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Deterministic Latency/Jitter-Aware Service Function Chaining Over Beyond 5G Edge Fabric

Deterministic Networking (DetNet) has recently attracted much attention. It aims at studying the deterministic bounded latency and low latency variation for time-sensitive applications (e.g., industrial automation). To improve the quality of service (QoS) guarantee and make the network management efficient, it is desirable for Internet Service Provider (ISP) to obtain an optimal service function chain (SFC) provision strategy while providing deterministic service performance for the time-sensitive applications. In this paper, we will study the deterministic SFC lifetime management problem in beyond 5G edge fabric with the objective of maximizing the overall profits and ensuring the deterministic latency and jitter of SFC requests. We first formulate this problem as a mathematical model with the maximal profits for ISP. Then, the novel Deterministic SFC Deployment algorithm (Det-SFCD) and SFC Adjustment algorithm (Det-SFCA) due to traffic load variation are proposed to efficiently solve the SFC lifetime management problem. Extensive simulation results show that our proposed algorithms can achieve better performance in terms of SFC request acceptance rates, overall profits and latency variation compared with the benchmark algorithm.

An Information-Centric Network Caching Method Based on Popularity Rating and Topology Weighting

Ubiquitous caching is a feature shared by all proposed information-centric network (ICN) architectures. Prioritising storage resources to popular content in the network is a proven way to guarantee hit rates, reduce the number of hops forwarded, and reduce user request latency. An ideal ICN caching mechanism should make the best use of relevant information such as content information, network state, and user requirements to achieve optimal selection and have the ability to adaptively adjust the decision cache content for dynamic scenarios. Since router nodes have limited cache space, it is then useless to accurately predict the popularity of the content with very low popularity, as this content has no chance of being cached. A more effective approach is to focus on content with high popularity that influences caching decisions. As for different nodes, they have different sets of popular content, and using this property, this paper designs a caching method based on the popularity hierarchy with topological weights. The method considers managing the cached content in nodes with a hierarchy of popularity and improving their distribution in terms of the importance of the nodes’ position in the network. Finally, the scheme is simulated by changing the parameter settings under different actual topologies on the simulation platform to confirm the feasibility of the scheme.

Pull request latency explained: an empirical overview

Pull request latency evaluation is an essential application of effort evaluation in the pull-based development scenario. It can help the reviewers sort the pull request queue, remind developers about the review processing time, speed up the review process and accelerate software development. There is a lack of work that systematically organizes the factors that affect pull request latency. Also, there is no related work discussing the differences and variations in characteristics in different scenarios and contexts. In this paper, we collected relevant factors through a literature review approach. Then we assessed their relative importance in five scenarios and six different contexts using the mixed-effects linear regression model. We find that the relative importance of factors differs in different scenarios, e.g., the first response time of the reviewer is most important when there exist comments. Meanwhile, the number of commits in a pull request has a more significant impact on pull request latency when closing than submitting due to changes in contributions brought about by the review process.

Reinforcement learning for cost-effective IoT service caching at the edge

In the edge computing environment, Internet of Things (IoT) application service providers can rent resources from edge servers to cache their service items such as datasets and code libraries, and thus significantly reducing the service request latency and the core network traffic. Since IoT service providers need to pay for the rented edge computing resources, it is essential to find a dynamical service caching strategy to minimize the service cost while optimizing the performance objective such as service latency reduction. However, most of the existing studies either overlooked the problem of collaborative service caching or failed to consider the system's long-term service cost and latency. In this paper, to address such a problem, we coordinate multiple edge servers to cache service items and formulate the collaborative service caching problem using a multi-agent multi-armed bandit model. Furthermore, we propose a utility-aware collaborative service caching (UACSC) scheme based on a multi-agent reinforcement learning. The UACSC scheme can coordinate multiple edge servers to make a dynamic joint caching decision, aiming at maximizing the system's long-term utility. To evaluate the performance of our proposed scheme, we implement four representative baseline algorithms and compare them with six different performance metrics. In addition, a real-world case study is also presented to demonstrate the effectiveness of the UACSC scheme. Comprehensive experimental results show that the UACSC scheme can effectively coordinate multiple edge servers to cache service items, and achieve higher service latency reduction and lower service cost compared with other baseline algorithms.

Leader Confirmation Replication for Millisecond Consensus in Private Chains

The private chain-based Internet-of-Things (IoT) system ensures the security of cross-organizational data sharing. As a widely used consensus model in private chains, the leader-based state-machine replication (SMR) model meets the performance bottleneck in IoT blockchain applications, where nontransactional sensor data are generated on a scale. We analyzed IoT private chain systems and found that the leader maintains too many connections due to high latency and client request frequency, which results in lower consensus performance and efficiency. To meet this challenge, we propose a novel solution for maintaining low request latency and high transactions per second (TPS): replicate nontransactional data by followers and confirm by the leader to achieve nonconfliction SMR, rather than all by the leader. Our solution, named leader confirmation replication (LCR), uses the newly proposed future log and confirmation signal to achieve nontransactional data replication on the followers, thereby reducing the leader’s network traffic and the request latency of transactional data. In addition, the generation replication strategy is designed to ensure the reliability and consistency of LCR when meeting membership changes. We evaluated LCR with various cluster sizes and network latencies. The experimental results show that in ms-network latency (2–30) environments, the TPS of LCR is 1.4X-1.9X higher than Raft, the transactional data response time is reduced by 40%–60%, and the network traffic is reduced by 20%–30% with acceptable network traffic and CPU cost on the followers. In addition, LCR shows high portability and availability since it does not change the number of leaders or the election process.

Request pattern change-based cache pollution attack detection and defense in edge computing

Through caching popular contents at the network edge, wireless edge caching can greatly reduce both the content request latency at mobile devices and the traffic burden at the core network. However, popularity-based caching strategies are vulnerable to Cache Pollution Attacks (CPAs) due to the weak security protection at both edge nodes and mobile devices. In CPAs, through initiating a large number of requests for unpopular contents, malicious users can pollute the edge caching space and degrade the caching efficiency. This paper firstly integrates the dynamic nature of content request and mobile devices into the edge caching framework, and introduces an eavesdropping-based CPA strategy. Then, an edge caching mechanism, which contains a Request Pattern Change-based Cache Pollution Detection (RPC2PD) algorithm and an Attack-aware Cache Defense (ACD) algorithm, is proposed to defend against CPAs. Simulation results show that the proposed mechanism could effectively suppress the effects of CPAs on the caching performance and improve the cache hit ratio.

Load-Balancing of Kubernetes-Based Edge Computing Infrastructure Using Resource Adaptive Proxy.

Kubernetes (K8s) is expected to be a key container orchestration tool for edge computing infrastructures owing to its various features for supporting container deployment and dynamic resource management. For example, its horizontal pod autoscaling feature provides service availability and scalability by increasing the number of replicas. kube-proxy provides traffic load-balancing between replicas by distributing client requests equally to all pods (replicas) of an application in a K8s cluster. However, this approach can result in long delays when requests are forwarded to remote workers, especially in edge computing environments where worker nodes are geographically dispersed. Moreover, if the receiving worker is overloaded, the request-processing delay can increase significantly. To overcome these limitations, this paper proposes an enhanced load balancer called resource adaptive proxy (RAP). RAP periodically monitors the resource status of each pod and the network status among worker nodes to aid in load-balancing decisions. Furthermore, it preferentially handles requests locally to the maximum extent possible. If the local worker node is overloaded, RAP forwards its requests to the best node in the cluster while considering resource availability. Our experimental results demonstrated that RAP could significantly improve throughput and reduce request latency compared with the default load-balancing mechanism of K8s.

Joint hierarchical placement and configuration of edge servers in C-V2X

To accommodate the high mobility and large-scale interconnection requirements of the Internet of Vehicles (IoVs) and to realize low-latency and high-reliability communication in IoVs, Cellular Vehicle-to-Everything (C-V2X) intelligent converged networks based on Mobile Edge Computing (MEC) is proposed. Various types of C-V2X applications impose diverse requirements on the network in terms of communication, computation, and storage, thus the MEC platform needs to be flexibly deployed in C-V2X. We can divide the MEC platform into Roadside-edge and Area-edge layers according to the service scope and business functions, and strategically select Road Side Units (RSUs) and Macro Base Stations (MBSs) with the optimal location in the Roadside-edge and Area-edge layer to place different types of Edge Servers (ESs). In the scenario of MEC and C-V2X convergence, we consider the trade-off between the response latency of computation requests and the placement cost, and modelled the hierarchical placement and configuration of ESs based on queuing theory. To reduce the complexity of the problem and improve the efficiency of the solution, heuristic algorithms are proposed to realize the joint hierarchical placement and configuration of ESs in C-V2X from the aspects of delay-aware, suitability evaluation, and spatial clustering. Finally, the real C-V2X scenarios are simulated based on the Poisson Line Process (PLP) and Poisson Point Process (PPP), and sufficient experimental results verify the effectiveness and superiority of the proposed algorithms.

Improving Bank-Level Parallelism for In-Memory Checkpointing in Hybrid Memory Systems

Checkpoint/recovery has been widely used in many high available and reliable systems. This paper proposes Shadow, an application-transparent and in-memory checkpointing mechanism based on hybrid memory system composed of DRAM and emerging Non-Volatile Memory (NVM). Shadow adopts a pre-copy based checkpointing mechanism to reduce the system downtime. It supports fine-grained and incremental checkpointing at frequencies up to 100 times per second. Under this context, the checkpointing can significantly degrade application performance due to memory contention between applications and the checkpointing process. Previous checkpointing mechanisms on hybrid memory systems have focused on the performance of checkpointing, and have overlooked the impact of memory contention on the application performance. In this paper, we mitigate the memory contention at the bank level by carefully scheduling memory requests to fully leverage the idle time slots of different memory banks. Moreover, if bank conflicts are unavoidable, Shadow promotes the priority of applications' memory requests to lessen their access latencies. By redesigning the memory controllers of DRAM and NVM, we implement a hardware-assisted checkpointing mechanism that can directly transfer data from working memory to the checkpoint in NVM, without any intervention of CPUs. Our evaluation shows that Shadow can reduce memory bank conflicts between applications and checkpointing by 75%, and decrease applications' memory read request latency by 28% on average compared to the pre-copy based checkpointing. Moreover, Shadow can also reduce checkpointing overhead by 42% and 16% on average compared to the stop-and-copy and pre-copy based checkpointing approaches, respectively.

Secured cloud SCADA system implementation for industrial applications

The proposed Remote SCADA System (RSS) is a smarter, faster and more reliable way to control high power machines and monitor their sensors, data, and failures. The proposed system focuses mainly on building our own complete SCADA software and not using open-source SCADA software. The proposed RSS can use unlimited number of added Remote Terminal Units (RTU) nodes and each of them can handling unlimited number of input/output and can be used under different operating systems like Windows and Android. Using RSS all machines can be monitored and controlled by a single click from anywhere at any time. By doing this a real-time response from RSS system can be achieved. It’s mainly based on standard communication techniques between remote nodes and single server-side application that talk to each node with its own ID and modify its instant database. So that every time accessing this web application, a real-time access to these nodes data and a virtual control room controls each General Purpose Input/Output (GPIO) in the selected node can be gotten. When a new event is happened in server-side program, it will be broadcasted to all related. On RSS there are two main points to deal with, the request latency and security of the system. This paper studied how the system latency and security are improved to obtain the needed values. The proposed RSS is a very secure program which have 4 security levels; authentication, authorization, RSA and CBC encryption system. Also, the encryption algorithms used in RSS are RSA and CBC block cipher encryption system. It is mixed way to prevent any attacker from breaking the cipher. First of all, RSA generates the public and private keys, and then CBC generates its Initialization vector and a random encryption key. Then a special function sends all of these keys encrypted with a pre-stored token in the data base and node memory which varied from node to another. Finally, node generates its private key from loaded public key. With this combination the speed of symmetric encryption system and the security of asymmetric encryption system can be achieved. On the other hand, the level of security firewalls needed to be Brocken by the attacker to brock the cipher is increased. The proposed system achieved low cost comparing with reported work; it is lower than Arduino + WIFI method by five times and 13 times lower than Raspberry-PI method. The proposed system is applied in educational systems, where it is used for teaching unlimited number of students Online.

Fog Computing Approach for Shared Mobility in Smart Cities

Smart transportation a smart city application where traditional individual models are transforming to shared and distributed ownership. These models are used to serve commuters for inter- and intra-city travel. However, short-range urban transportation services within campuses, residential compounds, and public parks are not explored to their full capacity compared to the distributed vehicle model. This paper aims to explore and design an adequate framework for battery-operated shared mobility within a large community for short-range travel. This work identifies the characteristics of the shared mobility for battery-operated vehicles and accordingly proposes an adequate solution that deals with real-time data collection, tracking, and automated decisions. Furthermore, given the requirement for real-time decisions with low latency for critical requests, the paper deploys the proposed framework within the 3-tier computing model, namely edge, fog, and cloud tiers. The solution design considers the power consumption requirement at the edge by offloading the computational requests to the fog tier and utilizing the LoRaWAN communication technology. A prototype implementation is presented to validate the proposed framework for a university campus using e-bikes. The results show the scalability of the proposed design and the achievement of low latency for requests that require real-time decisions.

Penalty- and Locality-aware Memory Allocation in Redis Using Enhanced AET

Due to large data volume and low latency requirements of modern web services, the use of an in-memory key-value (KV) cache often becomes an inevitable choice (e.g., Redis and Memcached). The in-memory cache holds hot data, reduces request latency, and alleviates the load on background databases. Inheriting from the traditional hardware cache design, many existing KV cache systems still use recency-based cache replacement algorithms, e.g., least recently used or its approximations. However, the diversity of miss penalty distinguishes a KV cache from a hardware cache. Inadequate consideration of penalty can substantially compromise space utilization and request service time. KV accesses also demonstrate locality, which needs to be coordinated with miss penalty to guide cache management. In this article, we first discuss how to enhance the existing cache model, the Average Eviction Time model, so that it can adapt to modeling a KV cache. After that, we apply the model to Redis and propose pRedis, Penalty- and Locality-aware Memory Allocation in Redis, which synthesizes data locality and miss penalty, in a quantitative manner, to guide memory allocation and replacement in Redis. At the same time, we also explore the diurnal behavior of a KV store and exploit long-term reuse. We replace the original passive eviction mechanism with an automatic dump/load mechanism, to smooth the transition between access peaks and valleys. Our evaluation shows that pRedis effectively reduces the average and tail access latency with minimal time and space overhead. For both real-world and synthetic workloads, our approach delivers an average of 14.0%∼52.3% latency reduction over a state-of-the-art penalty-aware cache management scheme, Hyperbolic Caching (HC), and shows more quantitative predictability of performance. Moreover, we can obtain even lower average latency (1.1%∼5.5%) when dynamically switching policies between pRedis and HC.

Building a Document-Oriented Warehouse Using NoSQL

The traditional data warehousing approaches should adapt to take into consideration novel needs and data structures. In this context, NoSQL technology is progressively gaining a place in the research and industry domains. This paper proposes an approach for building a NoSQL document-oriented warehouse (DocW). This approach has two methods, namely 1) document warehouse builder and 2) NoSQL-Converter. The first method generates the DocW schema as a galaxy model whereas the second one translates the generated galaxy into a document-oriented NoSQL model. This relies on two types of rules: structure and hierarchical rules. Furthermore, in order to help understanding the textual results of analytical queries on the NoSQL-DocW, the authors define two semantic operators S-Drill-Up and S-Drill-Down to aggregate/expand the terms of query. The implementation of our proposals uses MangoDB and Talend. The experiment uses the medical collection Clef-2007 and two metrics called write request latency and read request latency to evaluate respectively the loading time and the response time to queries.

IoT Traffic Offloading with MultiPath TCP

The concept of a fully connected world in the fifth generation technology envisions the massive rollout of small and constrained Internet of Things (IoT) devices. In such IoT environments, machine-to-machine (M2M) communications is key for allowing these devices (e.g., sensors, actuators, smart meters/monitors) to collect data toward a remote server. We address this particular scenario by building and evaluating an IoT multi-access reference architecture, with a multipath TCP (MPTCP) gateway connected to multiple cellular networks to offload M2M data traffic. Experimental results from CoAP-emulated traffic indicate that the our proposed system together with the benefits of MPTCP improve IoT application performance. More specifically, multipath-based M2M data traffic offloading increases throughput and significantly reduces latency of CoAP requests as the number of sensor networks increases.

Reliable scheduling and load balancing for requests in cloud-fog computing

Fog computing broadens the computing services to serve requests of Internet of Things (IoT) by resources at the edge of Cloud-Fog environments instead of serving these requests by resources at the environment’s core. The aim of fog computing is to reduce load of computing in data centers and reduce latency of requests, especially real-time ones. Load balancing and scheduling play essential roles and represent main key challenges to guarantee high throughput and reliability of services in Cloud-Fog environments. Therefore, this paper introduces a reliable scheduling approach for allocating customers’ requests to the resources of Cloud-Fog environments. The approach is called Load Balanced Service Scheduling Approach (LBSSA) and it considers load balancing among resources when assigning requests to them by classifying requests to real-time, important and time-tolerant. In addition, scheduling of requests in the proposed approach considers the failure rate of resources in order to provide high reliability for requested services. The approach has a set of algorithms for handling different types of requests. Simulation experiments using CloudSim are conducted to assess the LBSSA approach in terms of number of computing resources, utilization of resources, load balance variance and running time.

Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme

As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient resource management scheme. As a way of saving energy as well as maintaining cloud user’s quality of experience, the scheme presents a multitier cloud architecture by configuring physical machines (PMs) into two pools: a hot (running) pool and a warm (turned on, but in dynamic sleep) pool. Each PM is configured with a resource search engine (RSE) that finds an available virtual machine (VM) for the request, and a synchronous sleep mechanism is introduced to the warm pool. To analyze the end-to-end performance of the cloud system’s service with the proposed scheme, we establish a hybrid queueing system composed of three stochastic submodels by using a matrix-geometric solution. Accordingly, the average latency of requests and the energy-saving rate of the system are derived. Through numerical results, we show the influence of the synchronous sleep mechanism on the system performance. Moreover, from the perspective of economics, we build a system cost function to study the trade-off between different performance measures. An improved Salp Swarm Algorithm (SSA) is presented to minimize the system cost and optimize the sleep parameter.

Wireless edge caching based on content similarity in dynamic environments

Edge caching could greatly relieve the burden of the backbone network and reduce the content request latency experienced by end-user devices. This makes edge caching a promising technology for enabling data-intensive and latency-sensitive applications on the eve of the large-scale commercial operation of 5G. However, the slow-start phenomenon incurred by existing request history-based caching strategies limits the performance of wireless edge caching, especially in the dynamic scenario where both mobile devices and contents arrive and leave periodically. On the other hand, it is also a hard task for deep reinforcement learning-based methods to adapt to the dynamics of the environment. In this backdrop, a new caching algorithm, called Similarity-Aware Popularity-based Caching (SAPoC), is presented in this paper to promote the performance of wireless edge caching in dynamic scenarios through utilizing the similarity among contents. In SAPoC algorithm, a content’s popularity is determined by not only its requests history but also its similarity with existing popular ones to enable a quick-start of newly arrived contents. A series of simulation experiments are conducted to evaluate SAPoC algorithm’s performance. Results have shown that SAPoC outperforms several typical proposals in both cache hit ratio and energy consumption.

Mobility Management in Small Cell Cluster of Cellular Network

The installation of small cells in a 5G network extends the maximum coverage and provides high availability. However, this approach increases the handover overhead in the Core Network (CN) due to frequent handoffs. The variation of user density and movement inside a region of small cells also increases the handover overhead in CN. However, the present 5G system cannot reduce the handover overhead in CN under such circumstances because it relies on a traditionally rigid and complex hierarchical sequence for a handover procedure. Recently, Not Only Stack (NO Stack) architecture has been introduced for Radio Access Network (RAN) to reduce the signaling during handover. This paper proposes a system based on NO Stack architecture and solves the aforementioned problem by adding a dedicated local mobility controller to the edge cloud for each cluster. The dedicated cluster controller manages the user mobility locally inside a cluster and also maintains the forwarding data of a mobile user locally. To reduce the latency for X2-based handover requests, an edge cloud infrastructure has been also developed to provide high-computing for dedicated controllers at the edge of a cellular network. The proposed system is also compared with the traditional 3GPP architecture and other works in the context of overhead and delay caused by X2-based handover requests during user mobility. Simulated results show that the inclusion of a dedicated local controller for small clusters together with the implementation of NO Stack framework reduces the significant amount of overhead of X2-based handover requests at CN.

Service Chain Composition With Resource Failures in NFV Systems: A Game-Theoretic Perspective

For systems that are based on network function virtualization (NFV), it remains a key challenge to conduct effective service chain composition with the lowest request latency and the minimum network congestion. In such an NFV system, users are usually non-cooperative, i.e., they compete with each other to optimize their own benefits. However, existing solutions often ignore such non-cooperative behaviors of users. What is more, they may fall short in the face of unexpected resource failures such as breakdown of virtual machines and loss of connections to users. In this article, we formulate the service chain composition problem with resource failures in NFV systems as a non-cooperative game , and show that such a game is a weighted potential game , aiming to search for the optimal Nash equilibrium (NE). By adopting Markov approximation techniques, we devise a distributed scheme called MH-SCCA, which achieves a provably near-optimal NE and adapts to resource failures in a timely manner. For comparison, we also propose two baseline schemes (DRL-SCCA and MCTS-SCCA) for centralized service chain composition that are based on deep reinforcement learning (DRL) and Monte Carlo tree search (MCTS) techniques, respectively. Our simulation results demonstrate the effectiveness of the three proposed schemes in terms of both latency reduction and congestion mitigation, as well as the adaptivity of MH-SCCA when faced with resource failures.

Predictive Switch-Controller Association and Control Devolution for SDN Systems

For software-defined networking (SDN) systems, to enhance the scalability and reliability of control plane, existing solutions adopt either multi-controller design with static switch-controller association, or static control devolution by delegating certain request processing back to switches. Such solutions can fall short in face of temporal variations of request traffics, incurring considerable local computation costs on switches and their communication costs to controllers. So far, it still remains an open problem to develop a joint online scheme that conducts dynamic switch-controller association and dynamic control devolution. In addition, the fundamental benefits of predictive scheduling to SDN systems still remain unexplored. In this paper, we identify the non-trivial trade-off in such a joint design and formulate a stochastic network optimization problem which aims to minimize time-averaged total system costs and ensure long-term queue stability. By exploiting the unique problem structure, we devise a predictive online switch-controller association and control devolution (POSCAD) scheme, which solves the problem through a series of online distributed decision making. Theoretical analysis shows that without prediction, POSCAD can achieve near-optimal total system costs a tunable trade-off for queue stability. With prediction, POSCAD can achieve even better performance with shorter latencies. We conduct extensive simulations to evaluate POSCAD. Notably, with mild-value of future information, POSCAD incurs a significant reduction in request latencies, even when faced with prediction errors.