Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.

At present, security is significant for individuals and organizations. All information need security to prevent theft, leakage, alteration. Security must be guaranteed by applying some or combining cryptography algorithms to the information. Encipherment is the method that changes plaintext to a secure form called cipherment. Encipherment includes diverse types, such as symmetric and asymmetric encipherment. This study proposes an improved version of the advanced encryption standard (AES) algorithm called optimized advanced encryption standard (OAES). The OAES algorithm utilizes sine map and random number to generate a new key to enhance the complexity of the generated key. Thereafter, multiplication operation was performed on the original text, thereby creating a random matrix (4×4) before the five stages of the coding cycles. A random substitution-box (S-Box) was utilized instead of a fixed S-Box. Finally, we utilized the eXclusive OR (XOR) operation with digit 255, also with the key that was generated last. This research compared the features of the AES and OAES algorithms, particularly the extent of complexity, key size, and number of rounds. The OAES algorithm can enhance complexity of encryption and decryption by using random values, random S-Box, and chaotic maps, thereby resulting in difficulty guessing the original text.

Cloud computing is a model for delivering information technology services, wherein resources are retrieved from the Internet through web-based tools and applications instead of a direct connection to a server. The capability to provision and release cloud computing resources with minimal management effort or service provider interaction led to the rapid increase of the use of cloud computing. Therefore, balancing cloud computing resources to provide better performance and services to end users is important. Load balancing in cloud computing means balancing three important stages through which a request is processed. The three stages are data center selection, virtual machine scheduling, and task scheduling at a selected data center. User task scheduling plays a significant role in improving the performance of cloud services. This paper presents a review of various energy-efficient task scheduling methods in a cloud environment. A brief analysis of various scheduling parameters considered in these methods is also presented. The results show that the best power-saving percentage level can be achieved by using both DVFS and DNS.

Cloud computing is a model that relies on sharing computing resources rather than having local servers or personal devices to handle applications. Numerous studies have shown that by replacing traditional computing with cloud infrastructure, the total power consumption can be decreased because cloud computing power consumption is influenced by four key cloud factors: dynamic provisioning, multi-tenancy, server utilization, and data center efficiency. Considering the huge amount of energy consumed by computing equipment in the last several years, energy consumption and sustainability have become one of the main areas of research. Thus, obtaining high performance at a reduced cost in cloud environments has reached a turning point where computing power is no longer the most important concern. Calculate the amount of energy consumption in a real cloud computing infrastructure is difficult. Thus, simulation tools have been developed in recent years to enable cloud computing researchers to analyze a cloud computing environment. This paper will introduce and compare the two popular cloud simulators: Cloud Sim and Green Cloud. Both simulators provide scenarios for cloud computing based on utilization, virtualization, and load and energy consumption of the server. The comparison showed that the Green Cloud simulator is more advantageous than the Cloud Sim simulator based on an accurate measurement of energy consumption in a cloud computing environment.