Traditional Distributed Systems Research Articles

Optimization of Integrated Energy Systems Based on Two-Step Decoupling Method

An integrated energy system (IES) plays a key role in transforming energy consumption patterns and solving serious environmental and economic problems. However, the abundant optional schemes and the complex coupling relationship among each piece of equipment make the optimization of an IES very complicated, and most of the current literature focuses on optimization of a specific system. In this work, a simulation-based two-step decoupling method is proposed to simplify the optimization of an IES. The generalized IES is split into four subsystems, and a two-layer optimization method is applied for optimization of the capacity of each piece of equipment. The proposed method enables fast comparison among abundant optional configurations of an IES, and it is applied to a hospital in Beijing, China. The optimized coupling system includes the gas-fired trigeneration system, the GSHP, and the electric chiller. Compared with the traditional distributed systems, the emission reduction rate of CO2 and NOX for the coupling system reaches 153.8% and 314.5%, respectively. Moreover, the primary energy consumption of the coupling system is 82.67% less than that of the traditional distributed energy system, while the annual cost is almost at the same level.

Charlotte: Reformulating Blockchains into a Web of Composable Attested Data Structures for Cross-Domain Applications

Cross-domain applications are rapidly adopting blockchain techniques for immutability, availability, integrity, and interoperability. However, for most applications, global consensus is unnecessary and may not even provide sufficient guarantees. We propose a new distributed data structure: Attested Data Structures (ADS), which generalize not only blockchains but also many other structures used by distributed applications. As in blockchains, data in ADSs is immutable and self-authenticating. ADSs go further by supporting application-defined proofs ( attestations ). Attestations enable applications to plug in their own mechanisms to ensure availability and integrity. We present Charlotte , a framework for composable ADSs. Charlotte deconstructs conventional blockchains into more primitive mechanisms. Charlotte can be used to construct blockchains but does not impose the usual global-ordering overhead. Charlotte offers a flexible foundation for interacting applications that define their own policies for availability and integrity. Unlike traditional distributed systems, Charlotte supports heterogeneous trust: different observers have their own beliefs about who might fail, and how. Nevertheless, each observer has a consistent, available view of data. Charlotte’s data structures are interoperable and composable : applications and data structures can operate fully independently or can share data when desired. Charlotte defines a language-independent format for data blocks and a network API for servers. To demonstrate Charlotte’s flexibility, we implement several integrity mechanisms, including consensus and proof of work. We explore the power of disentangling availability and integrity mechanisms in prototype applications. The results suggest that Charlotte can be used to build flexible, fast, composable applications with strong guarantees.

P-RAN, an Indoor Solution for 5G and 6G in High Frequency Bands

With the progress of 5G commercial deployments worldwide, indoor coverage becomes a new issue because of the use of high frequency bands. While traditional distributed antenna systems (DAS) are available to mitigate the indoor coverage problem, service providers are reluctant to use them due to the required zoning and civil engineering difficulties in addition to the required higher capital expenditure (CAPEX). Device to device (D2D) technique has been proposed since Release 12 of LTE for cellular communications. Unfortunately, the commercial capabilities of D2D using the Side-Link standard have not been available in smartphones due to the lack of needed support from any of the major operators. In this paper, a launch of new services using D2D capabilities of smartphones will be discussed to mitigate the indoor coverage problem. This would mark the first time when one of the world major operators has provided a material support for the capabilities. If this effort can be fruitful, it can be a paradigm shifting approach for the indoor coverage problem and may pave the way for the D2D capabilities to be implemented into smartphones for many possible proximity services in the future.

Blockchain for Genomics: A Systematic Literature Review

Human genomic data carry unique information about an individual and offer unprecedented opportunities for healthcare. The clinical interpretations derived from large genomic datasets can greatly improve healthcare and pave the way for personalized medicine. Sharing genomic datasets, however, poses major challenges, as genomic data is different from traditional medical data, indirectly revealing information about descendants and relatives of the data owner and carrying valid information even after the owner passes away. Therefore, stringent data ownership and control measures are required when dealing with genomic data. In order to provide a secure and accountable infrastructure, blockchain technologies offer a promising alternative to traditional distributed systems. Indeed, the research on blockchain-based infrastructures tailored to genomics is on the rise. However, there is a lack of a comprehensive literature review that summarizes the current state-of-the-art methods in the applications of blockchain in genomics. In this article, we systematically look at the existing work both commercial and academic, and discuss the major opportunities and challenges. Our study is driven by five research questions that we aim to answer in our review. We also present our projections of future research directions which we hope the researchers interested in the area can benefit from.

Differential Privacy Meets Federated Learning Under Communication Constraints

The performance of federated learning systems is bottlenecked by communication costs and training variance. The communication overhead problem is usually addressed by three communication-reduction techniques, namely, model compression, partial device participation, and periodic aggregation, at the cost of increased training variance. Different from traditional distributed learning systems, federated learning suffers from data heterogeneity (since the devices sample their data from possibly different distributions), which induces additional variance among devices during training. Various variance-reduced training algorithms have been introduced to combat the effects of data heterogeneity, while they usually cost additional communication resources to deliver necessary control information. Additionally, data privacy remains a critical issue in FL and, thus, there have been attempts at bringing Differential Privacy to this framework as a mediator between utility and privacy requirements. This article investigates the tradeoffs between communication costs and training variance under a resource-constrained federated system theoretically and experimentally, and studies how communication reduction techniques interplay in a differentially private setting. The results provide important insights into designing practical privacy-aware federated learning systems.

Distributed predictive formation control for autonomous underwater vehicles under dynamic switching topology

Numerous existing autonomous underwater vehicles (AUVs) formation control works have focused on the preconfigured communication topology. However, the complex marine environment reveals the limitations and unreliability of the fix topology scheme. In this paper, the formation control of AUVs is investigated under dynamic switching topology. In order to realize this objective, the 2-D Delaunay triangulation and Jaccard index are introduced. A formation control system based on intermittent broadcast, rather than the traditional distributed control system, is established based on a leader-follower strategy. During the process of formation moving, the input constraints, vehicle collision avoidance and input saturation needed to be noted. To this end, the model predictive control algorithm is employed to design the formation controller, and the stability of the formation control system is proved by the average residence time notion and Lyapunov stability. The numerical simulations are exhibited to illustrate the effectiveness of the proposed formation control schemes in connectivity preservation and formation generation.

Adaptive Fault-Tolerant Strategy for Latency-Aware IoT Application Executing in Edge Computing Environment

Edge computing has recently evolved that offers to execute jobs efficiently by pushing cloud capabilities to edge of the network, this improves the quality of services to latency-oriented Internet of Things (IoT) applications when compared with cloud computing. By using current smart devices as edge nodes, edge computing can provide elastic resources that allow distributed data processing in a decentralized way. Still these smart devices are resource constrained in nature and tends to face a high failure rate than traditional distributed systems, the implementation of a fault-tolerant system that ensures the reliability and application availability becomes a key requirement. In this article, we propose a fault-tolerance methodology based on checkpointing and replication for the edge computing. Our proposed system uses a smart checkpointing for the IoT application tasks executing in a distributed edge network, and by replicating the checkpoint files on alternative edge nodes in the vicinity allowed to increase the system reliability. The experimental results show that our approach is effective in terms of reliability and availability of tasks executing in the edge network along with meeting deadlines of an IoT application.

Multi-objective optimization for scientific workflow scheduling based on Performance-to-Power Ratio in fog–cloud environments

The ever-expanding processing of large-scale scientific workflow applications has increased power consumption and reliability costs. However, considering the complexity of computational data processing in fog–cloud computing, existing workflow scheduling techniques conceived for traditional distributed systems is limited and powerless. Therefore, for profit-driven cloud service providers, minimizing power consumption and maximizing scheduling reliability of scientific workflows is a pressing concern. In this paper, we model the scientific workflow scheduling as a multi-objective optimization problem to compromise the conflicting energy consumption and scheduling reliability solution in two steps. The first step places the tasks with lower computational requirements on fog resources and computationally complex tasks on cloud resources, avoiding resources with high failure rates. In the second step, we adapt the Reliability-Aware stepwise Performance-to-Power Ratio procedure to further reduce energy consumption by sampling the machine’s utilization levels with a distinguished Performance-to-Power Ratio (PPR). The PPR is defined as the calculation of the number of transactions performed at a specific time divided by the active power consumed. Simulation results under synthesized and real-world workflow applications demonstrated that dis-RMEE achieved considerably higher reliability values under the minimized energy constraint compared with some opponent workflow mapping algorithms.

Repair Rates for Multiple Descriptions on Distributed Storage

In a traditional distributed storage system, a source can be restored perfectly when a certain subset of servers is contacted. The coding is independent of the contents of the source. This paper considers instead a lossy source coding version of this problem where the more servers that are contacted, the higher the quality of the restored source. An example could be video stored on distributed storage. In information theory, this is called the multiple description problem, where the distortion depends on the number of descriptions received. The problem considered in this paper is how to restore the system operation when one of the servers fail and a new server replaces it, that is, repair. The requirement is that the distortions in the restored system should be no more than in the original system. The question is how many extra bits are needed for repair. We find an achievable rate and show that this is optimal in certain cases. One conclusion is that it is necessary to design the multiple description codes with repair in mind; just using an existing multiple description code results in unnecessary high repair rates.

Reputation Detection for Information Diffusion in Social Network Systems

Reputation detection in traditional distributed systems (e.g., electronic commerce systems and peer‐to‐peer systems) relies heavily on the factor of interaction reputation, which can be derived from direct interactions between agents through bidirectional relationships. However, in the current information diffusion in social network systems (SNS) (e.g., Twitter), the characteristic of the unidirectional relationship between agents and the decay property of diffusion will result in lacking direct interactions; therefore, interaction reputations will be difficult to be obtained by agents in a distributed manner. To solve this problem, a novel distributed reputation detection model following the pattern “from path to individual” (FPTI) is proposed, which can provide a new reputation factor as an alternative to interaction reputation in such environments. The main idea is that the positive (or negative) observation of an information diffusion process increases (or decreases) the belief of the corresponding diffusion path, which further increases (or decreases) the reputation of each involved agent. Thus, the reputation of a target agent can be assessed by the superimposition of reputations of multiple paths on which this agent has participated in past information diffusion processes. Furthermore, being aware of agent’s limited capacity for reputation detection in SNS, we then propose the enhanced FPTI model (eFPTI), which simplifies the detection source to reduce detection costs and achieve the approximate performance as FPTI. Theoretical analyses and experimental evaluations validate the efficiency and effectiveness of our models and also show several properties of the models, for example, the robustness for dynamic environments.

Developing Check-Point Mechanism to Protect Mobile Agent Free-Roaming Against Untrusted Hosts

Mobile Agent has many benefits over traditional distributed systems such as reducing latency, bandwidth, and costs. Mobile Agent Systems are not fully utilized due to security problems. This paper focuses on mobile agent protection against malicious hosts. A new security mechanism called Checkpoints has been proposed. Checkpoint Mechanism (CPM) aims to protect Mobile Agent against malicious hosts in case of Capturing and Integrity attacks. CPM assumes using a free-roaming mobility mechanism by Mobile agent systems. The main idea behind CPM is to generate multiple versions of Mobile Agent. The multiple version is used to recover Mobile Agent from Capturing and Integrity attacks by untrusted hosts. MA versions are kept in Recovery Host(RH). RH plays a key role in CPM by controlling and monitoring MAs’ recovery processes. A prototype method has been used to prove the feasibility of CPM. The prototype was implemented by using the. Net framework and C#. full discussion for several scenarios has been done to analyze the feasibility and performance for CPM. As found from this research, CPM has a strong ability to protect Mobile Agents from Capturing and Integrity attacks completely. In addition, there is no negative impact on the overall performance of the mobile agent system.

Transient Simulation and Optimization of Regional Integrated Energy System

A modeling method of regional integrated energy system based on bus method and transient simulation is proposed, and the system optimization is based on the dynamic balance of supply and demand in the whole year energy supply cycle. A CCHP system of gas turbine coupled with ground source heat pump and electric refrigeration unit is constructed. The energy relationship of the system is described by bus structure, and the transient calculation model is built on TRNSYS platform. The weighted sum of annual total cost saving rate, primary energy saving rate and environmental pollutant shadow cost saving rate is taken as the optimized objective function, and on the basis of annual dynamic balance, the Hooke-Jeeves algorithm is used for optimization of the system configuration. A complex commercial area in Beijing is taken as an example, and different weighting coefficients are set for optimization of the system configuration. The results show that, from the perspective of economy, environmental benefit and primary energy consumption, performance of the system increases and then decreases with rise of gas turbine power; under the simulated cooling/heating load, the maximum number of optimum configuration is seen in the combination of 35 kW gas turbine + 723 kW GSHP and 1178 kW electric chiller; in comparison with traditional distributed system, the annual cost saving rate, primary energy saving rate and environmental pollutant shadow cost saving rate of the system are 29.4%, 49.6% and 58.2%, respectively.

Optimize Task Scheduling and Resource Allocation Using Nature Inspired Algorithms in Cloud based BDA

Task Scheduling and Resource allocation is a prominent research topic in cloud computing. There are several objectives associated with Optimize Task Scheduling and Resource allocation as cloud computing systems are more complex than the traditional distributed system. There are several challenges like resolving the task mapped to the node on which task to be executed. A simplified but near optimal proposed nature inspired algorithms are focus in this paper. In this paper basic idea about optimization, reliability and complexity is considered while design a solution for modern BDA (Big Data Application). Detailed analysis of experimental results, it is shown that the proposed algorithm has better optimization effect on the fair share policies which are presently available in most of the BDA. In this paper we focused on Dragonfly algorithm and Sea lion algorithms which are nature inspired algorithms. These algorithms are efficient for optimization purpose for solving task scheduling and resource allocation problem. Finally performance of the hybrid DA algorithm and Sea lion is compared with traditional techniques used for modern BDA using Hadoop MapReduce. Simulation results prove the efficacy of the suggested algorithms.

XERIS/APEX

Modern day cloud native applications have become broadly representative of distributed systems in the wild. However, unlike traditional distributed system models with conceptually static designs, cloud-native systems emphasize dynamic scaling and on-line iteration (CI/CD). Cloud-native systems tend to be architected around a networked collection of distinct programs ("microservices") that can be added, removed, and updated in real-time. Typically, distinct containerized programs constitute individual microservices that then communicate among the larger distributed application through heavy-weight protocols. Common communication stacks exchange JSON or XML objects over HTTP, via TCP/TLS, and incur significant overhead, particularly when using small size message sizes. Additionally, interpreted/JIT/VM-based languages such as Javascript (NodeJS/Deno), Java, and Python are dominant in modern microservice programs. These language technologies, along with the high-overhead messaging, can impose superlinear cost increases (hardware demands) on scale-out, particularly towards hyperscale and/or with latency-sensitive workloads.

Two-phase collaborative optimization and operation strategy for a new distributed energy system that combines multi-energy storage for a nearly zero energy community

The combination of a distributed energy system and multi-energy storage system has the potential to use renewable energy on a large scale and to further improve the system’s energy efficiency. Therefore, a new type of distributed energy system that combines multi-energy storage is proposed in this paper. Its novelty is that the three types of energy storage, i.e., cold, heat and electricity, are considered simultaneously, and the electric vehicle load is also included in the distributed energy system in combination with the multi-energy storage system, which allows full consideration of the effect of multi-energy storage on the distributed energy system. Three operation modes are proposed to give full play to the advantages of the new system according to the charging mode for electric vehicles. Subsequently, a two-phase collaborative optimization method for system configuration and operation optimization is proposed, and it is applied to a nearly zero energy community. The results show that the primary energy savings rate of the distributed energy system that combines multi-energy storage is 53.5% when the electric vehicle charging load is provided by the new system, which is 17.5% higher than that of the traditional distributed energy system, while the annual cost savings rate increased by only 8.3%. In addition, the hourly operating cost of the distributed energy system that combines multi-energy storage is also significantly reduced compared with the separated production system. Therefore, the method of two-phase collaborative optimization in the new system proposed in this paper can be used to realize the optimal system configuration and operation design for energy savings and consumption reduction. Finally, the new system can provide a feasible scheme for the energy supply of a nearly zero energy community in the future.

Intrusion detection of hierarchical distribution network system based on machine computation

In order to solve the problems of low detection accuracy and long detection time of traditional hierarchical distributed system intrusion detection method, a hierarchical distributed system intrusion detection method based on machine computing is proposed. By judging the Chinese protocol type of IP message and the control bit value of TCP, the network traffic is transformed into different sub-flows, and the characteristic parameters of traffic behaviour are extracted from the sub-flows. Based on the invasion behaviour characteristics obtained, the vector with six-dimensional characteristics is selected as the important symbol of invasion. By combining rule detection method, support vector machine and machine learning classification, the intrusion detection of hierarchical distribution network system is realised by embedding detection modules at different levels. Experimental results show that this method can effectively reduce intrusion detection time and improve detection accuracy.

Design and Implementation of a Distributed Versioning File System for Cloud Rendering

Rendering is widely used for visual effects in animations, video games, and movies. As the computational load in rendering workloads fluctuates greatly over time, it is attractive to use cloud infrastructures for cost-effective rendering. However, we analyze that cloud rendering has several technical challenges involved in the handling of rendering input data. In this article, we analyze the workload characteristics of popular rendering projects, and find out the following three observations. First, total size of rendering input files reaches tens to hundreds of gigabytes, and uploading these large data to cloud increases the startup latency of rendering significantly. Second, the consistency requirement of file systems in cloud rendering is complicated compared to that of traditional distributed file systems. Third, file accesses in rendering are highly skewed such that the top 20% files account for 60-80% of total accesses, whereas 40-70% are never used or used only once. Based on these observations, we design and implement a new file system for cloud rendering, which has the function of version control, on-demand fetch, and distributed cooperative caching for rendering data. This allows for minimizing data transmission overhead caused by the large input data of rendering and satisfying the rendering data consistency. Measurement studies under synthetic and real workloads show that the proposed file system performs better than the conventional uploading scheme and NFS by 55.4% and 29.5% on average, respectively.

Techno-Economic Assessment of Clean Oil Field Energy Supply System

In response to 2060's goal of fully achieving carbon neutrality, all walks of life are striving for low-carbon transformation. In the oilfield production and gathering system, due to the high water cut in the middle and late stages of development, the energy consumption of the oilfield will be very high, and the increase of energy consumption will lead to the increase of carbon emission at the same time. Therefore, the traditional energy model cannot meet the energy consumption demand of high water cut oil fields in the middle and later stages of development. This study investigated the current wind hydrogen coupling energy system and combined it with the traditional distributed oil field energy system to provide energy for the oil fields. To screen the scheme, this paper puts forward four different modes of the energy system, compares it with the traditional system, combined with the equilibrium energy cost and net present value, puts forward a comprehensive technical and economic evaluation method, and analyzes five scenarios. The annual energy consumption of the oil field combined station is set as 238 Gwh, the wind speed in the oilfield area has also been given. The results show that scenario 4 has the best economic and environmental benefits, in which the unit energy cost is 3.62 $/ Kwh and the net present value is 2.95×107 $, compared with the traditional energy model, the carbon emission is reduced by 44096080kg, the local consumption of renewable energy is realized, and the pressure on the power grid system caused by the grid connection of wind power generation is reduced. Finally, through sensitivity analysis, the effects of wind speed, power price, and oil field associated gas production on the economic performance of the system are analyzed.

Distributed guidance-based formation control of marine vehicles under switching topology

Because of the limited and unreliable communication topology in complex marine environment, the formation control of marine vehicles under switching topology is investigated in this paper, whereas the most previous works for marine formation focused on the fixed communication topology. In order to simplify the formation control design, a novel distributed guidance system, rather than the traditional distributed control system, is established based on leader-follower strategy only using a portion of communication links. To address the problem of input saturation of marine vehicle, the model predictive control is therefore employed to design the marine formation controller; furthermore, the predictive mechanism allows model predictive control to prevent the constraint violation in advance, which brings advantages against the large inertia of ship motion. Both formation tracking control and formation regulation control are studied herein. For application of formation tracking control of marine vehicles, i.e., the time-varying reference signal, the formation control can be realized under switching topology with the condition that the communication topology contains a directed spanning tree. For application of formation regulation control of marine vehicles, i.e., the constant reference signal, the formation control can be realized under switching topology with a relative milder condition that the union of communication topology contains a directed spanning tree. Multiple simulations are included to show the effectiveness of the presented distributed guidance-based MPC formation controllers.

Consensus Crash Testing: Exploring Ripple’s Decentralization Degree in Adversarial Environments

The inception of Bitcoin as a peer-to-peer payment system, and its underlying blockchain data-structure and protocol, has led to an increased interest in deploying scalable and reliable distributed-ledger systems that build on robust consensus protocols. A critical requirement of such systems is to provide enough fault tolerance in the presence of adversarial attacks or network faults. This is essential to guarantee liveness when the network does not behave as expected and ensure that the underlying nodes agree on a unique order of transactions over a shared state. In comparison with traditional distributed systems, the deployment of a distributed-ledger system should take into account the hidden game theoretical aspects of such protocols, where actors are competing with each other in an environment which is likely to experience various well-motivated malicious and adversarial attacks. Firstly, this paper discusses the fundamental principles of existing consensus protocols in the context of both permissioned and permissionless distributed-ledger systems. The main contribution of this work deals with observations from experimenting with Ripple’s consensus protocol as it is embodied in the XRP Ledger. The main experimental finding suggests that, when a low percentage of malicious nodes is present, the centralization degree of the network can be significantly relaxed ensuring low convergence times. Those findings are of particular importance when engineering a consensus algorithm that would like to balance security with decentralization.