A Model for Resource Sharing for Internet Data Center Providers within the Grid

Internet data center providers are still struggling to lower the operational costs of their data centers. One reason is the low utilization of servers over a long period of time during the day. The paper describes a system for optimizing the server resources within Internet data centers, which host different services such as web servers or enterprise resource planning systems. The system, called resource management system, allows Internet data center providers to allocate their resources in an economically efficient way. The results may indicate that there is free capacity or a lack of capacity. Based on the results, the resource management system can sell or purchase resources on the Grid. The idea behind this approach is to enable Internet data center providers to gradually transition from the current environment to an environment where utility computing is possible. Our approach separates between the local resource allocation and the external one (Grid)

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation research topics and seven performance management methods. Task allocation research areas are resource allocation, load-Balancing, and scheduling. Performance management includes monitoring and control, power and energy management, resource utilization optimization, quality of service management, fault management, virtual machine management, and network management. The study proposes new techniques to enhance cloud computing work allocation and performance management. Short-comings in each approach can guide future research. The research's findings on cloud data center task allocation and performance management can assist academics, practitioners, and cloud service providers in optimizing their systems for dependability, cost-effectiveness, and scalability. Innovative methodologies can steer future research to fill gaps in the literature

DYNAMIC RESOURCE ALLOCATION USING VIRTUAL MACHINES FOR CLOUD COMPUTING ENVIRONMENT

ABSTRACT Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of "skewness" to measure the unevenness in the multi-dimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance

FPGA Implementation of Advanced Encryption Standard

Security is a crucial parameter to be recognized with the improvement of electronic communication. Today most research in the field of electronic communication includes look into on security concern of communication. At present most by and large consumed and recognized standard for encryption of data is the Advanced Encryption Standard. AES was transformed to supplant the developing Data Encryption Standard. The AES calculation is fit for handling cryptographic keys which are of 256, 128, & 192 bits to encode & unscramble data in squares of 128 bits. The center of the calculation is made up of four key parts, which manage 8 bit data pieces. The whole 128 bit data to the calculation is dealt with into a 4 x 4 grid termed a state, to obtain the 8 bit square. Considering the complex nature of advance encryption standard (AES) algorithm, it requires a huge amount of hardware resources for its practical implementation. The extreme amount of hardware requirement makes its hardware implementation very burdensome. During this research, a FPGA scheme is introduced which is highly efficient in terms of resource utilization. In this scheme implementation of AES algorithm is done as a finite state machine (FSM). VHDL is used as a programming language for the purpose of design. Data path and control unit are designed for both cipher and decipher block, after that respective data path and control unit are integrated using structural modeling style of VHDL. Xilinx_ISE_14.2 software is being used for the purpose of simulating and optimizing the synthesizable VHDL code. The working of the implemented algorithm is tested using VHDL test bench wave form of Xilinx ISE simulator and resource utilization is also presented for a targeted Spartan3e XC3s500e FPGA