Heterogeneous Hybrid Research Articles

A Heterogeneous Parallel Computing Approach Optimizing SpTTM on CPU-GPU via GCN

Sparse Tensor-Times-Matrix (SpTTM) is the core calculation in tensor analysis. The sparse distributions of different tensors vary greatly, which poses a big challenge to designing efficient and general SpTTM. In this paper, we describe SpTTM on CPU-GPU heterogeneous hybrid systems and give a parallel execution strategy for SpTTM in different sparse formats. We analyze the theoretical computer powers and estimate the number of tasks to achieve the load balancing between the CPU and the GPU of the heterogeneous systems. We discuss a method to describe tensor sparse structure by graph structure and design a new graph neural network SPT-GCN to select a suitable tensor sparse format. Furthermore, we perform extensive experiments using real datasets to demonstrate the advantages and efficiency of our proposed input-aware slice-wise SpTTM. The experimental results show that our input-aware slice-wise SpTTM can achieve an average speedup of 1.310 × compared to ParTI! library on a CPU-GPU heterogeneous system.

Near‐Infrared InGaAs Intelligent Spectral Sensor by 3D Heterogeneous Hybrid Integration

The applications of near‐infrared spectroscopy (NIRS) are limited due to the bulky size, low integration, and poor intelligence in edge computing of traditional spectrometers. In this work, the authors develop an on‐chip InGaAs intelligent spectral sensor, which consists of a linear variable filter for wavelength selection, a linear focal plane array as detector and a chip processor for neural‐network inferring, based on an advanced 3D heterogeneous hybrid‐integration. More than 200 spectral channels with a spectral resolution of 1.25% central wavelength are acquired in a wide waveband from 900 to 1700 nm. Immediate results are provided onsite for users, especially applicable to nonspecialists, owing to the embedded algorithmic model in‐sensor. As a proof of applications, the authors experimentally detect adulterating green tea and achieve a real‐time identification with accuracy better than 90%. This spectral sensor with edge‐artificial‐intelligence breaks the dependence on external algorithms and paves the way for NIRS into miniaturization, integration, and intelligence, which brings more possibilities in incorporating with the consumer devices and the Internet of Things.

Homogeneous–Heterogeneous Hybrid Artificial Photosynthesis Induced by Organic Semiconductors with Controlled Surface Architectures

AbstractPhotocatalysis is considered an effective approach for converting CO2 into high‐value‐added chemicals. However, practical implementation of this technology is limited by the efficiency and stability of photocatalysis. Herein, an interfacial control strategy is proposed to optimize the homogeneous‐heterogeneous hybrid photocatalysis by enhancing the interaction between light‐harvesting semiconductors (LHS) and molecular active centers (MAC). Based on this strategy, self‐assembled organic semiconductors with controlled surface architectures are constructed using 1,6‐bis(phenylethynyl)pyrene building blocks to act as LHS. Combining with the classical MAC, an excellent CO2 photoreduction performance is achieved with a CO turnover number of > 2980 maintaining long‐term stability with a selectivity of > 90%, and an apparent quantum yield of > 2.3%. Theoretical calculations combined with in situ and transient spectroscopy studies reveal that the optimized biphase interface dominates the synergy between the homogeneous and heterogeneous photocatalysts. This strategy and the proposed mechanism of interactions will contribute to the design of future artificial photosynthesis systems.

Controlled Immobilization of a Palladium Complex/Laccase Hybrid into a Macrocellular Siliceous Host.

This study investigates the site-directed immobilization of a hybrid catalyst bearing a biquinoline-based-Pd(II) complex (1) and a robust laccase within cavities of a silica foam to favor veratryl alcohol oxidation. We performed the grafting of 1 at a unique surface located lysine of two laccase variants, either at closed (1⊂UNIK157 ) or opposite position (1⊂UNIK71 ) of the enzyme oxidation site. After immobilization into the cavities of silica monoliths bearing hierarchical porosity, we show that catalytic activity is dependent on the orientation and loading of each hybrid, 1⊂UNIK157 being twice as active than 1⊂UNIK71 (203 TON vs 100 TON) when operating under continuous flow. These systems can be reused 5 times, with an operational activity remaining as high as 40 %. We show that the synergy between 1 and laccase can be tuned within the foam. This work is a proof of concept for controlling the organization of a heterogeneous hybrid catalyst using a Pd/laccase/silica foam.

Is Perversion a Perverted Category? Introduction to the Special Issue

ABSTRACT This introductory paper outlines the clinical, ethical, and personal reasons that led to the preparation of this French-based issue on perversion. In traditional French psychoanalysis, perversion is conceived of as an autonomous psychopathological structure, with a special emphasis placed on narcissistic fragility, nullification of the other, and nonhegemonic sexual practices deemed as the abolishment of sex and generational differences. The author questions the relevance of perversion as a fixed category and proposes an inclusive approach that takes into account social, cultural, intrapsychic, and countertransference parameters. Finally, he overviews each article included in this special issue and highlights the epistemological diversity of the authors’ positions, thus promoting the paradigm of complexity, which is understood as heterogeneous hybrid thinking, noncompliant to dialectical synthesis.

Ag2O modified magnetic BaFe12O19/C3N4 photocatalysts with enhanced antibiotic removal: Photocatalytic mechanism and toxicity evaluation

Novel Ag2O-BaFe12O19/C3N4 composites were prepared via a grind, solvothermal and deposition–precipitation methods. The physicochemical properties of the samples were tested by FT-IR, XRD, SEM, TEM, XPS, DRS, ESR, VSM, photocurrent test, and electrochemical impedance spectra test, respectively. The characterization manifested that Ag2O was firmly anchored to the BaFe12O19/C3N4 to form heterogeneous hybrid. Most importantly, the formation of Ag2O-BaFe12O19/C3N4 effectively promoted the charge transfer, eventually enhancing the photocatalytic performance. Meanwhile, the activity of the photocatalysts were assessed by photocatalytic removal of tetracycline (TC) under visible light irradiation. The results showed that Ag2O-BaFe12O19/C3N4 photocatalyst has better photocatalytic activities than the pure component. The degradation rate reaches 80 % after 15 min, which is nearly 1.56 times higher than that of pure g-C3N4. The effects of Ag2O content, photocatalyst dosage, pH value on TC degradation performances were studied in detail, respectively. The experimental results verified that pH value exhibited significant effects on TC degradation. The TC degradation rate was highest only when the initial solution pH = 7. This indicates that the photocatalyst also has high activity in neutral environment, and no additional pH adjustment is required. The results of plant growth test (mung bean seeds) showed that the degradation intermediates showed lower toxicity. Based on experiments and characterization, the possible carrier migration and photocatalytic degradation mechanism were proposed.

An improved multi-population genetic algorithm with a greedy job insertion inter-factory neighborhood structure for distributed heterogeneous hybrid flow shop scheduling problem

Distributed heterogeneous hybrid flow shop scheduling problem (DHHFSP) is an extension of the classical hybrid flow shop scheduling problem (HFSP), which is an NP-hard problem. In the context of economic globalization, DHHFSP considers the collaboration and heterogeneity among multiple factories. The neighborhood structure plays an important role in continuously improving the current individuals for DHHFSP. The existing work has fully studied the inner-factory neighborhood structure, but the research on the inter-factory neighborhood structure is still immature. A greedy job insertion inter-factory neighborhood structure and a new move evaluation method are designed to ensure the efficiency of neighborhood movement. And an improved multi-population genetic algorithm (IMPGA) is proposed to solve the DHHFSP with makespan. To enhance the convergence speed and robustness of the IMPGA, a guided sub-populations information interaction and a re-initialization procedure with an individual resurrection strategy are designed respectively. In computational experiments, there are 480 instances (including the same proportion of small, medium, and large-scale problems) randomly generated. The proposed IMPGA obtains the best solutions for 457 instances. The analysis of experimental results shows that IMPGA significantly outperforms the reported state-of-the-art algorithms for DHHFSP. Finally, the proposed method is used to solve a polyester film manufacturing company case effectively.

Correlations between the protein content in grain with economic characters in winter bread wheat breeding accessions of hybrid origin on irrigation

Purpose and Objectives. To evaluate the expression of the "protein content in grain" trait in winter bread wheat lines derived from late-ripening accessions of the Western European ecotype; to construct correlation-regression models for the relationships of the protein content in grain with the "anthesis - grain ripeness" interphase period length and grain yield in elite accessions grown in breeding nurseries.
 Material and Methods. The field studies were carried out at the Institute of Irrigated Agriculture of NAAS in 2019–2021. Modern winter wheat varieties bred at the Institute, collection specimens of the Western European ecotype, which had been introduced from France (registration numbers Kf1...16), and their hybrids were studied. Individual selections of elite plants from F2 were brought to the control nursery and evaluated for protein content in grain, yield, "anthesis - ripeness" period and other economic characteristics. Biometric measurements, biochemical analyses, and yield records were conducted by traditional methods. The study methods were field, laboratory, biochemical, breeding-genetic, and statistical. The studies were carried out under irrigation conditions at the pre-irrigation field moisture capacity (FMC) in the 0–50 cm soil layer of 75%.
 Results and Discussion. The expression of the "protein content in grain" trait in the breeding accessions selected from hybrid populations, which had been originated from Western European ecotypes of winter bread wheat, was analyzed; the correlation-regression models of the relationships between the protein content in grain, the grain yield and the "anthesis - ripeness" period were constructed. The “anthesis – ripeness” period in the best accessions lasted 45 - 52 days, and each original hybrid combination had its own optimum, which was associated with a high yield and protein content in grain. Extension of the "anthesis - ripeness" period slightly increased the grain yields of the breeding accessions from some hybrid populations; however, analogous effect on the protein content in grain was insignificant.
 Conclusions. A concurrent increase in the grain yield and protein content in grain through traditional selections is possible; however, such parallel enhancement of these traits is more suitable for heterogeneous hybrid populations with low protein contents grain and yields, or with low values of one of these traits (low protein content). For each hybrid population from parents contrasting in vegetation length, it is necessary to develop a specific plan of selections with due account for intra-population correlation-regression models of yield capacity, protein content in grain and “anthesis – ripeness” period length.

LS-HH: A Learning-Based Selection Hyper-Heuristic for Distributed Heterogeneous Hybrid Blocking Flow-Shop Scheduling

As the development of economic globalization, the distributed manufacturing has become common in modern industries. The scheduling of production resources in multiple production centers becomes an emerging topic. This paper is the first attempt to address a distributed heterogeneous hybrid blocking flow-shop scheduling problem (DHHFSP-B) with the minimization of makespan. Compared with the traditional single flow-shop scheduling, DHHFSP-B considers the collaborative production of multiple hybrid flow lines with heterogeneous layout and processing performance as well as no intermediate buffers. We firstly present a mixed-integer linear programming model to formulate DHHFSP-B and then propose a learning-based selection hyper-heuristic framework (LS-HH) for solving it. The LS-HH contains high-level strategy and low-level heuristics. In the high-level strategy, a learning probability model is built to provide the guidance to choose the suitable perturbation heuristic during the optimization process. A simulated annealing-like move acceptance is employed to determine the updating of incumbent domain solution and prevent the search from trapping into local optimum. In the low-level heuristics, a constructive heuristic is proposed based on a novel assignment rule to create the initial domain solution. Four problem-specific perturbation heuristics and a variable neighborhood search-based improvement operator are employed to search the solution space. A comprehensive computational experiment is conducted. The comparative results show that the LS-HH significantly outperforms the Gurobi solver and several closely relevant optimization methods in solving the DHHFSP-B.

Semantics Foundation for Cyber-physical Systems Using Higher-order UTP

Model-based design has become the predominant approach to the design of hybrid and cyber-physical systems (CPSs). It advocates the use of mathematically founded models to capture heterogeneous digital and analog behaviours from domain-specific formalisms, allowing all engineering tasks of verification, code synthesis, and validation to be performed within a single semantic body. Guaranteeing the consistency among the different views and heterogeneous models of a system at different levels of abstraction, however, poses significant challenges. To address these issues, Hoare and He’s Unifying Theories of Programming (UTP) proposes a calculus to capture domain-specific programming and modelling paradigms into a unified semantic framework. Our goal is to extend UTP to form a semantic foundation for CPS design. Higher-order UTP (HUTP) is a conservative extension to Hoare and He’s theory that supports the specification of discrete, real-time, and continuous dynamics, concurrency and communication, and higher-order quantification. Within HUTP, we define a calculus of normal hybrid designs to model, analyse, compose, refine, and verify heterogeneous hybrid system models. In addition, we define respective formal semantics for Hybrid Communicating Sequential Processes and Simulink using HUTP.

Acceleration of Bi-Objective Optimization of Data-Parallel Applications for Performance and Energy on Heterogeneous Hybrid Platforms

Accelerating the bi-objective optimization of applications for performance and energy is crucial to achieving energy efficiency objectives and meeting quality-of-service requirements in modern high-performance computing platforms and cloud computing infrastructures. In this work, we highlight the crucial challenges to accelerate model-based methods proposed for the bi-objective optimization of data-parallel applications for performance and energy that employ workload distribution between the executing processors as the decision variable. The methods solve unconstrained bi-objective optimization problems and take input, the processors’ performance and energy profiles in the form of discrete functions of workload size, and output Pareto-optimal solutions (workload distributions), minimizing the execution time and the total energy consumption of computations during the parallel execution of the application. One of the challenges is the fast computation of Pareto-optimal solutions. We then formulate the bi-objective optimization problem of data-parallel applications for performance and energy through workload distribution on a cluster of p identical hybrid nodes, each containing h heterogeneous processors. The state-of-the-art algorithm for solving the problem is sequential and takes exorbitant execution times to find Pareto-optimal solutions for even moderate numbers of processors. We propose two algorithms that address this shortcoming. The first algorithm is an exact sequential algorithm that is more efficient and amenable to parallelization and achieves a complexity reduction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (m × h) over the state-of-the-art sequential algorithm where m is the cardinality of the input discrete execution time and dynamic energy functions. The second algorithm is a parallel algorithm executed by q identical parallel processes that reduces the complexity of our proposed sequential algorithm by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (q) and therefore achieves a complexity reduction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (m×h×q) over the state-of-the-art sequential algorithm. Finally, we experimentally analyze the practical efficacy of our proposed algorithms for two data-parallel applications, matrix multiplication and fast Fourier transform, on a heterogeneous hybrid node containing an Intel Haswell multicore CPU, an Nvidia k40c GPU, and an Nvidia P100 GPU and simulations of clusters of such hybrid nodes. The experiments demonstrate that our proposed algorithms provide tremendous speedups over state-of-the-art solutions.

Nickel-iron bimetallic sulfides nanosheets anchored on bacterial cellulose based carbon nanofiber for enhanced electrocatalytic oxygen evolution reaction

To meet demand for accelerating kinetics toward oxygen evolution reaction (OER), designing catalysts with outstanding activity and stability is of significance. Hence, nickel-iron sulfides and carbon nanofiber derived from bacterial cellulose (Ni-Fe-S/BCCNF) are coupled by facile hydrothermal method. With the optimal mass concentration of BCCNF suspension (1.0 mg mL−1), Ni-Fe-S/BCCNF shows preeminent electrocatalytic activity toward OER. Compared with pristine Ni-Fe-S, as-prepared Ni-Fe-S/BCCNF exhibits lower overpotential due to the fact that sulfide nanosheets are interwoven with network structure, exposing more active sites. Additionally, BCCNF functions as a steady platform for electron transport, while ensuring the durability during the uninterrupted water oxidation process. The strategy of designing heterogeneous hybrid material can be applied to not only nickel-iron bimetallic sulfides but also other metal sulfides, which is valuable for accelerating kinetics toward OER.

Fine-grained heterogeneous parallel direct solver for finite element problems

The improvement of computational effectiveness is a vital issue in the field of large-scale finite element analysis. The performance is fundamentally determined by the efficiency of solving sparse linear system equations using the implicit finite element method. This paper presents a direct linear solver based on heterogeneous hybrid parallel computing on CPUs and GPUs. This can efficiently utilize computing resources of multiple devices to achieve performance improvement. Initially, we partition the elimination tree into several subtrees to accomplish the task decomposition. Based on this, we build a dynamic programming mathematical model to balance the computational load of the various devices. Then, we develop a numerical decomposition strategy by combining node parallelism and tree parallelism for the CPUs. In addition, efficient numerical decomposition is achieved on the GPU through batch processing and maximizing the overlap between computations and data transfers. Numerical experiments show that, compared with MKL PARDISO, the performance of numerical factorization can be improved by up to 10 times by using CPU and dual-path GPU hybrid calculations, and the computation time of simulation can be reduced by one-third for the multicondition analysis of Body In White and by 20% for the large-scale nonlinear finite element deformation analysis.

Deep extractive and oxidative removal of 2- and 3-methylthiophene using a hierarchical architecture of α-Keggin-type polyoxotungstate

2-methylthiophene (2 MT) and 3-methylthiophene (3 MT) are two pollutants that are not removed through the usual purification in the gas refineries and lead to the release of SOX after combustion. A novel active hierarchical architecture of Fe2O3 hollow nanospindle coated by polyaniline-Zinc(II)-substituted α-Keggin type polyoxotungestate (Fe2O3NHS@PA/αSiW11Zn composites) is presented for removal of 2-methylthiophene (2 MT) and 3-methylthiophene (3 MT) as representative of alkylthiophenes. In this research, using a combined method based on simultaneous extraction techniques, 99.1% of 2 MT and 98.7% of 3 MT were successfully removed. The process involves the simultaneous use of ultrasound-assisted extraction phenomenon by an ionic liquid and catalytic oxidation by the heterogeneous hybrid nanocatalyst (UA-ECODS). The advantages of this process include the possibility of producing ultra-clean fuels, mild operational conditions, and cost-effectiveness. For this purpose, Box-Behnken Design (BBD) and response surface methodology (RSM) were used to design experiments and study the parameters. The affecting factors were optimized in terms of removal efficiencies by response surface methodology. The oxidation progress was studied more closely based on a pseudo-first-order kinetic model at different temperatures for various ultrasonic times. The Fe2O3NHS@PA/αSiW11Zn nanocomposite can be used as an efficient and recyclable phase-transfer heterogeneous catalyst to remove mercaptans from gas refinery products.

A network memetic algorithm for energy and labor-aware distributed heterogeneous hybrid flow shop scheduling problem

With the penetration of decentralization into factories, the production scheduling among non-identical factories has emerged as a hot issue for industrial demand response. However, little attention has been paid to differentiated production resources for distributed production scheduling. This paper studies an energy and labor-aware distributed heterogeneous hybrid flow shop scheduling problem (ELDHHFSP). The heterogeneous factors, i.e. different numbers and capabilities of machines, time-of-use electricity price, labor shift, salary and bonus of workers, are included in the ELDHHFSP. The mixed-integer linear programming (MILP) model of ELDHHFSP is presented with the objectives of minimization of total tardiness, total production cost, and total carbon emission. A network memetic algorithm (NMA) is proposed for the ELDHHFSP from the solution and strategy space. The NMA mainly includes two critical parts, i.e., probability network model and learning-based local search, corresponding to global and local search. A probability network model is established to guide the search towards the optimal regions from different directions by employing multiple objective weight vectors. The sampling part samples from the probability matrix of each node in the network to construct new solutions. The learning-based local search integrates several local search operators and a reward learning mechanism is designed to find suitable local search operators when evolutionary. A comprehensive experiment on extensive testing instances is conducted to investigate the effectiveness of the probability network and local-learning strategy. The comparison of NMA and other related algorithms shows the effectiveness and efficiency of NMA.

Modelling and optimization of distributed heterogeneous hybrid flow shop lot-streaming scheduling problem

More enterprises are facing rapid and changing market demand and small but frequent orders, and they extend the production capability of manufacturing systems to respond to these changes. This paper studies a distributed heterogeneous hybrid flow shop lot-streaming scheduling problem (DHHFLSP) with the minimization of makespan. In the DHHFLSP, the processing capacity of each factory is different and each job can be split into several sub-lots. These sub-lots are assigned to several non-identical factories. The mixed-integer linear programming model (MILP) of DHHFLSP is established. To solve the DHHFLSP, eighteen constructive heuristics and an iterated local search algorithm (ILS) are designed. In constructive heuristics, the jobs are sorted according to several time-based heuristic rules or they are divided into several groups according to bottleneck stages, and then these jobs are assigned into factories through two NEH-based job assignment rules. In the ILS, the NEH-based heuristic (NEHafter) plus Largest medium rule is used to generate an initial solution. Two greedy insertion operators with or without critical factories are adopted to generate the perturbation solutions. Four greedy local search operators are designed to make a deep search. The influence of the parameters and main components are investigated by a comprehensive analysis. The comparisons with several related algorithms on extensive testing instances demonstrate the effectiveness and efficiency of the ILS algorithm.

Nanocrystalline Iron Pyrophosphate-Regulated Amorphous Phosphate Overlayer for Enhancing Solar Water Oxidation

HighlightsInducing a localized crystalline iron pyrophosphate in amorphous iron phosphate overlayer.Enhanced photoelectrochemical water oxidation activity with long-term durability.The heterogeneous hybrid structure overcoming the energy barrier in water oxidation.

An Ant Colony Optimization Behavior-Based MOEA/D for Distributed Heterogeneous Hybrid Flow Shop Scheduling Problem Under Nonidentical Time-of-Use Electricity Tariffs

This article studies a distributed heterogeneous hybrid flow shop scheduling problem under nonidentical time-of-use electricity tariffs (DHHFSP-NTOU). The makespan and the total electricity charge are considered as the optimization objectives from the view of production and management. The DHHFSP-NTOU considers different processing capabilitie and time-of-use electricity tariffs for each factory. The mixed-integer linear programming (MILP) model of DHHFSP-NTOU is established. To solve the DHHFSP-NTOU, this article proposes an ant colony optimization behavior-based multiobjective evolutionary algorithm based on decomposition (ACO_MOEA/D). A problem-specific ant colony behavior is presented to construct offspring individuals. Eight neighborhoods within the factory and between factories are adopted to improve the quality of the individuals in the archive set. A right-shift movement is used to reduce the electricity charge. A large number of numerical experiments and comprehensive investigations are carried out to test the efficiency and effectiveness of ACO_MOEA/D. The experimental results show that each component (e.g., ant colony behavior, neighborhoods move operators, right-shift movement) contributes to the performance of ACO_MOEA/D. The comparisons with several related algorithms show the superiority of ACO_MOEA/D for solving the DHHFSP-NTOU. Note to Practitioners—From the managers’ insights, the electricity charge is a large cost in the production. The scheduling is an economical approach to reduce the electricity charge. For the time-of-use (TOU) tariffs, the managers can adjust the schedule to reduce the idle time or move some operations to the interval period with a lower electric price. This article studies a distributed heterogeneous hybrid flow shop scheduling problem under nonidentical TOU (UTOU) electricity. This model can be used in many manufacturing enterprises that have several heterogeneous factories. This article proposes an ant colony optimization behavior-based multiobjective evolutionary algorithm based on decomposition (ACO_MOEA/D) to minimize the makespan and the total electricity charge. The ACO_MOEA/D can provide the economy and high-efficiency schedules for practitioners. The computational results confirm its effectiveness and efficiency.

Efficient exact algorithms for continuous bi‐objective performance‐energy optimization of applications with linear energy and monotonically increasing performance profiles on heterogeneous high performance computing platforms

AbstractPerformance and energy are the two most important objectives for optimization on heterogeneous high performance computing platforms. This work studies a mathematical problem motivated by the bi‐objective optimization of data‐parallel applications on such platforms for performance and energy. First, we formulate the problem and present an exact algorithm of polynomial complexity solving the problem where all the application profiles of objective type one are continuous and strictly increasing, and all the application profiles of objective type two are linear increasing. We then apply the algorithm to develop solutions for two related optimization problems of parallel applications on heterogeneous hybrid platforms, one for performance and dynamic energy and the other for performance and total energy. Our proposed solution methods are then employed to solve the two bi‐objective optimization problems for two data‐parallel applications, matrix multiplication and gene sequencing, on a hybrid platform employing five heterogeneous processors, namely, two different Intel multicore CPUs, an Nvidia K40c GPU, an Nvidia P100 PCIe GPU, and an Intel Xeon Phi.

A Heterogeneous Hybrid Cloud Storage Service Using Storage Gateway with Transfer Acceleration and Diff Algorithm

Recently, the cloud service has the potential to replace conventional cluster and grid systems. The objective of migrating apps to the cloud is to minimize maintenance and procurement expenses while simultaneously boosting scalability and availability. However, embra=cing cloud technology created some challenges, such as the complexity of cloud storage. In addition, many clients underestimate if it is not plug-and-play. Each vendor has its access methods, and nonstandard application programming interfaces (APIs) make integrated applications, such as archiving or sharing data with cloud storage, complicated, costly, and require high throughput. Furthermore, organizations did not have many alternatives for implementing high-performance object storage systems in the cloud and on-premises data centers until now. In this paper, we would like to suggest a storage gateway as a solution to this issue and will optimize it using Transfer Acceleration and Diff algorithms to improve the performance, Intelligent Tiering to reduce costs, and Server-Side encryption for extra protection. Moreover, utilizing Storage Gateway has proven can provide more efficient integration between the on-premises data center environment and the AWS Cloud Storage ecosystem that is safer and more reliable. This technology can work in a common data center environment regardless of the vendor used by the company it can communicate seamlessly with the AWS Environment.