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1. SEMINAR PRESENTATION
(DECEMBER 2022)
SUPERVISOR : DR. PRIYANKA VASHISHT PRESENTED BY : SHRUTI SHARMA
CO-SUPERVISOR : DR. AASHIMA NARANG SEMESTER - 1

2. CLOUD COMPUTING
• CLOUD COMPUTING IS A MODEL FOR ENABLING UBIQUITOUS, CONVENIENT, ON-DEMAND NETWORK ACCESS TO A SHARED POOL OF
CONFIGURABLE COMPUTING RESOURCES (E.G., NETWORKS, SERVERS, STORAGE, APPLICATIONS, AND SERVICES) THAT CAN BE RAPIDLY
PROVISIONED AND RELEASED WITH MINIMAL MANAGEMENT EFFORT OR SERVICE PROVIDER INTERACTION.
• TYPES OF CLOUD
• PUBLIC CLOUD (FOR GENERAL PUBLIC, GOOGLE CLOUD)
• PRIVATE CLOUD (INFRASTRUCTURE IS SOLELY OPERATED FOR AN ORGANIZATION. E.G. HYPER-V)
• HYBRID CLOUD
• SERVICE MODEL
• INFRASTRUCTURE AS A SERVICE( AWS)
• PLATFORM AS A SERVICE (FORCE.COM)
• SOFTWARE AS A SERVICE( GOOGLE SPREADSHEET)

3. LITERATURE SURVEY
Sno Authors Journal Year Title Proposed method Parameters Software
1 Aburukba et
al
Future
Generation
computer System
2020 Scheduling Internet of
Things requests to
minimize latency in hybrid
Fog–Cloud​ computing
Customized GA Latency Lingo
2 Abd Elaziz et al
Future Generation Computer Systems
2021 Advanced optimization
technique for scheduling
IoT tasks in cloud-fog
computing environments
AEOSSA( modified
artificial ecosystem-
based optimization
(AEO),modification is
done using salp swarm
optimization)
Makespan,
Throughput,
Performance
improvement Rate
MATLAB
R2018b
3 Boveiri et al. Journal of
Ambient
Intelligence and
Humanized
Computing
2019 An efficient Swarm-
Intelligence approach
for task scheduling
in cloud based internet
of things applications
Max- Min Ant system
(Modified Ant colony
optimization) for
scheduling of static
graphs
Makespan,
Priority,Normalized
schedule
length(makespan /
weight of ndes on
critical path)
MS Visual
Basic 6.0
4
Sun et.al Wireless Pers
Commun
2018 Multi-objective
Optimization of Resource
Scheduling in Fog
Computing Using an
multiobjective
optimization technique
NSGA-II is
implemented with
Service Latency,
Stability of task
execution
MATLAB
5 Nazir et al Conference
paper
2019 Cuckoo Optimization Algorithm
Based Job Scheduling Using Cloud and
Fog Computing in Smart Grid
Cuckoo Optimization
Algorithm to distribute
tasks
Load
Balancing,Response
time,processing
Cloud
Analyst

4. 6 Agarwal et
al
Soft Computing:
Theories and
Applications,
2019 A PSO Algorithm-Based Task
Scheduling in Cloud
Computing
Particle swarm
optimization
Execution time cloudsim
7 Tychalas et
al
Simulation
Modelling
Practice and
Theory
2020 A Scheduling Algorithm for a
Fog Computing System with
Bag-of-Tasks Jobs:
Simulation and Performance
Evaluation
Heuristic approach cost, response
time,load balancing
C
Programmin
g language
8
Keshavarz
nejad et al
Cluster
Computing
2021 Delay-aware optimization of
energy consumption for task
offloading in fog
environments using
NSGA-II, BEE
algorithm
Power consumption,
Delay
IFogSIm
9 Meng et al. IEEE Access 2017 Delay-Constrained Hybrid
Computation Offloading with
Cloud and Fog
Computing(delay, computation
energy efficiency
Computation energy efficiency (The
computation energy efficiency
(CEE) is
defined as the amount of the
computation tasks that are
offloaded by consuming a unit of
energy.) based cloud and fog
offloading method
Energy
consumption, Delay
Not
mentioned
10 Tavana et al Computers &
Industrial
Engineering
2018 A discrete cuckoo
optimization algorithm for
consolidation in cloud
Discrete cuckoo
optimization
Energy,cost MATLAB

5. 11 Abbasi et al J Grid
Computing
2020 Workload Allocation in IoT-Fog-
Cloud Architecture Using a Multi-
Objective Genetic Algorithm
NSGA-II Delay, Energy
consumption
MATLAB
R2013a
12 Mohammad et al
IEEE
TRANSACTION
S ON MOBILE
COMPUTING,
2019 An Application Placement
Technique for Concurrent IoT
Applications in Edge and Fog
Computing Environments
Memetic algo Energy
consumption,
execution time
IfogSim
13 Jafari et al. Journal of
Ambient
Intelligence and
Humanized
Computing
2021` Joint optimization of energy
consumption and time delay
in IoT‑fog‑cloud computing
environments using NSGA‑II
metaheuristic algorithm
NSGA-II and BA with
minimax differential
evolution approach
Energy
consumption,
Response time
Ifogsim,
SPSS
14 Singh et al ACM
Computing
Surveys
2022 Towards Metaheuristic Scheduling
Techniques in Cloud and Fog: An
Extensive Taxonomic Review
Review paper Review paper NA
15
Tychalas et alPCI 2020,
November
2020 An Advanced Weighted Round
Robin Scheduling Algorithm
Advanced weighted
Round robin
Load balance,
Response time,utility
C
Language,H

6. TASK SCHEDULING APPROACHES IN FOG COMPUTING: A COMPREHENSIVE REVIEW
• PROPOSED METHOD:
• REVIEW PAPER
• EVALUATION PARAMETER:
• TASK SCHEDULING
• .

7. • SCHEDULING METHODS BASED ON COMPUTATION METHOD CLASSIFIED INTO
• WORKFLOW SCHEDULING
• RESOURCE SCHEDULING
• TASK SCHEDULING
ON THE BASIS OF ARCHITECTURE
CENTRALIZED : SINGLE SCHEDULER MAKES DECISION FOR SCHEDULING IF TASKS(FAULT
TOLERANCE IS LOW)
DISTRIBUTED : SEVERAL SCHEDULERS TAKES SCHEDULING DECISIONS (HIGHLY SCALABLE,
COMPLEX)
TASK SCHEDULING ALGORITHMS ARE CLASSIFIED AS
STATIC
DYNAMIC : CLASSIFIED INTO ONLINE AND BATCH GROUP
HEURISITIC
,HYBRID

8. CLASSIFICATION OF OPTIMIZATION PROBLEMS IN FOG COMPUTING
• PROPOSED METHOD:
• REVIEW PAPER
• EVALUATION PARAMETER:
• TAXONOMY OF OPTIMIZATION PROBLEM IN FOG COMPUTING
• .
Taxonomy of
optimization
problems
All three
layers(Cloud ,
fog, end devices)
End Devices and
Fog nodes
Fog nodes only
Fog nodes and
cloud

9. TAXONOMY OF OPTIMIZATION PROBLEMS IN FOG
COMPUTING
• MODEL BASED
• HEURISTIC
• META HEURISTIC
• METRICS
• DELAY NETWORK METRIC(MOSTLY CONSIDERED)
• ENERGY CONSUMPTION(ONLY FEW STUDIES)
• SIMULATOR
• IFOGSIM

10. METRICS USED IN OPTIMIZATION
• TECHNIQUES USED:
• HEURISTIC
• META HEURISTIC
• METRICS
• RESPONSE TIME, ENERGY CONSUMPTION, COST,
• LATENCY
• SIMULATOR
• IFOGSIM Static Dynamic Hybrid
metircs

11. TASK OFFLOADING
• TRANSFER COMPUTE INTENSIVE TASKS FROM RESOURCE LIMITED IOT DEVICES TO RESOURCE RICH COMPUTING NODES
• TECHNIQUES USED:
• MODEL BASED(MOSTLY USED)
• HEURISTIC(LESS USED)
• METRICS
• LATENCY
• ENERGY CONSUMPTION
• SIMULATOR
• MATLAB
Task
Offloading
Single Multiple

12. LOAD BALANCING
• TECHNIQUES USED:
• HEURISTIC
• METRICS
• LATENCY, RESPONSE TIME
• ENERGY CONSUMPTION(LESS USED)
• SIMULATOR
• CLOUDSIM
Load
balancing
Centralized Decentralized Hybrid

13. RESOURCE ALLOCATION
• TECHNIQUES USED:
• HEURISTIC
• METRICS
• MAKE SPAN, COST, UTILITY
• SIMULATOR
• IFOGSIM
Resource
allocation
Auction
based
Optimization

14. RESOURCE MANAGEMENT APPROACHES IN FOG COMPUTING: A COMPREHENSIVE REVIE
• FINDINGS:
• AUTHOR PRESENTED A SLR ON RESOURCE MANAGEMENT APPROACHES IN FOG COMPUTING IN TAXONOMY FORM
CATEGORIZED INTO APPLICATION PLACEMENT, RESOURCE SCHEDULING, TASK OFFLOADING, LOAD BALANCING,
RESOURCE ALLOCATION . AUTHOR REVIEWED ISSUES, APPROACH USED, METRICS USED AND SIMULATOR USED
FOR EVALUATION IN ALL SIX CATEGORIES.
• RESEARCH GAP: REVIEW OF PARAMETERS SLA PENALTY, PRIORITY OF TASKS COULD ALSO BE DONE.

15. TOWARDS METAHEURISTIC SCHEDULING TECHNIQUES IN CLOUD
AND FOG: AN EXTENSIVE TAXONOMIC REVIEW
• PROPOSED METHOD:
• REVIEW PAPER
• EVALUATION PARAMETER: METAHEURISTIC SCHEDULING TECHNIQUES
• SIMULATOR USED:
• LINGO
• RESULT:
• PROPOSED METHOD SHOWED BETTER PERFORMANCE THAN WAITED-FAIR QUEUING (WFQ), PRIORITY-STRICT QUEUING (PSQ), AND
ROUND ROBIN (RR) TECHNIQUES.
• FINDINGS:
• INTRODUCED MODIFIED GENETIC ALGORITHM TO OPTIMIZE TASK SCHEDULING IN HYBRID FOG CLOUD COMPUTING.
RESEARCHERS HAVE CONSIDERED TASK SCHEDULING OPTIMIZATION PROBLEM AS AN INTEGER PROGRAMMING PROBLEM
WITH OBJECTIVE TO REDUCE LATENCY AND CONSTRAINTS THAT EACH REQUEST IS ASSIGNED ONE RESOURCE AND
DEADLINE CRITERIA SHOULD BE MET.
• RESEARCH GAP:
• ANALYSIS IS DONE ONLY ON SMALL SIZE DATA AND PRE-EMPTION OF JOBS IS NOT CONSIDERED..

16. SCHEDULING INTERNET OF THINGS REQUESTS TO MINIMIZE LATENCY IN HYBRID
FOG-CLOUD COMPUTING
• PROPOSED METHOD:
• CUSTOMIZED GENETIC ALGORITHM TO SCHEDULE IOT TASKS IN CLOUD FOG ENVIRONMENT
• EVALUATION PARAMETER: LATENCY
• SIMULATOR USED:
• LINGO
• RESULT:
• PROPOSED METHOD SHOWED BETTER PERFORMANCE THAN WAITED-FAIR QUEUING (WFQ), PRIORITY-STRICT QUEUING (PSQ), AND
ROUND ROBIN (RR) TECHNIQUES.
• FINDINGS:
• INTRODUCED MODIFIED GENETIC ALGORITHM TO OPTIMIZE TASK SCHEDULING IN HYBRID FOG CLOUD COMPUTING.
RESEARCHERS HAVE CONSIDERED TASK SCHEDULING OPTIMIZATION PROBLEM AS AN INTEGER PROGRAMMING PROBLEM
WITH OBJECTIVE TO REDUCE LATENCY AND CONSTRAINTS THAT EACH REQUEST IS ASSIGNED ONE RESOURCE AND
DEADLINE CRITERIA SHOULD BE MET.
• RESEARCH GAP:
• ANALYSIS IS DONE ONLY ON SMALL SIZE DATA AND PRE-EMPTION OF JOBS IS NOT CONSIDERED..

17. A NATURE-INSPIRED-BASED MULTI-OBJECTIVE SERVICE PLACEMENT IN FOG COMPUTING
ENVIRONMENT
• PROPOSED METHOD:
• GENETIC ALGORITHM BASED ALGORITHM TO SOLVE APPLICATION PLACEMENT PROBLEM
• EVALUATION PARAMETER: MAKESPAN, ENERGY CONSUMPTION, COST
• SIMULATOR USED:
• YAFS (YET ANOTHER FOG SIMULATOR)
• RESULT:
• PROPOSED METHOD SHOWED BETTER PERFORMANCE THAN RANDOM PLACEMENT ALGORITHM.
• FINDINGS:
• AUTHOR PROPOSED A GENETIC ALGORITHM BASED ALGORITHM FOR PLACEMENT OF APPLICATIONS ON FOG NODES. TO FULLY AND
EFFICIENTLY UTILIZE THE RESOURCES APPLICATIONS ARE DIVIDED INTO INDEPENDENT SERVICES WHICH ARE THEN PLACED ON FOG
NODES TO ENSURE QUALITY OF SERVICES. PROBLEM IS EXPRESSED AS MULTIOBJECTIVE PROBLEM WITH MAKESPAN, ENERGY AND COST
WITH DIFFERENT WEIGHTS ARE OBJECTIVES AND CONSTRAINTS ARE BASED UPON DEADLINES . IT HAS BEEN OBSERVED AFTER
SIMULATION THAT GA BASED ALGORITHM OUTPERFORM RANDOM PLACEMENT ALGORITHM.
• RESEARCH GAP:
• ALGORITHM CAN BE TESTED WITH REAL DATA AND MORE QOS PARAMETER LIKE LATENCY CAN BE CONSIDERED FOR EVALUATION.

18. SCHEDULING INTERNET OF THINGS REQUESTS TO MINIMIZE LATENCY IN HYBRID
FOG-CLOUD COMPUTING
• PROPOSED METHOD:
• CUSTOMIZED GENETIC ALGORITHM TO SCHEDULE IOT TASKS IN CLOUD FOG ENVIRONMENT
• EVALUATION PARAMETER: EXECUTION TIME
• SIMULATOR USED:
• IFOGSIM
• RESULT:
• PROPOSED METHOD SHOWED BETTER PERFORMANCE THAN WAITED-FAIR QUEUING (WFQ), PRIORITY-STRICT QUEUING (PSQ), AND
ROUND ROBIN (RR) TECHNIQUES.
• FINDINGS:
• A GA BASED COST EFFICIENT SCHEDULING TECHNIQUE IS PROPOSED TO MAP APPLICATIONS MODULES TO VARIOUS
RESOURCES IN CLOUD FOG ENVIRONMENT WITH THE OBJECTIVE TO MINIMIZE EXECUTION TIME. MODULES WITH
COMPUTATION REQUIREMENT MORE THAN THRESHOLD VALUES ARE PASSED TO CLOUD. REMAINING MODULES ARE
PASSED TO GA AS INITIAL POPULATION. ONE POINT CROSSOVER AND SINGLE POINT MUTATION S USED.PROPOSED
SCHEDULING TECHNIQUES IS SIMULATED AND FOUND TO BE BETTER THAN GA AND RACE TECHNIQUES.
• ONLY ONE OBJECTIVE IS CONSIDERED FOR EVALUATION. ENERGY EFFICIENCY IS NOT EVALUATED.

19. MULTI-OBJECTIVE OPTIMIZATION OF RESOURCE SCHEDULING IN FOG COMPUTING USING AN IMPROVED
NSGA-II
• PROPOSED METHOD:
• TWO LEVEL RESOURCE SCHEDULING IS INVESTIGATED
• SCHEDULING AMONG FOG CLUSTERS AND SCHEDULING AMONG NODES WITHIN SAME CLUSTER
• IMPROVED NSGA-II TO SCHEDULE IOT TASKS AMONG FOG NODES IN SAME FOG CLUSTER.
• EVALUATION PARAMETER: SERVICE LATENCY, STABILITY(AS SOME FOG NODES ARE NOT RELIABLE)
• SIMULATOR USED:
• MATLAB
• RESULT:
• PROPOSED METHOD SHOWED BETTER PERFORMANCE THAN RANDOM( IT SELECTS ONE SOLUTION FOR RESOURCE SCHEDULING
RANDOMLY), FIRMM ( IT IS A FOG-BASED IOT RESOURCE MANAGEMENT MODEL AIMED AT SCHEDULING AND MANAGING
RESOURCES EFFICIENTLY AND IN TIME.)
• FINDINGS:
• INTRODUCED TWO LEVEL RESOURCE SCHEDULING APPROACH IS PRESENTED AS MODIFIED NSGA-II WITH AIM TO MINIMIZE
LATENCY AND TO ACHIEVE STABILITY. AUTHORS HAVE COMPARED IT WITH EXISTING RANDOM AND FIRMM SCHEDULING
TECHNIQUES AND ANALYSED THAT IN TERM OF AVERAGE LATENCY ALL THREE SCHEMES ARE EQUALLY EFFICIENT IF NUMBER OF
JOBS ARE LESS BUT PROPOSED SCHEME IS MORE EFFICIENT IF NUMBER OF TASKS ARE MORE. IN TERMS OF AVERAGE STABILITY
PROPOSED SCHEME DOMINANT OVER EXISTING SCHEMES
• RESEARCH GAP:
• COST AND ENERGY EFFICIENCY ARE NOT CONSIDERED.

20. SCHEDULING PROBLEMS
• RESOURCE SCHEDULING
• TASK SCHEDULING
• WORKFLOW SCHEDULING

21. OPTIMIZATION TECHNIQUES
GRADIENT VS NON GRADIENT BASED ALGORITHM
• GRADIENT BASED
• HILL CLIMBING
• NON GRADIENT BASED
• TRAJECTORY BASED( SIMULATED ANNEALING)
• POPULATION BASED(GENETIC ALGORITHM, PARTICLE SWARM OPTIMIZATION)

22. TASK SCHEDULING METRICS
• TASK SCHEDULING METRICS FOG PERFORMANCE HAS BEEN EVALUATED BY
SEVERAL PERFORMANCE METRICS.
THE MOST COMMONLY USED PERFORMANCE METRICS ARE:
RESOURCE UTILIZATION: RESOURCE UTILIZATION IS DETERMINED AS THE NUMBER
OF USED RESOURCES IN EXECUTING TASKS
RESPONSE TIME: THE RESPONSE TIME OF A TASK IS THE TIME INTERVAL AMONG
THIS TASK THAT IS ACHIEVED INTO THE SYSTEM UNTIL IT IS COMPLETED
COST: THE PAYMENTS OF A GIVEN TOTALITY OF MONEY TO SUGGEST THE
PERFORMANCE, WHICH IS REQUIRED TO DO IN THE FOGS
MAKESPAN: MAKESPAN IS USED TO APPROXIMATE THE LARGEST PART OF
COMPLETION TIME BY ANALYZING THE OVERTIME OF THE RECENT AFFAIR WHEN
ALL AFFAIRS ARE PLANNED .

23. RESEARCH OBJECTIVES
• TO UNDERSTAND RESOURCE MANAGEMENT PROBLEMS IN CLOUD/FOG
COMPUTING
• TO DO EXTENSIVE LITERATURE REVIEW
• TO PUBLISH RESEARCH PAPER

24. REFERENCES
 Aburukba, R. O., AliKarrar, M., Landolsi, T., & El-Fakih, K. (2020). Scheduling Internet of Things requests to minimize latency
in hybrid Fog–Cloud​ computing. Future Generation Computer Systems, 111, 539-551.
 Abd Elaziz, M., Abualigah, L., & Attiya, I. (2021). Advanced optimization technique for scheduling IoT tasks in cloud-fog
computing environments. Future Generation Computer Systems, 124, 142-154.
 Boveiri, H. R., Khayami, R., Elhoseny, M., & Gunasekaran, M. (2019). An efficient Swarm-Intelligence approach for task
scheduling in cloud-based internet of things applications. Journal of Ambient Intelligence and Humanized
Computing, 10(9), 3469-3479.
 Sun, Y., Lin, F., & Xu, H. (2018). Multi-objective optimization of resource scheduling in fog computing using an improved
NSGA-II. Wireless Personal Communications, 102(2), 1369-1385.
 Nazir, S., Shafiq, S., Iqbal, Z., Zeeshan, M., Tariq, S., & Javaid, N. (2018, September). Cuckoo optimization algorithm-based
job scheduling using cloud and fog computing in smart grid. In International Conference on Intelligent Networking and
Collaborative Systems (pp. 34-46). Springer, Cham
 Agarwal, M., & Srivastava, G. M. S. (2019). A PSO algorithm based task scheduling in cloud computing. International Journal
of Applied Metaheuristic Computing (IJAMC), 10(4), 1-17.
 Tychalas, D., & Karatza, H. (2020). A scheduling algorithm for a fog computing system with bag-of-tasks jobs: Simulation
and performance evaluation. Simulation Modelling Practice and Theory, 98, 101982
 Keshavarznejad, M., Rezvani, M. H., & Adabi, S. (2021) Delay-aware optimization of energy consumption for task offloading
in fog environments using metaheuristic algorithms. Cluster Computing, 24(3), 1825-1853.
 Meng, X., Wang, W., & Zhang, Z. (2017). Delay-constrained hybrid computation offloading with cloud and fog
computing. IEEE Access, 5, 21355-21367.

25.  TAVANA, M., SHAHDI-PASHAKI, S., TEYMOURIAN, E., SANTOS-ARTEAGA, F. J., & KOMAKI, M. (2018). A DISCRETE CUCKOO
OPTIMIZATION ALGORITHM FOR CONSOLIDATION IN CLOUD COMPUTING. COMPUTERS & INDUSTRIAL ENGINEERING, 115,
495-511.
 ABBASI, M., MOHAMMADI PASAND, E., & KHOSRAVI, M. R. (2020). WORKLOAD ALLOCATION IN IOT-FOG-CLOUD
ARCHITECTURE USING A MULTI-OBJECTIVE GENETIC ALGORITHM. JOURNAL OF GRID COMPUTING, 18(1), 43-56.
 GOUDARZI, M., WU, H., PALANISWAMI, M., & BUYYA, R. (2020). AN APPLICATION PLACEMENT TECHNIQUE FOR CONCURRENT
IOT APPLICATIONS IN EDGE AND FOG COMPUTING ENVIRONMENTS. IEEE TRANSACTIONS ON MOBILE COMPUTING, 20(4),
1298-1311.
 JAFARI, V., & REZVANI, M. H. (2021). JOINT OPTIMIZATION OF ENERGY CONSUMPTION AND TIME DELAY IN IOT-FOG-CLOUD
COMPUTING ENVIRONMENTS USING NSGA-II METAHEURISTIC ALGORITHM. JOURNAL OF AMBIENT INTELLIGENCE AND
HUMANIZED COMPUTING, 1-24.
 SINGH, R. M., AWASTHI, L. K., & SIKKA, G. (2022). TOWARDS METAHEURISTIC SCHEDULING TECHNIQUES IN CLOUD AND FOG:
AN EXTENSIVE TAXONOMIC REVIEW. ACM COMPUTING SURVEYS (CSUR), 55(3), 1-43.
 TYCHALAS, D., & KARATZA, H. (2020, NOVEMBER). AN ADVANCED WEIGHTED ROUND ROBIN SCHEDULING ALGORITHM.
IN 24TH PAN-HELLENIC CONFERENCE ON INFORMATICS (PP. 188-191).

26. THANK YOU