Cloud computing is a rapidly growing paradigm which has evolved from having a monolithic to microservices architecture. The importance of cloud data centers has expanded dramatically in the previous decade, and they are now regarded as the backbone of the modern economy. Cloud-based microservices architecture is incorporated by firms such as Netflix, Twitter, eBay, Amazon, Hailo, Groupon, and Zalando. Such cloud computing arrangements deal with the parallel deployment of data-intensive workloads in real time. Moreover, commonly utilized cloud services such as the web and email require continuous operation without interruption. For that purpose, cloud service providers must optimize resource management, efficient energy usage, and carbon footprint reduction. This study presents a conceptual framework to manage the high amount of microservice execution while reducing response time, energy consumption, and execution costs. The proposed framework suggests four key agent services: (1) intelligent partitioning: responsible for microservice classification; (2) dynamic allocation: used for pre-execution distribution of microservices among containers and then makes decisions for dynamic allocation of microservices at runtime; (3) resource optimization: in charge of shifting workloads and ensuring optimal resource use; (4) mutation actions: these are based on procedures that will mutate the microservices based on cloud data center workloads. The suggested framework was partially evaluated using a custom-built simulation environment, which demonstrated its efficiency and potential for implementation in a cloud computing context. The findings show that the engrossment of suggested services can lead to a reduced number of network calls, lower energy consumption, and relatively reduced carbon dioxide emissions.

Robust chaotic systems, due to their inherent properties of mixing, ergodicity, and larger chaotic parameter space, constitute a perfect candidate for cryptography. This paper reports a novel method to generate random numbers using modified robust logistic map (MRLM). The non-smooth probability distribution function of robust logistic map (RLM) trajectories gives an un-even binary distribution in randomness test. To overcome this disadvantage in RLM, control of chaos (CoC) is proposed for smooth probability distribution function of RLM. For testing the proposed design, cryptographic random numbers generated by MRLM were vetted with National Institute of Standards and Technology statistical test suite (NIST 800-22). The results showed that proposed MRLM generates cryptographically secure random numbers (CSPRNG).

Grid computing is the enabling technology for high performance computing in scientific and large scale computing applications. A number of powerful parallel systems have been built with increasing number of processors and multi-core solutions in order to meet the increased computational demands. Scheduling is a fundamental issue in achieving high performance on computational Grids. A Grid scheduling system must meet the functional requirements of heterogeneous domains (e.g., user domains, application domains, and network domains). Sometimes these requirements conflict with one another. Moreover, a Grid scheduling system must also satisfy non-functional requirements, such as reliability, efficiency (in terms of time consumption), effectiveness in resource utilization, and robustness.Many frameworks have been proposed for Grid scheduling system. Agents are able to behave autonomously, intelligently, learn from environment, and adaptability. Agent-based Grid scheduling is concerned with the design and development of software agents for the resource discovery, job scheduling, resource management, and allocation of computing resources on a computational Grid. This paper presents a new Agent based Grid scheduling framework which possesses high degree of robustness, performance, efficiency, scalability and heterogeneity.