Autonomous driving often suffer from a decoupled feedback loop between prediction and planning. While prediction losses focus on the accuracy of surrounding agents, planning losses typically imitate recorded Autonomous Vehicles’ (AVs) trajectories that may contain suboptimal or aggressive behaviors, leading to unstable interactions in mixed traffic. This paper presents HybridLoss, an adaptive planning-oriented objective that unifies prediction and planning through planner-in-the-loop supervision and interaction-aware consistency. HybridLoss integrates an adaptive motion-planning module which replaces ground-truth targets with optimized reference trajectories, and a multi-term loss combining prediction, adaptive planning, safety potential, and social force objectives. Evaluations on the INTERACTION dataset indicate that HybridLoss significantly outperforms strong baselines. Beyond standard metric improvements—reducing ADE/FDE from 1.36/1.64 m to 1.11/1.36 m and collision rates from 0.19% to 0.11%—extensive stress-testing reveals superior system maturity. First, HybridLoss exhibits the highest robustness under input perturbations, maintaining the lowest planning deviation and endpoint standard deviation. Second, it demonstrates strong generalization, maintaining stable success rates (87.7%) in unseen scenarios with high computational efficiency (64.3 Hz). Third, multi-objective analysis confirms that HybridLoss achieves the optimal Pareto trade-off between efficiency, safety, and comfort, avoiding the speed-safety collapse seen in baseline methods. Finally, social force evaluations highlight that HybridLoss fosters implicit cooperation, achieving higher yield rates and reduced conflict indices while maintaining safe interaction buffers. These results validate HybridLoss as a robust, socially compliant, and adaptive solution for end-to-end driving.

Isolation Levels (IL) act as correct contracts between applications and database management systems (DBMSs). The complex code logic and concurrent interactions among transactions make it a hard problem to expose violations of various ILs stated by DBMSs. With the recent proliferation of new DBMSs, especially the cloud ones, there is an urgent demand for a general way to detect bugs violating various ILs. The core challenges come from the requirements of: (a) lightweight (verifying without modifying the application logic in workloads and the source code of DBMSs), (b) generality (verifying various ILs), and (c) efficiency (performing efficient verification on a long running workload). To this end, we propose a powerful and practical bug-finding toolLeopard. For lightweight, we propose to infer transaction dependencies based on the time intervals of operations collected from the client-side, without modifying the source code of DBMSs. For generality, based on a thorough analysis of existing concurrency control protocols, we summarize and abstract four mechanisms which can implement ILs in all commercial DBMSs we have investigated. For efficiency, we design atwo-level pipelineto organize and sort massive time intervals in a time and memory conservative way; we propose amechanism-mirrored verificationto simulate the concurrency control protocols implemented in DBMSs for high throughputs; From experimental results,Leopardoutperforms existing methodsCobraandElle. In practice,Leopardhas a superpower to verify various ILs on any workload running on all commercial DBMSs. Moreover, it has successfully discovered 49 bugs that cannot be found by other existing methods.

Distributed stream processing systems rely on the dataflow model to define and execute streaming jobs, organizing computations as Directed Acyclic Graphs (DAGs) of operators. Adjusting the parallelism of these operators is crucial to handling fluctuating workloads efficiently while balancing resource usage and processing performance. However, existing methods often fail to effectively utilize execution histories or fully exploit DAG structures, limiting their ability to identify bottlenecks and determine the optimal parallelism. In this paper, we propose StreamTune, a novel approach for adaptive parallelism tuning in stream processing systems. StreamTune incorporates a pre-training and fine-tuning framework that leverages global knowledge from historical execution data for job-specific parallelism tuning. In the pre-training phase, StreamTune clusters the historical data with Graph Edit Distance and pre-trains a Graph Neural Network-based encoder per cluster to capture the correlation between the operator parallelism, DAG structures, and the identified operator-level bottlenecks. In the online tuning phase, Stream-Tu ne iteratively refines operator parallelism recommendations using an operator-level bottleneck prediction model enforced with a monotonic constraint, which aligns with the observed system performance behavior. Evaluation results demonstrate that StreamTune reduces reconfigurations by up to 29.6% and parallelism degrees by up to 30.8% in Apache Flink under a synthetic workload. In Timely Dataflow, StreamTune achieves up to an 83.3% reduction in parallelism degrees while maintaining comparable processing performance under the Nexmark benchmark, when compared to the state-of-the-art methods.

Cardinality Estimation (CardEst) is crucial for query optimization. Despite the remarkable achievement in DBMS, there is a pressing need to test or tune the work of CardEst. To satisfy the need, we introduce Artemis, a customizable workload generator, which can be used to generate various scenarios with the sensitive features for CardEst, including various data dependencies, complex SQL structures, and diverse cardinalities. It designs a PK-oriented deterministic data generation mechanism to plot various data characteristics; a search-based workload generation is proposed for composing queries with various complexities; it takes a constraint optimization-guided way to achieve a cost-effective cardinality calculation. In this demonstration, users can explore the core features of Artemis in generating workloads.

Various physical design (PD) options within a single database have emerged to optimize diverse workloads, including row-based PDs (e.g., index) and column-based PDs (e.g., column-store replica), each with its own acceleration advantages for different workloads. Determining the optimal combination of these two PDs is a labor-intensive and challenging task, yet it could result in significant performance improvements for the system. Recent automated index advisors (AIAs) have concentrated on identifying the most advantageous combination of row-based PDs. However, the extension of these efforts to the present problem has proven challenging due to 1) the larger search space of hybrid PD selections, 2) the inadequate consideration of the complex interactions between heterogeneous PDs, and 3) the inaccurate evaluation made by the what-if optimizer. To address these issues, we propose a Hybrid physical design advisor (Hyper) with multi-agent reinforcement learning. Hyper excels at recommending the optimal combination of PDs under any specific workload, with an overarching emphasis on both efficiency and quality. Comprehensive evaluations on well-established benchmarks show that our approach outperforms state-of-the-art methods.

Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researchers and practitioners alike. First, EasyTime enables one-click evaluation, enabling researchers to evaluate new forecasting methods using the suite of diverse time series datasets collected in the preexisting time series forecasting benchmark (TFB). This is achieved by leveraging TFB's flexible and consistent evaluation pipeline. Second, when practitioners must perform forecasting on a new dataset, a nontrivial first step is often to find an appropriate forecasting method. EasyTime provides an Automated Ensemble module that combines the promising forecasting methods to yield superior forecasting accuracy compared to individual methods. Third, EasyTime offers a natural language Q&A module leveraging large language models. Given a question like “Which method is best for long term forecasting on time series with strong seasonality?”, EasyTime converts the question into SQL queries on the database of results obtained by TFB and then returns an answer in natural language and charts. By demonstrating EasyTime11https://decisionintelligence.github.io/EasyTime, we aim to show how it simplifies the use of time-series forecasting and facilitates the development of new generations of time series forecasting methods.

The large language model (LLM)-based knob tuning method has attracted considerable attention due to its excellent in-context learning ability and generalizability. However, the existing LLM-based tuning methods do not effectively harmonize multi-source external knowledge, leading to missed opportunities for enhanced knob tuning. In light of this, we propose Rabbit, a novel approach that leverages Retrieval-augmented generation to enhance database knob tuning tools, which seamlessly integrates structured historical tuning experience with graph-encoded static knowledge. First, we introduce an experience-driven knob selection strategy, enhanced by dependency-aware external knowledge integration, to systematically select key knobs. Second, we develop a cutting-edge multi-agent knob domain pruning method, which ensures the reduced search space remains compact yet effective. Finally, we leverage the few-shot capabilities of LLMs to act as surrogate models, enabling rapid exploration of the pruned search space, followed by incremental optimization that expands the search space using historical insights. Moreover, we also design an adaptive strategy to transition between these two search spaces, striking an optimal balance between exploration and exploitation. Extensive experiments on well-established bench-marks demonstrate that Rabbit outperforms the state-of-the-art methods in both effectiveness and efficiency, pointing to a new paradigm for this area.

Modern analytical query engines (AQEs) are essential for large-scale data analysis and processing. These systems usually provide numerous query-level tunable knobs that significantly affect individual query performance. While several studies have explored automatic DBMS configuration tuning, they have several limitations to handle query-level tuning. Firstly, they fail to capture how knobs influence query plans, which directly affect query performance. Secondly, they overlook query failures during the tuning processing, resulting in low tuning efficiency. Thirdly, they struggle with cold-start problems for new queries, leading to prolonged tuning time. To address these challenges, we propose AQETuner, a novel Bayesian Optimization-based system tailored for reliable query-level knob tuning in AQEs. AQETuner first applies the attention mechanisms to jointly encode the knobs and plan query, effectively identifying the impact of knobs on plan nodes. Then, AQETuner employs a dual-task Neural Process to predict both query performance and failures, leveraging their interactions to guide the tuning process. Furthermore, AQETuner utilizes Particle Swarm Optimization to efficiently generate high-quality samples in parallel during the initial tuning stage for the new queries. Experimental results show that AQETuner significantly outperforms existing methods, reducing query latency by up to 23.7% and query failures by up to 51.2%.

This paper employs lateral and longitudinal trajectory planning to generate candidate trajectories and discards those that do not satisfy the constraints imposed by single-vehicle conditions. Next, a collaborative trajectory combination set for multiple vehicles is derived from the candidate trajectories, with multi-vehicle constraints applied to eliminate combinations that fail to meet the required conditions. The objective function for each candidate trajectory set is first calculated using a single-vehicle objective function, after which a multi-vehicle objective function based on the Kaldor-Hicks improvement principle is constructed. Finally, the paper introduces an improved particle swarm optimization method for multi-vehicle collaborative trajectory planning. The results demonstrate that the dynamic spatiotemporal occupancy growth rate, under varying planning times and frequencies, is at least 19%. Furthermore, the proposed algorithm ensures efficient allocation of travel resources, preventing competition among vehicles that could compromise system feasibility. When verified with HighD trajectory data, the algorithm not only delivers superior optimization results but also exhibits lower standard deviations in dynamic spatiotemporal occupancy and speed compared to real-world data. Finally, the algorithm’s superiority in real-time decision-making and stability is confirmed. Note to Practitioners—In the context of mixed traffic comprising both autonomous and human-driven vehicles, this paper tackles the challenge of coordinating autonomous vehicles to improve traffic efficiency and safety in real-time environments. It presents a promising approach to enhancing cooperative behavior in complex scenarios by integrating real-time data streams to optimize adaptability and ensure equitable driving efficiency across different vehicle types.

Urban public transportation is a complex and open system integral to urban mobility. Its operation is often disrupted by various random factors, necessitating robust scheduling solutions. This study develops a bus robust scheduling model based on mixed-integer linear programming to enhance system resilience. First, an arterial signal coordination model is proposed for mixed traffic environments, enabling autonomous public transport vehicles to traverse intersections without stopping. Second, a demand-deterministic bus scheduling model is constructed, integrating timetables, trajectories, and origin-destination transfer schemes to balance passenger waiting time fairness and efficiency. Third, to address stochastic passenger demand during actual operations, a robust bus scheduling model is developed by incorporating robust constraints. Numerical experiments demonstrate that the demand-deterministic model generates optimal scheduling schemes when passenger demand remains within bus capacity. However, when passenger demand exceeds capacity, the demand-deterministic model becomes infeasible. In such scenarios, the robust scheduling model produces feasible schemes, albeit with reduced optimization, and its robustness can be tuned by adjusting model parameters. Additionally, practical management insights are provided for real-world applications.

To optimize query parallelism techniques, substantial workloads are required with specific query plans and customized output size for each operator (denoted as cardinality constraint). To this end, a rich body of query-aware database generators (QAG) are proposed. However, the complex data dependencies hidden behind queries make previous QAGs suffer from deficiencies in supporting complex operators and controlling the generation errors. In this paper, we design a new generator Mirage supporting well for complex operators with low error bounds for cardinality constraints. First, Mirage leverages Query Rewriting and Set Transforming Rules to decouple dependencies between key and non-key columns, which could help generate each of them individually. Then, for the non-key columns, Mirage abstracts cardinality constraints of operators as placement requirements within each column's domain, and further models the generation problem as a classic bin packing problem. Finally, for the key columns, Mirage proposes a uniform representation of join cardinality constraints for all types of PK-FK joins and partitions the data according to the matching status between PK and F K columns. Then, it formulates the key population as a Constraint Programming problem, which can be solved by an existing CP Solver. The experiments show that Mirage conquers all previous work in either operator support or generation error.

Isolation level (IL) acts as a correctness contract between applications and DBMSs. Problematic IL implementations would cause incorrect transaction execution results and erroneous database states. However, existing studies could not efficiently generate various effective workloads for IL test. The core challenges come from the requirements of (a) black-box testing (trigger the IL code of a closed source DBMS), (b) effective testing (evade redundant and ineffective testing), and (c) anomaly-sensitive testing (test various ILs in a distinguishable way). For black-box testing, we investigate the IL implementations of 15 popular DBMSs and discover that they follow a generic framework that utilizes conflict graphs to manage all conflicts of a workload, and performs a verification policy to prevent non-serializable anomalies. For effective testing, we propose a lightweight data state mirroring method, which helps generate SQL operations that precisely access its expected records and participate the formation of specific conflict graphs. We also propose an efficient history-independent approach to generate dissimilar conflict graphs. It guarantees the graph generation overhead is irrelevant to the scale of historical graphs. For anomaly-sensitive testing, we propose an implantation-based approach to orchestrate conflict record accesses and inject them into different transactions according to the anomaly definition. Our approach outperforms existing approaches in testing effectiveness, efficiency, and coverage. Practically, we have successfully found 33 bugs in popular DBMSs.

The CPU overhead of the LSM-tree becomes increasingly significant when high-speed storage devices are utilized. In this article, we propose SuccinctKV , a key-value store based on LSM-tree that is optimized to improve CPU efficiency in mixed workload scenarios. To achieve this, SuccinctKV reduces the CPU overhead of compaction by writing scan-sorted data directly to the storage device. SuccinctKV also redesigns the merge-sort operation of the LSM-tree, enhancing CPU locality and reducing the unnecessary CPU overhead of cache accesses and I/O system calls. Additionally, SuccinctKV introduces a scheduler to resolve potential bursty I/O contention by autonomously initiating I/O requests at appropriate times and quickly relieving I/O pressure by terminating background I/O requests. We implement SuccinctKV on RocksDB and conduct extensive experiments to evaluate our proposed methods. The experimental results demonstrate that, compared with RocksDB, SuccinctKV achieves a maximum improvement of 2.6x in scan performance and reduces CPU overhead of compaction by up to 89% under mixed workloads.

In this study, we introduce a novel hybrid trajectory planning algorithm for autonomous driving, specifically designed to mitigate the risks posed by spoofing attacks on Connected and Autonomous Vehicles (CAVs). The research begins by assessing the safety implications of attacks and developing an adaptive safety model that is grounded in the fundamental assessment of state and decision data. This model incorporates the establishment of a posterior probability distribution for decision data, rooted in the pre-existing prior distribution but adjusted to account for the influence of spoofing attacks. The adjustment is achieved through Bayesian maximum posterior estimation, thereby refining the model to better adapt to potential threats. The adaptive safety model is then optimized dynamically, taking into consideration a set of indices—safety, comfort, and efficiency—that are critical to trajectory planning. In the subsequent phase, we introduce a composite trajectory planning algorithm that integrates a lateral trajectory selection sampling method with a longitudinal trajectory optimization approach. The adaptive safety model is seamlessly integrated into the trajectory planning process, influencing target position selection, the setting of constraints, and the formulation of the optimization objective function. The results demonstrate that the algorithm effectively limits the average standard deviation of lateral displacement to 0.5851 and achieves a significant increase in longitudinal speed growth rate, by up to 7.30%, surpassing the performance of benchmark algorithms. The proposed solution consistently delivers optimal safety and efficiency across various scenarios and under different parameter conditions.

The log-structured merge tree (LSM-tree) has been widely adopted as a backbone of modern key-value stores. However, the multiple exponentially increased levels of LSM-tree makes it suffer from high write amplification. Existing studies often improve the write performance by sacrificing the read performance, which is inefficient to make trade-offs between the update and search efficiency. In this paper, we exploit nonvolatile memory (NVM) to address the write amplification issue for systems with NVM-SSD hybrid storage, and further propose a reinforcement learning method to navigate between update and search efficiency on the varying workloads. Specifically, we first propose a lightweight hot data identification method to efficiently capture access recency as well as frequency in NVM with relative large capacity. On this basis, we can eliminate different versions of frequently updated data in high-performance NVM without pushing them to SSD. To improve the data access locality and facilitate fine-grained index tuning in each level, we devise a virtual-split method to partition the key space gradually without extra write amplification. Finally, we propose a cost based Q-learning algorithm to adaptively tune the data organizations of each partition according to the changing access patterns. Experimental results show that our approach outperforms existing methods by up to 2.67×.

Isolation Levels (IL) act as correct contracts between applications and database management systems (DBMSs). The complex code logic and concurrent interactions among transactions make it a hard problem to expose violations of various ILs stated by DBMSs. With the recent proliferation of new DBMSs, especially the cloud ones, there is an urgent demand for a general way to verify various ILs. The core challenges come from the requirements of: (a) lightweight (verifying without modifying the application logic in workloads and the source code of DBMSs), (b) generality (verifying various ILs), and (c) efficiency (performing efficient verification on a long running workload). For lightweight, we propose to deduce transaction dependencies based on time intervals of operations collected from client-sides without touching the source code of DBMSs. For generality, based on a thorough analysis of existing concurrency control protocols, we summarize and abstract four mechanisms which can implement ILs in all commercial DBMSs we have investigated. For efficiency, we design a two-level pipeline to organize and sort massive time intervals in a time and memory conservative way; we propose a mechanism-mirrored verification to simulate the concurrency control protocols implemented in DBMSs for high throughputs. Leopard outperforms existing methods by up to 114× in verification time with a relative small memory usage. In practice, Leopard has a superpower to verify various ILs on any workload running on all commercial DBMSs. Moreover, it has successfully discovered 23 bugs that cannot be found by other existing methods.

Predictive autoscaling is a key enabler for optimizing cloud resource allocation in Alibaba Cloud's computing platforms, which dynamically adjust the Elastic Compute Service (ECS) instances based on predicted user demands to ensure Quality of Service (QoS). However, user demands in the cloud are often highly complex, with high uncertainty and scale-sensitive temporal dependencies, thus posing great challenges for accurate prediction of future demands. These in turn make autoscaling challenging---autoscaling needs to properly account for demand uncertainty while maintaining a reasonable trade-off between two contradictory factors, i.e., low instance running costs vs. low QoS violation risks. To address the above challenges, we propose a novel predictive autoscaling framework MagicScaler , consisting of a Multi-scale attentive Gaussian process based predictor and an uncertainty-aware scaler. First, the predictor carefully bridges the best of two successful prediction methodologies---multi-scale attention mechanisms, which are good at capturing complex, multi-scale features, and stochastic process regression, which can quantify prediction uncertainty, thus achieving accurate demand prediction with quantified uncertainty. Second, the scaler takes the quantified future demand uncertainty into a judiciously designed loss function with stochastic constraints, enabling flexible trade-off between running costs and QoS violation risks. Extensive experiments on three clusters of Alibaba Cloud in different Chinese cities demonstrate the effectiveness and efficiency of MagicScaler , which outperforms other commonly adopted scalers, thus justifying our design choices.

The set similarity join finds all pairs of similar sets from two collections of sets. It has many real world applications, such as personalized recommendation and community mining. In this paper, we study the problem of computing the similarity join in a dynamic context, where the sets are updated dynamically. This, however, is inefficient with the state-of-the-art join methods, because they usually assume that data collections are static and have to compute the join result from scratch whenever a set is updated. To address this issue, we propose${{\sf ALJoin}}$, an adaptive filtering approach that computes the join result incrementally based on the update logs. We first investigate the effect of set updates on the similarity values, and on this basis we propose to build a neighborhood index for each set. The neighborhood index of a specific set consists of any other sets that can be transformed into its similar sets within a threshold number of update operations.${{\sf ALJoin}}$then uses this index to effectively identify both similar and dissimilar set pairs based on their update logs. To efficiently build the neighborhood index, we devise several filtering techniques and propose a “lazy-forward” method to reduce the computational cost. In addition, to improve the efficiency on varying workloads, we propose an analytical cost model, and design an online algorithm with performance guarantees to dynamically consolidate the update logs and adapt the neighborhood indexes. We evaluated our method using four real-world datasets. Experimental results show that our approach outperforms existing methods by up to$3.7\times$.

With the prevalence of location based services, activity trajectories are being generated at a rapid pace. The activity trajectory data enriches traditional trajectory data with semantic activities of users, which not only shows where the users have been, but also the preference of users. However, the large volume of data is expensive for people to explore. To address this issue, we study the problem of Diversity-aware Activity Trajectory Selection (DaATS). Given a region of interest for a user, it finds a small number of representative activity trajectories that can provide the user with a broad coverage of different aspects of the region. The problem is challenging in both the efficiency of trajectory similarity computation and subset selection. To tackle the two challenges, we propose a novel solution by: (1) exploiting a deep metric learning method to speedup the similarity computation; and (2) proving that DaATS is an NP-hard problem, and developing an efficient approximation algorithm with performance guarantees. Experiments on two real-world datasets show that our proposal significantly outperforms state-of-the-art baselines.

Approximate Nearest Neighbor (ANN) search in high-dimensional space is a fundamental task in many applications. Locality-Sensitive Hashing (LSH) is a well-known methodology to solve the ANN problem with theoretical guarantees and empirical performance. We observe that existing LSH-based approaches target at the problem of designing search optimized indexes, which require a number of separate indexes and high index maintenance overhead, and hence impractical for high-dimensional streaming data processing. In this paper, we present PDA-LSH, a novel and practical disk-based LSH index that can offer efficient support for both updates and searches. Experiments on real-world datasets show that our proposal outperforms the state-of-the-art schemes by up to 10× on update performance and up to 2× on search performance.

Flash memory based solid state drives (SSDs) have emerged as a new alternative to replace magnetic disks due to their high performance and low power consumption. However, random writes on SSDs are much slower than SSD reads. Therefore, traditional index structures, which are designed based on the symmetrical I/O property of magnetic disks, cannot completely exert the high performance of SSDs. In this paper, we propose an SSD-optimized linear hashing index called Self-Adaptive Linear Hashing (SALhashing) to reduce small random-writes to SSDs that are caused by index operations. The contributions of our work are manifold. First, we propose to organize the buckets of a linear hashing index into groups and sets to facilitate coarse-grained writes and adaptivity to access patterns. A group consisting of a fixed number of buckets is proposed to transform small random writes to buckets into coarsegrained writes and in turn improve write performance of the index. A set consists of a number of groups, and we propose to employ different split strategies for each set. With this mechanism, SAL-hashing is able to adapt to the changes of access patterns. Second, we attach a log region to each set, and amortize the cost of reads and writes by committing updates to the log region in batch. Third, in order to reduce search cost, each log region is equipped with Bloom filters to index update logs. We devise a cost-based online algorithm to adaptively merge the log region with the corresponding set when the set becomes search-intensive. Fourth, we propose a new technique called virtual split to optimize the search performance of SAL-hashing. Finally, we propose a new scheme for the management of the log buffer. We conduct extensive experiments on real SSDs. The results suggest that our proposal is self-adaptive according to the change of access patterns, and outperforms several competitors under various workloads.

A set containment join operates on two set-valued attributes with a subset (⊆) relationship as the join condition. It has many real-world applications, such as in publish/subscribe services and inclusion dependency discovery. Existing solutions can be broadly classified into union-oriented and intersection-oriented methods. Based on several recent studies, union-oriented methods are not competitive as they involve an expensive subset enumeration step. Intersection-oriented methods build an inverted index on one attribute and perform inverted list intersection on another attribute. Existing intersection-oriented methods intersect inverted lists one-by-one. In contrast, in this paper, we propose to intersect all the inverted lists simultaneously while skipping many irrelevant entries in the lists. To share computation, we utilize the prefix tree structure and extend our novel list intersection method to operate on the prefix tree. To further improve the efficiency, we propose to partition the data and use different methods to process each partition. We evaluated our methods using both real-world and synthetic datasets. Experimental results show that our approach outperforms existing methods by up to 10×.

With the popularization of portable devices, numerous applications continuously produce huge streams of geo-tagged textual data, thus posing challenges to index geo-textual streaming data efficiently, which is an important task in both data management and AI applications, e.g., real-time data streams mining and targeted advertising. This, however, is not possible with the state-of-the-art indexing methods as they focus on search optimizations of static datasets, and have high index maintenance cost. In this paper, we present NQ-tree, which combines new structure designs and self-tuning methods to navigate between update and search efficiency. Our contributions include: (1) the design of multiple stores each with a different emphasis on write-friendness and read-friendness; (2) utilizing data compression techniques to reduce the I/O cost; (3) exploiting both spatial and keyword information to improve the pruning efficiency; (4) proposing an analytical cost model, and using an online self-tuning method to achieve efficient accesses to different workloads. Experiments on two real-world datasets show that NQ-tree outperforms two well designed baselines by up to 10×.

change memory (PCM) as a newly developed storage medium has many attractive properties such as non-volatility, byte addressability, high density and low energy consumption. Thus, PCM can be used to build non-volatile main memory databases. However, PCM's long write latency and high write energy bring challenges to PCM-based memory systems. In this paper, we propose an improvement over the B+-tree for PCM. Particularly, we consider the read/write tendency of leaf nodes. For write-intensive leaf nodes, we use an overflow-node technique to reduce PCM writes, while for read-intensive ones, we adjust the tree structure to remove overflow nodes to improve read performance. Our experimental results suggest that our proposal outperforms the traditional B+-tree and the overflow B+-tree.

Flash memory based solid state drives (SSDs) have emerged as a new alternative to replace magnetic disks due to their high performance and low power consumption. However, random writes on SSDs are much slower than SSD reads. Therefore, traditional index structures, which are designed based on the symmetrical I/O property of magnetic disks, cannot completely exert the high performance of SSDs. In this paper, we propose an SSD-optimized linear hashing index called Self-Adaptive Linear Hashing (SAL-Hashing) to reduce small random writes to SSDs that are caused by index operations. The contributions of our work are manifold. First, we propose to organize buckets into groups and sets to facilitate coarse-grained writes and lazy-split so as to avoid intermediate writes on the hash structure. A group consists of a fixed number of buckets and a set consists of a number of groups. Second, we attach a log region to each set, and amortize the cost of reads and writes by committing updates to the log region in batch. Third, in order to reduce search cost, each log region is equipped with Bloom filters to index update logs. We devise a cost-based online algorithm to adaptively merge the log region with the corresponding set when the set becomes search-intensive. Finally, in order to exploit the internal package-level parallelisms of SSDs, we apply coarse-grained writes for merging or split operations to achieve a high bandwidth. Our experimental results suggest that our proposal is self-adaptive according to the change of access patterns, and outperforms several competitors under various workloads on two commodity SSDs.

Phase change memory (PCM) has become one of the most promising storage media particularly for memory systems, due to its byte addressability, high access speed, and low energy consumption. In addition, hybrid memory systems involving both PCM and DRAM can utilize the merits of both media and overcome some typical drawbacks of PCM such as high write latency and limited lifecycle. In this paper, we present a novel page replacement algorithm called APP-LRU (Access-Pattern-prediction-based LRU) for PCM/DRAM-based hybrid memory systems. APP-LRU aims to reduce writes to PCM while maintaining stable time performance. Particularly, we detect read/write intensity for each page in the memory, and put read-intensive pages into PCM while placing write-intensive pages in DRAM. We conduct trace-driven experiments on six synthetic traces and one real OLTP trace. The results show that our proposal is able to reduce up to 5 times of migrations more than its competitors.

In this paper, we consider the potential influences on energy efficiency when Device to Device (D2D) communication is introduced to small cell network with sleep scheme. To begin with, we obtain the optimal sleep strategy for small cells, then we define parameters sleep ratio (SR) and maximum sleeping cell (MSC) to describe system dormant situation. Furthermore, parameters λ and θ are defined to indicate the D2D offloading ratio as well as geographic distribution of D2D users. We show that D2D communication will always be energy beneficial to the base station side. However, when considering the overall energy consumption (users included), D2D Assisted Sleep Mode (DASM) may cost more energy than D2D Forbidden Sleep Mode (DFSM). We demonstrate that to obtain the λ scope where DASM is energy efficient, it equals to calculate the positive range of a quadratic function f(θ,λ). Therefore, D2D scheduling scheme should be self-adaptive according to the f(θ, λ) curve to keep the network energy efficient. The analysis method in this paper should be universal regardless of the specific system models.

Orthogonal experimental design (OED) is a well-known method in statistical experiment which aims to reduce the testing complexity. This paper tries applying OED to reduce the computational complexity in Multiple-input Multiple-output (MIMO) detection. Several times of sorted QR decomposition (SQRD) algorithm are applied firstly to get the diverse solutions. Then a subspace is constructed with the diverse results. Inside the subspace, OED shows its potential and value in reducing the computational complexity compares with the exhaustive method. The proposed QR-OED MIMO scheme provides nearly 8 dB gain compared with the traditional sorted QRD while the minimal computational complexity needed is only 3.7% compared with exhaustive method for the 6×6 MIMO system and 4-QAM modulation scheme.

With Device to Device (D2D) user equipment (DUE) sharing the same resources with traditional cellular user equipment (CUE), intra-cell interference is becoming a challenging issue nowadays in D2D underlaying network. However, due to the lack of convexity, globally optimal resource sharing scheme is generally unattainable. In this paper, we propose a genetic algorithm (GA) based user matching scheme (GAM) with optimal power allocation (OPA) to achieve the multi-dimension optimization. Firstly, we demonstrate that the optimal power allocation between DUE and CUE can be obtained by searching several finite solution sets in the feasible region boundries. Secondly, genetic algorithm is applied to obtain the near-optimal user matching in the whole network scope. Simulation results show that the GA based scheme can achieve 30Mbps system throughput gain comparing to the traditional greedy searching, while with a limited complexity increase.

Device to Device (D2D) underlaying cellular networks have drawn a lot of attention recently, in both Long Term Evolution (LTE) standardization as well as academic research, with its huge potential in increasing the system capacity. However, intra-cell interference is quite a challenging issue since D2D user equipment (DUE) may share the same resources with the traditional cellular user equipment (CUE). Moreover, due to the lack of convexity, globally optimal sharing strategy is generally unattainable. In this paper, we apply bipartite matching strategy from graph theory to optimize the resource allocation in D2D underlay network. Two different situations are considered in detail, namely optimal resource allocation with fixed power transmission (FPT) and optimal resource allocation with uplink power controlled transmission (PCT). Practical constraints such as minimum spectral efficiency and maximum transmit power limitations are also considered in the optimization. Simulations and analyses show that the proposed Bipartite Matching based Allocation (BMA) can improve the system capacity significantly comparing to those traditional methods.

The world is witnessing a tremendous increase in data demands which is subject to the new emerging technologies, applications and services. 60 GHz communication network is one of such technology, claiming data rate in multi-gigabits. In this paper, we propose a scheduling algorithm for device-to-device 60 GHz network having directional antennas. The proposed algorithm utilizes the vertex coloring scheme and is optimized to improve system throughput. A threshold minimum distance between conflicting flows is used to keep the accumulative interference limited. Also, when there are conflicts among different flows, those with better data rate prospects will be scheduled priorly. Simulation results show that our scheme has brought significant improvement to system throughput almost by 19% and average flow number per slot is improved by 12%, as compared to other scheduling algorithms.