## 2 Related Work
(2018) presented a new-list workflow scheduling using the fuzzy-based HEFT(FDHEFT), an algorithm that was classified into two phases of task prioritization and instance selection. In the task prioritization phase, tasks were sorted in descending order of high-rank values, i.e., high-rank values were assigned with higher priority. In the second phase, the FDHEFT sorted all the scheduled solutions produced according to their fuzzy dominance values and chose the best K solutions. The main objective of this algorithm was to minimize the makespan and cost of scheduling. However, the study noted that this algorithm increased the computation time. **Overview of workflows:**  
Cloud computing has recently been used by commercial and scientific applications to access storage and computing infrastructure. These applications are usually created as a workflow. Depending on the complexity of the problem, workflows in past studies have been grouped as memory-intensive-, CPU-intensive-, or input/output-intensive-based workflows [PERSON] et al. (2018). **Montage workflow:**  
The Montage workflow (Figure 2) is an input/output-intensive astronomy workflow used to construct custom mosaics of the sky depending on the size of the input images. The workflow structure is modified to accommodate for increases in inputs, which then correspond to an increase in the computational jobs. **Broadband workflow:**  
The Broadband workflow(Figure 3) was primarily designed to integrate the simulation codes for earthquake motions and their associated calculations. Based on the types of computations performed at each stage, the broadband workflow can be divided into four distinct phases. Figure 3: Broadband workflow. Figure 2: Montage workflow. LIGO:  
The LIGO (Figure 4) is a memory-intensive application used in the field of physics to detect gravitational waves. This workflow is extremely complex and is made up of several sub-workflows. Epigenomics:  
Epigenomics (Figure 5) is a largely pipelined application, with multiple pipelines processing independent data chunks in parallel. It begins with FastQSplit jobs and splits the lane into multiple chunks depending on the partition factor. The noise and contaminated data are then filtered from the chunks. The sol2 sanger utility then converts each chunk's data into the Maq DNA sequence-mapping software format. The Fastq2 bfq then converts the data into the binary fastQ format for faster processing and reduced disk usage. The map utility then aligns the remaining sequences with the reference genome. Next, mapMerge jobs combine individual map process results. After merging, the maqIndex utility retrieves reads about a specific region from the merged alignment file. **Cybershake:**  
Cybershake workflow (Figure 6) is a data- and memory-intensive workflow application used by the Southern California Earthquake Center. Cybershake can perform computations on particularly large datasets. The workflow starts by generating the strain green tensors (SGT) data from finite simulations derived from the master SGT files. The ExtractSGT job extracts rupture-specific SGT files from the master SGT. Thus, ExtractSGT jobs can be considered to be data partitioning. Seismogram-synthesis jobs generate synthetic seismograms for each rupture variation. Peak intensity values, specifically pertaining to spectral acceleration, are then calculated for each synthetic seismogram by the PeakValueCalcOkaya jobs. The ZipSeismograms and ZipPeakSA jobs collect and compress the synthesized seismograms and peak intensities, which are then staged and archived. These jobs are categorized as simple data collection jobs. Figure 4: LIGO. Figure 5: Epigenomics. Figure 6: Cybershake. Table 1 summarizes the existing workflow-scheduling algorithms for cloud computing. This summary also notes where existing algorithms can be improved pertaining to scheduling performance by considering parameters such as cloud utilization, resource utilization, load balancing, and energy consumption.