the resources, which are selected for tasks execution. It provides better QoS (Quality of Service).

The trust model is used to measures the trust values for the data centers. It is based on the parameters follows:

a) Initialization time: Time is taken to allocate the resource request.

b) PE: Number of cores assigned to the virtual machine (VM).

c) Machine instruction per second (MIPS): Number of instructions computed in a unit time interval

d) Bandwidth: Network capacity inits parameter which defines the internet characteristic and limit of the network throughput.

e) RAM: Random access memory of the virtual machine instance at datacenter node.

f) Fault rate: This is the number of faults in a time span, which is assigned by the IaaS cloud.

g) Execution time: Time is taken to complete the user requests.

The contemplated trust-based algorithm is divided into two aspects:

 Initialization:

In this phase trust level of the data center, and the clients are being initialized.

1) First, the datacenter trust is initialized based on the trust management model, which is used to initialize the trust value based on direct trust. In this scenario, if the datacenter is newly introduced, it is initialized with the default trust value, i.e., the direct trust.

Direct trust can be defined as:

𝐷𝑇 𝑖 = 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑣𝑎𝑙𝑢𝑒 + 𝑎 ∗ 𝑃𝐸 + 𝑏 ∗ 𝑅𝐴𝑀 + 𝑐 ∗ 𝑏𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ 𝐷𝑇 𝑖 : Direct trust for 𝑖^𝑡ℎ VM (Virtual Machine)

 Scheduling

In the first step when the system will start, and the datacenters have not yet served any requests so do not have any performance history, In that case, the system will use the direct trust, and relative trust becomes zero.

Trust can be defined as:

𝑇𝑅 𝑖 = 𝑎 ∗ 𝐷𝑇 𝑖 + 1 − 𝑎 ∗ 𝑅𝑇 𝑖 𝑇𝑅 𝑖 : Trust value corresponding to 𝑖^𝑡ℎ VM.

𝑅𝑇(𝑖, 𝑗): Relative trust referring to 𝑖^𝑡ℎ VM for 𝑗^𝑡ℎ request.

𝑅𝑇(𝑖) = 0 // In the initial state.

 Evaluation

After a short span of time, when the system has served some cloudlets, then the relative trust is measured. Where relative trust is defined as:

𝑅𝑇 𝑖, 𝑗 = 𝑁𝑜_𝑡𝑎𝑠𝑘_𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑑 𝑖 + 𝑙𝑜𝑎𝑑_𝑉𝑀 𝑖 − 𝑁𝑜_𝑡𝑎𝑠𝑘𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖 + 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒 𝑖, 𝑗 ;

Relative Trust𝑅𝑇 𝑖, 𝑗 : This trust value responds to the trust for the fitness of the virtual machine to complete a task with high reliability. It depends on the following parameters:

𝑁𝑜_𝑡𝑎𝑠𝑘_𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑑 𝑖 : Number of cloudlets completed by a virtual machine over a time„t.‟

𝑙𝑜𝑎𝑑_𝑉𝑀 𝑖 : Load on a virtual machine.

𝑁𝑜_𝑡𝑎𝑠𝑘𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖 : Number of tasks execution failed on the virtual machine over a time„t‟ can also be defined as fault rate.

𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒 𝑖, 𝑗 : Time required to complete the user requests, including the waiting time by 𝑖^𝑡ℎvirtual machine.

𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒 𝑖, 𝑗 = 𝑇𝑎𝑠𝑘 𝐿𝑒𝑛𝑔𝑡ℎ 𝑉𝑀_𝑀𝐼𝑃𝑆 ∗ 𝑉𝑀_𝑃𝐸

The evaluation step is repeated after a small equal interval of time to evaluate the relative trust. The relative trust helps in the study the performance of the system and change the trust value for a virtual machine correspondingly. The updated trust value is used by the scheduler which makes the system more reliable and provides better QoS to the users. Quality of service aware scheduling also improves the reliability of the system. The reliability improves using scheduling the task to the most reliable system and VM with least execution time at the same time.

Fault in the cloud may occur due to the software or hardware failure, which are random where the randomness of the system is defined by Poisson distribution in the system.

4.1. Proposed trust-based Max-Min Algorithm

The trust aware Max-Min technique is a basic Max-Min algorithm. In existing Max-Min algorithm fitness function is based on execution time. In the proposed trust-based Max-Min technique, the fitness function is based on trust value which finds a reliable solution. The reliability of the solution is defined by selecting a resource with least execution time, highest idle resources availability, and least failure probability.

The algorithm pseudo-code is as follows.

4.2 Proposed Trust-based Min-Min Algorithm

Analogous to the Max-Min algorithm, trust aware Min-Min algorithm is proposed for the performance study of the trust-based algorithm. The proposed algorithm aims to select the smallest task and schedule it on a resource, i.e., VM with minimum execution time and highest idle resources, and least failure probability. The solution is designed for allocation of a task in ascending order for completion of all the smaller tasks in parallel with the least waiting time. The algorithm assumes that the set of tasks consist of the majority of smaller task and few more significant tasks.

The algorithm pseudo-code is as follows.

5. Results & Discussions

In this section, we have presented the simulation setup using CloudSim 3.0. It exhibits the comparative study of the basic scheduling algorithms with the proposed trust aware virtual machine provisioning algorithms. Cloudsim toolkit provides a basic simulation environment to test a proposed trust aware algorithms. The simulator does not have any trust model defined in it, so a trust model to evaluate the trust value is defined, and the proposed trust aware TMin -Min and TMax-Min are implemented in Cloudsim 3.0.

The trust aware provisioning technique is tested using various test cases with five servers D1-D5 at the datacenter, and Poisson distribution model is used for the random request, and fault model is used in a distributed computing environment.

Results are presented for various workflow types like montage, cybershake. SIPHT, ligo, and inspired for 50-1000 user requests (cloudlets). Configuration of simulation scenarios is provided in Table 1, Table 2, Table 3, and Table 4 respectively. It provides the configurations of cloud entities data center, virtual machine, and cloudlets.

Table 1. Datacenter Configuration

Datacenter ID

Memory (Gb)

RAM

(Gb) PE Hosts CORE

D1 100000 64 6 2 4

D2 100000 64 6 2 4

D3 100000 64 6 2 4

D4 100000 64 6 2 4

D5 100000 64 6 2 4

Table 2. Datacenter Network Delay Configuration

VM ID

Cloud Controller to VM Network Delay (ms)

VM1 10

VM2 15

VM3 20

VM4 15

VM5 20

VM6 15

VM7 10

VM8 17

VM9 12

VM10 14

Table 3.Virtual Machines (VM’s) Configurations

VM Type

Image Size

RAM MIPS PE Bandwidth Failure Probability

VM1 1000 1024 500 3 1000 0.2

VM2 1000 512 400 4 1000 0.3

VM3 1000 2048 600 2 1000 0.5

Table 4 Type of Tasks

Task Type Number of Tasks

Number of Tasks

Number of Tasks

Number of Tasks

Montage 30 50 100 1000

Cybershake 30 50 100 1000

SIPHT 30 60 100 1000

Epigenomics 24 46 100 997

Inspiral 30 50 100 1000

The performance metrics to perform comparative analysis are:

𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒: 𝑇𝑖𝑚𝑒 𝑖𝑠 𝑡𝑎𝑘𝑒𝑛 𝑡𝑜 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒 𝑎𝑙𝑙 𝑡𝑎𝑠𝑘𝑠 (𝑢𝑠𝑒𝑟 𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑠).

𝐹𝑎𝑖𝑙𝑢𝑟𝑒 𝑃𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦: 𝑇𝑕𝑒 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑎𝑙𝑙 𝑡𝑕𝑒 𝑡𝑎𝑠𝑘𝑠.

𝑅𝑒𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦: 𝑇𝑕𝑒 𝑖𝑛𝑣𝑒𝑟𝑠𝑒 𝑜𝑓 𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦.

Reliability = 1- Failure Probability.

𝑇𝑜𝑡𝑎𝑙 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑑 𝑡𝑎𝑠𝑘𝑠: 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑢𝑠𝑒𝑟 𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑠 𝑒𝑥𝑒𝑐𝑢𝑡𝑒𝑑.

𝑇𝑜𝑡𝑎𝑙 𝐹𝑎𝑖𝑙𝑒𝑑 𝑇𝑎𝑠𝑘𝑠: 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑢𝑠𝑒𝑟 𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑠 𝑓𝑎𝑖𝑙𝑒𝑑.

5.1 Proposed Trust aware Max-Min (TMax-Min) Experiment and Results This section demonstrates the results of experiments based on the proposed trust aware Max-Min algorithm and compared with existing Max-Min algorithm. Figure 1 exhibits a comparative analysis of the execution time in a workflow of tasks type montage and cybershake. The requests size is varying from 25 to 1000 tasks in a workflow which exhibits that the proposed Trust based Max -Min (TMax-Min) technique performs better than the existing simple Max -Min algorithm.

(a) (b)

Figure 1. (a) Comparison of execution time for montage workflow tasks (b)Comparison of execution time for cybershake workflow tasks Figure 3 to 5 exhibits the reliability and failure probability of the system to complete all tasks in a workflow. The result has shown that the proposed trust aware Max-Min and trust aware Min-Min algorithm give reliability and least failure probability, which enhances the quality of service. The reliability of the system is being tested over montage and cybershake workflows. The proposed TMaxMin technique outperforms simple Max-Min. The performance is measured using performance metrics reliability and failure probability.

(a) (b)

Figure 3. (a) Comparison of reliability for montage workflow tasks (b) Comparison of failure probability for montage workflow tasks

(a) (b)

Figure 4.(a) Comparison of reliability for cybershake workflow tasks (b) Comparison of failure probability for cybershake workflow tasks

(a) (b)

Figure 5. (a) Comparison of the number of completed tasks for montage workflow tasks(b) Comparison of the number of failed tasks for montage

workflow tasks

Figure 6 is a comparative study of the number of tasks completed, and several tasks failed using the existing algorithm and proposed trust aware algorithm over multiple workflows on montage and cybershake type. The result shows that the proposed TMaxMin algorithm reduces the number of tasks failed with an increase in task completion count.

(a) (b)

Figure 6. (a) Comparison of the number of completed tasks for montage workflow tasks (b) Comparison of the number of failed tasks for montage

workflow tasks

5.2 Proposed Trust aware Min-Min (TMin-Min) results

This section of the article demonstrates the results of experiments performed on the proposed trust aware Max-Min algorithm. The results are compared with existing Min-Min algorithm. Figure 7 discourses comparative analyses of execution time taken to complete all tasks in a workflow of type montage and cybershake. The size of workflow is varying from 25 to 1000 task, which shows the proposed Trust base Min-Min (TMinMin) performs better than existing algorithms simple Min -Min technique.

(a) (b)

Figure7. (a) Comparison of execution time for cybershake workflow tasks (b) Comparison of execution time for montage workflow tasks

Figure 8 and 9 discourses the reliability and failure probability of the system to complete all tasks in a workflow. The result has shown that the proposed trust aware Min-Min algorithm provides reliability and least failure probability, which enhances the QoS ensured to the users and reliability of the system as a whole. The reliability of the system is being tested over montage and cybershake workflows.

(a) (b)

Figure 8. (a) Comparison of failure probability for montage workflow tasks (b)Comparison of failure probability for cybershake workflow tasks

(a) (b)

Figure 9. (a) Comparison of reliability tasks for montage workflow tasks (b) Comparison of reliability tasks for cybershake workflow tasks

(a) (b)

Figure 10(a)Comparison of the number of failed tasks for montage workflow tasks (b) Comparison of the number of completed tasks for montage

workflow tasks

Figure 10 and 11 is a comparative study of the number of tasks completed, and several tasks failed using an existing algorithm and proposed algorithm over multiple workflows on montage and cybershake type. Results exhibit that the proposed TMin-Min algorithm reduces the number of tasks failed with increasing the task completion count.

(a) (b)

Figure 11(a) Comparison of the number of failed tasks for cybershake workflow tasks (b) Comparison of the number of completed tasks for

cybershake workflow tasks

5.3 Comparative analysis

This section shows the comparative results of two proposed trust aware TMax -Min, TMin-Min algorithms on performance matrix as the number of tasks failed, and several completed tasks. Figure 12and13 demonstrate the performance of the proposed TMax-Min and TMin-Min algorithm over workflows of time montage and cybershake with varying workflow size from 25-1000 tasks. Results display that TMax-Min performs better than TMin-Min, where TMax-Min has least number of task failed and a large number of completed tasks. The results reflect that TMax - Min provides better reliability and quality of service to the system and client having the least failure probability.

(a) (b)

Figure 12. (a) Comparison of the proposed algorithm in term of number of failed tasks for montage workflow tasks (b) Comparison of the proposed algorithm in term of number of completed tasks for montage workflow tasks

(a) (b)

Figure 13. (a) Comparison of the proposed algorithm in term of number of failed tasks for cybershake workflow tasks (b) Comparison of the proposed

algorithm in term of the number of completed tasks for cybershake workflow tasks.

6. Conclusions & Future Works

Scheduling of the tasks on virtual machines is a challenging issue in a scalable cloud simulation environment. Researchers focused only on tasks completion time, execution time, and average resource cost. Reliability and failure probability are challenging concerns in workflow scheduling. In this work, we concentrated on the allocation of tasks on virtual machines using the workflow scheduling technique.

The proposed TMin-Min and TMax-Min workflow scheduling techniques provide better performance than simple Max-Min and Min-Min techniques. The trust aware scheduling technique measures the performance using trust metric, reliability, execution time, and failure probability. The simulation results exhibit that the proposed trust aware TMin-Min, and TMax-Min workflow scheduling provide the quality of service-aware scheduling of the workflow. The proposed trust aware TMin-Min, and TMax-Min scheduling technique outperforms the simple Max -Min and Min-Min techniques. The performance is measured using various type of tasks which are allocated on virtual machines. In future work, we will test the trust aware scheduling techniques in a real cloud computing environment. The proposed TMin - Min and TMax-Min techniques will use for reliability testing of the real cloud computing environment which supports the workflow scheduling of tasks on virtual machines. The trust level of the proposed TMax-Min and TMin-Min model depends on execution time, failure probability, and reliability.

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