Reduce Communication Overhead Research Articles

An improved task scheduling algorithm for scientific workflow in cloud computing environment

As an emerging business computing model, cloud computing needs to deal with the scientific workflow submitted by user groups. How to efficiently schedule massive tasks of scientific workflow is an important problem in cloud computing. In order to minimize the total execution time of workflow, reduce the consume of cloud resources, reduce execution costs of users, a new task scheduling algorithm based on task duplication and task grouping is proposed in this paper. The new algorithm is composed of four steps. Firstly, the join nodes are duplicated, a DAG is converted into an in-tree graph, then all tasks are divide into task groups, it reduces communication overhead between tasks; then some task groups are merged by utilizing the idle time between tasks in a task group, it reduces the use of the processors; lastly, Assign the tasks to processors by making full use of the idle time of the processors, it increases resource utilization. The new algorithm is compared with TDS and TDCS by simulation platform CloudSim. The performance indicators for comparison include makespan of workflow, the number of used processors and resource utilization. The experiment results show that the new algorithm has a smaller makespan of workflow, fewer processors are used, and has higher resource utilization for both compute-intensive and data-intensive workflow, especially for data-intensive workflow, the new algorithm has obvious advantages on the three performance indicators.

Universal secure rank-metric coding schemes with optimal communication overheads

We study the problem of reducing the communication overhead from a noisy wire-tap channel or storage system where data is encoded as a matrix, when more columns (or their linear combinations) are available. We present its applications to reducing communication overheads in universal secure linear network coding and secure distributed storage with crisscross errors and erasures and in the presence of a wire-tapper. Our main contribution is a method to transform coding schemes based on linear rank-metric codes, with certain properties, to schemes with lower communication overheads. By applying this method to pairs of Gabidulin codes, we obtain coding schemes with optimal information rate with respect to their security and rank error correction capability, and with universally optimal communication overheads, when n ≤ m, being n and m the number of columns and number of rows, respectively. Moreover, our method can be applied to other families of maximum rank distance codes when n > m. The downside of the method is generally expanding the packet length, but some practical instances come at no cost.

OverWatch: A Cross-Plane DDoS Attack Defense Framework with Collaborative Intelligence in SDN

Distributed Denial of Service (DDoS) attacks are one of the biggest concerns for security professionals. Traditional middle-box based DDoS attack defense is lack of network-wide monitoring flexibility. With the development of software-defined networking (SDN), it becomes prevalent to exploit centralized controllers to defend against DDoS attacks. However, current solutions suffer with serious southbound communication overhead and detection delay. In this paper, we propose a cross-plane DDoS attack defense framework in SDN, called OverWatch, which exploits collaborative intelligence between data plane and control plane with high defense efficiency. Attack detection and reaction are two key procedures of the proposed framework. We develop a collaborative DDoS attack detection mechanism, which consists of a coarse-grained flow monitoring algorithm on the data plane and a fine-grained machine learning based attack classification algorithm on the control plane. We propose a novel defense strategy offloading mechanism to dynamically deploy defense applications across the controller and switches, by which rapid attack reaction and accurate botnet location can be achieved. We conduct extensive experiments on a real-world SDN network. Experimental results validate the efficiency of our proposed OverWatch framework with high detection accuracy and real-time DDoS attack reaction, as well as reduced communication overhead on SDN southbound interface.

Regional Load Balancing Circuitous Bandwidth Allocation Method Based on Dynamic Auction Mechanism

Aiming at the problem of link congestion caused by the shortage of network bandwidth resources at the user end, this paper first proposes a regional load balancing idea. Then, for the problem of bandwidth resource allocation in regional load balancing, a bandwidth allocation model is established and a dynamic auction algorithm is proposed. The algorithm calculates the link quality and stability by constructing a link model, and introduces the auction bandwidth to the auctioneer's incentive degree to obtain the auction bidding function. The simulation results show that the algorithm can effectively improve the user's network status, reduce the service response delay, increase the throughput, and at the same time can effectively prevent the auction user's false bidding behavior, so that the auction quote quickly converges to the maximum quote, reduces the number of auctions, and reduces Communication overhead.

Intelligent clustering protocol design and optimisation in wireless sensor networks

Communication overhead is one of the big troubles in distributed clustering for which protocols that are able to guarantee limited flow of control packets shall be introduced. Considering that most of fuzzy based protocols are vulnerable to overhead of control packets in cluster setup phase, new approach has been introduced in this paper to reduce communication overhead and consequently optimising distributed cluster formation. Clusters are formed using fuzzy logic and optimal routes towards the base station are established based on ant colony optimisation (ACO) algorithm. The optimisation problem consists of finding routes towards the base station such that the tour length is minimised and the balance of energy consumption is maintained among routes in the path. The fuzzy logic and ACO toolboxes are developed in C++ and integrated with OMNeT++ simulation platform to implement the protocol. The experimental results prove that the proposed protocol extends the network lifetime compared to LEACH and CHEF protocols.

Adaptive Consistency Strategy of Multiple Controllers in SDN

In large-scale software-defined networking (SDN), the logically centralized controller usually uses multiple distributed controllers to manage and operate the network in a cooperative manner from the perspective of global network view. One of the major challenges with respect to deploying multiple controllers in SDN is how to synchronize the state among controllers to maintain information consistency. Aiming at solving this problem, we propose a periodically adaptive synchronization strategy of controllers. At first, the consistency level of controllers in the network is quantified based on the characteristics of SDN. Then, an adaptive synchronization strategy is proposed. By the use of this strategy, controllers are divided into three kinds of roles, and the synchronization period is adjusted dynamically by an adaptive function according to the current network state to attain a certain consistency level. The simulation results show that the proposed strategy has merits of reducing communication overhead of controllers as well as improving network availability compared with the other non-adaptive strategies. However, it lacks consideration of load balance among controllers, which will be our future research direction.

A Novel Dynamic Link Connectivity Strategy Using Hello Messaging for Maintaining Link Stability in MANETs

Maintaining link stability among randomly deployed network nodes is one of the key challenges for effective communication in mobile ad hoc networks (MANETs). Under uniform speed and random trajectory of mobile nodes, there must be a unified model to determine an adequate strategy that addresses the issue of link stability in MANETs. We present a novel dynamic link connectivity (DLC) strategy that maintains link stability through efficient link connectivity among the neighboring nodes using Hello messaging. We also perform stochastic analysis of the proposed strategy, which predicts the future link status among the neighboring nodes at different time steps of a Markov process. We find that the link stability is affected by the received signal strength, signal‐to‐noise ratio, transition rates between the connection and disconnection states, and probabilities of link connectivity and disconnectivity at steady state. Analytical and simulation results indicate efficacy of the proposed strategy in terms of reduced communication overhead, lower propagation delay, and better energy efficiency of the network. The results also demonstrate that the proposed strategy minimizes the average response time, increases the throughput, and reduces the packet loss ratio, thereby, maintaining efficient link stability among the neighboring nodes.

Reliable Resource Provisioning Using Bankers’ Deadlock Avoidance Algorithm in MEC for Industrial IoT

Multi-access edge computing (MEC) is a new 5G enabling technology proposed to reduce latency by bringing cloud computing capability closer to IoT and mobile device users. MEC may be prone to unreliable communication as a result of deadlock during resource provisioning. Deadlock may occur due to a huge number of devices contending for a limited amount of resources if adequate measures are not put in place. It is crucial to eradicate deadlock while scheduling and provisioning of resources on MEC to achieve highly reliable and available system. In this paper, a deadlock avoidance resource provisioning algorithm is proposed for Industrial IoT devices using MEC platforms to ensure the higher reliability of network interactions. The proposed scheme incorporates a banker’s resource-request algorithm using software-defined networking to reduce communication overhead. Simulation Results have shown that system deadlock can be prevented by applying the proposed algorithm which ultimately leads to a more reliable network interaction between mobile stations and MEC platforms.

Accurate and Communication-Efficient Detection of Widespread Events

Monitoring agents are deployed in distributed network environments to collect data and report events to a central coordinator. A widespread event is defined as an event that is observed by all monitoring agents simultaneously. Identifying widespread events can assist in detecting problems, such as cyberattacks, network malfunctions, and abnormal usages. However, detecting widespread events is challenging and a significant communication overheadcan be incurred, because every monitoring agent must report any observed event to a central coordinator. In this paper, we study the problem of detecting widespread events accurately. Our proposed schemes can significantly reduce communication overhead and achieve perfect detection accuracy, which was not achieved by any previous studies. The new schemes can also identify generalized widespread events, defined as events observed by more than apredefined number of monitoring agents.

Intelligent clustering protocol design and optimisation in wireless sensor networks

Communication overhead is one of the big troubles in distributed clustering for which protocols that are able to guarantee limited flow of control packets shall be introduced. Considering that most of fuzzy based protocols are vulnerable to overhead of control packets in cluster setup phase, new approach has been introduced in this paper to reduce communication overhead and consequently optimising distributed cluster formation. Clusters are formed using fuzzy logic and optimal routes towards the base station are established based on ant colony optimisation (ACO) algorithm. The optimisation problem consists of finding routes towards the base station such that the tour length is minimised and the balance of energy consumption is maintained among routes in the path. The fuzzy logic and ACO toolboxes are developed in C++ and integrated with OMNeT++ simulation platform to implement the protocol. The experimental results prove that the proposed protocol extends the network lifetime compared to LEACH and CHEF protocols.

Nessie: A Decoupled, Client-Driven Key-Value Store Using RDMA

Key-value storage systems are an integral part of many data centre applications, but as demand increases so does the need for high performance. This has motivated new designs that use Remote Direct Memory Access (RDMA) to reduce communication overhead. Current RDMA-enabled key-value stores (RKVSes) target workloads involving small values, running on dedicated servers on which no other applications are running. Outside of these domains, however, there may be other RKVS designs that provide better performance. In this paper, we introduce Nessie, an RKVS that is fully client-driven, meaning no server process is involved in servicing requests. Nessie also decouples its index and storage data structures, allowing indices and data to be placed on different servers. This flexibility can decrease the number of network operations required to service a request. These design elements make Nessie well-suited for a different set of workloads than existing RKVSes. Compared to a server-driven RKVS, Nessie more than doubles system throughput when there is CPU contention on the server, improves throughput by 70 percent for PUT -oriented workloads when data value sizes are 128 KB or larger, and reduces power consumption by 18 percent at 80 percent system utilization and 41 percent at 20 percent system utilization compared with idle power consumption.

A distortion free multi-secret image sharing with efficient shadow communication overhead

Traditional secret image sharing deals with encoding one secret image into two shares. Natural extension of traditional secret image sharing is multiple secret encoding into the same number of shares which reduces the communication and storage overhead. This paper proposes a multiple secret image sharing technique with minimum pixel expansion and share size. In (2, 2) secret image sharing generally two shares of equal size are required to be stored. While in the proposed scheme, one of the shares is replaced with a key which is taking the size of only one row of the share. However minimum bits are required to be 100 for the security reason, so if the bits in key is less than this value, it is made up to this size by padding extra random bits. So now in total, only one share with almost one extra row is needed to be stored resulting in the reduction of communication and storage overhead to almost half. The size of the shares and original secret image is approximately same so the reconstructed secret image will be distortion free. The advantages and its applicability of the propose scheme has been demonstrated by experimental results.

Survey on: Communication Overhead Reduction for Remote Data Sharing to Prevent Data Privacy

Survey on: Communication Overhead Reduction for Remote Data Sharing to Prevent Data Privacy

Distributed Bayesian Filter Using Measurement Dissemination for Multiple Unmanned Ground Vehicles With Dynamically Changing Interaction Topologies

This paper presents a novel distributed Bayesian filtering (DBF) method using measurement dissemination (MD) for multiple unmanned ground vehicles (UGVs) with dynamically changing interaction topologies. Different from statistics dissemination (SD)-based algorithms that transmit posterior distributions or likelihood functions, this method relies on a full-in and full-out (FIFO) transmission protocol, which significantly reduces the transmission burden between each pair of UGVs. Each UGV only sends a communication buffer (CB) and a track list (TL) to its neighbors, in which the former contains a history of sensor measurements from all UGVs, and the latter is used to trim the redundant measurements in the CB to reduce communication overhead. It is proved that by using FIFO, each UGV can disseminate its measurements over the whole network within a finite time, and the FIFO-based DBF is able to achieve consistent estimation of the environment state. The effectiveness of this method is validated by comparing with the consensus-based distributed filter (CbDF) and the centralized filter (CF) in a multitarget tracking problem.

Optimized state update for mobile games in cloud networks

The emergence of mobile cloud computing indicates that abundant applications such as mobile games are available. Clouds have many interesting characteristics including on demand service, measured service, scalable elastic service, “anytime, anywhere” network access in cloud networks. Benefit due to the service controlled by communication between objects is now being increased by people who use these services in real life. In a very large scale mobile gaming environment with limited wireless network bandwidth, the command that game users give should be reflected to the game immediately and should be transferred to several objects at once, and it takes a certain amount of time to get the command pass to other users. An efficient mechanisms for state update are crucial to allow graceful real time interaction for many players and limited network bandwidth. Therefore, we propose a scheme for an optimized state update based on event locking and analytical models for mobile game in this paper. We design and implement the gaming server applying to our proposed scheme. The experimental results show that the proposed scheme can effectively improve the performance for mobile games and reduce communication overhead in cloud networks.

Workload prediction and balance for distributed reachability processing for large‐scale attribute graphs

SummaryReachability query with label constraint in an attribute graph is one of the most fundamental and important operations in semantic network analysis. However, ever‐growing graph size has resulted in intractable reachability problems on single machines.This work aims to devise efficient solutions for the reachability with label constraint problem in an attribute graph in a distributed environment. We focus on two issues in distributed processing—data locality and workload balancing—since data locality reduces communication overhead and workload balancing improves the efficiency of cluster use. We propose three novel techniques to address the two issues: (1) a partition replication method that improves data locality while conserving community property, (2) a workload‐prediction method that accurately predicts machine workloads for a given quer, and (3) a workload balancing method that uses these predictions to shift partial workloads among machines to produce a balanced workload. Experimental results suggest that these techniques significantly improve performance and reduce total execution time by 40%.

SDNHAS: An SDN-Enabled Architecture to Optimize QoE in HTTP Adaptive Streaming

HTTP adaptive streaming (HAS) is receiving much attention from both industry and academia as it has become the de facto approach to stream media content over the Internet. Recently, we proposed a streaming architecture called SDNDASH [1] to address HAS scalability issues including video instability, quality of experience (QoE) unfairness, and network resource underutilization, while maximizing per player QoE. While SDNDASH was a significant step forward, there were three unresolved limitations: 1) it did not scale well when the number of HAS players increased; 2) it generated communication overhead; and 3) it did not address client heterogeneity. These limitations could result in suboptimal decisions that led to viewer dissatisfaction. To that effect, we propose an enhanced intelligent streaming architecture, called SDNHAS, which leverages software defined networking (SDN) capabilities of assisting HAS players in making better adaptation decisions. This architecture accommodates large-scale deployments through a cluster-based mechanism, reduces communication overhead between the HAS players and SDN core, and allocates the network resources effectively in the presence of short- and long-term changes in the network.

Distributed low-rank adaptive estimation algorithms based on alternating optimization

This paper presents a novel distributed low-rank scheme and adaptive algorithms for distributed estimation over wireless networks. The proposed distributed scheme is based on a transformation that performs dimensionality reduction at each agent of the network followed by transmission of a reduced set of parameters to other agents and reduced-dimension parameter estimation. Distributed low-rank joint iterative estimation algorithms based on alternating optimization strategies are developed, which can achieve significantly reduced communication overhead and improved performance when compared with existing techniques. A computational complexity analysis of the proposed and existing low-rank algorithms is presented along with an analysis of the convergence of the proposed techniques. Simulations illustrate the performance of the proposed strategies in applications of wireless sensor networks and smart grids.

CSDA: a novel cluster-based secure data aggregation scheme for WSNs

With the development of wireless sensor networks, privacy-preserving has become a very important problem in numerous wireless sensor networks (WSN) applications. This paper presents a novel energy-efficient secure data aggregation scheme cluster-based private data aggregation (CSDA) based on cluster privacy-preserving. It has good flexibility and practical applicability using the slice-assemble technology. And, the number of fragments will dynamically change from the change of the network scale. Then, it can reduce communication overhead and energy consumption. Finally, the simulation results show that the proposed aggregation method demonstrates better performance in data aggregation precision, privacy-preserving and communication efficiency than other methods.

An algorithm for network and data-aware placement of multi-tier applications in cloud data centers

Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.