Caching Scheduling Research Articles

Design and Application of Legal Information Systems Based on Big Data Technology

China's exploration of the legal application of big data is still far from thorough enough. This article proposes a universal suitable for hierarchical big data storage systems, which can quickly add new cache policies when needed and provide cache scheduling strategies. The neural training algorithm used in legal information systems implements a complete parallel computing framework for large-scale neural network training, supporting distributed storage and management of large-scale sample data. The experimental results show that the framework has good scalability and fault tolerance, and can quickly train legal information systems, improving their efficiency and response speed. This provides new ideas and methods for the design and development of legal information systems.

Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN

The Internet of Medical Things (IoMT) is the network of medical devices, hardware infrastructure, and software applications used to connect the healthcare information technology. Massive traffic growth and user expectations cause challenges in the current exhausting models of IoMT data. To reduce the IoMT traffic, Information Centric Network (ICN) is a suitable technique. ICN uses persistent naming multicast communication that reduces the response time. ICN in IoMT provides a promising feature to reduce the overhead due to the distribution of commonly accessed contents. Some parameters such as energy consumption, communication cost, etc., influence the performance of sensors in the IoMT network. Excessive and unbalanced energy consumption degrades the network performance and lifetime. This article presents a framework called Dynamic Cache Scheme (DCS) that implements energy-efficient cache scheduling in IoMT over ICN to reduce network traffic. The proposed framework establishes a balance between the multi-hop traffic and data item freshness. The technique improves the freshness of data; thus, updated data are provided to the end-users via the effective utilization of caching in IoMT. The proposed framework is tested on important parameters, i.e., cache-hit-ratio, stretch, and content retrieval latency. The results obtained are compared with the state-of-the-art models. Results’ analysis shows that the proposed framework outperforms the compared models in terms of cache-hit-ratio, stretch, and content retrieval latency by 59.42%, 32.66%, and 18.8%, respectively. In the future, it is intended to explore the applicability of DCS in more scenarios and optimize further.

Data collection of multi-player cooperative game based on edge computing in mobile crowd sensing

As the number and type of mobile crowd sensing (MCS) data collection increases, more and more computation and processing are required, resulting in higher service cost and service delay, posing a huge challenge to traditional MCS. Currently, edge computing is being introduced to MCS to collect data to reduce service cost and service latency. In the offline mode, there are two issues that need to be addressed with the edge computing-based MCS data collection. First, for large-scale, multi-player, and multiple types of task data, edge servers are limited in computational resources and need to address issues such as task offloading and service cache scheduling. Second, in traditional MCS data collection, workers usually carry in a single type of task data, but it has now been proposed that workers need to carry multiple types of task data. To address the above problems, this paper proposes a joint optimization strategy for edge computing based on multi-player cooperative game and greedy differential evolution algorithm (MCG-GDE) to improve the service rate of edge servers and minimize the service cost and service latency in the data collection. We build a mathematical optimization problem for edge computing based on MCS data collection. The formulation of the optimization problem proves to be a NP-hard problem, so this optimization strategy constructs a task propagation scheme for multi-player cooperative games (MCG), where tasks carried by workers are reassigned to effectively reduce problem complexity and produce sub-optimal solutions to the mathematical model. Then, on the basis of the suboptimal solution, the optimal solution of the problem is obtained by the greedy differential evolution algorithm (GDE). Simulation results demonstrate that MCG-GDE outperforms other baseline strategies.

Efficient Data Dissemination Strategy for UAV in UAV-Assisted VANETs

With development of emerging intelligent transportation systems (ITSs), unmanned aerial vehicles (UVAs) are gaining growing attention to achieve UAV-assisted VANETs for improving performance of data dissemination. However, in UAV-assisted VANETs, the performance of data dissemination can be significantly compromised by the limited bandwidth resource of UAVs. In this paper, we propose a scheduling strategy to provide efficient data dissemination. In particular, first, we take into account a scenario with either unavailable or overloaded infrastructure, where a UAV is employed to provide data dissemination. Furthermore, considering the limited bandwidth resource of the UAV, we formulate a coding-based cooperative caching data dissemination (C3D2) problem which makes use of the synergy between network coding and vehicular caching with the objective of maximizing the bandwidth efficiency of UAVs. Meanwhile, the NP-hardness of the C3D2 problem is proved by constructing a polynomial time reduction from the simultaneous matrix completion problem. Based on the effectiveness of the binary particle swarm optimization (BPSO) framework in solving the complex problems, we propose a BPSO-based cooperative caching scheduling (BPSO_C2S) algorithm consisting of a series of strategies, such as particle representation, particle evaluation, particle update and infeasible solution amendment. In addition, we analyze the computational complexity of the BPSO_C2S. Finally, we build the simulation model and give a comprehensive performance evaluation through the real-world map and taxi trajectories. Simulation results demonstrate the effectiveness and superiority of the proposed solution.

SwNEMO_v4.0: an ocean model based on NEMO4 for the new-generation Sunway supercomputer

Abstract. The current large-scale parallel barrier of ocean general circulation models (OGCMs) makes it difficult to meet the computing demand of high resolution. Fully considering both the computational characteristics of OGCMs and the heterogeneous many-core architecture of the new Sunway supercomputer, swNEMO_v4.0, based on NEMO4 (Nucleus for European Modelling of the Ocean version 4), is developed with ultrahigh scalability. Three innovations and breakthroughs are shown in our work: (1) a highly adaptive, efficient four-level parallelization framework for OGCMs is proposed to release a new level of parallelism along the compute-dependency column dimension. (2) A many-core optimization method using blocking by remote memory access (RMA) and a dynamic cache scheduling strategy is applied, effectively utilizing the temporal and spatial locality of data. The test shows that the actual direct memory access (DMA) bandwidth is greater than 90 % of the ideal bandwidth after optimization, and the maximum is up to 95 %. (3) A mixed-precision optimization method with half, single and double precision is explored, which can effectively improve the computation performance while maintaining the simulated accuracy of OGCMs. The results demonstrate that swNEMO_v4.0 has ultrahigh scalability, achieving up to 99.29 % parallel efficiency with a resolution of 500 m using 27 988 480 cores, reaching the peak performance with 1.97 PFLOPS.

Cooperative Computation and Cache Scheduling for UAV-Enabled MEC Networks

Since the flight of Unmanned Aerial Vehicle (UAV) greatly consumes the limited on-board energy and affects the ability to afford computation services, it is urgent and challenging to minimize UAV’s endurance time while satisfying varieties of constraints from the UAVs and users, such as energy budgets, cache capacities, computation resources, and latency requirements. In this paper, the UAVs are endowed with the capability to temporarily buffer some offloaded tasks and process them later, then adopt cooperative computation and cache scheduling to minimize their total endurance time. Following this, the formulated problem is a mix-integer nonlinear programming problem, which can be further transformed into three more tractable subproblems: 1) PO, offloading decision and scheduling strategy for computing & caching; 2) PH, hovering trajectory design; 3) PC, computation resource allocation, and then iteratively tackled in a sequence. Due to the high complexity of solving PO, the low-complexity algorithm based on the K-means clustering and Penalty Method is proposed to reduce complexity and facilitate the processing procedure, which can save about 91.04%-189.36% running time and ONLY sacrifice 8.05%-22.08% of the total endurance time. Simulation results show that cooperative computation and cache scheduling has a great effect on saving endurance time.

Cache-Enabled Data Rate Maximization for Solar-Powered UAV Communication Systems

Currently, deploying fixed terrestrial infrastructures is not cost-effective in temporary circumstances, such as natural disasters, hotspots, and so on. Thus, we consider a system of caching-based UAV-assisted communications between multiple ground users (GUs) and a local station (LS). Specifically, a UAV is exploited to cache data from the LS and then serve GUs’ requests to handle the issue of unavailable or damaged links from the LS to the GUs. The UAV can harvest solar energy for its operation. We investigate joint cache scheduling and power allocation schemes by using the non-orthogonal multiple access (NOMA) technique to maximize the long-term downlink rate. Two scenarios for the network are taken into account. In the first, the harvested energy distribution of the GUs is assumed to be known, and we propose a partially observable Markov decision process framework such that the UAV can allocate optimal transmission power for each GU based on proper content caching over each flight period. In the second scenario where the UAV does not know the environment’s dynamics in advance, an actor-critic-based scheme is proposed to achieve a solution by learning with a dynamic environment. Afterwards, the simulation results verify the effectiveness of the proposed methods, compared to baseline approaches.

Cooperative coding and caching scheduling via binary particle swarm optimization in software-defined vehicular networks

With recent development in vehicular communication technologies, much attention has been paid to data dissemination in vehicular networks. In particular, the infrastructure-to-vehicle (I2V) communication is one of the primary technologies to provide a variety of information services. To enhance the bandwidth efficiency of I2V communication, this work considers in a software-defined vehicular networks (SDVN), aiming at exploiting synergistic effects of network coding and vehicular caching. First, we consider a data service scenario in which roadside unites (RSUs) are connected with the controller, which exercises scheduling decisions based on service requests received from vehicles. On this basis, we formulate a cooperative coding and caching scheduling problem with the objective of maximizing the bandwidth efficiency of I2V communication. Then, we propose a binary particle swarm optimization (BPSO)-based coding scheduling (BPSO_CS) algorithm. Finally, we build the simulation model and give a comprehensive performance evaluation. The results conclusively demonstrate the superiority of the proposed solution.

Integrated Cache Scheduling Replacement Algorithm to Reduce Cache Pollution

Cache memory management has recently become one of the most crucial research topics in the area of high performance computing. The cache memory has gained popularity due to its capability to offer faster access compared to main memory so processor can access data directly through cache very efficiently. As processor execution speed is faster than accessing memory so the goal is to keep programs rapidly accessible for processing. Cache memory helps not only to access data quickly but also improves the performance of the system. Various cache management policies have been developed to improve the performance of the system. In this paper we focus the issues related with the existing replacement policies and present a new algorithm to overcome these issues. In this paper, we proposed Integrated Cache Scheduling Replacement Algorithm (ICSRA) that will remove the object on the basis of integration of all the parameters like size, frequency and recency if the cache is full. This algorithm consider all size of object to replace with the condition of frequency and timestamp parameter to reduce the cache pollution [1]. Cache pollution means the cache unnecessary waste its space by holding such an object which is no longer be accessed. ICSRA helps to enhance the utilization of cache memory in an efficient manner as well as maximize the cache hit ratio to improve the performance of the system. A detailed classification has also been included based on different parameters which are depending on the analysis of the existing techniques.

A software defined caching framework based on user access behavior analysis for transparent computing server

A large number of resource access requests from heterogeneous terminals bring severe challenges to ensuring the performance and efficiency of Tranparent Computing server. Caching mechanism plays a significant role in performance improvement of transparent computing systems. Nevertheless, the existing caching mechanisms do not take into account the complex and volatile runtime context in the server-side, such as the change in users’ access requirements for the resources and server performance status, so that their cache scheduling strategies are lack of flexibility and diversity.Thus, in this paper, we proposed a software defined cache scheduling framework that can dynamically and flexibly schedule appropriate caching policies according to the monitored information to achieve optimal caching performance for transparent computing server. First, we constructed a multi-layer and linked virtual disk storage model and its resource access mechanism. Then, based on this storage model, in order to perceive changes in the users’ demand for server resources, we adopted information entropy to model and analyze the user access behavior, and predict it with exponential smoothing algorithm. Finally, the cache scheduling is defined as an optimization problem from two aspects of prefetching and replacement, and some heuristic algorithms are used to obtain the approximate optimal solutions based on the conclusions of user access behavior analysis and prediction. We made experiments on the real data and tested the effectiveness of our approach, and the results show that our approach can achieve better caching performance than traditional methods, thus improving the service quality and user experience of transparent computing effectively.

HSCS: a hybrid shared cache scheduling scheme for multiprogrammed workloads

The traditional dynamic random-access memory (DRAM) storage medium can be integrated on chips via modern emerging 3D-stacking technology to architect a DRAM shared cache in multicore systems. Compared with static random-access memory (SRAM), DRAM is larger but slower. In the existing research, a lot of work has been devoted to improving the workload performance using SRAM and stacked DRAM together in shared cache systems, ranging from SRAM structure improvement to optimizing cache tags and data access. However, little attention has been paid to designing a shared cache scheduling scheme for multiprogrammed workloads with different memory footprints in multicore systems. Motivated by this, we propose a hybrid shared cache scheduling scheme that allows a multicore system to utilize SRAM and 3D-stacked DRAM efficiently, thus achieving better workload performance. This scheduling scheme employs (1) a cache monitor, which is used to collect cache statistics; (2) a cache evaluator, which is used to evaluate the cache information during the process of programs being executed; and (3) a cache switcher, which is used to self-adaptively choose SRAM or DRAM shared cache modules. A cache data migration policy is naturally developed to guarantee that the scheduling scheme works correctly. Extensive experiments are conducted to evaluate the workload performance of our proposed scheme. The experimental results showed that our method can improve the multiprogrammed workload performance by up to 25% compared with state-of-the-art methods (including conventional and DRAM cache systems).

Mobility-Aware Caching Scheduling for Fog Computing in mmWave Band

As an extension of cloud computing, fog computing at the edge of networks provides low latency, location awareness, and real-time interactions. At the same time, millimeter wave (mmWave) communications are able to provide directional multi-gigabit transmission rates with large available bandwidth. Based on the user mobile trajectories in a region, several activity hotspots that users pass by frequently can be obtained. By caching popular content at the edge nodes near the hotspots, users can download the cached content directly at a short distance, and the user experience can be significantly improved. Considering multiple hotspots in a region, how to efficiently schedule the transmission for the caching at edge nodes becomes a key problem. In this paper, we focus on the problem of mobility aware transmission scheduling for caching at edge nodes near hotspots, and utilize multi-hop relaying and concurrent transmissions to achieve better performance. After formulating the optimal scheduling problem as a stochastic nonlinear mixed integer program, we propose a mobility aware caching scheduling scheme, called MHRC (Multi-Hop Relaying based Caching), where multi-hop D2D paths are established for edge nodes, and concurrent transmissions are exploited in the scheduling of caching at edge nodes. Extensive performance evaluation demonstrates MHRC achieves more than 1x higher expected cached data amount compared with state-of-the-art schemes.

Toward multi-programmed workloads with different memory footprints: a self-adaptive last level cache scheduling scheme

With the emerging of 3D-stacking technology, the dynamic random-access memoryDRAM) can be stacked on chips to architect the DRAM last level cacheLLC). Compared with static random-access memorySRAM), DRAM is larger but slower. In the existing research papers, a lot of work has been devoted to improving the workload performance using SRAM and stacked DRAM together, ranging from SRAM structure improvement, to optimizing cache tag and data access. Instead, little attention has been paid to designing an LLC scheduling scheme for multi-programmed workloads with different memory footprints. Motivated by this, we propose a self-adaptive LLC scheduling scheme, which allows us to utilize SRAM and 3D-stacked DRAM efficiently, achieving better workload performance. This scheduling scheme employs (1) an evaluation unit, which is used to probe and evaluate the cache information during the process of programs being executed; and linebreak (2) an implementation unit, which is used to self-adaptively choose SRAM or DRAM. To make the scheduling scheme work correctly, we develop a data migration policy. We conduct extensive experiments to evaluate the performance of our proposed scheme. Experimental results show that our method can improve the multi-programmed workload performance by up to 30% compared with the state-of-the-art methods.

What to cache: differentiated caching resource allocation and management in information-centric networking

Information-centric networking (ICN) aims to improve the efficiency of content delivery and reduce the redundancy of data transmission by caching contents in network nodes. An important issue is to design caching methods with better cache hit rate and achieve allocating on-demand. Therefore, an in-network caching scheduling scheme for ICN was designed, distinguishing different kinds of contents and dynamically allocating the cache size on-demand. First discussing what was appropriated to be cached in nodes, and then a classification about the contents could be cached was proposed. Furthermore, we use AHP to weight different contents classes through analyzing users' behavior. And a distributed control process was built, to achieve differentiated caching resource allocation and management. The designed scheme not only avoids the waste of caching resource, but also further enhances the cache availability. Finally, the simulation results are illustrated to show that our method has the superior performance in the aspects of server hit rate and convergence.

Cache-aware interfaces for compositional real-time systems

Interface-based compositional analysis is by now a fairly established area of research in real-time systems. However, current research has not yet fully considered practical aspects, such as the effects of cache interferences on multicore platforms. This position paper discusses the analysis challenges and motivates the need for cache scheduling in this setting, and it highlights several research questions towards cache-aware interfaces for compositional systems on multicore platforms.

An effective cache scheduling scheme for improving the performance in multi-threaded processors

Abstract Although in a multi-threaded processor, the processor may execute more than one process simultaneously to maximize the overall throughput of the system, the executing processes may compete with each other in using shared caches of the processor. This can seriously affect the average performance of the processes as the probability of cache hit for each process could be lowered. In this paper, we propose a new algorithm called the sharable cache partitioning algorithm (ShaParti), for scheduling the processor caches amongst co-running processes. In ShaParti, each executing process has its own cache and a priority scheme is designed for them to share the caches belonging to other executing processes. The performance goals of ShaParti are to improve the cache hit rates of the processes and at the same time the cache miss rates of other concurrent processes will not be lowered compared with the case in which each process has its own cache. Extensive experiments have been performed to illustrate the effectiveness of ShaParti in improving the performance in accessing shared processor caches.

Efficient Support of Streaming Videos through Patching Proxies in the Cloud

Multimedia streaming applications such as mobile conferencing, e-learning and mobile cinema are becoming popular for the Internet and mobile devices. Patching can enable a client to receive multicast streaming by listening to ongoing transmission of the same video clip in order to reduce retransmission from the media server. However, multimedia streams multicasting to clients must be stored in the client buffer in advance; most fractions of ongoing streaming frames are lost due to limited space. The authors first propose a patching cache scheduling algorithm to patch and cache the shared frames of the ongoing streams on cloud-based patching proxy servers. The patching proxy cloud is responsible for patching and caching streaming data and mobile devices in the same cloud share bandwidth and cooperatively stream video frames to distribute the load. The simulation results show that the proposed patching proxy cloud can efficiently improve the cache utility, decrease the user-perceived latency, and reduce the transmission overhead between the proxy and the original media server when a video clip is very popular.

P2P traffic optimization

Peer-to-peer (P2P) based content distribution systems have emerged as the main form for content distribution on the Internet, which can greatly reduce the distribution cost of content providers and improve the overall system scalability. However, the mismatch between the overlay and underlay networks causes large volume of redundant traffic, which intensifies the tension between P2P content providers and ISPs. Therefore, how to efficiently use network resources to reduce the traffic burden on the ISPs is crucial for the sustainable development of P2P systems. This paper surveys the state-of-art P2P traffic optimization technologies from three perspectives: P2P cache, locality-awareness and data scheduling. Technological details, comparison between these technologies and their applicabilities are presented, followed by a discussion of the issues that remain to be addressed and the direction of future content distribution research.

Efficient Reduction of Access Latency through Object Correlations in Virtual Environments

Object correlations are common semantic patterns in virtual environments. They can be exploited to improve the effectiveness of storage caching, prefetching, data layout, and disk scheduling. However, we have little approaches for discovering object correlations in VE to improve the performance of storage systems. Being an interactive feedback-driven paradigm, it is critical that the user receives responses to his navigation requests with little or no time lag. Therefore, we propose a class of view-based projection-generation method for mining various frequent sequential traversal patterns in the virtual environments. The frequent sequential traversal patterns are used to predict the user navigation behavior and, through clustering scheme, help to reduce disk access time with proper patterns placement into disk blocks. Finally, the effectiveness of these schemes is shown through simulation to demonstrate how these proposed techniques not only significantly cut down disk access time, but also enhance the accuracy of data prefetching.

Efficient LRU Algorithm for Cache Scheduling in a Disk Array System

Redundant Arrays of Inexpensive Disks (RAID) technology is an efficient way to solve the bottleneck problem between CPU processing ability and I/ O subsystem. Usually, there exist very large amounts of cache memory on the disk array controller board. How to schedule data in the cache so as to enhance the hit-ratio and reduce the time for the disk array to access data from the disks in the array is important for the design of the disk array controller. It is the key factor to measure the performance of the disk array. In this paper, a new cache-scheduling algorithm for the disk array, called track-based improved LRU algorithm, is proposed. The basic element, during the scheduling, is a track of disk drive. It is the combination of read-ahead caching, fast-write, elevator write-back and LRU replacement policy. Track-based improved algorithm uses read-ahead caching as the read policy, fast write as the write policy, elevator write-back as the destage policy and LRU as the replacement policy. Compared to the traditional cache-scheduling algorithm, the performance of disk array can be greatly improved, as the simulation results demonstrate.