a green energy-aware task scheduling using the dvfs...

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Journal of Advances in Computer Research Quarterly pISSN: 2345-606x eISSN: 2345-6078 Sari Branch, Islamic Azad University, Sari, I.R.Iran (Vol. 10, No. 1, February 2019), Pages: 1-10 www.jacr.iausari.ac.ir

A Green Energy-aware task scheduling using the DVFS technique in Cloud Computing

Alireza Ghonoodi*

Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran [email protected]

Receivzed: 2018/06/17; Accepted: 2018/10/03

Abstract Reducing energy consumption for high end computing can bring various benefits

such as reducing costs, increasing system reliability & availability, and environmental respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cloud data center with the Dynamic Voltage Frequency Scaling (DVFS) technique and task duplication. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS-enabled processors, and energy consumption. In this paper, we develop a new scheduling algorithm called Energy Aware Scheduling Algorithm based on DVFS technique and task duplication strategy, called EADUPDVFS. Models and scheduling heuristics are examined with a simulation study. Using simulations we show our algorithm not only maintains good performance, but also has a good improvement on energy efficiency for parallel applications.

Keywords: Cloud Computing, Dynamic Voltage Frequency Scaling (DVFS), Task Duplication,

Makespan

1. Introduction

Nowadays, energy consumption as a critical issue in distributed computing systems with high performance has become so green computing tries to energy consumption, carbon footprint and CO2 emissions in high performance computing systems (HPCs) such as clusters, Grid and cloud that a large number of parallel processors made up. Thus, the scheduling of precedence-constrained parallel applications, one of the applications used in the fields of science and engineering, the homogeneous computing system (HPS) and heterogeneous like cloud computing infrastructures taking into account the amount of energy consumption and other performance parameters, is essential [1,2,3,4,5,6]. Thus, the temperature computing systems because of the large amount of energy consumed will increase, evidence shows that the temperature increase per 100 c in data centers, the expected failure rate will double, this will reliability and availability can affect the performance of the system to be harmful. Recent studies show that about 1.5% - 2% of world energy consumption by Data Centers used and the enormous growth due to the popularity of platforms, distributed computing platform such as clusters, grid and Cloud. Studies also show that about 52% of energy in data centers by computing systems and the rest support systems.

The interconnections networks and processors that is part of the computing systems, data centers consume a significant amount of energy. The total energy consumed by data centers is shown in Figure 1.

A Green Energy-aware task scheduling … A. Ghonoodi

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Fig1. Energy consumption in Data Centers

So green design and production software for scheduling precedence-constrained parallel application can have a direct impact on performance parameters and the energy consumed by processors and communication networks in data centers cloud computing.

Our objective of this paper propose green-oriented scheduling heuristic strategy and time-oriented scheduling heuristic algorithm named EADUPDVFS for energy-aware task duplication based scheduling algorithm of parallel tasks on cloud data centers. In order to meet performance and energy consumption for a given parallel application, we propose a novel task duplication scheduling algorithm based on dynamic voltage frequency scaling (DVFS) technique and clustering and duplication design pattern. The proposed algorithm aims to reduce the communication energy by using task duplication and clustering, however, these duplicate-based scheduling strategies replicate tasks and clustering by another task only according to the energy difference between current task computation energy and communication energy of these two tasks. Allocate the slack times to the appropriate non critical tasks for downscaling their frequencies and voltages with DVFS technique to try to minimize energy consumption.

2. Related Work

Task scheduling techniques in parallel and distributed systems have been studied in great detail with the aim of making use of these systems efficiently. Task scheduling algorithms are typically classified into two subcategories: static scheduling algorithms and dynamic scheduling algorithms. In static task scheduling algorithms, the task assignment to resources is determined before applications are executed. Information about task execution cost and communication time is supposed to be known at compilation time. Static task scheduling algorithms normally are non-preemptive—a task is always running on the resource to which it is assigned [11]. Dynamic task scheduling algorithms normally schedule tasks to resources in the runtime to achieving load balance among PEs. are based on the redistribution [12,13].

The list scheduling algorithm is the most popular algorithm in the static scheduling [7, 8]. List based scheduling algorithms assign priorities to tasks and sort tasks into a list ordered in decreasing priority. Then tasks are scheduled based on the priorities. In this paper, we build a list based scheduling heuristic for parallel tasks the PALS algorithm. The task execution information, such as task execution cost and communication cost, can be obtained by some profiling tools and compiler aides in advance. The task graph clustering technique [9, 10] is an effective static scheduling heuristic for scheduling parallel tasks. Given a task graph,"clustering" is the process of mapping task graph nodes onto labeled clusters. All tasks of the same cluster are executed in the same processor. In traditional task scheduling heuristics, the process of clustering tasks is an optimization of reducing the makespan of the scheduled graph. In this paper, we proposed the PATC algorithm, whose process of clustering tasks is guided by reducing the total power consumption of the scheduled graph.

Dynamic voltage and frequency scaling (DVFS) is accepted as a technique can be considered as an effective mechanism to reduce energy consumption of processors by reduce frequency and supply voltage for each slack time slot of tasks and communication and idle phase. The authors in [20] proposed energy aware scheduling heuristic algorithm named PALS and PATC for reduce energy consumption task parallel in cluster with DVFS technique and makespan, This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task’s execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. The scheduling algorithm takes into account the maximum job (Fmax) and minimum job (Fmin) frequencies given for each job and the multiple server Si that are running at maximum Si (Fmax) and minimum Si (Fmin) frequencies. For specific jobs, the scheduling algorithm efficiently assign proper servers that runs between (Fmin , Fmax) to jobs according to requirements of jobs frequencies.

Journal of Advances in Computer Research (Vol. 10, No. 1, February 2019) 1-10

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Yikun Hu et al [21] proposed algorithm with named EASLA for scheduling parallel applications by DVFS technique and considering SLA on cluster platform. The main idea of EASLA algorithm is to allocates each slack to a maximum set of independent tasks for each task using compatible task matrix and scale frequencies down to try to minimize energy consumption within certain extension rate of makespan that accept between user and service provider.

The hybrid genetic algorithm multi-objective parallel to solve the scheduling problem of parallel applications with priority Limited with the aim of reducing the total execution time tasks and the energy consumed in cloud computing are provided [22]. DVFS is used for energy storage techniques, so that each processor can be clocked at different frequencies. This approach has been evaluated with the FFT task graph which is a real word application. Cloud computing offers utility-oriented IT services to consumers based on pay-as-you-go model. This model is a payment method for services that charges based on usage only resources that are needed.

3. System Model

In this section we introduce a formal definition for system Architecture model, parallel task model, DVFS model, resource model, energy consumption model in processors and interconnections, performance model under some assumption and restrictions, which are employed for problem formulation.

3.1 Architecture Model This architecture model proposed schedule parallel tasks we introduce our environment. Architectural

model, consists of three layers, each layer contains different sections. 3.1.1 User Layer

In this layer, users their applications, including source code is to be executed by a processor or processing elements that exist in cloud data centers, are sent.

3.1.2 COMP Superscalar Layer COMP Superscalar (COMPSs) layer is a framework which aims to ease the development and execution

of task-based applications for distributed infrastructure, such as clusters, Grids and Clouds and a runtime system which manage several aspects of the applications execution. Beside, it keeps the underlying infrastructure transport to the application. Some important functionalities implemented by the COMPSs runtime are:

· Task dependency analyzer: tasks are the basis for the parallelism in COMPSs. The runtime automatically finds the data dependencies between tasks based on the direction of their parameters. With this information, it dynamically builds a task dependency graph is called Directed Acyclic Graph (DAG).

· Task scheduler: when tasks are free of dependencies received from the task analyzer, they are scheduled by the runtime in the available distributed resources.

· Job manager: it is in charge of job submission and monitoring. It receives the scheduled tasks from the task scheduler and delegates the necessary file transfers to the file manager.

· File manager: it takes care of all the operations where file are involved. It is a composite component which encompasses the file information provider and the file transfer manager components.

3.1.3 Data Center Resource Layer Layers of data center resources, including several computational nodes, which each computing node

contains multiple virtual machines and virtual machine also includes several processor, disk, memory and network communication. DVFS processor has the ability and responsibility to carry out their duties.


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3.2 Parallel Task Model A parallel task with precedence constrains is modeled as a Directed Acyclic Graph

v : consist of a set of tasks in G. all tasks are the components of the

application code (nodes in a DAG). These tasks are scheduled to run over different processors in the systems. T = � , Where

§ is a task in DAG. § is the total number of tasks. § is weight on task represents the instruction number of task § is the start time of task . § is execution time (computation time) of task on processor which is

indivisible and its execution can not be interrupted, the task execution time is calculated as follow:

� � � Í� ��

§

§ is the end(finish) time of task that calculated as follow: ��

v :

§


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3.3 Cloud Data Center Resource Model

§

§ .

§

� � �� § ;

§

§

3.4 DVFS Model

� � Table1. Voltage - frequency pairs of AMD Athlon-64 processors [18]

� � �� Í� �� Í� �� Í� ��

Level Frequency (GHz) Voltage (V)

Speed (MIPS)

Relative speed Power

0 0.8 0.9 4000 40 2.03 1 1.0 1.0 5000 50 3.85 2 1.2 1.1 6000 60 4.84 3 1.4 1.2 7000 70 5.95 4 1.6 1.3 8000 80 6.35 5 1.8 1.4 9000 90 7.2 6 2 1.5 10000 100 8.4


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3.5 Estimation Model

3.5.1 Makespan Estimation

��

3.5.2 Latency Model

3.5.3 Energy Consumption Model

3.5.3.1 Computation Energy

��

��

��.�� .�� Í ��Í �� Í �� .�� ℎ��ℎ��2 ℎ��ℎ��

��.�� .�� Í �� Í ��

��

��

��.�� .�� Í �� Í ��

��

�� Í � � � ��Í � � ��


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��.�� Í � � � ��Í � � ��

. �� .�� .��

3.5.3.2 Communication Energy

�� Í ��

�� ∈ ��(��) ��Í ��

��

�� = �� (��.��) �� (��.��) �� (��)

3.5.4 Objective Function

, �� (��.��) �� (��.��) �� (��)

�� Í�� Í �� Í ��

��Í

4. Proposed Algorithm

.

Input: G (T, D, et, ct, C, nRep), �, � ) ΄, nRep΄(T, D, et, ct, C ΄Output: G

← G ;΄1 G },�= {�́| �́← { ΄2 C T}; �

;�)�́) ← et(�́, nRep( ΄C �́3 )΄get Throughput(G ��4

5 If �� then 6 { �,� �,� � � � � � � } 7 for �,�΄ �,� � do

�,�΄is the destination task of �́, �,�΄ is the source task of �́8 ΄← G�,΄← G� 9

10 cluster �́ � �́ � 11 duplicate �́ � 12 �←getLatency �


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13 �←getLatency � 14 if � � then

;�← ΄15 G 16 else

;�← ΄17 G 18 end

);΄getPowerConsumtion(G ��19 20 end 21 end 22 while �� 23 { �,� �,� � � � � � � }; 24 if then 25 return NULL; 26 end 27 �,�΄ �,� 28 �́ is the source task of �,�΄ �́ is the destination task of �,�΄ 29 cluster �́ �́

);΄getPowerConsumtion(G ��30 31 end

5. Performance Result With Simulation

Table2. Operating points for the Turion MT-34 processor Frequency (GHz) Voltage (V)

1.8 1.2 1.6 1.15 1.4 1.1 1.2 1.05

1 1 0.8 0.9

Table 3. Comparison of energy savings between different energy aware scheduling algorithm Energy aware DAG scheduling algorithm Energy saving (%)

EADUS & TEBUS [29] 16.8 Energy reduction algorithm [33] 25

LEneS [23] 28 ECS [32] 38

PATC 39.7 PALS 44.3

EADUPDVFS (proposed algorithm) 48.3


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6. Conclusion And Future Work

References

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[15] Ziliang Zong, Adam Manzanares, Brian Stinar, Xiao Qin, Energy-aware duplication strategies for scheduling precedence-constrained parallel tasks on clusters, in: Proceedings of the 2006 IEEE International Conference on Cluster Computing, 2006.

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