Efficient Energy Aware Task Scheduling for Parallel · application on a cloud environment to...

Efficient Energy Aware Task Scheduling for Parallel Workflow Tasks on Hybrids Cloud Environment

Thanawut Thanavanich School of Computer and Information Technology

Chiangrai Rajabhat University Chiangrai, Thailand

[email protected]

Putchong Uthayopas Department of Computer Engineering

Faculty of Engineering, Kasetsart University Bangkok, Thailand

[email protected]

Abstract— In this paper, the challenge of scheduling a parallel application on a cloud environment to achieve both time and energy efficiency is addressed. Two energy-aware task scheduling algorithms called the EHEFT and the ECPOP are proposed to address the challenge. These algorithms have the objective of trying to sustain the makespan and energy consumption at the same time. The concept is to use a metric that identify the inefficient processors and shut them down to reduce energy consumption. Then, the task is rescheduled to use fewer processors to obtain more energy efficiency. The experimental results from the simulation show that our enhanced algorithms not only reduce the energy consumption, but also maintain a good quality of the scheduling. This will enable the efficient use of the cloud system as a large scalable computing platform.

Keywords-task scheduling; energy-aware; cloud computing; heuristic algorithm

I. INTRODUCTION Recent development of cloud computing technology

enables users to obtain a very cost effective, scalable, and massive computing power and storage for large scale application. Nevertheless, the efficient scheduling of these large scale and complex applications is a challenging problem. There are many works that address this problem such as in [1], [2], [3], [4], [5], [6], [7], [8], [9]. An efficient task scheduling not only increases the system utilization, but also reducing the energy consumption which can lead to a substantial cost reduction. Recent study estimates that the power consumption in data center is now account for as much as 0.5 percent of the world’s total power usage [10], [11]. Therefore, the problem of finding of a good task scheduling algorithm for a cloud that try to get a minimal completion time and low energy consumption becomes a substantially important research topic.

In this paper, we present two energy-aware scheduling heuristics that take into consideration not only the makespan conservation, but also energy reduction. Both scheduling algorithms are devised with measure to identify the degree of task computation efficiency relative to the application completion time. The degree is used as a utilization value to identify a level of virtue for executing task on processor, and implied an effectively energy consumption of that processor. The main different between the proposed algorithms and two previous scheduling, HEFT and CPOP [2], [3], is to apply a measure to the utilization of computation of task assigned on

each processor. In this work, the HEFT (Heterogeneous Earliest Finish Time) and the CPOP (Critical Path on a Processor) were applied to enhance energy-aware scheduling called EHEFT (Enhancing Heterogeneous Earliest Finish Time) and ECPOP (Enhancing Critical Path on a Processor), respectively.

The rest of paper is organized as follows: Section 2 reviews related work. In section 3, the cloud system model, the cloud application, the energy consumption, and scheduling model are described. The EHET and the ECPOP algorithm present in Section 4. The experimental results and evaluate studies are presented in Section 5. Finally, Section 6 concludes the paper.

II. RELATED WORK In recent literature, many efforts in task scheduling have

been focused on the minimization of application execution time (schedule length or makespan). Generally, the objective of scheduling is to find an optimal solution for scheduling the given tasks with the minimal amount of completion time. Many works [8], [6], [15], [16], [17] studied the problem of parallel application scheduling. There is a growing interest in energy-aware scheduling for single processor [18], [19] homogeneous resources [7], [14] and heterogeneous resources [8], [6], [16].

Many efficient techniques exist for reducing the energy consumption of a processor [23]. One of most effective technique is Dynamic Voltage Scaling (DVS) [12], [13], [14], [24]. This technique enables processor to operate at different voltage supply level. By lower the level of voltage during idle time slot of the scheduling a large reduction in power consumption can be achieved. Thus, several scheduling algorithms applied this technique to improve the performance of scheduling with energy-aware concept [6], [8], [15], [25], [7]. Nevertheless, the application of scheduling with DVS may results in a lower power consumption but longer execution time.

In [15], [6], [8], [16], the scheduling of tasks that try to minimize both schedule length and power consumption are studied. For example, a concept of makespan conservation with energy reduction technique is proposed [8]. This technique is based on adopting the slack reclamation to achieve the energy reduction. Since scheduling with optimal solution is NP-Complete problem, both heuristic [8] and metaheuristic [16] methods often applied to strike this problem. However, most

2013 International Computer Science and Engineering Conference (ICSEC): ICSEC 2013 English Track Full Papers

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recent works were attended heuristic method for scheduling a set of task with a low time complexity. In the previous works, we found that scheduling with slack management technique still consumes energy at a low level of supply voltage while waiting data of precedence task, and is in idle state of task execution. Scheduling that try to achieve the balance between schedule length and energy consumption is considered in [8] [15]. Their scheduling aims to schedule task within a deadline and also reduce the power consumption. Those scheduling techniques applied the decision model for balancing two performance goals to identify a trade-off value between these factors. Our scheduling algorithm focuses on reducing the energy consumption by reducing number of processor execute tasks inefficiently.

Many studies on task scheduling with energy-aware are applied to single processor and cluster system. Usually, the tasks in these systems are modeled as both dependent and independent task using both static and dynamic task scheduling [25]. In heterogeneous system [6], scheduling algorithm with DVS technique need to apply to reduce the power consumption and improve the benefit to the providers. The concept of slack management proposed to enhance a utilization of task execution within idle phase in processors enabled DVS [13]. The slack time occur in processor that executes a task with earlier completion. Task scheduling for energy-aware with slack management are proposed. However, they are not considering a trade-off between the performance of schedules and energy consumption.

III. MODELS In this section, the system, applications, and energy model

is presented.

A. Cloud System Model In this work, computing resources in a cloud is modeled as

set of services. The data center is consists of a set P of heterogeneous processors. Each processor Pp j ∈ is DVS-enable which can be operated with different voltage levels and clock frequencies. We define the supply voltage on processor

jp as set jV of v and the clock frequency on processor jp as set

jF of f . While the supply voltage operates at level lv , the clock frequency also performs in lf . A processor in an idle state will operate at a lowest voltage level ( lowv ). In this paper, we assume that the overhead of the frequency transition can be ignored (equal to zero). In addition, the inter-processor communication is assumed to be at the same speed for all processors. It is also assumed that data can be exchanged among processors while a task is executing.

B. Cloud Application Model A cloud application is a workflow represented by a directed

acyclic graph (DAG) as shown in Fig. 1. The task graph, ),( EVG = that consists of a set of vertices V and set of edges E . Each vertex represents a task tagged with the computation cost of the application and each edge represents the precedence constraints of the task such that task in must

complete its processing before task jn start. The weight of each edge is the communication cost between tow tasks. In general, a task without precedence is called an entry task entryn and a task without successor is called an exit task exitn . After all tasks completed its execution, the finish time of latest task is called schedule length and the longest path of the scheduled task graph is the critical path )(CP .

Figure 1. Task graph

The estimated task execution of a given application denotes as W is represents the computation cost matrix pv × , in which each ijw is an execution time of task jn on processor jp . An average computation cost of task in can defined as iw . The task communication, denote as ijc represents the communication cost between task in and jn . The communication cost is ignored when both tasks are allocated to the same processors.

C. Energy Model The energy consumption of a task execution on any

processor totalEnergy , is composed of a dynamic energy consumption dynamicE and a static energy consumption staticE . In this work, the static energy consumption can be ignored since the dynamic power dissipation is the most significant factor of the energy consumption [6], [8]. The dynamic power dissipation dynamicP is computed as ljldynamic fVCAP ... 2= where A is the number of switches per clock cycle, C is the total capacitance load, jlV is the supply voltage at level l on processor jp , and f is the operating frequency that operated the supply voltage at level l . The parameters A and C , which are device related constant, depend on each device capacity. Thus, the dynamic energy consumption is computed as

ijdynamicdynamic wPE Δ= . where ijwΔ is the computation cost of task

in on the processor jp (the amount of time used to execute task in ). In the task execution period, we assume that the processor jp operate at the highest level of supply voltage

2,highjV and the highest frequency highlf , . On the other hand, the

processor scales down the voltage and frequency to lowest supply voltage, 2

,lowjV and lowest frequency, lowjf , , for idle


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period. The energy consumption for all the idle period idleE can be represented as jidlelowjlowjidle wfVCAE ,,

2, .... Δ= where jidlew ,Δ is an

idle time slot that spent to execute the empty task. Then, the total energy consumption totalEnergy can be represented as

idledynamictotal EEEnergy += . (1)

IV. THE PROPOSED ENERGY-AWARE SCHEDULING In this work, the task scheduling problem is to allocate a set

V of v tasks to a set P of p processors and minimizing schedule length and energy consumption as much as possible. After the scheduling of v task is completed, the schedule length or makespan is obtained through the completion time of exitn .

A. Motivation Case Fig. 2 (a) shows a schedule of task graph from Fig. 1

scheduled by HEFT algorithm. Since the primary performance goal of the HEFT is to assign all tasks to minimize the completion time, the scheduling will not consider the reduction of number of processor used to increase the energy efficiency. Thus, by taking some processor off line, there is possibility of saving some energy while maintaining the same makespan or slightly increases the makespan (as shown in Fig. 2(b)). The result shows that the schedule has length, which is equal to 92, and energy consumption, which is equal to 538. Thus, this work try to identify an opportunity of shutdown some processors for energy saving. Although, there are a few works that use different approach for saving the energy (e.g. [8], [6]), this work uses the schedule length and energy consumption of HEFT with simple DVFS technology as based line algorithm.

(a)

(b) Figure 2. (a) Scheduling with HEFT (b) Rescheduling with EHEFT

B. Proposed EHEFT Scheduling In this subsection, an approach to enhance a HEFT

scheduling algorithm called EHEFT (as shown in Fig. 3) is proposed. The main idea of the algorithm is to reduce the energy consumption by shutting down a set of processors that execute the task inefficiently. All of the processors allocated to

a set of tasks are evaluated applying the ratio between active time and makespan as criteria. This EHEFT algorithm consists of three phases as follows.

1) Task Scheduling Phase: First, Heterogeneous Earliest-Finish Time (HEFT) is used to allocate the tasks to the processor which minimize its earliest finish time. The result of the HEFT is schedule which is an assignment of tasks from task graph to processors.

2) Discovering Inefficient Processors Phase : This phase trying to discover the inefficient processors using a performance metrics called Ratio of Effectiveness (RE) of the assigning task on processors. The RE value can be used to identified a utilization of task execution on the processor. The RE value on the processor jp are denoted as jRE . Its RE is obtained by computing the ratio of total active time to schedule length on the processor. (The RE is presented in (2))

makespan

wRE jpi ij

j∑∈

= (2)

Let ijw denote an active time slot which runs a task in on the processor jp . The output of this step is the sorted RE list in an ascending order. For a processor with a low RE value, most of the energy is consumed by idle time slot not the task execution. When a low RE processor is shutdown task will be reschedule by HEFT to another processor.

3) Rescheduling Phase: The EHEFT uses the set of the assigned processors that exclude a set of inefficient processors. Let 'P be set of processors after a set of inefficient processor 'p was shutdown. We can define a set of 'P as fellows ineffPPP −=' where set ineffP is set of inefficient of processor execution. After the new set of processor has been identified, the same task graph is rescheduling again using HEFT. For example, EHEFT is applied to schedule tasks in Fig. 2 (b). The set of processors is used is },,,{ 4310 pppp . After inefficient processors was switch off, we can save energy consumption of execution task on those processors. In many cases, a high concurrency application can save a lot of the energy than a low concurrency application.

Algorithm 1: EHEFT (Enhancing Heterogonous Earliest Finish Time) Input : G(V,E) and a set of P processor Output: EHEFT schedule of G onto P Phase 1: # Task Scheduling Run HEFT Scheduling with set P of processor # Output is to assign a set V of tasks to a set P of processors. Phase 2: # Discovering inefficient processor Compute ),( MakespanwRE ijj for every processor

Shutdown inefficient processor that has minimum RE value. Phase 3: #Rescheduling Run HEFT scheduling with set 'P of processor

Figure 3. The EHEFT algorithm


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C. Proposed ECPOP Scheduling In this subsection, the enhanced scheduling algorithm,

which included with the efficient technique of maintaining makespan, called ECPOP (as shown in Fig. 5) is proposed. The goal is to reduce the energy consumption by increasing the utilization of task execution while trying to conserve the makespan. In this algorithm, critical processor is defined as a processor that execute task that is on the critical path. On the other hand, any processor that is assigned with non-critical task is called non-critical processor. A set of non-critical processors are evaluated for the utilization. The measure used is RE (ratio of effectiveness). The makespan conservative technique applied to the ECPOP is to shutdown non-critical processor as many as possible. The ECPOP algorithm consists of three phases as follows.

(a)

(b) Figure 4. (a) Scheduling with CPOP (b) Rescheduling with ECPOP

1) Task Scheduling Phase: First, the traditional Critical

Path on a Processor (CPOP) algorithm is used to allocate the critical tasks to the processor which has the minimium total of exeuction time of the critical tasks. The result of the CPOP is schedule which is an assignment of tasks from task graph to processors.

2) Discovering Non-Critical Processor Phase: Let nCPP denote a set of processors that only execute non-critical

task. We can define a set of nCPP as CPnCP PPP −= where set CPP is a set of processor that executes the critical task. In the parallel task execution, non-critical task often arises and can extend its execution time unless the time exceed the slack time. After the scheduling determine the set nCPP , its RE value of each processor is computed to identify the inefficient processor that can be shutdown without increasing the makespan. Thus, the RE measure is given in (3).

makespan

wRE

nCPjpi ij

j∑∈

= (3)

Let ijw denote an active time slot which runs a task in on the processor nCP

jp . The output of this step is the sorted RE list in an ascending order. Next, a set of task allocated on the lowest

RE processor is assigned to the processor nCPkp where k

lowestj RERE < . The reassigned step is repeated till

set of task cannot allocate to the slack slot of the next processor.

Algorithm 2: ECPOP (Enhancing Critical Path on a Processor) Input : G(V,E) and a set of P processor Output: ECPOP schedule of G onto P Phase 1: # Task Scheduling Run CPOP Scheduling with set P of processor # Output is to assign a set V of tasks to a set P of processors. Phase 2: # Discovering inefficient non-critical processor Compute ),( MakespanwRE ijj for non-critical processor

Shutdown inefficient processor that has minimum RE value. Phase 3: #Rescheduling Run CPOP scheduling with set '

nCPP of processor

Figure 5. The ECPOP algorithm

3) Rescheduling Phase: The ECPOP used the CPOP algorithm for rescheduling a set of non-critical task that was assigned on the lowest RE processor lowest

jp . The enhanced algorithm needs to reschedule those tasks for executing on new set of processor that exclude the lowest RE processor. (Thus, saving the energy). Let '

nCPP be the new assigned processors that exclude a processor lowest

jp . We can define a set of '

nCPP as fellows lowestjnCPnCP pPP −=' . For example, as

illustrated in Fig. 4 (b), ECPOP is applied to rearrange the given task in Fig. 4 (a) that scheduled with the CPOP algorithm. In this example, a set of processor that selected as the critical path processor nCPP is },,{ 310 ppp and a set of non-critical path processor '

nCPP is }{ 2p . After the RE value of set '

nCPP is evaluated, the scheduling then selected the processor 2p (it is the lowest RE lowest

jp processor) as the target of shutdown processor. In this case, we found that the ECPOP is not only save the energy consumption from shutdown the inefficient execution of processor, but also maintain the makespan of task execution.

V. EXPERIMENTAL RESULT AND DISCUSSION This section presents the evaluation of our algorithm. The

results are obtained from the comparison of scheduling length and energy saving between EHEFT, ECPOP, HEFT and CPOP.

A. Experimental Setting In this study, the performance of the proposed algorithms

were studied using randomly generated synthetic task graphs that take into account the various characteristics of cloud applications. The simulation parameters [2], [8], which depend on several application characteristic, were given as fellow. Number of tasks in the random DAG, we use the set {10, 20, 40}. For DAG workload on task, we assigned the communication to computation ratio (CCR) with the set {0.5, 1.0, 5.0} – (if CCR value is very high, i.e. 5.0, it can be considered as a communication intensive application.). The α used as depict a shape of the graph and the value of α in this simulation are 0.5 and 1.0 (if α value is high, it generated with high degree of concurrency (high parallelism) application). For heterogeneity factor for processor


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speed )(β , we choose the set {0.5, 1.0} as a range of computation cost (The computation cost among the processors has significantly different, if β is high.). A set of processor that is available to schedule is {5, 10, 15}. In our experiments, the number of total task graph evaluated is around 1000 DAGs.

B. Comparison Metrics The performance of algorithms is evaluated with two

metrics: First, the Schedule Length Ratio (SLR) which is obtained by dividing the makespan by the summation of the minimum computation cost of task on the critical path. This is given by

∑ ∈ ∈=

CPn ijPpi jw

MakespanSLR}{min

. (4)

Second, the Energy Consumption Ration (ECR) which is obtained from dividing energy consumption by the summation of the energy consumption of task, the ECR is given by

2, }{max}{min

, kjVvCPn ijPp vwEnergyECR

jkji j ∈∈ ∈ ×=∑

. (5)

C. Results and Discussion From the result, it can be seen that EHEFT executes a task

graph with low parallelism degree and computation intensive, i.e. α < 1.0 and low CCR, can maintain the SLR values in exactly the same as the HEFT. And, the EHEFT clearly signifies the performance of executing the communication intensive application over the HEFT as shown in Fig. 6 (a), SLR. Although the average SLR of ECPOP on low parallelism degree, computation intensive, and low CCR has its SLR value worse than EHEFT and HEFT, it can sustain its makespan. Specifically, EHEFT and ECOP can reduce more energy consumption than HEFT and CPOP for various application characteristics as shown in Fig. 6 (b), ECR. When shape of application changed to square (i.e. height of graph is equal to depth), the average SLR of EHEFT have greater than HEFT for computation intensive application but ECPOP still maintain its SLR value which is less than EHEFT. On average, the average SLR of both the proposed scheduling on communication intensive application can achieve its SLR value of less than EHEFT and HEFT. Nevertheless, EHEFT provide promising results that has the significantly decreased of ECR value, and also has been proven that it obtained more excellent solution for reducing energy consumption than ECPOP, HEFT and CPOP in this application characteristic as shown in Fig. 6 (c), (d), (e) and (f).

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(e) (f) Figure 6. Average SLR and ECR (a) and (b) SLR and ECR with 5.0=α , (c) and (d) SLR and ECR with 0.1=α , (e) and (f) SLR and ECR with 0.2=α

Finally, the results for our study are summarized in Fig. 7. Obviously, the EHEFT algorithm mostly outperformed the HEFT with communication intensive application. Although the EHEFT take effect to slightly increase a schedule length in computation intensive application, the averages of its ECR value on all generated graphs are decreased significantly over the three others scheduling (ECPOP, HEFT, and CPOP). Since the EHEFT cannot maintain the quality of scheduling a computation intensive application, the ECPOP is proposed to schedule parallel applications that use the quality of scheduling as performance constraint since this algorithm trying to maintain the makespan and reduce energy consumption at the same time.

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Figure 7. Trend of energy reduction and makespan growth


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VI. CONCLUSION In this paper, two energy aware scheduling for the cloud

called EHEFT and ECPOP are proposed. Both of algorithms trying to achieve more energy reduction while maintaining the same performance as much as possible. The proposed approach is to use performance metric called RE to help identify inefficient processor in the system. The simulation results show that the proposed method can help reduce the energy consumption without increasing schedule length substantially. In addition, the experimental results show that EHEFT and ECPOP can reduce the total power consumption with many class of parallel applications, both computation and communication intensive. The result of this work can lead to a more energy efficient cloud. In the future, the reduction of time complexity of this algorithm can be explored along with how to make more efficient use of DVS support that is built into the processor.

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