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The growing popularity of mobile and cloud computing raises new challenges related to energy efficiency. This work evaluates four various SQL and NoSQL database solutions in terms of energy efficiency. Namely, Cassandra, MongoDB, Redis, and MySQL are taken into consideration. This study measures energy efficiency of the chosen data storage solutions on a selected set of physical and virtual computing nodes by leveraging Intel RAPL (Running Average Power Limit) technology. Various database usage scenarios are considered in this evaluation including both local usage and remote offloading. Different workloads are benchmarked through the use of YCSB (Yahoo! Cloud Serving Benchmark) tool. Extensive experimental results show that (i) Redis and MongoDB are more efficient in energy consumption under most usage scenarios, (ii) remote offloading saves energy if the network latency is low and destination CPU is significantly more powerful, and (iii) computationally weaker CPUs may sometimes demonstrate higher energy efficiency in terms of J/ops. An energy efficiency measurement framework is proposed in order to evaluate and compare different database solutions based on the obtained experimental results.

The performance of a computer network is commonly measured by the maximum minimum time required to move a certain amount of data between any 2 nodes in the network. Due to the advances in technology, certain links in the network may be upgraded, for instance to optical fibre links, so that better performance can be achieved. In this paper, we study the LINK UPGRADE problem for networks. We first show that the LINK UPGRADE problem is NP-complete. We also show that, a closely related problem, the MINIMUM COST LINK UPGRADE problem is NP-complete even if the underlying topology of the network is a linear array. However, for certain classes of networks, the LINK UPGRADE problem can be solved in polynomial time. For general networks, we provide effective heuristics for the above problems.