The continually increasing energy consumption represents a critical issue in modern heterogeneous computing systems. With the aid of dynamic voltage frequency scaling (DVFS), task scheduling is considered an effective software-based technique for reducing the total energy consumption and minimizing the overall schedule length (makespan). A natural solution is to reclaim the slack time in a given time-efficient schedule, which is also referred to as a "two-pass" method or a "rescheduling" method. A number of studies have focused on slack reclamation to achieve energy reductions through heuristics; although, these methods offer suboptimal solutions. In this article, the rescheduling optimization problem is formulated as a linear program for minimizing an energy objective function subject to precedence and deadline constraints implied in the given schedule. Two types of decision variables, ie, frequency duty factors and task intervals, are defined to set up the linear model. Consequently, an optimal solution to the problem can be provided in a straightforward manner by a linear programming solver, which suggests that such a rescheduling problem belongs to the P (polynomial time) class. The experimental results show the effectiveness of the proposed approach and demonstrate that the performance is superior to that of other competitive algorithms in terms of both energy saving and runtime efficiency. KEYWORDS dynamic voltage frequency scaling (DVFS), energy efficiency, heterogeneous computing system, linear programming, slack reclamation, task scheduling 1 INTRODUCTION A heterogeneous computing system (HCS) is a computing platform with diverse sets of heterogeneous computing resources connected by a high-speed network for processing parallel applications. 1 In recent decades, HCSs have been widely used in both scientific and commercial fields, such as supercomputing, cloud computing, and big data. For example, the world's fastest supercomputer "Sunway TaihuLight" has 40,960 computer nodes, each with 4 management processing elements (MPEs) and 256 computing processing elements (CPEs). Although it provides the most powerful computing capability, "Sunway TaihuLight" also consumes a tremendous amount of electrical energy, estimated at approximately 15,371 kW. 2,3At an approximate price of $0.1/kWh, its energy cost is approximately $13.46 million per year, which is far beyond the acceptable cost for many HCS operators. Furthermore, the energy cost of powering a typical data center doubles every five years. 4 In certain cases, the power cost may exceed the hardware purchase costs. 5 Besides, high energy consumption creates a number of environmental problems. 6,7 For example, in 2014, global data centers consumed up to 3% of the world's electricity production while causing 200 million metric tons of carbon emissions, thus accounting for approximately 2% of the world's greenhouse gas emissions. 8 On the other hand, a large portion of servers inside these data centers have relatively low average utilization efficiency. Observa...
Vehicular nodes are equipped with more and more sensing units, and a large amount of sensing data is generated. Recently, more and more research considers cooperative urban sensing as the heart of intelligent and green city traffic management. The key components of the platform will be a combination of a pervasive vehicular sensing system, as well as a central control and analysis system, where data-gathering is a fundamental component. However, the data-gathering and monitoring are also challenging issues in vehicular sensor networks because of the large amount of data and the dynamic nature of the network. In this paper, we propose an efficient continuous event-monitoring and data-gathering framework based on fog nodes in vehicular sensor networks. A fog-based two-level threshold strategy is adopted to suppress unnecessary data upload and transmissions. In the monitoring phase, nodes sense the environment in low cost sensing mode and generate sensed data. When the probability of the event is high and exceeds some threshold, nodes transfer to the event-checking phase, and some nodes would be selected to transfer to the deep sensing mode to generate more accurate data of the environment. Furthermore, it adaptively adjusts the threshold to upload a suitable amount of data for decision making, while at the same time suppressing unnecessary message transmissions. Simulation results showed that the proposed scheme could reduce more than 84 percent of the data transmissions compared with other existing algorithms, while it detects the events and gathers the event data.
Summary In Vehicular Ad hoc Networks (VANETs), because of the selfishness of the vehicles, the resource allocation in VANET has become one of the primary tasks. Exploiting the Road Side Units (RSUs), which constructs the Cloud Computing environment, provides more data access opportunities and stable communication time for the vehicles. We investigated the cloud resource allocation for data access with noncooperative game based on a Gauss–Seidel iteration method. We further proposed a repeated game scheme, which can approximately achieve the near Pareto‐optimal flow allocation among the vehicles. Considering the vehicles' irrational behavior, a punishment strategy was designed to prevent the vehicles from behavior deviation. The analysis based on these models lays a theoretical method foundation on cloud resource allocation process. The validity of the modeling and the accuracy of the analysis were verified through the extensive simulations, which also guide the future design of more sophisticated cloud resource allocation schemes. Copyright © 2016 John Wiley & Sons, Ltd.
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