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
Murgesh V. Jambigi1, Dr. M.V. Vijay Kumar2, Dr. D.V. Ashoka3, Dr. Prabha R4
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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
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• 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.
Murgesh V. Jambigi1, Dr. M.V. Vijay Kumar2, Dr. D.V. Ashoka3, Dr. Prabha R4
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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).
Energy Aware Resource Utilization Technique for Workflow Scheduling in Cloud Computing Environment
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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.
Murgesh V. Jambigi1, Dr. M.V. Vijay Kumar2, Dr. D.V. Ashoka3, Dr. Prabha R4
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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
Energy Aware Resource Utilization Technique for Workflow Scheduling in Cloud Computing Environment
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,
Murgesh V. Jambigi1, Dr. M.V. Vijay Kumar2, Dr. D.V. Ashoka3, Dr. Prabha R4
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.
Energy Aware Resource Utilization Technique for Workflow Scheduling in Cloud Computing Environment
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
Sipht 30 Sipht 60 Sipht 100 Sipht 1000
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
Murgesh V. Jambigi1, Dr. M.V. Vijay Kumar2, Dr. D.V. Ashoka3, Dr. Prabha R4
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
Sipht 30 Sipht 60 Sipht 100 Sipht 1000
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
Sipht 30 Sipht 60 Sipht 100 Sipht 1000
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
Energy Aware Resource Utilization Technique for Workflow Scheduling in Cloud Computing Environment
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.
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Sipht 30 Sipht 60 Sipht 100 Sipht 1000
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
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50
100
150
200
250
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7472
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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.
[13] 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.
[14] Z. Zhu, G. Zhang, M. Li and X. Liu, "Evolutionary Multi-Objective Workflow Scheduling in Cloud," in
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(DATE), March 2016, pp. 1469–1472.
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2020.
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