Hybrid scheduling algorithms in cloud computing: a review

Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, quality of service (QoS) parameters, and future directions for hybrid scheduling algorithms

Hybrid heuristic algorithm for better energy optimization and resource utilization in cloud computing

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a Hybrid Heuristic algorithm based Energy-efficient cloud Computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the Chaotic based Particle Swarm Optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the Hybrid Heuristic Workflow Scheduling algorithm (HHWS) and Distributed Dynamic VM Management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods

Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution

Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched

Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim

With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, and lower collection efficiency. Moreover, public remote data sources have restrictions on user settings, such as access to IP, frequency, and bandwidth. In order to satisfy users’ demand for accessing public remote sensing data collection nodes and effectively increase the data collection speed, this paper proposes a TSCD-TSA dynamic task scheduling algorithm that combines the BP neural network prediction algorithm with PSO-based task scheduling algorithms. Comparative experiments were carried out using the proposed task scheduling algorithms on an acquisition task using data from Sentinel2. The experimental results show that the MAX-MAX-PSO dynamic task scheduling algorithm has a smaller fitness value and a faster convergence speed

IKH-EFT: An improved method of workflow scheduling using the krill herd algorithm in the fog-cloud environment

peer reviewedGiven the increase diversity of smart devices and objectives of the application management such as energy consumption, makespan users expect their requests to be responded to in an appropriate computation environment as properly as possible. In this paper, a method of workflow scheduling based on the fog-cloud architecture has been designed given the high processing capability of the cloud and the close communication between the user and the fog computing node, which reduces delay in response. We also seek to minimize consumption and reduce energy use and monetary cost in order to maximize customer satisfaction with proper scheduling. Given the large number of variables that are used in workflow scheduling and the optimization of contradictory objectives, the problem is NP-hard, and the multi-objective metaheuristic krill herd algorithm is used to solve it. The initial population is generated in a smart fashion to allow fast convergence of the algorithm. For allocation of tasks to the available fog-cloud resources, the EFT (earliest finish time) technique is used, and resource voltage and frequency are assumed to be dynamic to reduce energy use. A comprehensive simulation has been made for assessment of the proposed method in different scenarios with various values of CCR. The simulation results indicate that makespan exhibits improvements by 9.9, 8.7% and 6.7% on average compared with respect to the methods of IHEFT, HEFT and IWO-CA, respectively. Moreover, the monetary cost of the method and energy use have simultaneously decreased in the fog-cloud environment

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date:September 30th 2022

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

免疫学的および進化的アルゴリズムに基づく改良された群知能最適化に関する研究

富山大学・富理工博甲第175号・楊玉・2020/3/24富山大学202

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Improvements of task scheduling and load balancing in cloud environment by swarm intelligence metaheuristics

Klaud racunarstvo pripada grupi novijih racunarskih paradigmi, koja se poput paradigme mrežnog racunarstva, bazira na grupisanju resursa i na korišcenju mrežnih i Internet tehnologija. U opštem smislu, klaud racunarstvo se odnosi na novi nacin isporuke racunarskih resursa u vidu usluge, gde se pod resursima podrazumeva gotovo sve, od podataka i softvera, do hardverskih komponenti, kao što su procesirajuci elementi, memorija i skladišta. Klaud racunarstvo je aktuelna i važna multidisciplinarna oblast, o cemu svedoci veliki broj objavljenih radova u vrhunskim me unarodnim casopisima i prikazanih na najznacajnijim svetskim skupovima. Na osnovu naucnih rezultata prikupljenih u objavljenim radovima iz ovog domena, može da se zakljuci da u klaud okruženju postoji veliki broj izazova i problema, za cije rešavanje mogu da se prona u bolje metode, tehnike i algoritmi. Jedan od najvažnijih izazova savremenog klaud okruženja je raspore ivanje zahteva krajnjih korisnika za izvršavanje na ogranicenom skupu raspoloživih resursa (virtuelnih mašina). Problem raspore ivanja na klaudu odnosi se na definisanje rasporeda izvršavanja zadataka na ogranicenom skupu raspoloživih resursa uzimajuci pritom u obzir potencijalna ogranicenja i funkciju cilja koju je potrebno optimizovati. Raspore ivanje poslova vrše algoritmi raspore ivanja, koji mogu da se podele na staticke i dinamicke. U slucaju statickog raspore ivanja, gde se poslovi ne mogu dinamicki prebacivati sa preopterecnih na manje opterecene virtuelne mašine, zadaci se raspore uju za izvršavanje na raspoložive virtuelne mašine pre pocetka izvršavanja. S druge strane, primenom metoda dinamickog raspore ivanja, koje je u literaturi poznato pod nazivom balansiranje opterecenja, vrši se preraspodela poslova izme u aktivnih virtuelnih mašina tokom samog izvršavanja programa raspore ivanja. Preraspodela se vrši tako što se zadaci sa virtuelnih mašina koje imaju vece opterecenje dinamicki prebacuju za izvršavanje na virtuelnim mašinama koje imaju manje opterecenje. Za potrebe dinamickog raspore ivanja koriste se uglavnom heuristicke i metaheuristicke optimizacione metode i algoritmi, koji postižu dobre rezultate. Problemi raspore ivanja poslova i balansiranja opterecenja na klaudu pripadaju grupi NP teških kombinatornih i/ili globalnih problema sa ili bez ogranicenja. Na osnovu publikovanih rezultata u relevantnim literaturnim izvorima, vidi se da su metaheuristike inteligencije rojeva, koje spadaju u grupu prirodom-inspirisanih algoritama, uspešno testirane na bencmark problemima i primenjivane na prakticnim NP teškim optimizacionim problemima (globalnim i kombinatornim) i da mogu da postignu bolje rezultate u smislu brzine konvergencije i kvaliteta rešenja, od drugih metoda, tehnika i algoritama. Polazeci od navedenog, u ovom radu je ispitivano da li je moguce dalje unaprediti rešavanja problema raspore ivanja poslova i balansiranja opterecenja na klaudu primenom metaheuristika inteligencije rojeva. Tokom sprovedenog istraživanja, unapre eno je i adaptirano više metaheuristika inteligencije rojeva za rešavanje problema raspore ivanja poslova i balansiranja opterecenja u klaud okruženju. U disertaciji su detaljno prikazane implementacije dva unapre ena algoritma rojeva - algoritma optimizacije monarh leptirovima i algoritma optimizacije jatom kitova. Za potrebe testiranja, rešavana su dva modela raspore ivanja poslova na klaudu. Prvi model, koji pripada grupi jednokriterijumske optimizacije, uzima u obzir minimizaciju vremena izvršavanja svih zadataka na klaudu, dok drugi, višekriterijumski model uzima u obzir minimizaciju vremena izvršavanja svih zadataka na klaudu i budžeta, tj. troškova za izvršavanje svih zahteva krajnjih korisnika. Simulacije su vršene u robusnom okruženju CloudSim simulatora i oba algoritma su testirana sa skupom veštackih podataka, generisanih u okviru CloudSim platforme, i realnih podataka, koji su preuzeti iz globalno dostupne bencmark baze. Osim testiranja za praktican izazov na klaudu, da bi se preciznije utvrdila unapre- enja modifikovanih metaheuristika u odnosu na osnovne verzije, obe metaheuristike su verifikovane i testiranjima na standardnim skupovima bencmark funkcija za globalnu optimizaciju bez ogranicenja. Upore ivanjem generisanih rezultata (kvalitet rešenja i brzina konvergencije) sa rezultatima najboljih poznatih metaheuristika i heuristika iz literature, koje su primenjivane na iste instance problema (na praktican problem raspore ivanja na klaudu i bencmark testove), dokazan je kvalitet implementiranih algoritama, cime je potvr ena i osnovna hipoteza ovog rada da se rešavanje izazova raspore ivanja poslova i balansiranja opterecenja u klaud okruženju mogu dalje unaprediti primenom metaheuristika inteligencije rojeva