Page-based virtual memory relies on TLBs to accelerate the address translation. Nowadays, the gap between application workloads and the capacity of TLB continues to grow, bringing many costly TLB misses and making the TLB a performance bottleneck. Previous studies seek to narrow the gap by exploiting the contiguity of physical pages. One promising solution is to group pages that are both virtually and physically contiguous into a memory range. Recording range translations can greatly increase the TLB reach, but ranges are also hard to index because they have arbitrary bounds. The processor has to compare against all the boundaries to determine which range an address falls in, which restricts the usage of memory ranges. In this article, we propose a tagged-pointer-based scheme, FlexPointer, to solve the range indexing problem. The core insight of FlexPointer is that large memory objects are rare, so we can create memory ranges based on such objects and assign each of them a unique ID. With the range ID integrated into pointers, we can index the range TLB with IDs and greatly simplify its structure. Moreover, because the ID is stored in the unused bits of a pointer and is not manipulated by the address generation, we can shift the range lookup to an earlier stage, working in parallel with the address generation. According to our trace-based simulation results, FlexPointer can reduce nearly all the L1 TLB misses, and page walks for a variety of memory-intensive workloads. Compared with a 4K-page baseline system, FlexPointer shows a 14% performance improvement on average and up to 2.8x speedup in the best case. For other workloads, FlexPointer shows no performance degradation.

Page-based virtual memory suffers from costly page walks because of the gap between application workload sizes and TLB capacity. In this paper, we propose a tagged-pointer-based system, FlexPointer, to solve this problem. FlexPointer creates memory ranges from objects larger than a certain threshold by allocating contiguous physical pages for them. Virtual addresses within a range share a common translation entry, thus greatly expanding the TLB capacity. Because such objects are rare, we can assign each of them a unique ID and pass it to the hardware in pointer tags. According to our trace-based simulation results, FlexPointer can reduce nearly all the L1 TLB misses and page walks for a variety of memory-intensive workloads, providing a 14% performance improvement on average.

A tagged-pointer-based memory spatial safety protection system utilizes the unused bits in a pointer to store the boundary information of an object. This paper proposed a hybrid metadata management scheme, MetaTableLite, for tagged-pointer-based protections. We observed that objects of a large size only take a minority part of all the objects in a program. However, recording their boundary metadata with traditional pointer tags will incur large memory overheads. Based on this observation, we introduce a small supplementary table to maintain metadata for these few large objects. For small ones, MetaTableLite represents their boundaries with a 14-bit pointer tag, well utilizing the unused 16 bits in a conventional 64-bit pointer. MetaTableLite can achieve a 6% average memory overhead without alternating the conventional pointer representation.

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. UAVs can greatly support data collecting and processing for Internet of Things devices (IoTDs) in mobile edge computing (MEC) systems due to their advantages of high environmental flexibility. This paper focuses on the scenario where multiple heterogeneous rotary-wing UAVs complete data collection and processing missions cooperatively. This paper introduces an energy minimization problem for UAV-assisted MEC system which attempts to optimize route planning and task allocation of UAVs. The energy consumption of a UAV includes hovering energy and flight energy depending on its configuration. By jointly choosing optimal UAVs for tasks and routes, we aim to obtain a sub-optimal solution of allocating IoTD tasks to UAVs and UAV flying route design while minimizing energy consumption. The Ant Colony System (ACS) algorithm is employed to obtain a high-quality near-optimal solution to solve this optimization problem. Finally, the simulation results show the effectiveness and efficiency of our proposed solution.

In this paper, we study a general Near-Far Computing Enhanced C-RAN (NFC-RAN), in which users can offload the tasks to the near edge cloud (NEC) or the far edge cloud (FEC). We aim to propose a profit-aware task allocation model by maximizing the profit of the edge cloud operators. We first prove that this problem can be transformed to a Multiple-Choice Multi-Dimensional 0-1 Knapsack Problem (MMKP), which is NP-hard. Then, we solve it by using a low complexity heuristic algorithm. The simulation results show that the proposed algorithm achieves a good tradeoff between the performance and the complexity compared with the benchmark algorithm.

In real world multiple extended target tracking problems, the presence of merged measurements is a frequently occurring phenomenon, however, most existing tracking algorithms in the literature assume that each target generates independent measurements. When this measurement merging phenomenon occurs, it increases the computational complexity of the tracking algorithms. Recently, the conditional joint decision and estimation (CJDE) algorithm based on the generalized Bayes risk was proposed to solve problems of joint detection, tracking and classification (JDTC) of targets. In this paper, we develop a principled Bayesian solution to the important problem involving inter-dependent decision and estimation conditioned on data based on the theory of random finite sets (RFS), and a tractable implementation based on the recently proposed generalized labeled multi-Bernoulli (GLMB) filter. The performance of the proposed technique is demonstrated by simulation of a multi-target bearings-only tracking scenario, where measurements become merged due to finite resolution effects.

Bus headway regularity heavily affects transit riders' attitude for choosing public transportation and also serves as an important indicator for transit performance evaluation. Therefore, an accurate estimate of bus headway can benefit both transit riders and transit operators. This paper proposed a relevance vector machine (RVM) algorithm to predict bus headway by incorporating the time series of bus headways, travel time, and passenger demand at previous stops. Different from traditional computational intelligence approaches, RVM can output the probabilistic prediction result, in which the upper and lower bounds of a predicted headway within a certain probability are yielded. An empirical experiment with two bus routes in Beijing, China, is utilized to confirm the high precision and strong robustness of the proposed model. Five algorithms [support vector machine (SVM), genetic algorithm SVM, Kalman filter, k-nearest neighbor, and artificial neural network] are used for comparison with the RVM model and the result indicates that RVM outperforms these algorithms in terms of accuracy and confidence intervals. When the confidence level is set to 95%, more than 95% of actual bus headways fall within the prediction bands. With the probabilistic bus headway prediction information, transit riders can better schedule their trips to avoid late and early arrivals at bus stops, while transit operators can adopt the targeted correction actions to maintain regular headway for bus bunching prevention.