Abstract: Techniques for optimizing power and computational density of data centers are described. According to various embodiments, a benchmark test is performed by a computer data center system. Thereafter, transaction information and power consumption information associated with the performance of the benchmark test are accessed. An efficiency metric value is then generated based on the transaction information and the power consumption information. In some implementations, the efficiency metric value indicates a number of transactions executed via the computer data center system during a specific time period per unit of power consumed in executing the transactions during the specific time period. The generated efficiency metric value is then compared to a target threshold value. Thereafter, a performance summary report indicating the generated efficiency metric value, and indicating a result of the comparison of the generated efficiency metric value to the target value, is generated.

Abstract: Techniques for optimizing power and computational density of data centers are described. According to various embodiments, a benchmark test is performed by a computer data center system. Thereafter, transaction information and power consumption information associated with the performance of the benchmark test are accessed. A service efficiency metric value is then generated based on the transaction information and the power consumption information, the service efficiency metric value indicating a number of transactions executed via the computer data center system during a specific time period per unit of power consumed in executing the transactions during the specific time period. The generated service efficiency metric value is then compared to a target threshold value. Thereafter, a performance summary report indicating the generated service efficiency metric value, and indicating a result of the comparison of the generated service efficiency metric value to the target value, is generated.

Abstract: Techniques for optimizing power and computational density of data centers are described. According to various embodiments, a benchmark test is performed by a computer data center system. Thereafter, transaction information and power consumption information associated with the performance of the benchmark test are accessed. A service efficiency metric value is then generated based on the transaction information and the power consumption information, the service efficiency metric value indicating a number of transactions executed via the computer data center system during a specific time period per unit of power consumed in executing the transactions during the specific time period. The generated service efficiency metric value is then compared to a target threshold value. Thereafter, a performance summary report indicating the generated service efficiency metric value, and indicating a result of the comparison of the generated service efficiency metric value to the target value, is generated.

Abstract: Techniques for optimizing power and computational density of data centers are described. According to various embodiments, a benchmark test is performed by a computer data center system. Thereafter, transaction information and power consumption information associated with the performance of the benchmark test are accessed. A service efficiency metric value is then generated based on the transaction information and the power consumption information, the service efficiency metric value indicating a number of transactions executed via the computer data center system during a specific time period per unit of power consumed in executing the transactions during the specific time period. The generated service efficiency metric value is then compared to a target threshold value. Thereafter, a performance summary report indicating the generated service efficiency metric value, and indicating a result of the comparison of the generated service efficiency metric value to the target value, is generated.

Abstract: Techniques for optimizing power and computational density of data centers are described. According to various embodiments, a benchmark test is performed by a computer data center system. Thereafter, transaction information and power consumption information associated with the performance of the benchmark test are accessed. A service efficiency metric value is then generated based on the transaction information and the power consumption information, the service efficiency metric value indicating a number of transactions executed via the computer data center system during a specific time period per unit of power consumed in executing the transactions during the specific time period. The generated service efficiency metric value is then compared to a target threshold value. Thereafter, a performance summary report indicating the generated service efficiency metric value, and indicating a result of the comparison of the generated service efficiency metric value to the target value, is generated.

Abstract: Techniques for optimizing power and computational density of data centers are described. According to various embodiments, a benchmark test is performed by a computer data center system. Thereafter, transaction information and power consumption information associated with the performance of the benchmark test are accessed. A service efficiency metric value is then generated based on the transaction information and the power consumption information, the service efficiency metric value indicating a number of transactions executed via the computer data center system during a specific time period per unit of power consumed in executing the transactions during the specific time period. The generated service efficiency metric value is then compared to a target threshold value. Thereafter, a performance summary report indicating the generated service efficiency metric value, and indicating a result of the comparison of the generated service efficiency metric value to the target value, is generated.

Abstract: Techniques for optimizing power and computational density of data centers are described. According to various embodiments, a benchmark test is performed by a computer data center system. Thereafter, transaction information and power consumption information associated with the performance of the benchmark test are accessed. A service efficiency metric value is then generated based on the transaction information and the power consumption information, the service efficiency metric value indicating a number of transactions executed via the computer data center system during a specific time period per unit of power consumed in executing the transactions during the specific time period. The generated service efficiency metric value is then compared to a target threshold value. Thereafter, a performance summary report indicating the generated service efficiency metric value, and indicating a result of the comparison of the generated service efficiency metric value to the target value, is generated.

Abstract: Techniques for optimizing power and computational density of data centers are described. According to various embodiments, a benchmark test is performed by a computer data center system. Thereafter, transaction information and power consumption information associated with the performance of the benchmark test are accessed. A service efficiency metric value is then generated based on the transaction information and the power consumption information, the service efficiency metric value indicating a number of transactions executed via the computer data center system during a specific time period per unit of power consumed in executing the transactions during the specific time period. The generated service efficiency metric value is then compared to a target threshold value. Thereafter, a performance summary report indicating the generated service efficiency metric value, and indicating a result of the comparison of the generated service efficiency metric value to the target value, is generated.

Abstract: Techniques for optimizing power and computational density of data centers are described. According to various embodiments, a benchmark test is performed by a computer data center system. Thereafter, transaction information and power consumption information associated with the performance of the benchmark test are accessed. A service efficiency metric value is then generated based on the transaction information and the power consumption information, the service efficiency metric value indicating a number of transactions executed via the computer data center system during a specific time period per unit of power consumed in executing the transactions during the specific time period. The generated service efficiency metric value is then compared to a target threshold value. Thereafter, a performance summary report indicating the generated service efficiency metric value, and indicating a result of the comparison of the generated service efficiency metric value to the target value, is generated.

Abstract: Techniques for optimizing power and computational density of data centers are described. According to various embodiments, a benchmark test is performed by a computer data center system. Thereafter, transaction information and power consumption information associated with the performance of the benchmark test are accessed. A service efficiency metric value is then generated based on the transaction information and the power consumption information, the service efficiency metric value indicating a number of transactions executed via the computer data center system during a specific time period per unit of power consumed in executing the transactions during the specific time period. The generated service efficiency metric value is then compared to a target threshold value. Thereafter, a performance summary report indicating the generated service efficiency metric value, and indicating a result of the comparison of the generated service efficiency metric value to the target value, is generated.