Traditionally, the primary performance goal of computer systems has focused on reducing the execution time of applications while increasing throughput. This performance goal has been mostly achieved by the development of high-density computer systems. As witnessed recently, these systems provide very powerful processing capability and capacity. They often consist of tens or hundreds of thousands of processors and other resource-hungry devices. The energy consumption of these systems has become a major concern. In this paper, we address the problem of scheduling precedence-constrained parallel applications on multiprocessor computer systems and present two energy-conscious scheduling algorithms using dynamic voltage scaling (DVS). A number of recent commodity processors are capable of DVS, which enables processors to operate at different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. To effectively balance these two performance goals, we have devised a novel objective function and a variant from that. The main difference between the two algorithms is in their measurement of energy consumption. The extensive comparative evaluations conducted as part of this work show that the performance of our algorithms is very compelling in terms of both application completion time and energy consumption.

A blockchain is a form of distributed ledger technology where transactions as data state changes are permanently recorded securely and transparently without the need for third parties. Besides, introducing smart contracts to the blockchain has added programmability, revolutionizing the software ecosystem toward decentralized applications. Although promising, the usability of smart contracts is primarily limited to on-chain data without access to the external systems (i.e., off-chain) where real-world data and events reside. This connectability to off-chain data for smart contracts and blockchain is an open practical problem referred to as the “oracle problem” and is defined as how real-world data can be transferred into/from the blockchain. Hence, Blockchain oracles are introduced and implemented in the form of application programming interfaces connecting the real world to the blockchain for mitigating such a limitation. This article studies and analyzes how blockchain oracles provide final feedback (i.e., outcome) to smart contracts and survey blockchain oracle technologies and mechanisms regarding data integrity and correctness. Since the existing solutions are extensive in terms of characteristics and usage, we investigate their structure and principles by classifying the blockchain oracle implementation techniques into two major groups voting-based strategies and reputation-based ones. The former mainly relies on participants’ stakes for outcome finalization, while the latter considers reputation and performance metrics in conjunction with authenticity-proof mechanisms for data correctness and integrity. We present the result of this classification with a thorough discussion of the state of the art and provide the remaining challenges and future research directions in the end.