With the increasing attraction of unmanned aerial vehicles (UAVs) in civil, public, and military applications, multi-UAV systems can perform environmental and disaster monitoring, border surveillance, and search and rescue. It is foreseen that these multi-UAV-based applications will be an important trend for edge computing scenarios. However, due to UAVs’ limited energy supplies as well as their continuous increase in the number of sensors, energy efficiency is a critical issue in multi-UAV systems. We believe that the fusion of edge computing and cloud computing can provide effective support for energy savings. This article presents an energy-efficient edge cloud architecture called RESERVE for intelligent multi-UAV. Under RESERVE, we study the energy-efficient computation offloading decision-making problem in a decentralized manner. The problem is formulated as a three-layer game in which the discretionary approach to reaching Nash Equilibrium is presented. Based on the proposed game, we design decentralized algorithms for two different cases. The algorithms can both achieve Nash Equilibrium. Furthermore, we propose a decentralized computation offloading mechanism and analyze the performance of the game by its efficiency ratio. We conduct simulation experiments and design a framework prototype. Evaluation results demonstrate that the proposed game methods can achieve more than 30 percent extra energy consumption reduction compared with the state-of-the-art decentralized algorithm and less than 10 percent performance loss relative to the centralized solution. The prototype framework we have developed proves the concept we propose.

Database queries can contain multiple predicates. The optimization of conjunctive predicates is still vital to the overall performance of analytic data processing tasks. Prior work proposes several memory-efficient storage layouts, e.g., BitWeaving and ByteSlice, to significantly accelerate predicate evaluation, as circuit-level intra-cycle parallelism available in modern CPUs can be exploited such that the total number of instructions can be dramatically reduced. However, the performance potential of conjunctive predicates has not been harvested yet under such storage layouts as there is no accurate cost model to provide necessary insights that guide the optimization process. In this paper, we propose a hybrid empirical/analytical cost model (Understanding) to unveil the performance characteristics of such storage layouts when applying to predicate evaluation. Our cost model takes into account effect of non-linear factors, e.g., cache miss and branch misprediction, and easily applies to different CPUs. The main finding from our cost model is to distinguish high-cost instruction (which suffers from cache miss and/or branch misprediction) from low-cost instruction (which enjoys cache hit and correct branch prediction) in the context of predicate evaluation under these storage layouts. Guided by such a finding, we propose a simple execution scheme Hebe (Optimizing), which is order-oblivious while maintaining high performance. Hebe is attractive to the query optimizer (QO), as the QO does not need to go through a sampling process to decide the optimal evaluation order in advance. The intuition behind Hebe is to significantly reduce the number of high-cost instructions while keeping low-cost instructions unchanged. Our finding from Hebe sheds light on the importance of accurate cost model that guide us to derive an efficient execution scheme for query processing on modern CPUs.

The optimization of conjunctive predicates is still critical to the overall performance of analytic data processing tasks, especially on a denormalized table, where queries with time-consuming joins on the original normalized tables are converted into simple scans. Existing work relies on the query optimizer to do the selectivity estimation and then produce the optimal evaluation order of predicates. In this paper, we argue for an order-oblivious approach, based on memory-efficient storage layouts. Accordingly, we propose Hebe, a simplified execution scheme which is attractive to the query optimizer, as it does not need to go through a sampling process to determine an optimal evaluation order of predicates. Compared with the state-of-theart implementation with the optimal evaluation order, Hebe can also achieve up to 153% performance improvement.

From the perspective of economic and efficient benefits, modern data center oversubscribes power supplies to deploy as many servers as possible. The oversubscription is based on the varied loads among servers to modulate power demand. Nevertheless, power oversubscription has potential threats to system availability, the data center may collapse as a result of overloading. Current solutions to the oversubscription usually focus on managing the datacenter workload to avoid the peak power demand time in the data center. However, none of the current research considers the influence of the failure of the power or utility components, where the component failure may affect the effectiveness of these strategies. Meanwhile, none of these current research can answer the question that how many servers should be deployed in the data centers under an availability constraint. In this paper, we propose quantitative availability analysis of hierarchical datacenter under power oversubscription. To this end, we use Markov chain and Stochastic Reward Net (SRN) to model the failure and repair processes of data center components. The servers at the bottom level are distributed in two pools: main pool and backup pool, where running servers are in main pool and turned-off servers in backup pool. Migration from backup pool to main pool is conducted once any running server fails. SRNs are implemented to model these two pools, and Markov chain is used to model the components in the upper level. The evaluation is based on the real-life Google and Wikipedia traces. The result shows the relationship between oversubscription and data center availability, which can guide the data center operators to choose the appropriate oversubscription ratio under the availability constraint.

To reduce the operation cost incurred by the rapidly growing energy consumption in internet data centers (IDCs), more and more internet service providers have spontaneously started using energy storage in various forms. The approach of energy storage is used to store cheap electricity energy when the electricity price from smart gird is low or the renewable energy is used. There are two typical forms of energy storage equipped in many IDCs, i.e., the battery storage and thermal energy storage. Recent work shows the energy storage can significantly reduce the operation cost for IDCs. However, the cost of the energy storage devices are still at a high level, and it may increase the operation cost for IDCs. In this paper, we investigate the comprehensive understanding on the operation cost reduction for IDCs using the energy storage. To this end, we conduct a quantitative analysis on the normalized electricity price in the two energy storage forms. The experiments demonstrate that the cost of the storage devices and renewable energy supply are largely affected by the storage capacity and the location of data centers, and we conclude that it does not always reduce operation cost using energy storage for IDCs.

Content service is a type of Internet cloud service that provides end-users plentiful contents. To ensure high performance for content delivering, content service utilizes a technology known as content multihoming: contents are generated from multiple geographically distributed data centers and delivered by multiple distributed content distribution networks (CDNs). The electricity costs for data centers and the usage costs for CDNs are major contributors to the contents service cost. As electricity prices vary across data centers and usage costs vary across CDNs, scheduling data centers and CDNs has a tremendous consequence for optimizing content service cost. In this paper, we propose a novel framework named Cost Optimization for Internet Content Multihoming (COMIC). COMIC dynamically balances end-users' loads among data centers and CDNs so as to minimize the content service cost. Using real-life electricity prices and CDN traces, the experiments demonstrate that COMIC effectively reduces the content service cost by more than 20 percent.

In cybernetics filed, the benefit of event-triggered control reaches a consensus, but in practice the event-triggered control is hampered by the lack of a system theory. This paper provides a Cyber-Physical System (CPS) viewpoint of the relation between the controller and the physical plant to explain the event detection logic of the event-triggered control. The relation between the controller and the physical plant is considered as communication, and based on this communication process the controller and the physical plant exchange the information entropy and thermodynamic entropy. The physical plant generates the thermodynamic entropy and the controller provides the negative thermodynamic entropy based on the predictability of the mathematical model for the physical plant, where there is an entropy balance process between the thermodynamic entropy and information entropy. The adjustment of the controller cycle period is the process to adjust the entropy balance process, which implies the event detection logic.