Reduce Server Load Research Articles

An energy efficient clustering algorithm based on density and fitness for mobile crowd-sensing network

Mobile crowd-sensing (MCS) is a cutting-edge paradigm that gathers sensory data and generates valuable insights for a multitude of users by utilizing built-in sensors and social applications in mobile devices. This enables a broad spectrum of Internet of Things (IoT) services. We introduce a novel MCS algorithm, Mobile Crowd-sensing Low Energy Clustering (MCLEC), which employs advanced clustering techniques to address issues of data oversampling and energy inefficiency prevalent in MCS networks. MCLEC innovatively adjusts clustering radii based on local node density and the proximity of nodes to the cloud server, thus optimizing data transmission paths and reducing energy consumption. A pivotal enhancement in MCLEC is its cluster head election strategy, which prioritizes leaders based on their energy levels and mobility, thereby enhancing network stability and minimizing the frequency of head re-elections. Our comparisons with established algorithms such as LEACH, LEACH-C, LEACH-M, DEEC, and SEP show that MCLEC significantly improves energy efficiency, reduces server load, and prolongs the lifespan of network nodes, establishing it as an effective solution for IoT applications dependent on MCS. Additionally, MCLEC was compared with other novel clustering algorithms including E-FLZSEPFCH, DFLC, ECPF, ACAWT, UCR, CHEF, and Gupta's algorithm. The results indicate that MCLEC also surpasses most of these algorithms in terms of energy consumption and network lifetime.

Non-orthogonal multiple access-based MEC for energy-efficient task offloading in e-commerce systems

Mobile edge computing (MEC) reduces the latency for end users to access applications deployed at the edge by offloading tasks to the edge. With the popularity of e-commerce and the expansion of business scale, server load continues to increase, and energy efficiency issues gradually become more prominent. Computation offloading has received widespread attention as a technology that effectively reduces server load. However, how to improve energy efficiency while ensuring computing requirements is an important challenge facing computation offloading. To solve this problem, using non-orthogonal multiple access (NOMA) to increase the efficiency of multi-access wireless transmission, MEC supporting NOMA is investigated in the research. Computing resources will be divided into separate sub-computing that will be handled via e-commerce terminals or transferred to edge sides by reutilizing radio resources, we put forward a Group Switching Matching Algorithm Based on Resource Unit Allocation (GSM-RUA) algorithm that is multi-dimensional. To this end, we first formulate this task allocation problem as a long-term stochastic optimization problem, which we then convert to three short-term deterministic sub-programming problems using Lyapunov optimization, namely, radio resource allocation in a large timescale, computation resource allocating and splitting in a small-time frame. Of the 3 short-term deterministic sub-programming problems, the first sub-programming problem can be remodeled into a 1 to n matching problem, which can be solved using the block-shift-matching-based radio resource allocation method. The latter two sub-programming problems are then transformed into two continuous convex problems by relaxation and then solved easily. We then use simulations to prove that our GSM-RUA algorithm is superior to the state-of-the-art resource management algorithms in terms of energy consumption, efficiency and complexity for e-commerce scenarios.

DATA AVAILABILITY IN DECENTRALIZED DATA STORAGE USING FOUR-NODE INTERPLANETARY FILE SYSTEM

Centralized storage is a data storage model in which data is stored and managed in a single physical location or centralized system. In this model, all data and information are stored on servers or data centers managed by one entity or organization. This model also has disadvantages such as risk of system failure against distributed denial of service (DDoS) attacks, natural disasters, and hardware failures causing a single point of failure. This threat results in loss of data and a lack of user confidence in the availability of data in centralized storage. This study proposes to evaluate the availability of data in decentralized data storage using a four-node interplanetary file system (IPFS) that is interconnected with a swarm key as the authentication key. Unlike centralized storage which has only one data center, four-node IPFS allows users to upload and download data from four interconnected data centers. This can avoid dependence on the central server and reduce server load. The evaluation results show that decentralized data storage using a four-node IPFS system is three times more resilient than centralized storage against a single point of failure. This system can increase data availability so that organizations can minimize data loss from the threat of system failure.

Desain dan Analisis Sistem CyberShare Menggunakan Four Node Interplanetary File System (IPFS)

Cybersecurity information sharing is a proactive and collaborative measure in enhancing organizational security by exchanging cybersecurity information using a centralized repository service. However, in practice, the use of centralized services poses a threat to distributed denial-of-service (DDoS) attacks which can result in system failure and cause single point of failure as well as man-in-the-middle (MITM) attacks which can result in modification of information and theft of exchanged information. This threat results in a lack of user confidence in the confidentiality, integrity, and availability of information. This study proposes the design of a secure cybersecurity information sharing (CyberShare) system using a private interplanetary file system (IPFS) network as a decentralized information storage. Unlike centralized storage which only has a single-node, CyberShare systems use four-node IPFS interconnected with swarm keys as authentication keys. This system allows users to store and share information from the sender to the recipient of information, avoiding dependence on a central server and reducing server load. The results of the analysis show that the proposed CyberShare system can guarantee the confidentiality, integrity, and availability of cyber security information. CyberShare systems can enhance the security of the information exchanged so that organizations can safely share and utilize cybersecurity information.

WebArrayDB

Geospatial array DBMSs operate on georeferenced N -d arrays. They provide storage engines, query parsers, and processing capabilities as their core functionality. Traditionally, those have been too heavy for a Web browser to support. Hence, Web Applications, mostly Geographic Information Systems (GISs), run array management on their server back-ends that return small portions of the results via the network. We showcase WebArrayDB, the first geospatial array DBMS that can run completely inside a Web browser. We demonstrate that modern Web browsers, JavaScript, and respective software libraries are sufficiently mature to build and run such a feature-rich and powerful DBMS. A Web-based array DBMS should reduce server load, enable offline work, decrease network I/O, and improve user experience. We also present ArrayGIS, our new Web GIS based on WebArrayDB, and invite everyone to explore both via a freely accessible, informative, and interactive Web GUI.

QoS Improvement Using In-Network Caching Based on Clustering and Popularity Heuristics in CCN.

Content-Centric Networking (CCN) has emerged as a potential Internet architecture that supports name-based content retrieval mechanism in contrast to the current host location-oriented IP architecture. The in-network caching capability of CCN ensures higher content availability, lesser network delay, and leads to server load reduction. It was observed that caching the contents on each intermediate node does not use the network resources efficiently. Hence, efficient content caching decisions are crucial to improve the Quality-of-Service (QoS) for the end-user devices and improved network performance. Towards this, a novel content caching scheme is proposed in this paper. The proposed scheme first clusters the network nodes based on the hop count and bandwidth parameters to reduce content redundancy and caching operations. Then, the scheme takes content placement decisions using the cluster information, content popularity, and the hop count parameters, where the caching probability improves as the content traversed toward the requester. Hence, using the proposed heuristics, the popular contents are placed near the edges of the network to achieve a high cache hit ratio. Once the cache becomes full, the scheme implements Least-Frequently-Used (LFU) replacement scheme to substitute the least accessed content in the network routers. Extensive simulations are conducted and the performance of the proposed scheme is investigated under different network parameters that demonstrate the superiority of the proposed strategy w.r.t the peer competing strategies.

Research on remote data access and visualization for long pulse experiment based on web technologies

Model Drive System plus (MDSplus) is an efficient and convenient data storage and management software designed by MIT specifically for pulse experiments. After years of development, MDSplus has become a data management system that integrates data acquisition, storage and processing of complex scientific data. However, with the development of the experimental pulse, long pulse experiment has now become the main mode of pulse-type experiment. Compared with the short pulse experiment, the data acquisition under the long pulse experiment speed is faster, the experiment time is longer, and the amount of data is larger. In view of the fact that most of the existing visualization systems are based on client/server (C/S) architecture, which can only display local visualization. Therefore, In order to enrich the way for experimenters to obtain data, this paper designs a visual platform based on browser/server (B/S) that using the WAMP (Windows+apache+MySQL+php) architecture to build the server. The bootstrap framework is used to build the front-end. Before the visualization data transmission, the original data is processed by segmentation technology to reduce server load and improve transmission utilization so that experimenters can query and obtain the required data remotely through the platform.

USAGE OF SESSION METRICS TO TASKS SCHEDULING IN BROWSER-BASED VOLUNTEER COMPUTING SYSTEMS

Context. The study is devoted to the development an adaptive method for scheduling tasks in distributed browser-based volunteer computing systems. which is constructed on the session metrics of a volunteer user. This allows server to allocate tasks to each volunteer for computing, depending on the characteristics of his computing processes. More productive clients will receive more tasks, maximizing resource utilization, reducing server load and facilitating scaling; less productive clients will receive fewer tasks, solving the problem of excessive blocking of input data.Objective. The aim of the study is increasing the efficiency of distributed browser-based volunteer computing systems by using session metrics to tasks scheduling.Method. The session metrics that determine the behavior of volunteer users are described. The indicators that characterize the progress of the computational process on user systems were introduced. The formula to calculate the amount of input data for distribution to client is proposed. It takes into account the capacity of the client's system and the tendency of processes on its system. Simulation was done using different methods to calculate the amount of input data on the basis of the existing system of volunteer computing. To evaluate the performance of such systems, the total number of user hits to the server part and the total number of calculated results were used.Results. The proposed method is applied in the existing browser-based volunteer computing system. A comparison of the results of the proposed method with the existing ones showed a decrease in server load and an increase of the number of calculated results when session metrics are used.Conclusions. The simulation confirmed the convenience and efficiency of session metrics usage to tasks scheduling. The presented approach provides scalability of distributed browser-based volunteer computing systems. The prospects of further research are to use the users’ statistics and information from their browsers to calculate the amount of input data for distribution to client.

Load Balancing using Docker and Kubernetes: A Comparative Study

Still in its early years, containers are increasingly being used in production environments. Containers offer a streamlined approach, easy deployment, and secure method of implementing infrastructure requirements also provide a much-improved alternative to virtual machines. A load balancer is required to distribute traffic across clusters. And now, with multiple container environments becoming widespread, load balancers are becoming a necessity to distribute traffic and reduce server load. Different load balancing algorithms provide a solution to this with varying efficiency. This paper presents a study on the latest methods which are being implemented to perform effective load balancing on containers. Docker Swarm and Kubernetes are the most widely used systems for deploying and managing a cluster of containers in an environment. The paper further demonstrates how Docker Swarm and Kubernetes can be used to minimize load traffic through load balancing techniques. We have introduced load balancing and different algorithms. Also, we have shown the implementations of load balancing algorithms in Docker and Kubernetes and finally compared the results. The paper finally concludes why Kubernetes is often preferred over Docker Swarm for load balancing.

Optimized content centric networking for future internet: Dynamic popularity window based caching scheme

Content-Centric Networking (CCN) has emerged as a promising future Internet architecture that concentrates on content-centric information retrieval instead of current host-based infrastructure. In-network content caching has been integrated with CCN that allows the transformation from IP-based architectures to a completely decentralized environment which is based on the Content-Centric approach for future Internet architecture. In traditional caching schemes, each intermediate router caches a copy of content before forwarding it towards the requester. However, the caching capacity of routers is much smaller as compared to the forwarded traffic through them. For comprehensive utilization of available cache space, a novel content caching scheme called DPWCS (Dynamic Popularity Window-based Caching Scheme) has been proposed that efficiently places contents in the network cache. By intelligently determining the size of popularity window using request rate and the total number of distinct contents in the network, DPWCS is able to improve network performance by caching popular contents in the routers. The proposed scheme also translates the threshold determination issue into an optimization problem and propose cache size and Zipf distribution based heuristics for its computation. Extensive simulation results demonstrate the necessity of DPWCS, as the default caching scheme is unable to achieve significant network performance with realistic request rates, cache sizes, and requests distribution patterns. It is shown that the proposed scheme increases the cache hit-ratio, hop reduction ratio and server-load reduction ratio up-to ≈ 13%, ≈ 11% and ≈ 13% from peer competing caching schemes respectively with widely accepted Least-Recently-Used (LRU) cache replacement policy and ≈ 23% reduction in average network hop count under various simulation scenarios. This makes it suitable for Industry 4.0 and futuristic Internet architectures with 5G.

Toward Efficient Short-Video Sharing in the YouTube Social Network

The past few years have seen an explosion in the popularity of online short-video sharing in YouTube. As the number of users continue to grow, the bandwidth required to maintain acceptable quality of service (QoS) has greatly increased. Peer-to-peer (P2P) architectures have shown promise in reducing the bandwidth costs; however, the previous works build one P2P overlay for each video, which provides limited availability of video providers and produces high overlay maintenance overhead. To handle these problems, in this work, we novelly leverage the existing social network in YouTube, where a user subscribes to another user’s channel to track all his/her uploaded videos. The subscribers of a channel tend to watch the channel’s videos and common-interest nodes tend to watch the same videos. Also, the popularity of videos in one channel varies greatly. We study real trace data to confirm these properties. Based on these properties, we propose SocialTube, which builds the subscribers of one channel into a P2P overlay and also clusters common-interest nodes in a higher level. It also incorporates a prefetching algorithm that prefetches higher-popularity videos. To enhance the system performance, we further propose the demand/supply-based cache management scheme and reputation-based neighbor management scheme. Extensive trace-driven simulation results and PlanetLab real-world experimental results verify the effectiveness of SocialTube at reducing server load and overlay maintenance overhead and at improving QoS for users.

Evaluation of the Caching Placement Algorithms in Named Data Network for Video on Demand Service

In the existing literature for Information-Centric Networking (ICN) architectures improvement metrics are well researched and studied including the cache hit rate, origin server load reduction, and the reduction in the overall network footprint [1]. Metrics could be optimized using various aspects of caching including the content placement to make a decision which routers should cache the content on a request patch [2-5]. Impact of different ICN caching placement strategies on the main cloud TV performance metrics is deeply researched in this work, and results are presented. Seven cache placement algorithms are evaluated including four cache sizes of 1 GB, 10 GB, 100 GB, and 1 TB.

On the Complexity of Optimal Content Placement in Hierarchical Caching Networks

The ever increasing demand for content is straining operators’ networks, thereby necessitating development of alternative content delivery mechanisms. Distributed caching architectures constitute a promising solution for mitigating the effects of demand growth by placing popular content files in proximity to users, rather than in a central site. In this paper, we study the content placement problem in multiple-level hierarchical caching networks where user requests for files are routed upwards toward content servers, satisfied by intermediate nodes if the latter have cached the requested files. Our goal is to reduce the server load by serving as many requests as possible by the caches. We show that the problem is NP-Hard in its general form, but it can be solved optimally in polynomial-time when the caches are installed on a single hierarchy path. For the general case, we develop an algorithm achieving a provably better approximation ratio than the best-known counterparts. Numerical experiments show up to 56% reduction in server load over existing algorithms, with gains increasing as the content popularity distributions get steeper and more diverse across nodes, and the cache capacities at the upper hierarchy levels increase. Our evaluation code is publicly available for the benefit of research community.

Triple Pattern Fragments: A low-cost knowledge graph interface for the Web

Billions of Linked Data triples exist in thousands of RDF knowledge graphs on the Web, but few of those graphs can be queried live from Web applications. Only a limited number of knowledge graphs are available in a queryable interface, and existing interfaces can be expensive to host at high availability. To mitigate this shortage of live queryable Linked Data, we designed a low-cost Triple Pattern Fragments interface for servers, and a client-side algorithm that evaluates SPARQL queries against this interface. This article describes the Linked Data Fragments framework to analyze Web interfaces to Linked Data and uses this framework as a basis to define Triple Pattern Fragments. We describe client-side querying for single knowledge graphs and federations thereof. Our evaluation verifies that this technique reduces server load and increases caching effectiveness, which leads to lower costs to maintain high server availability. These benefits come at the expense of increased bandwidth and slower, but more stable query execution times. These results substantiate the claim that lightweight interfaces can lower the cost for knowledge publishers compared to more expressive endpoints, while enabling applications to query the publishers’ data with the necessary reliability.

Triple Pattern Fragments: A Low-Cost Knowledge Graph Interface for the Web

Billions of Linked Data triples exist in thousands of RDF knowledge graphs on the Web, but few of those graphs can be queried live from Web applications. Only a limited number of knowledge graphs are available in a queryable interface, and existing interfaces can be expensive to host at high availability. To mitigate this shortage of live queryable Linked Data, we designed a low-cost Triple Pattern Fragments interface for servers, and a client-side algorithm that evaluates SPARQL queries against this interface. This article describes the Linked Data Fragments framework to analyze Web interfaces to Linked Data and uses this framework as a basis to define Triple Pattern Fragments. We describe client-side querying for single knowledge graphs and federations thereof. Our evaluation verifies that this technique reduces server load and increases caching effectiveness, which leads to lower costs to maintain high server availability. These benefits come at the expense of increased bandwidth and slower, but more stable query execution times. These results substantiate the claim that lightweight interfaces can lower the cost for knowledge publishers compared to more expressive endpoints, while enabling applications to query the publishers’ data with the necessary reliability.

Distributed Information Retrieval: Developments and Strategies

Opposed to centralized search where Websites are crawled and indexed, Distributed Information Retrieval (DIR), also known as Federated Search, is a powerful way to comprehensively search multiple databases in real-time simultaneously. DIR is preferred to centralized search environments in a number of ways, characteristically among them are: 1. the diversity of resources that are made available; 2. improving scalability and reducing server load and network traffic; 3. the leverage of accessing the hidden or deep Web.There are three major phases/tasks of a DIR (i) resource description or collection representation (ii) resource selection and (iii) result merging. This paper aims at providing a comprehensive review on the various phases of DIR and also some current strategies being recommended in enhancing and improving the smooth implementation of a DIR system.

Cache-Centric Video Recommendation

In this article, we take advantage of the user behavior of requesting videos from the top of the related list provided by YouTube to improve the performance of YouTube caches. We recommend that local caches reorder the related lists associated with YouTube videos, presenting the cached content above noncached content. We argue that the likelihood that viewers select content from the top of the related list is higher than selection from the bottom, and pushing contents already in the cache to the top of the related list would increase the likelihood of choosing cached content. To verify that the position on the list really is the selection criterion more dominant than the content itself, we conduct a user study with 40 YouTube-using volunteers who were presented with random related lists in their everyday YouTube use. After confirming our assumption, we analyze the benefits of our approach by an investigation that is based on two traces collected from a university campus. Our analysis shows that the proposed reordering approach for related lists would lead to a 2 to 5 times increase in cache hit rate compared to an approach without reordering the related list. This increase in hit rate would lead to reduction in server load and backend bandwidth usage, which in turn reduces the latency in streaming the video requested by the viewer and has the potential to improve the overall performance of YouTube's content distribution system. An analysis of YouTube's recommendation system reveals that related lists are created from a small pool of videos, which increases the potential for caching content from related lists and reordering based on the content in the cache.

A Social-Network-Aided Efficient Peer-to-Peer Live Streaming System

In current peer-to-peer (P2P) live streaming systems, nodes in a channel form a P2P overlay for video sharing. To watch a new channel, a node depends on the centralized server to join in the overlay of the channel. In today's live streaming applications, the increase in the number of channels triggers users' desire of watching multiple channels successively or simultaneously. However, the support of such watching modes in current applications is no better than joining in different channel overlays successively or simultaneously through the centralized server, which if widely used, poses a heavy burden on the server. In order to achieve higher efficiency and scalability, we propose a Social-network-Aided efficient liVe strEaming system (SAVE). SAVE regards users' channel switching or multichannel watching as interactions between channels. By collecting the information of channel interactions, nodes' interests, and watching times, SAVE forms nodes in multiple channels with frequent interactions into an overlay, constructs bridges between overlays of channels with less frequent interactions, and enables nodes to identify friends sharing similar interests and watching times. Thus, a node can connect to a new channel while staying in its current overlay, using bridges or relying on its friends, reducing the need to contact the centralized server. We further propose the channel-closeness-based chunk-pushing strategy and capacity-based chunk provider selection strategy to enhance the system performance. Extensive experimental results from the PeerSim simulator and PlanetLab verify that SAVE outperforms other systems in system efficiency and server load reduction, as well as the effectiveness of the two proposed strategies.

Request routing through collaborative in‐network caching for bandwidth optimization: a methodology

AbstractTo reduce data access latency, network traffic volume and server load, in‐network caching was proposed and has become an intrinsic component of the content‐centric network (CCN) architecture. The content‐oriented characteristics of in‐network caching, such as arbitrary topology and volatile content locations, make routers content‐aware and supportive of fast content distribution. Meanwhile, they also raise new challenges in content placement and request routing, namely, how to optimally make content storage decisions and forward user requests towards a ‘best’ (e.g. closest) available content replicas, so as to minimize the bandwidth cost under storage and link capacity limit. To address this problem, we build a distributed in‐network caching model to formulate the content placement and request routing in CCN, aiming at minimizing the bandwidth cost with strict storage and bandwidth constraints. Based on the proposed model, we design a scalable, adaptive and low‐complexity in‐network caching scheme for content placement and request routing and analyse the performance gains via simulations on a real Internet Service Provider (ISP) network topology and traffic traces. The experimental results show the proposed model and scheme are superior. Compared with the existing works, we also observe significant performance enhancements in terms of hit ratio of requests, reduction of server load and bandwidth cost. Copyright © 2015 John Wiley & Sons, Ltd.

Efficient cache availability management in Information-Centric Networks

In-network caching is one of the fundamental operations of Information-Centric Networks (ICN). The default caching strategy taken by most of the current ICN proposals is caching along-default-path, which makes popular objects to be cached redundantly across the network, resulting in a low utilization of available cache space. On the other hand, efficient use of network-wide cache space requires possible cooperation among caching routers without the use of excessive signaling burden. While most of the cache optimization efforts strive to improve the latency and the overall traffic efficiency, we have taken a different path in this work and improved the storage efficiency of the cache space so that it is utilized to its most.In this work we discuss the ICN caching problem, and propose a novel distributed architecture to efficiently use the network-wide cache storage space based on distributed caching. The proposal achieves cache retention efficiency by means of controlled traffic redirection and selective caching. We utilize the ICN mechanisms and routing protocol messages for decision making, thus reducing the overall signaling need. Our proposal achieves almost 9-fold increase in cache storage efficiency, and around 20% increase in server load reduction when compared to the classic caching methods used in contemporary ICN proposals.