Energy Aware Resource Utilization Technique for Workflow

Turkish Journal of Computer and Mathematics Education Vol.12 No.10 (2021), 7461-7472

Research Article

7461

Energy Aware Resource Utilization Technique for Workflow Scheduling in Cloud

Computing Environment

Murgesh V. Jambigi1, Dr. M.V. Vijay Kumar2, Dr. D.V. Ashoka3, Dr. Prabha R4

1Department of Computer Science & Engineering A.I.T.M., Belgaum, India 2Department of Information Science & Engineering Dr. A.I.T., Bengaluru, India 3Department of Information Science & Engineering JSS Academy of Technical Education Bengaluru, India 4Department of Computer Science & Engineering Dr. A.I.T., Bengaluru, India

Article History: Received: 10 January 2021; Revised: 12 February 2021; Accepted: 27 March 2021; Published online: 28

April 2021

Abstract—Heterogeneous multicore computational environment are increasingly being used for executing scientific

workload. Heterogeneous computational framework aid is reducing energy dissipation for executing real-time data intensive

workload by employing Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS).

However, reducing energy and improving performance is becoming major constraint in modelling workload scheduling

model in heterogeneous computational environment. Recently, number of multi-objective based workload scheduling aimed

at minimizing power budget and meeting task deadline constraint. However, these model induce significant overhead when

demand and number of processing core increases. For addressing research problem this work assume that different task will

have different execution path, I/O access, memory, active processing, and cache requirement. Thus, this paper present Energy

Aware Resource Utilization (EARU) model by minimizing energy dissipation and utilizing cache resource more efficiently.

The EARU model achieves much lesser execution time, energy consumption, and power consumption when compared with

existing multi-objective based and DVFS-based workload scheduling algorithm.

Keywords—DVFS, Energy aware scheduling, Heterogeneous cloud computing environment, multi-objective optimization

problem, Scientific workload, Quality of service.

I. INTRODUCTION

Consistent development and progression in manufacture industry, number of semiconductors can be

effortlessly installed into one single chip, additionally ongoing improvement in processor configuration has caused

for adding a few CPU center and enormous store in single chip to upgrade the exhibition. Anyway this has

additionally caused a few force system issue which influences the gadget dependability, gadget execution and

battery life of gadget. Besides power utilization is considered as one of the significant explanation that lead to the

design move towards the multicore chips, this will in general deal with the interest in expansion in recurrence.

Anyway to keep up the performance immaculate many number of centers should have been added to the given

processor, this makes the force one of the significant factor once more; the fundamental test is overseeing

immense number of centers for conveying superior with low force utilization. The performance can be

extemporized through two situations for example either through expanding the center and correspondence width

or through expanding recurrence. These two situations can bring about power utilization; further energy is

considered as the result of execution and force. Thus it is another inspiration for investigating the relationship

among them.

Power utilization issue has been handled through the different existing method like DPM (Dynamic Power

management) and DVFS (Dynamic Voltage Frequency Scaling) [1], in here DVFS regulator distinguishes the

computational examples in execution measure and further decides the voltage scaling and frequency scaling of

CPU center to diminish the energy utilization. In addition a few processor plans have embraced DVFS per center.

For example Intel's lift innovation and LITTLE cores, where each center holds the capacity for scaling the degree

of voltage and frequency; further in multi-core model, expanded control convention is suggested for controlling

the center to lessen power utilization [2]. Further different investigations have centered energy productivity by

implication through execution ad lib as in [3] [4], AI based methodology is utilized for the mind boggling issue

such of limiting the multicore power consumption [5]. These system can be apply productively profoundly,

anyway such technique can't be scaled to the given chip level where the board is needed to be applied in given

facilitated type. Besides the overhead of given method are inadmissible as the quantity of center increments [6].

Further, support learning is utilized to deal with the force utilization due to earlier information and furthermore it

is versatile [7]. Anyway all postulations DVFS experiences vigorously the low inventory voltage requirement and

neglected to think about the store unwavering quality and furthermore none of them considered the force

distribution among chip clusters progressively to improve the performance or to save power, all the while keeping

up the adaptability of model.

Central processor usage control utilizing input data [8] has shown astounding execution to give the continuous

assurances through responsibility varieties adaption dependent on given powerful criticism. Additionally the

fundamental point of use control is implementing the legitimate usage of schedulable limits progressively situation

on whole processors. This should be accomplished in spite of vulnerability in responsibility. Subsequently it is

seen that use control is equipped for fulfilling constantly constraints of ongoing without appropriate information

on responsibility, for example, execution seasons of errand. Further force mindful usage is engaged by hardly any

specialist to accomplish the decrease in power utilization and constant certifications [9]. [10], [11], anyway the


7462

current work on this chiefly relies upon the DVFS through presumption that execution season of assignment can

be effectively adjusted alongside CPU recurrence. Besides the supposition that is a lot of substantial considering

the ongoing situation for task that are memory concentrated and calculation escalated and have 75% of directions

as store or burden. Besides when specific processor is in measure CPU utilization and memory escalated

assignments are set to most significant level, further use can likewise surpass the schedulable bound this outcomes

in missing undesired cutoff times. Also reserve size are isolated to center and can be amplified to limit the store

access idleness and store miss rate which is happened because of the less memory access delay. Consequently

CPU use is brought down for productive continuous situation, further if use is nearly lower than given bound

while the recurrence is at the least level, size of dynamic store can be limited further and other reserve units which

are seldom utilized can be utilized through putting low force mode to decrease the reserve spillage power [12].

Fig. 1. Workflow management.

In this paper, two-stage use was proposed for energy proficiency in the continuous situation for heterogeneous

multi-center handling climate, at the center level this component uses dynamic L2 store segment and per-center

DVFS for tending to the goals, for example, diminishing the center energy utilization and controlling the CPU use

for each center. Also, use that happened because of the intermittent constant can be resolved through recurrence-

free and recurrence subordinate execution time. Further store dividing and per-center DVFS is utilized for

receiving the needy and autonomous parts of recurrence, individually [13], [14]. Nonetheless, the principal

challenge here is the customary control hypothesis streamlining model [13], [14], and [15] doesn't deal with the

advancement targets. Further, both DVFS and LLC regulator choices are made freely. Accordingly when the LLC

regulator attempting to limit the worldwide LLC can influence singular undertaking execution prompting QoS

infringement. Further, these model are not proficient in advancing energy as the reserve parcel regulator doesn't

think about the impact of DVFS

For instance, a little reserve asset distribution for executing certain errands can build the handling component

voltage-recurrence and welcomes quadratic impact on preparing component energy utilization. Thus in this paper,

a proficient asset the board strategy is created which depended on the multi-target control hypothesis [16] [17] to

improve the over two destinations (i.e., use store asset all the more effectively with negligible execution time for

planning logical responsibility under heterogeneous cloud computational climate). The EARU procedure is

planned by incorporating asset the board system that advance center DVFS and LLC parceling under shared

multicore distributed computing climate. The EARU model can facilitate the store size from the given center and

further, the unique reserve is resized to decrease the spillage in power utilization of last level stores (LLCs).

The commitment of examination work is as per the following:

• This paper introduced productive asset the board strategy for executing logical responsibility with

negligible energy utilization under heterogeneous cloud computational climate.

Energy Aware Resource Utilization Technique for Workflow Scheduling in Cloud Computing Environment

7463

• EARU model lessens LLC disappointments with better store and V/F scaling enhancement in a powerful

way; in this way uses asset all the more proficiently.

• The EARU model achieve preferable execution over existing asset the board strategy as far as to force

utilization, preparing time, and energy production.

The paper association is as per the following: In segment II, a study for hidden advantage and limit of utilizing

condition of-craftsmanship responsibility booking is depicted. In segment III, the proposed Energy-Aware

Resource Utilization working model is portrayed. In area IV, the test result got by EARU over different existing

responsibility booking models is nitty-gritty. Finally, the exploration is finished up and future bearing work are

examined.

II. LITTERATURE SURVEY

This segment presents an examination of different existing responsibility booking calculations under

distributed computing climate. In [9] saw that current techniques attempt to turn off a few processors through

consolidating the errand on fewer processors for diminishing the energy utilization for the cutoff time compelled.

In any case, it is seen that turning of processor probably won't be important to decrease the energy utilization,

consequently, they proposed EPM (Energy-mindful processor combining) instrument to pick the specific

processor to turn off for energy utilization and Quick-EPM was created to limit the computational overheads. In

[20] saw that solitary calculation doesn't have ideal arrangement under various force settings, dynamic leeway,

and different jobs, further the gadget design variety influences the DVFS calculation. Henceforth considering the

flexibility this paper zeroed in on building up the support discovering that takes execution method set which is

particular to deal with the different conditions and changes to the best procedure thinking about the circumstances.

In [10] proposes CEAS (cost and energy mindful planning) method for the cloud scheduler to lessen the work

process execution cost and limit the energy utilization which complies with the time constraint essential.

Overall CEAS contains five calculations, from the start VM determination approach is utilized that applies the

expense utility idea to plan the errand to their ideal VM-types through the making length imperative. Later two

errands mindful methods were utilized to limit the energy utilization and execution cost, further to reuse the VM

Instance, VM reuse strategy is created and finally slack time recovery gets used to lessen the energy of these VM

Instances. In [17] proposed two creative work process planning strategies which think about the financial expense

just as make a range. Consequently from the outset, a single target work process is created named DCOH which

was principally cutoff time compelled cost upgraded to limit the booking cost under the given cutoff time. Besides

thinking about DCOH, multi-target advancement is proposed for half and half cloud named MOH to streamline

the financial expense and execution season of work processes.

In [18] created load adjusting approach for apportioning the non-continuous on the given heterogeneous hubs,

further they presented the preparing hub recurrence of the entire cycle for every one of the positions that are doled

out. In [19] built up an ILP based string which takes the contribution through equipment execution which decides

the qualities of the string. Here they utilized the Last level reserve and directions each second as a proportion of

memory transfer speed and CPU load. Besides, they utilized execution metrics for improving the worldwide string

to the central task. Anyway it isn't reasonable for the constant situation and it needs to take care of the issue of ILP

occasionally and thus this brings about the barely booking overhead. In [15] showed heterogeneous multi-center

preparing is embraced basically in installed framework, as it gives the energy utilization minimization through

applying the mainstream method like DPM and DVFS. Besides compelling administration of energy-based

strategy misuses the product and equipment level energy minimization method

EEP (Energy Efficiency Partitioning) is a product-level method where task designation to the given

heterogeneous groups impacts the entire energy model. Thus a procedure was created which couples the energy

proficient segment issue alongside task booking as errand vary regarding SoC hardware, dynamic preparing,

execution way, I/o access, memory, store, and guidance blend, these influences the interest in power. Also,

equipment recurrence scaling is utilized for scaling to limit the model energy.

In [14] proposed LEAD for example learning empowered EAD (Energy-Aware Dynamic Voltage) scaling for

given multicore design utilizing support learning and managed learning. Further, LEAD bunches the connection

and its switch into a similar voltage-recurrence space and further executes the administration techniques of

proactive DVFS which essentially depend on the AI-based disconnected model to give the voltage-recurrence

choice among the sets of voltage/recurrence. Further, three directed learning model were created dependent on

energy/throughput change, cradle use change and support use, these permits the proactive mode determination

mode based on the outright forecast. Further support learning models were created which improves the

determination of DVFS mode straightforwardly and furthermore eliminates the necessity for edge and name

designing. In [8] saw that energy is one of the significant concerns while planning the multicore chips, here

execution and force are two essential energy segments which are contrarily identified with one another, in here

multicore chips improvement which measures on the equal burden utilizing either execution enhancement or force

streamlining.


7464

Consequently to accomplish that AI model was created dependent on the dynamic and worldwide regulator of

force the executives, also the regulator is utilized for lessening the force utilization and builds the presentation in

the given force financial plan. Further, it is seen that the regulator is versatile and doesn't have a lot of overhead as

there is expansion popular. In [13] saw that any expansion in chip temperature has different circuit mistakes,

additionally, there is an enormous expansion in spillage power utilization. Thus it applied undertaking movement

or customary DTM method for decreasing the center temperature as these centers have high temperature,

additionally to remunerate with appeal in information, LLC(last level reserve) is connected which helps in

diminishing on spillage of force by possessing the chip region. Further to lessen the force utilization store size is

made to shrivel progressively, contracting of reserve size helps in spillage of force utilization as well as helps in

making on-chip warm cradles to additionally limit the temperature of chip however misusing heat move. In

addition resizing of the store is done depends on the reserve area of interest produced while execution.

From a broad overview, it is seen the current responsibility planning embracing multi-target improvement

incite calculation intricacy in light of NP-difficult issue. Further, DVFS based approaches are exceptionally

affected by variance of clock recurrence and when the supply voltage is low these models initiate critical

corruption in execution. Further, zeroing in just on responsibility execution makespan metric will bring about the

inappropriate estimation of energy dissemination. This is on the grounds that distinctive errands will have different

execution ways, natural store utilization, I/O access design. Further, it is seen that force won't generally be the

same regardless of whether responsibility is executed on the same sort of preparing component. This is on the

grounds that the positions with higher reserve and memory openness would cause higher energy. Accordingly, for

beating research issue this work presents an Energy-Aware Resource Utilization model for executing logical

responsibility on heterogeneous computational climate in the next segment.

III. AN ENERGY AWARE RESOURCE UTILIZATION TECHNIQUE FORWORKFLOW SCHEDULINGIN CLOUD

COMPUTING ENVIRONMENT

This part presents Energy-Aware Resource Utilization (EARU) strategy for booking logical responsibility on

Cloud figuring climate. For improving asset usage in distributed computing structure for executing logical

responsibility this work think about compelling use of reserve assets. This work target planning a logical

responsibility booking model that limits Last Level Cache (LLC) disappointments. The reserve mindful asset use

model pointed tending to asset distribution limitation of shared stores. At that point, the registering hub is

relocated for lessening LLC disappointments in heterogeneous computational climates. The asset distribution

model is made out of two stages. In stage one, each virtual computing nodes (VCN) is joined with a working hub

that divides the reserved memory between them. This guide in upgrading framework limit and data transmission.

Here the scheduler will reconfigure VCMs at the hubs where LLC disappointment in a computational climate of

stage two. The functioning cycle of the EARU model appears in Algorithm 1.

Here whole VCMs at each handling hubs are assembled in like manner as for LLC disappointments and afterward

consolidated as per their missed LLC of shared memory climate. The VCMs with maximal LLC disappointment

are put into bunch A, at that point, VCMs with most (i.e., Maximal) last level reserves are put in bunch B. In a

comparative way, the VCMs with negligible LLC disappointment are put in Group A, at that point, VCM with

insignificant last level stores are put in bunch B. In the EARU booking model, the undertaking is executed

utilizing VCMs of both gatherings. Hence, the planning acknowledges two sorts of virtual figuring machines, for

example, VCM with insignificant LLC disappointments and VCMs with maximal LLC disappointments in stage

2. Further, if the variety among last level reserve disappointment is higher than the given limit, in such cases the

VCMs are exchanged utilizing the EARU model. In this work, the EARU model carries out two-stage planning for

reformist way by lessening the last level reserve disappointments in the heterogeneous computational systems in

an occasional way.

Algorithm 1: Energy Aware Resource Utilization (EARU) technique for scheduling scientific workload in

cloud computing environment.

Step 1. Start

Step 2. Compute and establish 𝑁𝕃 (i.e., last level cache miss of each virtual computing machine (VCM).

Step 3.Compute and establish 𝑊𝕃 (i.e., last level cache miss of virtual computing machines in every processing

nodes.

Phase 1-

Step 4. For each processing node 𝑗 from 1 to 𝑦do (i.e, establish last level cache failure of each virtual computing

machine in processing node 𝑗.

Step 5.𝑛𝑥𝑗 ← collects(𝑗)

Step 6. 𝑊𝕃 ← 𝑠𝑜𝑟𝑡 (𝑛𝑥𝑗) (i.e., arrange virtual computing node with last level cache failures).


7465

Step 7. Obtain(𝑊𝕃)

Step 8. End for

Phase 2-Obtain processing node with maximal and minimal last level cache failures

Step 9.𝑀𝑎𝑥𝑖𝑚𝑎𝑙𝑁𝑜𝑑𝑒 ← 𝑓𝑖𝑛𝑑 𝑀𝑎𝑥𝑖𝑚𝑎𝑙𝑁𝑜𝑑𝑒(𝑁𝕃)

Step 10.𝑀𝑖𝑛𝑖𝑚𝑎𝑙𝑁𝑜𝑑𝑒 ← 𝑓𝑖𝑛𝑑 𝑀𝑖𝑛𝑖𝑚𝑎𝑙𝑁𝑜𝑑𝑒(𝑁𝕃)

// establish (i.e., find) virtual computing machine that composed of maximal and minimal last level cache failures

Step 11.𝑀𝑎𝑥𝑖𝑚𝑎𝑙𝑉𝐶𝑀 ← 𝑓𝑖𝑛𝑑𝑚𝑎𝑥𝑖𝑚𝑎𝑙𝑉𝐶𝑀(𝑀𝑎𝑥𝑖𝑚𝑎𝑙𝑁𝑜𝑑𝑒)

Step 12.𝑀𝑖𝑛𝑖𝑚𝑎𝑙𝑉𝐶𝑀 ← 𝑓𝑖𝑛𝑑𝑚𝑖𝑛𝑖𝑚𝑎𝑙𝑉𝐶𝑀(𝑀𝑖𝑛𝑖𝑚𝑎𝑙𝑁𝑜𝑑𝑒)

Step 13. if𝑇 < 𝑚𝑎𝑥𝑖𝑚𝑎𝑙𝑁𝑜𝑑𝑒𝕃𝕃ℂ − 𝑚𝑖𝑛𝑖𝑚𝑎𝑙𝑁𝑜𝑑𝑒𝕃𝕃ℂthen

Step 14. interchange(𝑚𝑎𝑥𝑖𝑚𝑎𝑙𝑉𝐶𝑀, 𝑚𝑖𝑚𝑖𝑚𝑎𝑙𝑉𝐶𝑀)

Step 15. Stop.

Here we present an Energy-Aware Resource Utilization strategy that limits energy scattering under

heterogamous (for example multi-center) distributed computing climate by utilizing compelling store streamlined

based responsibility planning. The EARU model guide in lessening energy scattering of distributed computing

handling hubs by limiting reserve use by eliminating dynamic bouncing minimization issue. The energy dispersal

P(t) under heterogeneous figuring climate is assessed utilizing the following condition

𝑃(𝑡) = 𝑒(𝑡)𝑀𝑎 , (1)

where𝑃(𝑡) depicts the energy dissipation under heterogeneous cloud framework processing element for certain

(i.e., 𝑡𝑡ℎ) interval time, 𝑒(𝑡) depicts the total power dissipated under heterogeneous cloud framework processing

element that composed of operating frequency level of processing core 𝐵𝑘 and present 𝐿2 cache size. Then, the

𝐿2 cache size and workload of the framework will generally remain constant for certain operation period𝑀𝑎. Here

𝑀𝑎 depicts the operating period for releasing various information of each workload task in process of 𝑡𝑡ℎ

operating period.

For acquiring tradeoff limiting energy utilization for setting reserve parcel size and recurrence necessity is gotten

utilizing following condition

min𝑎𝑘(𝑡)|1 ≤𝑘≤𝑖,𝕗𝑘(𝑡)|1≤𝑘≤𝑖

∑[𝑉𝑘 − 𝑣𝑘(𝑡)]2,

𝑖

𝑘=1

(2)

𝑚𝑖𝑛𝑎𝑘(𝑡)|1 ≤𝑘≤𝑖,𝕗𝑘(𝑡)|1≤𝑘≤𝑖

𝑃(𝑡) (3)

where 𝑣𝑘(𝑡) depicts processing core utilization 𝐵𝑘 in 𝑡𝑡ℎ session instance, 𝑉𝑘 depicts resource utilization sets 𝑉 =

[𝑉1, … … . , 𝑉𝑖]𝑇 for respective frequency range of [𝑅↑,𝑘, , 𝑅↓,𝑘] for each processing core 𝐵𝑘, {𝑎𝑘(𝑡)|1 ≤ 𝑘 ≤ 𝑖 }

depicts cache memory partition size and {𝕗𝑘(𝑡)|1 ≤ 𝑘 ≤ 𝑖} depicts processing core operating frequency at

𝑡𝑡ℎsession instance for reducing variance among processing core utilization 𝑣𝑘(𝑡) and utilization sets(𝑉𝑘).

The processing element of cloud computing framework is composed of two cache element namely L1 cache

and L2 cache. These caches are shared among different core in multi-core shared computational framework. Here

each processing element has DVFS capability. Thus aid in saving significant amount of energy resource. The

cache memory is portioned for carrying out various task. Therefore, the L2 cache partition size is represented

by𝑎𝑘(𝑡)considering processing core size𝐵𝑘. The peak frequency size under certain 𝐵𝑘 is represented by 𝕗𝑘↑(𝑡).

The Eq. (2) and Eq. (3) must satisfy following constraint described below

𝑅↓,𝑘 ≤ 𝕗𝑘(𝑡) ≤ 𝑅↑,𝑘 𝑤ℎ𝑒𝑟𝑒, (1 ≤ 𝑘 ≤ 𝑖) (4)

∑ 𝑎𝑘(𝑡) ≤ 𝐴

𝑖

𝑘=1

(5)

where 𝐴 depicts total L2 cache size available in heterogeneous cloud computing environment.


7466

The Eq. (2) depict the minimum energy dissipation for executing certain task under heterogeneous cloud

computational framework under certain power generation 𝑒(𝑡) for𝑡𝑡ℎsession instance. The Eq. (3) depicts the

processing machine frequency leis within in range of each processing core using EARU model. The frequency

range variation depends on kind of processing element being used. The Eq. (5) depicts summation of every

partitioned cache memory which is almost equal to total memory available.

For each processing core, the variation among resource (i.e., core) utilization𝑣𝑘(𝑡) and utilization sets 𝑉𝑘 is

reduced utilizing cache aware resource utilization method by modifying the cache partition size and its core

frequencies. However, optimizing frequencies based on different cache partition size in static manner induces

overhead and affect the processing time of heterogeneous computational environment. Thus, for improving

processing time a dynamic optimization model is presented. The model maintain ideal relationship among

balancing𝑣𝑘(𝑡), core frequency𝕗𝑘(𝑡), and optimizing feature 𝑎𝑘(𝑡) in 𝑡𝑡ℎ session instance. First, for respective

core 𝐵𝑘 , the dynamic optimization model gives an ideal relationship among𝑏𝑘𝑝(𝑡), job operational time 𝑀𝑘𝑝 and

optimizing feature 𝕗𝑘(𝑡)in𝑡𝑡ℎ session instance and𝑎𝑘(𝑡). Then, the relationship parameter𝑏𝑘𝑝(𝑡) can be optimized

in different manner such as frequency independent or frequency dependent as described in below equation

𝑏𝑘𝑝(𝑡) = 𝑠𝑘𝑝(𝑡) + 𝑖𝑘𝑝. (𝕗𝑘(𝑡))−1

, (6)

where 𝑠𝑘𝑝(𝑡) depicts frequency independent segment considering respective operational session instance 𝑀𝑘𝑝as

processing time of I/O devices does not rely upon frequency of individual coreand 𝑖𝑘𝑝 . (𝕗𝑘(𝑡))−1

depicts

frequency dependent segment because it depends on frequency of operating cores. The reserved cache memory for

respective job operational instance 𝑀𝑘𝑝 considering certain I/O device doesn’t take part for executing jobs can be

depicted as 𝑠𝑘𝑝(𝑡). The parameter 𝑠𝑘𝑝(𝑡) plays an ideal relationship among cache failure and cache memory size.

The ideal relationship among 𝑠𝑘𝑝(𝑡),𝑎𝑘𝑝(𝑡), and allocated caches for heterogeneous computational framework 𝐵𝑘

can be estimated using following equation

𝑠𝑘𝑝(𝑡) = {𝐷𝑘𝑝𝑎𝑘𝑝(𝑡) + 𝐻𝑘𝑝 0 ≤ 𝑎𝑘𝑝(𝑡) ≤ 𝑋𝑘𝑝

𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 𝑎𝑘𝑝(𝑡) ≥ 𝑋𝑘𝑝

(7)

where 𝐷𝑘𝑝,𝐻𝑘𝑝 are quantified jobs features, and𝑋𝑘𝑝depicts the operational set size within job operational session

instance 𝑀𝑘𝑝. The Eq. (7) shows that whenever operation set size 𝑋𝑘𝑝 is higher than 𝑎𝑘𝑝(𝑡), the cache memory

size improves and aiding in minimizing operation session instance. In similar manner, if operation set size 𝑋𝑘𝑝 is

lower than 𝑎𝑘𝑝(𝑡), then cache failure will be higher and can’t be addressed by allocating additional cache

memory. Thus, for managing job execution of real-time scientific application, the relationship among total

independent frequency and operation session instance of each job in heterogeneous computing processing element

𝐵𝑘 and total cache size 𝑎𝑘(𝑡) given to processing element 𝐵𝑘 is established using following equation

𝑠𝑘(𝑡) = {∑ 𝐷𝑘𝑝

′𝑎𝑘(𝑡) + ∑ 𝐻𝑘𝑝

𝑝𝑝

0 ≤ 𝑎𝑘(𝑡) ≤ 𝑋𝑘

𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 𝑎𝑘(𝑡) ≥ 𝑋𝑘

(8)

where, 𝐷𝑘𝑝′ =

𝐷𝑘𝑝𝑎𝑘𝑝(𝑡)

(𝑎𝑘(𝑡))and𝑋𝑘 = ∑ 𝑋𝑘𝑝𝑝 . The Eq. (8) depicts cumulating of Eq. (7) for every job on

heterogeneous computational processing element 𝐵𝑘. Then, the proposed EARUmodel aids in minimizing

interference among different processing element shared caches can be described using following equation

ℎ𝑘(𝑡) = ∑ 𝑖𝑘𝑝

𝑝

𝑞𝑘𝑝 . (𝕗𝑘(𝑡))−1

+ ∑ 𝐷𝑘𝑝′𝑞𝑘𝑝𝑎𝑘(𝑡) + ∑ 𝐻𝑘𝑝 𝑞𝑘𝑝

𝑝𝑝

(9)

where ℎ𝑘(𝑡) depicts the estimated processing element resource utilization and 𝑞𝑘𝑝 depicts job rate within

operational session instance 𝑀𝑘𝑝 for heterogeneous computing environment 𝐵𝑘. Using Eq. (9), it can be shown

that ℎ𝑘(𝑡) is proportionally inverse with respect to processing element frequency 𝕗𝑘(𝑡). The estimated variation in

resource utilization ∆ℎ𝑘(𝑡) for heterogeneous computing framework 𝐵𝑘 is described using following equation

∆ℎ𝑘(𝑡) = 𝑙𝑘(𝑡) ∑ 𝑖𝑘𝑝

𝑝

𝑞𝑘𝑝 + ∆𝑎𝑘(𝑡) ∑ 𝐷𝑘𝑝′𝑞𝑘𝑝

𝑝

(10)

where∆ℎ𝑘(𝑡) is a linear function with respect to 𝑙𝑘(𝑡) and ∆𝑎𝑘(𝑡), 𝑙𝑘(𝑡) = (1

𝕗𝑘(𝑡)) − (

1

𝕗𝑘(𝑡−1)) and ∆𝑎𝑘(𝑡) =

𝑎𝑘(𝑡) − 𝑎𝑘(𝑡 − 1). The Eq. (10) substitute direct frequency utilization of processing element 𝕗𝑘(𝑡)to𝑙𝑘(𝑡). The

Eq. (10) verifies that ∆ℎ𝑘(𝑡)proportional with respect to 𝑖𝑘𝑝 and 𝐷𝑘𝑝′. Therefore, the cost function of

heterogeneous computational environment can be minimized using regulator for heterogeneous processing

element 𝐵𝑘 using following equation


7467

𝑍𝑘(𝑡) = ∑‖𝑣𝑘(𝑡 + 𝑐 − 1|𝑡) − 𝛽𝕗𝑘(𝑡 + 𝑐 − 1|𝑘)‖2 + ‖𝑢𝑘(𝑡|𝑡) − 𝑢𝑘(𝑡 − 1|𝑡)‖2

𝐸

𝑐=1

(11)

where,

𝑅↓,𝑘 ≤ 𝕗𝑘(𝑡) ≤ 𝑅↑,𝑘 (12)

𝑎𝑘(𝑡) ≤ 𝑎𝑞𝑢𝑜𝑡𝑎,𝑘 (13)

where 𝛽𝕗𝑘(𝑡 + 1|𝑡) depicts the pattern considering resource utilization influence/feature 𝑣𝑘(𝑡 + 𝑐 − 1|𝑡) must

change its present utilization influence𝑣𝑘(𝑡) to 𝑉𝑘, 𝑢𝑘(𝑡) = [ 𝑙𝑘(𝑡)

∆𝑎𝑘(𝑡)] and 𝐸depicts the computed range for

estimating the pattern of the device in 𝐸 operational session instances. The cache size 𝑎𝑘(𝑡) for heterogeneous

computational framework𝐵𝑘 is bounded by 𝑎𝑞𝑢𝑜𝑡𝑎,𝑘 for satisfying Eq. (5). Thus, using dynamic model, the least

square problem can be minimized and cache memory can be optimized in efficient manner. The power

consumption optimization can be described using Energy Aware Resource Utilizationmodel can be described

using following equation

𝑒𝑘(𝑡) = 𝑆𝑘𝕗𝑘(𝑡)3 + 𝑌𝑘𝑎𝑘(𝑡) + 𝐶𝑘 (14)

where,

𝑅↓,𝑘 ≤ 𝕗𝑘(𝑡) ≤ 𝑅↑,𝑘 (15)

𝑎𝑘(𝑡) ≤ 𝑎𝑞𝑢𝑜𝑡𝑎,𝑘 (16)

where 𝑆𝑘 , 𝑌𝑘, and 𝐶𝑘 depicts the power factors of the heterogeneous computation framework processing element

of virtual computing nodes. The power consumption of heterogeneous computational framework processing

element can be described as cumulative of power consumed by different shared caches and processing element.

The total power consumption are dependent on leakage power 𝐶𝑘 and dynamic power component𝑆𝑘𝕗𝑘(𝑡)3. Thus,

the cache memorypower consumption can be optimized using the EARU model. Thus, using EARUwe can bring

good tradeoffs between minimizing energy dissipation and improving system performance of heterogeneous cloud

computing environment which is experimentally shown in below section.

IV. RESULT AND ANALYSIS

Here, the presentation of the framework is tried on logical work process SIPHT utilizing proposed Energy-Aware

Resource Utilization model to confirm high proficiency and lower energy utilization of proposed reserve mindful

asset usage model in heterogeneous computational structure. In this advanced time, heterogeneous multi-center

models have dazzled all over across the globe in various territories like businesses, exchanging offices, clinical

applications, and so on Subsequently, because of broad interest in multi-center structures, distributed computing

has likewise expressed to add multi-center engineering support. Also, GPU occasions are supported rather than

conventional CPU-based assets to improve the speed and proficiency of the framework. Nonetheless, ill-advised

asset planning and gigantic measure of energy utilization can decrease the exhibition of the model in a broadway.

Accordingly, a reserve mindful Energy-Aware Resource Utilization logical responsibility planning strategy is

acquainted with guarantee low energy utilization, the elite of the model, and legitimate asset booking utilizing

heterogeneous multi-center designs. This procedure assists with accelerating the cycle and execution of the model.

Here, we have led different tests utilizing the proposed EARU model to discover energy utilization,

power whole, reenactment time, and normal force results which are exhibited in Table 1 with the assistance of

the SIPHT logical dataset for different positions 30, 60, 100, and 1000. Our proposed procedure guarantees

exceptionally less energy utilization for running SIPHT logical dataset for SIPHT 30 is 2812.991014 Watts,

SIPHT 60 is 3158.219947 Watts, SIPHT 100 is 3174.261302 Watts and SIPHT 1000 is 11211.22691 Watts

shown in Table 1 which is profoundly diminished contrasted and other condition of-workmanship strategies

utilizing comparative insights. Table 2 likewise shows Execution time to complete the assignment utilizing the

proposed EARU strategy for different positions as 30, 50,100, and 1000 with the assistance of the SIPHT

benchmark. The normal force results for SIPHT 30 is 21.99945901 W, SIPHT 60 is 21.9994593 W, SIPHT 100

is 21.9994591 W and SIPHT 1000 is 21.99946127 W. Here, Table 2 addresses the Average Simulation time

examination of proposed EARU strategy with other State-of-craftsmanship procedures utilizing logical model

SIPHT. Here, we have led different investigations utilizing the proposed EARU model to discover energy

utilization, power whole, reenactment time, and normal force results which are shown in Table 1 with the

assistance of the SIPHT logical dataset for different positions 30, 60, 100, and 1000. Our proposed method

guarantees extremely less energy utilization for running SIPHT logical dataset for SIPHT 30 is 2812.991014

Watts, SIPHT 60 is 3158.219947 Watts, SIPHT 100 is 3174.261302 Watts and SIPHT 1000 is 11211.22691

Watts shown in Table 1 which is profoundly diminished contrasted and other condition of-craftsmanship

procedures utilizing comparable insights. Table 2 likewise shows Execution time to complete the assignment

utilizing the proposed EARU procedure for different positions as 30, 50,100, and 1000 with the assistance of the

SIPHT benchmark. The normal force results for SIPHT 30 is 21.99945901 W, SIPHT 60 is 21.9994593 W,


7468

SIPHT 100 is 21.9994591 W and SIPHT 1000 is 21.99946127 W. Here, Table 2 addresses the Average

Simulation time correlation of proposed EARU strategy with other State-of-craftsmanship procedures utilizing

logical model SIPHT.

Table 1: Energy efficiency and execution time performance evaluation of proposed EARU model over existing

DVFS based workload scheduling algorithm

Paramete

rs

DVFS [10] EARU

𝑆𝑖𝑝ℎ𝑡 30 𝑆𝑖𝑝ℎ𝑡 60 𝑆𝑖𝑝ℎ𝑡 100 𝑆𝑖𝑝ℎ𝑡 1000 𝑆𝑖𝑝ℎ𝑡 30 𝑆𝑖𝑝ℎ𝑡 60 𝑆𝑖𝑝ℎ𝑡 100 𝑆𝑖𝑝ℎ𝑡 1000

Power

Sum (W)

107348

00.6

2256602

0.6

33620561.

4

33596926

9.8

9783513.6

2

1029117

3.3

9934905.1

3

16022788.

43

Average

Power

(W)

28.6557

284

28.65572

03

28.655721

04

28.655722

39

21.999459

01

21.99945

93

21.999459

1

21.999461

27

Power

Consump

tion

(𝑊ℎ)

4367.65

8563

11228.74

085

20813.047

76

1070996.9

31

2812.9910

14

3158.219

947

3174.2613

02

11211.226

91

Simulati

on Time

(sec)

3746.13

7874.87 11732.58 117243.34 4447.16 4677.92 4515.98 7283.26

Table 2: Computation efficiency performance evaluation of proposed EARU model over existing multi

objective-based and DVFS-based workload scheduling algorithm

DAGs Number

of nodes

Average Simulation time (s)

DCOH [17] DVFS [10] EARU

𝑆𝑖𝑝ℎ𝑡 30 30 178.92 124.871 148.2386667

𝑆𝑖𝑝ℎ𝑡 60 60 194.48 131.2478333 77.96533333

𝑆𝑖𝑝ℎ𝑡 100 100 175.55 117.3258 45.1598

𝑆𝑖𝑝ℎ𝑡 1000 1000 179.05 117.24334 7.28326

Further, this part gives a graphical portrayal of our reenacted tests for different positions utilizing the SIPHT

logical dataset and contrasted and conventional condition of-craftsmanship strategies as far as normal force,

energy utilization, reproduction time, and force aggregate. Here, figure 1 shows power whole outcomes as

opposed to DVFS strategy utilizing proposed EARU model for logical dataset SIPHT for various positions as 30,

60,100, and 1000. Here, figure 2 shows normal force brings about the difference to DVFS method utilizing

propose EARU model for logical dataset SIPHT for various positions as 30, 60,100 and 1000. Here, figure 3

shows energy utilization brings about the difference to DVFS procedure utilizing proposed EARU model for

logical dataset SIPHT for various positions as 30, 60, 100, and 1000. Here, figure 4 shows execution time brings

about the difference to DVFS strategy utilizing proposed EARU model for logical dataset SIPHT for various

positions as 30, 60, 100, and 1000. These results close the predominance of the proposed EARU model regarding

normal force, power utilization, and force entirety utilizing the SIPHT logical dataset. Additionally, figure 5

exhibits normal execution time examination with DVFS and DCOH method utilizing our proposed EARU model

for logical benchmark SIPHT for different positions as 30, 50,100, and 1000.


7469

Fig. 1. Power sum comparison using proposed EARU model with DVFS-based workload scheduling

algorithm.

Fig. 2. Average power consumption comparison using proposed EARU model with DVFS-based workload

scheduling algorithm.

Sipht 30 Sipht 60 Sipht 100 Sipht 1000

DVFS (W) 10734800.61 22566020.59 33620561.59 335969269.8

ERU(W) 9783513.615 10291173.27 9934905.132 16022788.43

0

50000000

100000000

150000000

200000000

250000000

300000000

350000000

400000000

PO

WER

CO

NSU

MED

(W

ATT

)

Power sum consumption performance evalaution


DVFS (W) 28.65571784 28.6557203 28.65572104 28.65572239

ERU (W) 21.99945901 21.9994593 21.9994591 21.99946127

0

5

10

15

20

25

30

35

PO

WER

CO

NSU

MER

(W

ATT

)

Average power comparison performance evaluation


7470

Fig. 3. Energy consumption comparison using proposed EARU model with DVFS-based workload scheduling

algorithm.

Fig. 4. Execution time comparison using proposed EARU model with DVFS-based workload scheduling

algorithm.

VM=30 VM=30 VM=30 VM=30


DVFS (Wh) 4367.658563 11228.74085 20813.04776 1070996.931

ERU(Wh) 2812.991014 3158.219947 3174.261302 11211.22691

0

200000

400000

600000

800000

1000000

1200000

ENER

GY

CO

NSU

MED

(W

ATT

HO

UR

)

Energy consumption performance evaluation of ERU and DVFS

VM=30 VM=30 VM=30 VM=30


DVFS (sec) 3746.13 7874.87 11732.58 117243.34

ERU(sec) 4447.16 4677.92 4515.98 7283.26

0

20000

40000

60000

80000

100000

120000

140000

EXEC

UTI

ON

TIM

E (S

ECO

ND

S)

Execution time performance evaluation of ERU and DVFS


7471

Fig. 5. Average execution time comparison using proposed EARU model with multi-objective based DVFS-

based workload scheduling algorithm.

V. CONCLUSION

Responsibility planning considering dynamic reserve memory streamlining under heterogeneous multicore climate

is a difficult assignment. As of late, a number of systems have pointed toward bringing great tradeoffs among

diminishing energy and improving responsibility execution. A successful method of decreasing energy dispersal is

to utilize the DVFS strategy, and for using assets all the more proficiently and comply with task time constraints

that require powerful reserve improvement procedures. Along these lines, this paper introduced a two-stage store

asset improvement procedure empowering V/F scaling in a powerful way. From the test, it tends to be seen EARU

improves energy productivity by 44.29% over the existing DVFS responsibility booking procedure. Further,

lessens execution time 43.215% and 61.72% over existing DVFS and DCOH responsibility booking methods,

separately. The proposed EARU responsibility booking model acquires great tradeoffs fulfilling task time

constraints with insignificant execution time and energy utilization. Future work would consider assessing the

execution of EARU thinking about assorted information serious responsibility; and furthermore, consider utilizing

developmental or profound learning strategy to screen and upgrade QoS for executing responsibility.

REFERENCES

[1] Y. Chen, Y. Tang, Y. Liu, A. C. -. Wu and T. Hwang, "A Novel Cache-Utilization-Based Dynamic Voltage-

Frequency Scaling Mechanism for Reliability Enhancements," in IEEE Transactions on Very Large Scale

Integration (VLSI) Systems, vol. 25, no. 3, pp. 820-832, March 2017.

[2] S. Chakraborty and H. K. Kapoor, "Analysing the Role of Last Level Caches in Controlling Chip

Temperature," in IEEE Transactions on Sustainable Computing, vol. 3, no. 4, pp. 289-305, 1 Oct.-Dec.

2018.

[3] Q. Fettes, M. Clark, R. Bunescu, A. Karanth and A. Louri, "Dynamic Voltage and Frequency Scaling in

NoCs with Supervised and Reinforcement Learning Techniques," in IEEE Transactions on Computers, vol.

68, no. 3, pp. 375-389, 1 March 2019.

[4] A. Suyyagh and Z. Zilic, "Energy and Task-Aware Partitioning on Single-ISA Clustered Heterogeneous

Processors," in IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 2, pp. 306-317, 1 Feb.

2020.

[5] Z. Zhu, G. Zhang, M. Li and X. Liu, "Evolutionary Multi-Objective Workflow Scheduling in Cloud," in

IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 5, pp. 1344-1357, 1 May 2016.

[6] Junlong Zhou et al., Cost and makespan-aware workflow scheduling in hybrid clouds.

https://doi.org/10.1016/j.sysarc.2019.08.004, 2019.

[7] M. U. K. Khan, M. Shafique, A. Gupta, T. Schumann, and J. Henkel, “Power-efficient load-balancing on

heterogeneous computing platforms,” in 2016 Design, Automation Test in Europe Conference Exhibition

(DATE), March 2016, pp. 1469–1472.

[8] V. Petrucci, O. Loques, D. Moss´e, R. Melhem, N. A. Gazala, and S. Gobriel, “Energy-efficient thread

assignment optimization for heterogeneous multicore systems,” ACM Trans. Embed. Comput. Syst., vol. 14,

no. 1, pp. 15:1–15:26, Jan. 2015.

[9] F. M. M. u. Islam and M. Lin, "Hybrid DVFS Scheduling for Real-Time Systems Based on Reinforcement

Learning," in IEEE Systems Journal, vol. 11, no. 2, pp. 931-940, June 2017.


DCOH 178.92 194.48 175.55 179.05

DVFS 124.871 131.2478333 117.3258 117.24334

ERU 148.2386667 77.96533333 45.1598 7.28326

0

50

100

150

200

250

EXEC

UTI

ON

TIM

E (S

ECO

ND

S)

Average execution time performance evaluation


7472

[10] Y. Chen, Y. Tang, Y. Liu, A. C. -. Wu and T. Hwang, "A Novel Cache-Utilization-Based Dynamic Voltage-

Frequency Scaling Mechanism for Reliability Enhancements," in IEEE Transactions on Very Large Scale

Integration (VLSI) Systems, vol. 25, no. 3, pp. 820-832, March 2017.

[11] S. Chakraborty and H. K. Kapoor, "Analysing the Role of Last Level Caches in Controlling Chip

Temperature," in IEEE Transactions on Sustainable Computing, vol. 3, no. 4, pp. 289-305, 1 Oct.-Dec.

2018.

[12] Q. Fettes, M. Clark, R. Bunescu, A. Karanth and A. Louri, "Dynamic Voltage and Frequency Scaling in

NoCs with Supervised and Reinforcement Learning Techniques," in IEEE Transactions on Computers, vol.

68, no. 3, pp. 375-389, 1 March 2019.



2020.

[14] Z. Zhu, G. Zhang, M. Li and X. Liu, "Evolutionary Multi-Objective Workflow Scheduling in Cloud," in

IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 5, pp. 1344-1357, 1 May 2016.



[16] M. U. K. Khan, M. Shafique, A. Gupta, T. Schumann, and J. Henkel, “Power-efficient load-balancing on

heterogeneous computing platforms,” in 2016 Design, Automation Test in Europe Conference Exhibition

(DATE), March 2016, pp. 1469–1472.

[17] V. Petrucci, O. Loques, D. Moss´e, R. Melhem, N. A. Gazala, and S. Gobriel, “Energy-efficient thread

assignment optimization for heterogeneous multicore systems,” ACM Trans. Embed. Comput. Syst., vol. 14,

no. 1, pp. 15:1–15:26, Jan. 2015.

[18] F. M. M. u. Islam and M. Lin, "Hybrid DVFS Scheduling for Real-Time Systems Based on Reinforcement

Learning," in IEEE Systems Journal, vol. 11, no. 2, pp. 931-940, June 2017.



2020.



Documents

Energy Aware Resource Utilization Technique for Workflow