Inter-server Communication Research Articles

Multi-cloud security model: establishment of inter-server communication for authentication integrity

Multi-cloud security model: establishment of inter-server communication for authentication integrity

A Survey of Multi-Access Edge Computing and Vehicular Networking

With the introduction of 5G and the Internet of Things, Multi-access Edge Computing (MEC) has become an evolving distributed compute and storage capability at the network edge. MEC will support task offloading, mobility, resource allocation, and management of inter-server communications to improve quality of service and satisfy real-time applications that require low latency. Thus, MEC is regarded as a crucial capability that will provide computation and storage at the network edge as an extension of the more traditional cloud. By placing MEC at the network edge, a broad range of new applications and services can be offered in close proximity to users, including support for vehicular networks. This paper provides a current and comprehensive review of MEC-enabled vehicular networks. It first introduces MEC by providing a definition, architecture, applications, and challenges. The paper then investigates MEC support for vehicular network applications and services and identifies current research and future challenges.

GCSA Codes With Noise Alignment for Secure Coded Multi-Party Batch Matrix Multiplication

A secure multi-party batch matrix multiplication problem (SMBMM) is considered, where the goal is to allow a master to efficiently compute the pairwise products of two batches of massive matrices, by distributing the computation across S servers. Any X colluding servers gain no information about the input, and the master gains no additional information about the input beyond the product. A solution called Generalized Cross Subspace Alignment codes with Noise Alignment (GCSA- NA) is proposed in this work, based on cross-subspace alignment codes. The state of art solution to SMBMM is a coding scheme called polynomial sharing (PS) that was proposed by Nodehi and Maddah-Ali. GCSA-NA outperforms PS codes in several key aspects—more efficient and secure inter-server communication, lower latency, flexible inter-server network topology, efficient batch processing, and tolerance to stragglers. The idea of noise alignment can also be combined with N-source Cross Subspace Alignment (N-CSA) codes and fast matrix multiplication algorithms like Strassen’s construction. Moreover, noise alignment can be applied to symmetric secure private information retrieval to achieve the asymptotic capacity.

An extended infrastructure security scheme for multi-cloud systems with verifiable inter-server communication protocol

Cloud service providers need to have elevated level of security than local system users, as users outsource their data to a remote system relying on its genuineness and also data has to be protected from intruders that exist globally. Multi-cloud systems are more prone to various kinds of attacks due to numerous internal communications of sensitive data. Multi-cloud infrastructure security can be enhanced by using stringent encryption algorithms or protocols with several integrity checks on each transaction, but that may drastically reduce the efficiency of the system. This research is one of a kind where we propose a multi-cloud framework comprising different techniques for data security as well as internal server communication security that can efficiently secure the privacy of data from eavesdroppers. Use of efficient cryptographic algorithms, modified Diffie-Hellman key exchange scheme and fragmentation technique assures overall efficiency of a system along with high security. The newest variant of SHA, i.e., SHA-3 outperformed other variants in terms of time efficiency and security. Results on multi-cloud setup demonstrate efficiency of proposed framework in terms of time taken for data storage, computations and retrieval while eliminating risks of attacks.

An extended infrastructure security scheme for multi-cloud systems with verifiable inter-server communication protocol

An extended infrastructure security scheme for multi-cloud systems with verifiable inter-server communication protocol

QuickPoint: Efficiently Identifying Densest Sub-Graphs in Online Social Networks for Event Stream Dissemination

Efficient event stream dissemination is a challenging problem in large-scale Online Social Network (OSN) systems due to the costly inter-server communications caused by the per-user view data storage. To solve the problem, previous schemes mainly explore the structures of social graphs to reduce the inter-server traffic. Based on the observation of high cluster coefficients in OSNs, a state-of-the-art social piggyback scheme can save redundant messages by exploiting an intrinsic hub-structure in an OSN graph for message piggybacking. Essentially, finding the best hub-structure for piggybacking is equivalent to finding a variation of the densest sub-graph. The existing scheme computes the best hub-structure by iteratively removing the node with the minimum weighted degree. Such a scheme incurs a worst computation cost of $O(n^2)$ O ( n 2 ) , making it not scalable to large-scale OSN graphs. Using alternative hub-structure instead of the best hub-structure can speed up the piggyback assignment. However, they greatly sacrifice the communication efficiency of the assignment schedule. Different from the existing designs, in this work, we propose a QuickPoint algorithm, which removes a fraction of nodes in each iteration in finding the best hub-structure. We mathematically prove that QuickPoint converges in $O(log_an) (a>1)$ O ( l o g a n ) ( a > 1 ) iterations in finding the best hub-structure for efficient piggyback. We implement QuickPoint in parallel atop Pregel, a vertex-centric distributed graph processing platform. Comprehensive experiments using large-scale data from Twitter and Flickr show that our scheme is 38.8× more efficient compared to existing schemes.

Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective

Mobile Edge Computing (MEC) is considered an essential future service for the implementation of 5G networks and the Internet of Things, as it is the best method of delivering computation and communication resources to mobile devices. It is based on the connection of the users to servers located on the edge of the network, which is especially relevant for real-time applications that demand minimal latency. In order to guarantee a resource-efficient MEC (which, for example, could mean improved Quality of Service for users or lower costs for service providers), it is important to consider certain aspects of the service model, such as where to offload the tasks generated by the devices, how many resources to allocate to each user (specially in the wired or wireless device-server communication) and how to handle inter-server communication. However, in the MEC scenarios with many and varied users, servers and applications, these problems are characterized by parameters with exceedingly high levels of dimensionality, resulting in too much data to be processed and complicating the task of finding efficient configurations. This will be particularly troublesome when 5G networks and Internet of Things roll out, with their massive amounts of devices. To address this concern, the best solution is to utilize Machine Learning (ML) algorithms, which enable the computer to draw conclusions and make predictions based on existing data without human supervision, leading to quick near-optimal solutions even in problems with high dimensionality. Indeed, in scenarios with too much data and too many parameters, ML algorithms are often the only feasible alternative. In this paper, a comprehensive survey on the use of ML in MEC systems is provided, offering an insight into the current progress of this research area. Furthermore, helpful guidance is supplied by pointing out which MEC challenges can be solved by ML solutions, what are the current trending algorithms in frontier ML research and how they could be used in MEC. These pieces of information should prove fundamental in encouraging future research that combines ML and MEC.

Piggyback Game: Efficient Event Stream Dissemination in Online Social Network Systems

Event stream dissemination dominates the workloads in large-scale Online Social Network (OSN) systems. Based on the de facto per-user view data storage, event stream dissemination raises a large amount of inter-server traffic due to the complex interconnection among OSN users. The state-of-the-art schemes mainly explore the structure features of social graphs to reduce the inter-server communications for event stream dissemination. Different sub-graph structures are exploited for achieving approximated optimal solutions. However, such schemes incur prohibitively high cost of either computation or communication. In this work, we follow a different design philosophy by using a game theoretic approach, which decomposes the highly complex graph computation problem into rational decision making of every individual social link. Specifically, we propose a novel social piggyback game to achieve a more efficient solution. We mathematically prove the existence of the Nash Equilibrium of the social piggyback game. Moreover, we propose an efficient best response dynamic algorithm to achieve the Nash Equilibrium, which quickly converges in a small number of iterations for large-scale OSNs. We further show that the communication cost of this design achieves a 1.5-approximation of the theoretical social optimum. We conduct comprehensive experiments using large-scale real-world traces from popular OSN systems as well as implement a prototype system to evaluate the performance of this design. Results show that the social piggyback game achieves a significant 302× improvement in system efficiency compared to existing schemes.

The Design Principles of Intelligent Load Balancing for Scalable WebSocket Services Used with Grid Computing

Large-scale grid compute projects may use computational capabilities of web browser sessions on numerous client devices to run distributed data processing algorithms. WebSocket protocol is the basis of duplex communications between browser worker nodes. Publish/Subscribe pattern defines communication scheme for client-server messaging. The downside of WebSocket-based Publish/Subscribe implementations is the stateful nature of long-living network connections. This aspect makes WebSocket scaling and load balancing a challenging task. In present work, we state that the intelligent balancer needs to consider different behavior types of client connections. We propose the negotiation-like mechanism between client and the load balancing system about the target server to set the client on. Our construction principles of scalable multi-server infrastructure with effective inter-server communications intensively use Redis instances, because Redis is a mature, well-tested, high performance system, rich of functions.

A Cost-Efficient Approach to Storing Users’ Data for Online Social Networks

As users increasingly befriend others and interact online via their social media accounts, online social networks (OSNs) are expanding rapidly. Confronted with the big data generated by users, it is imperative that data storage be distributed, scalable, and cost-efficient. Yet one of the most significant challenges about this topic is determining how to minimize the cost without deteriorating system performance. Although many storage systems use the distributed key value store, it cannot be directly applied to OSN storage systems. And because users’ data are highly correlated, hash storage leads to frequent inter-server communications, and the high inter-server traffic costs decrease the OSN storage system’s scalability. Previous studies proposed conducting network partitioning and data replication based on social graphs. However, data replication increases storage costs and impacts traffic costs. Here, we consider how to minimize costs from the perspective of data storage, by combining partitioning and replication. Our cost-efficient data storage approach supports scalable OSN storage systems. The proposed approach co-locates frequently interactive users together by conducting partitioning and replication simultaneously while meeting load-balancing constraints. Extensive experiments are undertaken on two realworld traces, and the results show that our approach achieves lower cost compared with state-of-the-art approaches. Thus we conclude that our approach enables economic and scalable OSN data storage.

CBL: Exploiting Community Based Locality for Efficient Content Search Service in Online Social Networks

Retrieving relevant data for users in online social network (OSN) systems is a challenging problem. Cassandra, a storage system used by popular OSN systems, such as Facebook and Twitter, relies on a DHT-based scheme to randomly partition the personal data of users among servers across multiple data centers. Although DHT is highly scalable for hosting a large number of users (personal data), it leads to costly inter-server communications across data centers due to the complex interconnection and interaction among OSN users. In this paper, we explore how to retrieve the OSN content in a cost-effective way by retaining the simple and robust nature of OSNs. Our approach exploits a simple, yet powerful principle called Community-Based Locality (CBL), which posits that if a user has a one-hop neighbor within a particular community, it is very likely that the user has other one-hop neighbors inside the same community. We demonstrate the existence of community-based locality in diverse traces of popular OSN systems such as Facebook, Orkut, Flickr, Youtube, and Livejournal. Based on the observation, we design a CBL-based algorithm to build the content index in OSN systems. By partitioning and indexing the relevant data of users within a community on the same server in the data center, the CBL-based index avoids a significant amount of inter-server communications during searching, making retrieving relevant data for a user in large-scale OSNs efficient. In addition, by using CBL-based scheme we can provide much faster search response and balanced loads. We conduct comprehensive trace-driven simulations to evaluate the performance of the proposed scheme. Results show that ourscheme significantly reduces the network traffic by 73 percent while reduces the query latency by 35 percent compared with existing schemes.

Scalability Issues in Online Social Networks

The last decade witnessed a tremendous increase in popularity and usage of social network services, such as Facebook, Twitter, and YouTube. Moreover, advances in Web technologies coupled with social networks has enabled users to not only access, but also generate, content in many forms. The overwhelming amount of produced content and resulting network traffic gives rise to precarious scalability issues for social networks, such as handling a large number of users, infrastructure management, internal network traffic, content dissemination, and data storage. There are few surveys conducted to explore the different dimensions of social networks, such as security, privacy, and data acquisition. Most of the surveys focus on privacy or security-related issues and do not specifically address scalability challenges faced by social networks. In this survey, we provide a comprehensive study of social networks along with their significant characteristics and categorize social network architectures into three broad categories: (a) centralized, (b) decentralized, and (c) hybrid. We also highlight various scalability issues faced by social network architectures. Finally, a qualitative comparison of presented architectures is provided, which is based on various scalability metrics, such as availability, latency, interserver communication, cost of resources, and energy consumption, just to name a few.

English

Web optimization is the process of optimizing the web to increase visibility or rank of websites in search engines. Furthermore, this process is also viewed from multiple perspectives, from optimizing inter-server communication that offers the best responses to users’ queries and provides targeted advertisements to users of a website. With this regard, the process of automatic classification and information extraction from users’ comments, also known as Sentiment Analysis (SA) or opinion mining, becomes vital to offer users the best online experience, based on their preferences. There are numerous algorithms available for SA. Therefore before applying any algorithm for polarity detection, pre-processing on comments is carried out. This study analyzes how we can write an algorithm for performing SA, and how different types of processing that are applied to initial data such as stemming or eliminating stop words affect the performance of this algorithm. The results show that even when a small sample is used, sentiment analysis can be done with a high accuracy (over 70%) if appropriate natural language processing algorithms are applied. Having a method for guessing sentiments could enable us, to excerpt opinions from the internet and predict online customer’s favorites, which could ascertain valuable for commercial or marketing research. Key words: Sentiment analysis, natural language processing, python programming language, machine learning, web optimization.

Minimizing Inter-Server Communications by Exploiting Self-Similarity in Online Social Networks

Efficiently operating on relevant data for users in large-scale online social network (OSN) systems is a challenging problem. Storage systems used by popular OSNs often rely on key-value stores, where randomly partitioning the data of users among servers across the data centers is the defacto standard. Although by using DHTs, the random partition scheme is highly scalable for hosting a large number of users, it leads to costly inter-server communications across data centers due to the complexity of interconnection and interaction between OSN users. In this paper, we explore how to reduce the inter-server communications by retaining the simple and robust nature of OSNs. We propose a data placement solution atop OSN systems to divide users among servers according to the interaction-locality-based structure. Our approach exploits a simple, yet powerful principle of OSN interactions, self-similarity, which reveals that the inter-server communication cost is minimized under such intrinsic structure. Our algorithm avoids a significant amount of inter-server traffic as well as achieves load balance among servers across the data centers. We demonstrate the existence of self-similarity in large-scale Facebook traces including 10 million Facebook users and 24 million interaction events. We conduct comprehensive trace-driven simulations to evaluate this design. Results show that our scheme significantly reduces the traffic and latency of OSN systems comparing to existing schemes.

Consistency-Aware Zone Mapping and Client Assignment in Multi-Server Distributed Virtual Environments

In Distributed Virtual Environments (DVEs), the primary task is to maintain a consistent view of the virtual world among all users. Multi-server architecture has been shown to have good scalability to support a large population of users in DVEs. In the zone-based Multi-server Distributed Virtual Environment (MSDVE), zone mapping and client assignment are two key issues in the design of an efficient and scalable MSDVE. Most of the existing work on the zone mapping and client assignment issues in MSDVE aims to either balance workload among servers, reduce inter-server communication, and/or reduce transmission delay. In this paper, we study the zone mapping and client assignment issues from a new perspective aiming to reduce the inconsistency of a DVE. Using the time-space inconsistency metric, we formally formulate the problem as a mix integer programming problem. The zone mapping and client assignment strategy is proposed which consists of a centralized algorithm for initialization and a distributed adaptive tuning algorithm for running system. Extensive experiments were conducted both by simulation and real system for evaluating the performance of the proposed solutions and results are reported in the paper.

IJARCCE - Computer and Communication Engineering

Inter server communication is most common word for the distributed system, it provide an interactive performance among the system. With the modern internet facility connection of server or interconnected networks is used for share the resources. An ideal system in the distributed architecture provide equal responsibility and computational power but an engaged system it fails to provide that facility among the distributed network and it also provide a load balancing problem when two or more packet send at the same time and some of the data were loosed because of collusion. For this issue many researchers try to balance the load of the server, but it improved in slight level only and if sequence of data occurred it may cause congestion problem. To recover the problem here we propose a Heuristic Algorithm (HA) to fully solve the load balancing and traffic avoidance problem and we also focus on security problem with the help of DSA Algorithm.

Efficient Keyword Searching in Large-scale Social Network Service

Different from traditional web searching, the relevant information for a social network system (SNS) is commonly the content from his/her friends. Such a difference makes content indexing extremely difficult for an online social network (OSN) search system because every user has an individual view during searching. Building such a per-user view index over existing SNS Key-Value stores raises a large amount of communication cost due to the complex interconnections among OSN users, making the search system unscalable. To address the problem, we propose a novel protocol called summary index to support keyword searching. In the protocol, each user keeps a directory of the succinct summaries of his/her neighbors, and checks these summaries for potential hits before sending any queries. Two factors contribute to the low overhead of our design: the summary index representations are memory efficient, and the summary dissemination for index updating is communication efficient. First, we design an incremental scalable Bloom filter for summarizing the content constantly generated by a neighbor of a user. For an issued query by a user, the search system first checks against the summary index for a user's neighbor to predict the neighbors likely having desired content. Thus, the search system saves a significant inter-server communication cost by avoiding exhaustively transmitting the query to all the neighbors. Second, to further reduce the overhead for maintaining the social index, we leverage the piggyback strategy which exploits the links with high social strengths to avoid redundant messages during updating the per-user view summary index. We conduct comprehensive simulations using traces from real world systems to evaluate this design. Results show that our scheme significantly outperforms existing schemes for OSN searching in terms of inter-sever traffic by 98 percent.

웰니스 환경에서 암호화 프로토콜 적용을 위한 모바일과 원격 서버간 트래픽 성능 평가

U-Wellness Healthcare System(U-WHS)이란, 웰빙(wellbeing)과 피트니스(fitness)를 결합한 원격 건강 관리 시스템을 말한다. 이러한 시스템에서는 시간과 공간에 제약 없이 언제 어디서나 환자의 생체정보를 측정 및 관리 할 수 있는 것이다. 본 논문에서는 스마트모바일기기와 HIS(Hospital Information System)간 생체정보 전송시 암호화 모듈이 통신 평가에 끼치는 영향을 알아보기 위해 수행하였다. U-WHS 모델의 경우 클라이언트는 iOS Xcode환경의 Objective-c 개발 언어를 이용하여 SEED, HIGHT 암호화 모듈 적용을 하였다. HIS의 경우 클라이언트와 서버간 통신을 위하여 HTML5의 WebSocket API와 관계형 데이터베이스 관리 시스템인 MySQL를 적용하였다. 그리하여 WIFI 통신 환경에서 Wireshark를 사용, 분석하여 생체 정보의 데이터 전송율, 지연율, 손실율에 대한 평가를 확인하였다. U-WHS refers to a means of remote health monitoring service to combine fitness with wellbing. U-WHS is a system which can measure and manage biometric information of patients without any limitation on time and space. In this paper, we performed in order to look into the influence that the encryption module influences on the communication evaluation in the biometric information transmission gone to the smart mobile device and Hospital Information System.In the case of the U-WHS model, the client used the Objective-c programming language for software development of iOS Xcode environment and SEED and HIGHT encryption module was applied. In the case of HIS, the MySQL which is the Websocket API of the HTML5 and relational database management system for the client and inter-server communication was applied. Therefore, in WIFI communication environment, by using wireshark, data transfer rate of the biometric information, delay and loss rate was checked for the evaluation.

The Little Engine(s) That Could: Scaling Online Social Networks

The difficulty of partitioning social graphs has introduced new system design challenges for scaling of online social networks (OSNs). Vertical scaling by resorting to full replication can be a costly proposition. Scaling horizontally by partitioning and distributing data among multiple servers using, for e.g., distributed hash tables (DHTs), can suffer from expensive interserver communication. Such challenges have often caused costly rearchitecting efforts for popular OSNs like Twitter and Facebook. We design, implement, and evaluate SPAR, a Social Partitioning and Replication middleware that mediates transparently between the application and the database layer of an OSN. SPAR leverages the underlying social graph structure in order to minimize the required replication overhead for ensuring that users have their neighbors' data colocated in the same machine. The gains from this are multifold: Application developers can assume local semantics, i.e., develop as they would for a single machine; scalability is achieved by adding commodity machines with low memory and network I/O requirements; and N+K redundancy is achieved at a fraction of the cost. We provide a complete system design, extensive evaluation based on datasets from Twitter, Orkut, and Facebook, and a working implementation. We show that SPAR incurs minimum overhead, can help a well-known Twitter clone reach Twitter's scale without changing a line of its application logic, and achieves higher throughput than Cassandra, a popular key-value store database.

A multi-agent memetic algorithm approach for distributed object allocation

In this paper, we present a multi-agent system (MAS) approach to allocate objects over different servers in distributed computing environment (DCE). We show that distributed object allocation problem in DCE with an objective to allocate objects over different servers so that the number of inter-server communications can be minimized is similar to the graph bisection problem. We test the performance of the MAS approach against other heuristic approaches and semidefinite programming (SDP) based lower bounds, and show that the MAS approach fares well when compared to other competing heuristic approaches.