workflow scheduling
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2023 ◽  
Vol 12 (6) ◽  
pp. 1
Author(s):  
Ravi S. Singh ◽  
Swati Gupta ◽  
Isha Agarwal

2022 ◽  
Author(s):  
K. Kalyana Chakravarthi ◽  
P. Neelakantan ◽  
L. Shyamala ◽  
V. Vaidehi

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0

Fog computing and Edge computing are few of the latest technologies which are offered as solution to challenges faced in Cloud Computing. Instead of offloading of all the tasks to centralized cloud servers, some of the tasks can be scheduled at intermediate Fog servers or Edge devices. Though this solves most of the problems faced in cloud but also encounter other traditional problems due to resource-related constraints like load balancing, scheduling, etc. In order to address task scheduling and load balancing in Cloud-fog-edge collaboration among servers, we have proposed an improved version of min-min algorithm for workflow scheduling which considers cost, makespan, energy and load balancing in heterogeneous environment. This algorithm is implemented and tested in different offloading scenarios- Cloud only, Fog only, Cloud-fog and Cloud-Fog-Edge collaboration. This approach performed better and the result gives minimum makespan, less energy consumption along with load balancing and marginally less cost when compared to min-min and ELBMM algorithms


Author(s):  
V. Lakshmi Narasimhan ◽  
V. S. Jithin ◽  
M. Ananya ◽  
Jonathan Oluranti

Author(s):  
Emmanuel Bugingo ◽  
Wei Zheng ◽  
Zhenfeng Lei ◽  
Defu Zhang ◽  
Samuel Rene Adolphe Sebakara ◽  
...  

Author(s):  
Rama Rani ◽  
Ritu Garg

AbstractThe enormous energy consumed by cloud data centers (CDCs) increases the carbon footprints, operational cost and decreases the system reliability, so it becomes a great challenge for CDCs providers. Dynamic voltage and frequency scaling (DVFS) is an efficient approach for energy efficiency, which reduces the operating frequency, and supply voltage of the processor during the task’s execution. Recent research shows that scaling of the supply voltage and operating frequency has negative impact on the system’s reliability as it increases transient fault rate of the resources. Thus, the system’s reliability and the energy consumption are two prime concerns in a cloud computing environment that requires attention. Most workflow scheduling algorithms in literature do not consider energy and reliability simultaneously. In this paper, we proposed the ε-fuzzy dominance based reliable green workflow scheduling (FDRGS) algorithm, which optimizes the application’s reliability and energy consumption simultaneously using the ε-fuzzy dominance mechanism. The simulation results obtained using fast Fourier transform (FFT) and gaussian elimination (GE) task graphs manifest that our scheduling algorithm is more efficient in optimizing energy consumption and lifetime system’s reliability jointly than several widely used algorithms. The proposed algorithm will help scientists and engineers for further insight into future research in the area of cloud.


2021 ◽  
Author(s):  
KARPAGAM M

Abstract An inevitable part of the cloud computing environment is virtualization, as it can multiplex or combine many virtual machines in a single physical machine, and simultaneously an isolated environment is provided to every virtual machine. An important issue in cloud computing is workflow scheduling, which maps tasks of workflow to VMs based on various functional and non-functional requisites. Workflow scheduling is an NP-hard optimization problem and it is quite hard to achieve an optimal schedule. Metaheuristic algorithms helped in solving the problem of cloud task scheduling and this was compared to other heuristics. Reactive Search (RSO) and its structure will consist of a local heuristic based on a certain neighborhood complemented by making use of a memory-based mechanism. The Shuffled Frog Leaping Algorithm (SFLA) is based on swarm evolution that imitates information exchange divided into memeplexes when searching for food. This paper proposes a new set of optimization heuristics along with hybrid optimizations (RSO - SFLA) to solve problems in combinatorial optimization.


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