Virtual Machine System Research Articles

Minimization of residual stress, surface roughness and tool wear in Electro Discharge Machining of inconel 625

Electro Discharge Machining (EDM) is a well-known non-traditional machining technique which is widely used to make die castings and turbine blades out of difficult-to-cut materials like Inconel 625. The machined part's surface quality and dimensional accuracy are affected by the EDM electrode's wear. Moreover, surface roughness and residual stress augmentation of EDM-machined components can reduce the workpiece's lifespan by decreasing the fatigue life of machined parts. To enhance the precision and durability of EDM machined components, residual stress, tool wear, and surface roughness should be evaluated and minimized. The majority of published research works for the assessment and optimization of EDM machining parameters are based on experimental works which are limited to testing, workpiece materials, and machining conditions. In order to improve the quality of machined surfaces and reduce tool wear and residual stress during Inconel 625 EDM processes, a virtual machining approach has been developed in the research work. To predict the cutting temperature during EDM operations, the modified Johnson Cook model of Inconel alloys is used. The finite element approach is then used to calculate the generated residual stress during the EDM process. The proposed virtual machining approach is also applied in order to predict the surface roughness of EDM machined parts. To minimize the residual stress, tool wear, and surface roughness during EDM operations of Inconel 625, the machining parameters of gap voltage, peak current, pulse-off time, and pulse-on time are optimized using the Taguchi optimization approach. Experiments and simulations are then conducted to verify the developed virtual machining system in the study. So, using the optimal machining parameters, the residual stress, surface roughness of machined items and wear of EDM electrode are minimized by 24.5 %, 25.4 %, and 25.4 % respectively. Thus, the quality and reliability of components made using EDM processes may be enhanced by the suggested virtual machining system.

Maximizing Profit for Cloud Brokers in The Era of Cloud Computing

Abstract: Cloud computing has experienced significant growth in recent years, with an increasing number of applications migrating to the cloud, where they can centrally manage a wide range of resources, including hardware and software. These resources are delivered over the internet as services, catering to customer demands. One of the key features of cloud computing is the pay-as-you-go model. However, due to the one-hour billing cycle and the discount structure favoring long-term users, many customers end up paying more than their actual usage. To address this issue, cloud brokers rent reserved Virtual Machines (VMs) from cloud providers at a reduced cost and offer them to users on an on-demand basis at a more affordable rate than what the cloud providers offer. Moreover, cloud brokers operate on a shorter billing cycle compared to cloud providers. This study focuses on configuring a cloud broker and determining the optimal pricing for its VMs to maximize its profits. Most cloud providers offer two primary payment options for their instances: On-Demand and Reserved Instances. With On-Demand instances, users pay for compute capacity per hour, making them suitable for short-term workloads. Reserved Instances provide significant discounts but require users to commit to long rental periods, disadvantaging short-term customers. The introduction of the cloud broker serves as a novel intermediary between cloud providers and cloud users, addressing the issues of both onehour billing cycles and the limitations on discounts for short-term users. This manuscript begins by introducing a multi-server queuing model, a revenue model, and a cost model, followed by a comprehensive analysis of the factors affecting them. It defines an optimization problem aiming to determine the optimal configuration for multi-server setups and VM pricing, all aimed at profit maximization. To address this optimization problem, a heuristic method is proposed, which combines partial derivatives and a bisection search. This method offers a practical approach to finding the optimal VM price and system scale, ultimately maximizing profit. This manuscript explores the critical role of cloud brokers in the cloud computing landscape and presents a comprehensive approach to optimizing their operations, ultimately benefitting both cloud providers and users.

MINIMIZATION OF DEFLECTION ERROR IN FIVE AXIS MILLING OF IMPELLER BLADES

The 5-axis CNC machine tools are used for manufacturing free form surfaces of sophisticated parts such as turbine blades, airfoils, impellers, and aircraft components. The virtual machining systems can be used in order to analyze and modify the 5-axis CNC machine tools operations. Cutting forces and cutting temperatures induce deflection errors in thin-walled structures such as impeller blades through machining operations. Thin-walled impeller blades' flexibility can result in machining errors such as overcutting or undercutting. So, decreasing the deflection error during machining operations of impeller blades can achieve the desired accuracy in produced parts. Optimized machining parameters can be obtained to minimize the deflection of machined impeller blades. In terms of precision and efficiency enhancement in component production processes, a virtual machining system is developed to predict and minimize deflection errors of 5-axis milling operations of impeller blades. The deflection error in machined impeller blades is calculated by using finite element analysis. The optimization methodology based on the genetic algorithm is applied to minimize the deflection error of impeller blades in machining operations. To validate the integrated virtual machining system in the study, the impeller is milled by using a 5-axis CNC machine tool. The CMM machine is used in order to measure and analyze deflection error in the machined impeller blades. As a result, by using the developed virtual machining system in the study, accuracy and efficiency in 5-axis milling operations of impellers can be increased.

Deformation error compensation in 5-Axis milling operations of turbine blades

The precision and performance of machined flexible parts are under influence of deformation errors during end milling operations. Thus, prediction and compensation of deformation errors during milling operations of flexible parts can provide a key tool in accuracy enhancement of part production. In this study, an improved virtual machining system is proposed in order to assess and compensate deformation errors caused by cutting temperature and forces in 5-axis milling operations of flexible parts. The improved Johnson–Cook model is utilized to investigate the cumulative impact of strain rate and deformation temperatures on flow stress during milling operations of turbine blade. To estimate deformation errors caused by cutting forces and temperature on the workpiece and cutting tool, the finite element analysis is then applied. As a result, volumetric vectors of deformation error at each cutting location along the machining pathways are then generated in order to be compensated within new compensated machining tool paths. Thus, the deformation error created by cutting forces and temperature on the workpiece and cutting tool are compensated in order to enhance accuracy during 5-axis milling operation of flexible turbine blades. Experiments are carried out using a 5-axis CNC machine tool and errors are quantified using a CMM to verify the developed strategy in the study. As a consequence, precision of machining operations on flexible turbine blades can be enhanced by employing the developed virtual machining system in the study.

The effects of coolant on the cutting temperature, surface roughness and tool wear in turning operations of Ti6Al4V alloy

The titanium alloys are widely used in aeronautical engineering and medical device materials due to exceptional mechanical properties such as tensile resistance and toughness of fractures. High thermo-mechanical loads occur in metal cutting of Titanium alloy Ti6Al4V, which can decrease life of cutting tool and increase cost of part production. In this paper, the coolant effects on the cutting temperature, surface roughness and tool wear are investigated by using the developed virtual machining system. The cutting forces during turning operations of Ti6Al4V alloy are accurately calculated in order to be used in calculation of cutting temperature and tool wear. The modified Johnson–Cook methodology is utilized to obtain the cutting temperatures along machining paths. Then, the Coupled Eulerian-Lagrangian (CEL) approach is investigated to predict and evaluate the effects of coolants on the cutting temperature in turning operations of Ti6Al4V alloy. The finite element approach is employed to predict tool wear by using the Takeyama–Murata analytical model and modifying the cutting tool geometry during the chip production process. To verify the developed methodology in the study, the results of experiments for the measured cutting temperatures, surface quality and wear rate are compared to the results of virtual machining system obtained by the finite element simulation. Thus, utilizing the proposed virtual machining system in the study, cutting temperatures, surface quality and tool wear during the turning operations of Ti6Al4V alloys with and without coolant can be accurately predicted to enhance the accuracy as well as productivity in the CNC machining operations.

Dimensional, geometrical, thermal and tool deflection errors compensation in 5-Axis CNC milling operations

ABSTRACT The dimensional, geometrical, thermal and tool deflection errors which have a big portion of the overall error of machined parts need more attention in precision of components produced by using CNC machine tools. As a result, it is essential to simulate and compensate the errors in the machined components in order to increase accuracy of machined parts. In order to simulate and analyse the real manufactured components in virtual environments, virtual machining systems are proposed. In this paper, application of virtual machining system is investigated in order to simulate and compensate dimensional, geometrical, thermal and tool deflection errors in 5-axis milling operations of free form surfaces. The volumetric error vectors regarding the dimensional, geometrical, thermal and tool deflection errors at each cutting tool location throughout the machining pathways are calculated and compensated utilising the study’s created virtual machining technology. In order to validate the study, a sample workpiece free from surfaces is milled by using the 5-axis CNC machine tool. The machine part is then measured by suing the CMM machine in order to obtain the dimensional, geometrical, thermal and tool deflection errors during milling operations of free form surfaces. Thermal sensors are also installed to the different locations of CNC machine tool in order to measure the thermal error of CNC machine tool during machining operations. Finally, in order to improve accuracy in 5-axis milling operations of free form surfaces, new cutting tool paths regarding the compensated volumetric errors of dimensional, geometrical, thermal, and tool deflection errors are generated. As a result, by utilising the proposed virtual machining system in the research work, precision as well as reliability during 5-axis milling operations of free form surfaces can be enhanced.

Minimization of surface roughness and residual stress in abrasive water jet cutting of titanium alloy Ti6Al4V

Concrete, ceramics, rocks, metal-matrix composites, titanium alloys and other hard-to-cut materials are frequently cut using the non-traditional cold processing method known as abrasive water jet machining (AWJM). A virtual machining system is proposed in the study to minimize roughness of surface and residual stress during AWJM of titanium alloy Ti6Al4V. The water and workpiece are simulated in virtual environments using the smoothed particle hydrodynamics (SPH) approach and finite element method, respectively. Then, the Johnson–Cook law model of titanium alloy is used to obtain the cutting temperature during cutting operations. To calculate residual stress during the AWJM operations, the finite element method is then utilized. The Taguchi optimization method is implemented to minimize surface roughness and residual stress in AWJM operations. Thus, the optimized machining parameters such as water jet pressure, traverse speed, abrasive mass flow rate and standoff distance are calculated to minimize the surface roughness and residual stress during the AWJM of titanium alloy Ti6Al4V. Experimental and computational examinations are carried out in order to validate the effectiveness and efficiency of proposed virtual machining system in minimization of surface roughness and residual stress during abrasive waterjet cutting of titanium alloy Ti6Al4V. As a result, by using the optimized machining parameters, 23.7% and 27.1% reduction in the measured and predicted residual stress of sample machined part are obtained. Also, the sample machined part’s surface roughness is reduced by 26.7% and 27.9%, for the measured and predicted surface roughness of sample machined part, respectively. So, the quality and reliability of the machined parts can be enhanced using the developed virtual machining system in the study to minimize the surface roughness and residual stress in AWJM operations.

Research and Development of Virtual Assembly and Machining System for Metal Cutting Petroleum Machinery Based on Kinect v2

Research and Development of Virtual Assembly and Machining System for Metal Cutting Petroleum Machinery Based on Kinect v2

Cutting tool wear minimization in drilling operations of titanium alloy Ti-6Al-4V

Cutting tool wear during drilling operations can cause damage to cutting tools, machine tools, and workpieces which should be analyzed and minimized. Cutting tool wear impacts not just tool life but also the quality of the final product in terms of dimensional accuracy and surface integrity. High mechanical and thermal loads are generated during drilling operations of difficult-to-cut materials such as Ti-6Al-4V alloy which can reduce the life of cutting tool during chip formation process. Thus, to increase the accuracy of drilled parts from titanium alloy Ti6-Al-4V, the cutting tool wear during drilling operations should be accurately predicted in order to be minimized. Application of virtual machining systems is developed in the study in order to predict and minimize the cutting tool wear during drilling operations of titanium alloy Ti-6Al-4V. To predict the tool wear during drilling operations, cutting forces and temperature are calculated. Then, the finite element method (FEM) is utilized to predict the tool wear using the analytical model of Takeyama–Murata and updating the cutting tool geometry during chip formation process. To minimize the cutting tool wear during drilling operations, the optimum drilling parameters of feed rate and spindle speed are obtained using the Taguchi method-based response surface analysis algorithm. As a result, optimized drilling parameters are used in order to minimize the cutting tool wear during drilling operations. To validate the study, the experimental works are implemented to the sample workpiece from titanium alloy Ti6-Al4-V and the values of tool wear are then measured. To present the effectiveness of the proposed virtual machining system in minimization of cutting tool wear, the obtained results with and without optimized machining parameters are evaluated and compared. So, precision and productivity in drilling operations of titanium alloy Ti6-Al4-V can be enhanced using the developed virtual machining system in the study.

RETRACTED ARTICLE: Minimization of Surface Roughness and Residual Stress in Grinding Operations of Inconel 718

The residual stress and surface roughness enhancement of ground surfaces can decrease the lifetime of workpiece by reducing its fatigue life. It is critical to accurately predict and minimize the residual stress and surface toughness of ground surfaces in order to enhance efficiency of part production using grinding operations. A virtual machining system is developed in the research work to minimize the surface integrity and residual stress during grinding operations of Inconel 718. The cutting temperature during grinding operations is calculated using the Inconel alloy Johnson–Cook law models. Then, using the finite element method, residual stress during the grinding operation is estimated. Utilizing the established virtual machining method, the surface roughness is predicted in the study. Using the Taguchi optimization approach, the grinding parameters of depth of cut and feed velocity are optimized in order to reduce surface roughness as well as residual stress throughout grinding operations on Inconel 718 superalloy. To confirm the effectiveness of the developed technique in the study, simulation and experimentation are conducted. As a result, the quality as well as reliability of the ground surfaces can be enhanced by using the developed virtual machining system in the study to minimize the surface roughness and residual stress of produced parts using grinding operations.

HB+-MHT: Lightweight and Efficient Data Integrity Verification Scheme for Cloud Virtual Machines

With the rapid development of cloud computing, cloud storage is widely used. In the cloud environment, users’ virtual machine system mirrors and data are stored in the cloud server. The escape of virtual machines and Trojan virus attacks make it challenging to ensure the integrity of virtual machine systems. Trusted computing is expensive to randomly verify data integrity and does not adapt to dynamic data changes. Provable data integrity is a potential solution to this problem. Merkle Hash Tree (MHT) model is widely adopted in provable data integrity. Although MHT requires only a small amount of evidence for verification, the verifier’s number of hash calculations and the server’s efficiency of evidence query are not optimal. Moreover, the verification frequency of each piece of data is not considered by MHT. Properly handling these factors can improve the actual verification performance. In this paper, a lightweight and efficient data integrity verification approach called HB+-MHT is proposed for the tenant virtual machine (TVM) in cloud computing. In HB+-MHT, the Huffman hash tree scheme is used for small file verification to ensure that the hot file has a shorter path, which reduces the required amount of evidence for verification. Meanwhile, the B+ hash tree scheme is used for big files verification, which can effectively reduce evidence query time and hash calculation times. The experimental results show that the scheme proposed in this paper can perform data integrity verification well, with reduced computing and storage overhead.

Formal Specification and Verification of Data Separation for Muen Separation Kernel

Introduction: Development of integrated mixed-criticality systems is becoming increasingly popular for application-specific systems, which needs separation mechanism for available onboard resources and the processors equipped with hardware virtualization. Hardware virtualization allow the partitions to physical resources, which include processor cores, memory, and I/O devices, among guest virtual machines (VMs). For building mixed criticality computing environment, traditional virtual machine systems are inappropriate because they use hypervisors to schedule separate VMs on physical processor cores. In this article, we discuss the design of an environment for mixed-criticality systems: The Muen an x86/64 separation kernel for high assurance. The Muen Separation Kernel is an Open Source microkernel which has no runtime errors at the source code level. The Muen separation kernel has been designed precisely to encounter the challenging requirements of high-assurance systems built on the Intel x86/64 platform. Muen is under active development, and none of the kernel properties of it has been verified yet. In this paper, we present a novel work of verifying one of the kernel properties formally. Method: The CTL used in NuSMV is a first-order modal along with data-depended processes and regular formulas. CTL is a branching-time logic, meaning that its model of time is a tree-like structure in which the future is not determined; there are different paths in the future, any one of which might be an actual path that is realized . This section shows the verification of all the requirements mentioned in section 3. In NuSMV tool the command used for verification of the formulas written in CTL is checkctlspec -p ”CTL-expression”. The nearest quantifier binds each occurrence of a variable in the scope of the bound variable, which has the same name and the same number of arguments. Result: Formal methods have been applied to various projects for specification and verification purpose. Some of them are the SCOMP , SeaView , LOCK,and Multinet Gateway projects. The TLS was written formally. Several mappings were done between the TLS and the SCOMP code: Informal English language to TLS, TLS to actual code , and TLS to pseudo-code. The authors present an ACL2 model for a generic separation kernel also known as GWV approach. Conclusion: We consider the formal verification of data separation property which is one of the crucial modules to achieve the separation functionality. The verification of the data separation manager is carried out on the design level using the NuSMV tool. Furthermore, we present the complete model of the data separation unit along with its code written in the NuSMV modelling language. Finally, we have converted the non-functional requirements into the formal logic, which then has verified the model formally.

Minimization of surface roughness in 5-axis milling of turbine blades

The Reynolds number of turbine blades can be reduced by decreasing the surface roughness of the blades, which decreases energy loss in energy production systems. Due to the challenges of the polishing process in reducing roughness of machined blades, the application of optimized machining parameters to decrease surface roughness is a practical solution. Virtual machining system is explored in this research to improve the efficiency of machined parts. The aim of this research is to provide a virtual machining system that can predict and effectively reduce roughness in gas turbine blade 5-axis machining operations. The machining parameters were optimized using Genetic algorithm to reduce surface roughness. The turbine blades were machined using a 5-axis CNC machine tool, and the surface roughness of the machined parts was measured using a CMM machine to validate the developed methodology in the study. The optimized machining parameters result in a 26.2% reduction in observed surface roughness and a 27.3% reduction in expected surface roughness in the machined blades. The developed virtual machining system can be used to improve the surface quality of machined turbine blades and hence improve the efficiency of gas turbines during the power production in gas turbines.

Investigation of a dynamics-oriented engineering approach to ultraprecision machining of freeform surfaces and its implementation perspectives

In current precision and ultraprecision machining practice, the positioning and control of actuation systems, such as slideways and spindles, are heavily dependent on the use of linear or rotary encoders. However, positioning control is passive because of the lack of direct monitoring and control of the tool and workpiece positions in the dynamic machining process and also because it is assumed that the machining system is rigid and the cutting dynamics are stable. In ultraprecision machining of freeform surfaces using slow tool servo mode in particular, however, account must be taken of the machining dynamics and dynamic synchronization of the cutting tool and workpiece positioning. The important question also arises as to how ultraprecision machining systems can be designed and developed to work better in this application scenario. In this paper, an innovative dynamics-oriented engineering approach is presented for ultraprecision machining of freeform surfaces using slow tool servo mode. The approach is focused on seamless integration of multibody dynamics, cutting forces, and machining dynamics, while targeting the positioning and control of the tool–workpiece loop in the machining system. The positioning and motion control between the cutting tool and workpiece surface are further studied in the presence of interfacial interactions at the tool tip and workpiece surface. The interfacial cutting physics and dynamics are likely to be at the core of in-process monitoring applicable to ultraprecision machining systems. The approach is illustrated using a virtual machining system developed and supported with simulations and experimental trials. Furthermore, the paper provides further explorations and discussion on implementation perspectives of the approach, in combination with case studies, as well as discussing its fundamental and industrial implications.

Effects of Variable Weather Conditions on Baled Proportion of Varied Amounts of Harvestable Cereal Straw, Based on Simulations

All harvestable cereal straw cannot be collected every year in regions where wet periods are probable during the baling season, so some Swedish studies have used ’recovery coefficients’ to estimate potential harvestable amounts. Current Swedish recovery coefficients were first formulated by researchers in the early 1990s, after discussions with crop advisors, but there are no recent Swedish publications on available baling times and recovery proportions. Therefore, this study evaluated baling operations over a series of years for representative virtual farms and machine systems in four Swedish regions, to determine the available time for baling, baled straw ratio and annual variation in both. The hourly grain moisture content of pre-harvested cereals and swathed straw was estimated using moisture models and real weather data for 22/23 years, and the results were used as input to a model for simulating harvesting and baling operations. Expected available baling time during August and September was estimated to be 39–49%, depending on region, with large annual variation (standard deviation 22%). The average baling coefficient was estimated to be 80–86%, with 1400 t·year−1 harvestable straw and 15 t·h−1 baling capacity, and the annual variation was also considerable (s.d. 20%).

Virtual Minimization of Residual Stress and Deflection Error in the Five-Axis Milling of Turbine Blades

To simulate and analyse the real machined parts in virtual environments, virtual machining systems are applied to the production processes. Due to friction, chip forming, and the heat produced in the cutting zone, parts produced using machining operation have residual stress effects. The machining force and machining temperature can cause the deflection error in the machined turbine blades, which should be minimized to increase the accuracy of machined blades. To minimize the residual stress and deflection error of machined parts, optimized machining parameters can be obtained. In the present research work, the application of a virtual machining system is presented to predict and minimize the residual stress and deflection error in a five-axis milling operations of turbine blades. In order to predict the residual stress and deflection error in machined turbine blades, finite element analysis is implemented. Moreover, to minimize the residual stress and deflection error in machined turbine blades, optimized parameters of machining operations are obtained by using a genetic algorithm. To validate the research work, experimentally determining residual stress by using a X-ray diffraction method from the machined turbine blades is compared with the finite element results obtained from the virtual machining system. Also, in order to obtain the deflection error, the machined blades are measured by using the CMM machines. Thus, the accuracy and reliability of machined turbine blades can be increased by analysing and minimizing the residual stress and deflection error in virtual environments.

Joint optimization of mission abort and component switching policies for multistate warm standby systems

Safety-critical systems have been widely applied in various practical engineering fields to perform specific missions. Failures of such systems will lead to significant damage, thus, mission abort polices can be implemented to enhance system survivability. Warm standby is one of the most commonly used techniques in mission-critical and safety-critical systems to reduce failure risk during mission execution. Switching between working component and standby component is a feasible way to balance component degradation and extend system lifetime. Motivated by these results, we propose a joint optimization model that captures both component switching and mission abort policies for multistate warm standby systems. The optimal component switching and mission abort policies based on component condition are obtained to balance the trade-off between mission success probability and system survivability and to minimize the long-run expected total economic loss. A numerical study of virtual machine system is conducted to demonstrate the proposed model and obtained results.

Small‐signal analysis of multiple virtual synchronous machines to enhance frequency stability of grid‐connected high renewables

The virtual synchronous machine technology is considered as an important technique to effectively control the shortcomings of renewable energy-based power electronics interfaces, providing backup inertia and regulating grid stability. Conventionally, the virtual synchronous machine with a large capacity is responsible for controlling the entire grid stability against renewable penetration. It is usually operated as a centralised control system. But what if virtual synchronous machines with small capacities are independently operated by their additional droop control schemes, and will they present better performance than the single virtual synchronous machine? This study proposes the multiple virtual synchronous machine system with different active power-frequency (P-f) droop characteristics to improve inertia support regarding frequency stability improvement. The comprehensive small-signal modelling of the multiple virtual synchronous machine unit is designed to include the additional P-f droop characteristics. Then, the dynamic characteristics (steady-state and transient responses) and static stability of the multiple virtual synchronous machines are compared with the single virtual synchronous machine at the same rated capacity in both eigenvalue/sensitivity-domain and time-domain analysis. The obtained results reveal that the system with the presence of several virtual synchronous machines is more stable than the system with single virtual synchronous machine, maintaining stable and secure system operation during the contingency.

An investigation into the method of energy monitoring and reduction for machining systems

Improving the energy efficiency of machining systems (MSs) is an urgent and challenging issue in sustainable manufacturing. Considering the dynamics and uncertainty of MSs, a five-dimension model is developed to monitor and reduce the energy consumption (EC) of MSs. In this model, the automatic acquisition of EC data from physical MSs is driven by Radio Frequency IDentification (RFID) read events. The EC service model provides monitoring and scheduling services for reducing the EC of physical MSs by connecting with virtual MSs in real time. The procedures of the EC reduction method for the MSs are presented. A case study of MSs for auto parts was conducted to illustrate the proposed method. The results showed that the time and EC of MSs were reduced by 148 s and 49.67 KJ, respectively.

An Improved Efficient Dynamic Load Balancing Scheme Under Heterogeneous Networks in Hybrid Cloud Environment

In the rapid development of computer network technology. The cloud computing is a novel technology had become a highly demanded service due to several new challenges to all organizations the advantages of high computing power, cost of services, scalability, accessibility and availability. However, Cloud computing supports virtual machines system is more complex while dispatching variety of tasks to server’s applications simultaneously. That dispatching tasks to the servers is a challenge since there has a large number of applications in the heterogeneous cloud environment servers, all application services need to cooperate with each other in the cloud computing environment network. The huge number of tasks, an appropriate and effective scheduling algorithm is to allocate these tasks to appropriate servers within the minimum completion time, and to achieve the load balancing of performance workload of the cloud system. In this paper, we present a novel improved efficient dynamic load balancing scheme to organizing the virtualized resources algorithm, called Improved Efficient Scheme (IES) algorithm in the cloud computing network. The main concept of the IES algorithm is to allocate the tasks to server host by comparing all value of makespan time of the server nodes between each task. Basically, the IES algorithm can obtain better task completion time than previous works and can achieve dynamic load balancing in cloud computing environment.