Cloud-edge-terminal computing has become a promising approach to improve resource utilization and service for communication and network applications. It is crucial to rationalize task offloading and resource allocation to better satisfy quality of experience (QoE). However, without strong incentives, cloud servers (CSs) and edge servers (ESs) may be reluctant to assist user terminals (UTs). In this paper, we propose TriStack, a three-stage Stackelberg game-based offloading incentive mecha-nism for the cooperation and competition among the cloud server, edges servers and user terminals. We optimize the benefits of game players under the comprehensive consideration of economic factors, delay sensitivity and energy consumption cost, and prove the existence of Nash equilibrium (NE) at each stage using backward induction. In addition, TriStack is designed to find Stackelberg equilibrium (SE) that maximizes the benefits of CS, ESs, and UTs. The simulation results show that TriStack has good convergence. Compared with uniform pricing strategy and two-stage game, TriStack can enhance players' benefits by 11.45% and 20.07 %, respectively.

The Unmanned Aerial Vehicle (UAV) has recently become a cost-effective and flexible data collection tool for Wireless Sensor Networks (WSNs). However, UAVs are typically energy-constrained with limited battery capacity, so their hover positions and flight trajectory during the data collection journey require careful planning to be efficient to have longer serving time and more coverage. In addition, reconfigurable antennas can provide adjustable coverage area for UAVs, which can further improve the efficiency of data collection. In order to address the efficient WSN data collection problem using a UAV equipped with reconfigurable antenna, in this paper, we propose BROAD, an iterative algorithm that can complete all data collection under sensor energy constraints while minimizing the total UAV energy consumption. Due to the non-convexity of the data collection problem, to determine the UAV hovering position, antenna beamwidth, and UAV travel path, we decompose the original problem into three sub-problems. We first use the linearized objective function to jointly optimize hover positions and beamwidth, then determine the covered sensor allocation. Finally, we use the travel salesman model to determine the UAV visit order. Through extensive simulations comparing with two benchmark algorithms under various parameters, BROAD manages to provide 27.5% more energy savings, which is very close to the theoretical lower bound.

Stream processing systems commonly work with auto-scaling to ensure resource efficiency and quality of service (QoS). Existing auto-scaling solutions lack accuracy in resource allocation because they rely on static QoS-resource models that fail to account for high workload variability and use indirect metrics with much distractive information. Moreover, different types of QoS metrics present different characteristics and thus need individual auto-scaling methods. In this paper, we propose a versatile auto-scaling solution for operator-level parallelism configuration, called AuTraScale+, to meet the throughput, processing-time latency, and event-time latency targets. AuTraScale+ follows the Bayesian optimization framework to make scaling decisions. First, it uses the Gaussian process model to eliminate the negative influence of uncertain factors on the performance model accuracy. Second, it leverages the expected improvement-based (EI-based) acquisition function to search and recommend the optimal configuration quickly. Besides, to make a more accurate scaling decision when the new model is not ready, AuTraScale+ proposes a transfer learning algorithm to estimate the benefits of all configurations at a new rate based on existing models and then recommend the optimal one. We implement and evaluate AuTraScale+ on the Flink platform. The experimental results on three representative workloads demonstrate that compared with the state-of-the-art methods, AuTraScale+ can reduce 66.6% and 36.7% resource consumption, respectively, in the scale-down and scale-up scenarios while achieving their throughput and processing-time latency targets. Compared with other methods of optimizing event-time latency, AuTraScale+ saves 26.9% of resources on average.

Power optimization for data center networks (DCNs) has recently received increasing research attention, since a DCN can account for 10 to 20 percent of the total power consumption of a data center. An effective power-saving approach for DCNs is traffic consolidation, which consolidates traffic flows onto a small set of links and switches such that unused network devices can be shut down dynamically for power savings. While this approach has shown great promise, existing solutions are mostly centralized and do not scale well for large-scale DCNs. In this article, we propose DISCO, aDIStributed traffic flowCOnsolidation framework, with correlation analysis and delay constraints, for large-scale data center network. DISCO features two distributed traffic consolidation algorithms that provide different trade-offs (as desired by different DCN architectures) between scalability, power savings, and network performance. First, a flow-based algorithm is proposed to conduct consolidation for each flow individually, with greatly improved scalability. Second, an even more scalable switch-based algorithm is proposed to consolidate flows on each individual switch in a distributed fashion. We evaluate the DISCO algorithms both on a hardware testbed and in large-scale simulations with real DCN traces. The results show that, compared with state-of-the-art centralized solutions, DISCO can achieve nearly the same power savings while decomposing the global problem into sub-problems that are three orders of magnitude smaller. As a result, DISCO can run$\mathbf {10^4}$to$\mathbf {10^6}$times faster for a DCN at the scale of 10K servers. The convergence of DISCO has also been proven theoretically and examined experimentally.

In the era of cloud computing, data center network (DCN) can consume a significant amount of power (e.g., 10 to 20 percent) in large-scale data centers. To reduce the power consumption of DCN, traffic consolidation has been recently proposed as an effective approach to reduce the number of DCN devices in use. However, existing consolidation approaches do not sufficiently consider the flow completion time (FCT) requirement. On one hand, missing the FCT deadlines can cause serious violation of service-level agreement, especially for delay-sensitive networking services, such as web search and E-commerce. On the other hand, keeping all the devices on to make FCTs much shorter than the desired requirements is unnecessary because 1) users may not be able to perceive the difference, and 2) such a greedy strategy can lead to unnecessarily high DCN power consumption and thus more electricity costs. In this paper, we propose FCTcon, a dynamic FCT control strategy for DCN power optimization. FCTcon is designed rigorously based on control theory to dynamically control the FCT of delay-sensitive traffic flows exactly to the requirements, such that the desired FCT performance is guaranteed while the maximum amount of DCN power savings can be achieved. Results from both hardware experiments and simulation evaluation demonstrate that, compared to the state-of-the-art DCN power optimization schemes, FCTcon can improve the DCN FCT performance, while achieving nearly the same or even more power savings. Consequently, FCTcon can result in more than 22.0 to 62.2 percent extra net profits for a data center with 50K servers. In addition, we further propose two extended designs of FCTcon to handle the coflow abstraction recently proposed for DCN, which successfully reduce the coflow deadline miss ratio by 12 to 15 percent.

In a Vehicular Cyber Physical System (VCPS), ensuring the real-time delivery of safety messages is an important research problem for Vehicle to Vehicle (V2V) communication. Unfortunately, existing work relies only on one or two pre-selected control channels for safety message communication, which can result in poor packet delivery and potential accident when the vehicle density is high. If all the available channels can be dynamically utilized when the control channel is having severe contention, then safety messages can have a much better chance to meet their real-time deadlines. In this article, we propose MC-Safe, a multi-channel V2V communication framework that monitors all the available channels and dynamically selects the best one for safety message transmission. During normal driving, MC-Safe monitors periodic beacons sent by other vehicles and estimates the communication delay on all the channels. Upon the detection of a potential accident, MC-Safe leverages a novel channel negotiation scheme that allows all the involved vehicles to work collaboratively, in a distributed manner, for identifying a communication channel that meets the delay requirement. MC-safe also features a novel coordinator selection algorithm that minimizes the delay of channel negotiation. Once a channel is selected, all the involved vehicles switch to the same selected channel for real-time communication with the least amount of interference. Our evaluation results both in simulation and on a hardware testbed with scaled cars show that MC-Safe outperforms existing single-channel solutions and other well-designed multi-channel baselines by having a 23.4% lower packet delay on average compared with other well-designed channel selection baselines.

Autonomous vehicles often need human driver to take over in some complicated conditions. Such a sudden takeover could jeopardize the vehicle's safety and stability if not han-dled properly. Hence, if the driver's takeover intention can be recognized as early as possible, the vehicle can have sufficient time to make important takeover preparation. The existing in-car monitoring systems are mostly based on camera, which have several key limitations, such as brightness condition and motion obscurity. On the other hand, WiFi-based wireless sensing has recently shown a great promise in human activity recognition, but mainly for large-scale movements performed in the room environment. In this paper, we propose WiDrive, a real-time in-car driver activity recognition system based on Channel State Information (CSI) changes of WiFi signals. WiDrive consists of three major components: A novel algorithm to extract small-scale in-car human activity features, a real-time recognition system based on Hidden Markov Model (HMM), and an online adaptation algo-rithm to adapt for different drivers and vehicles. We implement WiDrive with commercial WiFi devices and evaluate it in real cars. Our results show that WiDrive has an average recognition accuracy of 91.3% and improves the takeover safety.

Ensuring the real-time delivery of safety messages is an important research problem for Vehicle to Vehicle (V2V) communication. Unfortunately, existing work relies only on one or two pre-selected control channels for safety message communication, which can result in poor packet delivery and potential accident when the vehicle density is high. If all the available channels can be dynamically utilized when the control channel is having severe contention, safety messages can have a much better chance to meet their real-time deadlines. In this paper, we propose MC-Safe, a multi-channel V2V communication framework that monitors all the available channels and dynamically selects the best one for safety message transmission. MC-Safe features a novel channel negotiation scheme that allows all the vehicles involved in a potential accident to work collaboratively, in a distributed manner, for identifying a communication channel that meets the delay requirement. Our evaluation results both in simulation and on a hardware testbed with scaled cars show that MC-Safe outperforms existing single-channel solutions and other well-designed multi-channel baselines by having a 12.31% lower deadline miss ratio and an 8.21% higher packet delivery ratio on average.

Data center network (DCN) can consume a significant amount of power (e.g., 10% to 20%) in large-scale data centers. To reduce the power consumption of DCN, traffic consolidation has been recently proposed as an effective approach to reduce the number of DCN devices in use. However, existing consolidation approaches do not sufficiently consider the flow completion time (FCT) requirement. On one hand, missing the FCT deadlines can cause serious violation of service-level agreement, especially for delay-sensitive networking services, such as web search and E-commerce. On the other hand, keeping all the devices on to make FCTs much shorter than the desired requirements is unnecessary because 1) users may not be able to perceive the difference, and 2) such a greedy strategy can lead to unnecessarily high DCN power consumption and thus more electricity costs. In this paper, we propose FCTcon, a dynamic FCT control strategy for DCN power optimization. FCTcon is designed rigorously based on control theory to dynamically control the FCT of delay-sensitive traffic flows exactly to requirements, such that the desired FCT performance is guaranteed while the maximum amount of DCN power savings can be achieved. Results from both hardware experiments and simulation evaluations demonstrate that, compared to the state-of-the-art DCN power optimization schemes, FCTcon can improve the DCN FCT performance, while achieving nearly the same or even more power savings. Consequently, FCTcon can result in more than 22.0% to 62.2% extra net profits for a data center with 50K servers.

Recently, a lot of research efforts have been made to optimize the large amounts of energy consumed by different devices in data centers, including servers, cooling, and the data center network (DCN). Unfortunately, current research addresses these devices mostly in a separate manner, leading to inferior optimization results. This paper proposes PowerNetS, a power optimization framework that coordinates servers and DCN, as well as cooling, for minimized power consumption of a data center. PowerNetS leverages workload correlation analysis for more energy savings during server and traffic consolidations. More importantly, PowerNetS tries to change the DCN topology during server consolidation, in order to have more intra-server traffic and shorter flows that go through fewer switches. For example, two virtual machines previously located on two different servers can now be migrated to the same server, so that the flow between them no longer needs to use switches, which allows more devices to sleep for energy savings without network performance degradation. PowerNetS has been implemented on a physical testbed with 6 servers and 10 virtual switches that are configured using a production 48-port OpenFlow switch. Our evaluation with Wikipedia, Yahoo!, and IBM traces shows that PowerNetS can save up to 51.6% of energy by coordinating servers and DCN, which is 44.3% and 15.8% more than two state-of-the-art baselines, respectively. By further coordinating with cooling to utilize different cooling efficiencies at different locations within a data center, PowerNetS can achieve 8.8%-14.6% additional energy savings.

Power optimization has become a key challenge in the design of large-scale enterprise data centers. Existing research efforts focus mainly on computer servers to lower their energy consumption, while only few studies have tried to address data center networks (DCNs), which can account for 10-20 percent of the total energy consumption of a data center. In this paper, we propose CARPO, a correlation-aware power optimization algorithm that dynamically consolidates traffic flows onto a small set of links and switches in a DCN and then shuts down unused network devices for energy savings. In sharp contrast to existing work, CARPO is designed based on a key observation from the analysis of real DCN traces that the bandwidth demands of different flows do not peak at exactly the same time. As a result, if the correlations among flows are considered in consolidation, more energy savings can be achieved. In addition, CARPO integrates traffic consolidation with link rate adaptation for maximized energy savings. We implement CARPO on a hardware testbed composed of 10 virtual switches configured with a production 48-port OpenFlow switch and 8 servers. Our empirical results with traces from Wikipedia and Yahoo! data centers demonstrate that CARPO can save up to 50 percent of network energy for a DCN, while having only negligible delay increases. CARPO also outperforms two state-of-the-art baselines by 19.6 and 95 percent on energy savings, respectively. Our simulation results with a large-scale DCN also show that CARPO can achieve more energy savings than the baselines for typical DCN topologies, such as fat tree and BCube.

Power optimization is a challenging task for a large scale data centre. Among all the major power consumption contributors of the entire data centre, data centre networks (DCNs) can account for up to 20% of the total power consumption. To save power for DCNs, several different approaches have been proposed. Unfortunately, none of these approaches addresses the performance impact on the flow completion times (FCTs) of delay-sensitive flows. In fact, FCTs of the delay-sensitive flows have been identified as one of the most important service-level agreement metrics of the data centre networking services. In this paper, we propose Power FCT, a multi-dimensional DCN power saving scheme that jointly conducts traffic consolidation and leverages the different power states of multiple components inside the DCN switches, including CPU, network processor, switch fabric, packet buffer and cooling fans, to maximize the power savings for DCNs. In sharp contrast to existing work that neglects the performance impacts, PowerFCT features a novel FCT model and explicitly enforces the desired FCT requirements in the process of power optimization. As a result, the desired FCTs for delay-sensitive flows can be effectively maintained. Both hardware experiments and simulation evaluations show that PowerFCT can achieve the power savings up to 62% for DCNs, which outperforms two state-of-the-art schemes by 21% and 14%, respectively. For large-scale data centers, this savings can lead to millions of dollars of savings in operating expenses. In addition, PowerFCT has the lowest FCT miss ratio among all the solutions.

Data center power optimization has recently received a great deal of research attention. For example, server consolidation has been demonstrated as one of the most effective energy saving methodologies. Likewise, traffic consolidation has also been recently proposed to save energy for data center networks (DCNs). However, current research on data center power optimization focuses on servers and DCN separately. As a result, the optimization results are often inferior, because server consolidation without considering the DCN may cause traffic congestion and thus degraded network performance. On the other hand, server consolidation may change the DCN topology, allowing new opportunities for energy savings. In this paper, we propose PowerNetS, a power optimization strategy that leverages workload correlation analysis to jointly minimize the total power consumption of servers and the DCN. The design of PowerNetS is based on the key observations that the workloads of different servers and DCN traffic flows do not peak at exactly the same time. Thus, more energy savings can be achieved if the workload correlations are considered in server and traffic consolidations. In addition, PowerNetS considers the DCN topology during server consolidation, which leads to less inter-server traffic and thus more energy savings and shorter network delays. We implement PowerNetS on a hardware testbed composed of 10 virtual switches configured with a production 48-port OpenFlow switch and 6 servers. Our empirical results with Wikipedia, Yahoo!, and IBM traces demonstrate that PowerNetS can save up to 51.6% of energy for a data center. PowerNetS also outperforms two state-of-the-art baselines by 44.3% and 15.8% on energy savings, respectively. Our simulation results with 72 switches and 122 servers also show the superior energy efficiency of PowerNetS over the baselines.