Abstract: Serverless computing is a rapidly growing cloud application model, popularized by Amazon's Lambda platform. Serverless cloud services provide fine-grained provisioning of resources, which scale automatically with user demand. Function-as-a-Service (FaaS) applications follow this serverless model, with the developer providing their application as a set of functions which are executed in response to a user- or system-generated event. Functions are designed to be short-lived and execute inside containers or virtual machines, introducing a range of system-level overheads. This paper studies the architectural implications of this emerging paradigm. Using the commercial-grade Apache OpenWhisk FaaS platform on real servers, this work investigates and identifies the architectural implications of FaaS serverless computing. The workloads, along with the way that FaaS inherently interleaves short functions from many tenants frustrates many of the locality-preserving architectural structures common in modern processors. In particular, we find that: FaaS containerization brings up to 20x slowdown compared to native execution, cold-start can be over 10x a short function's execution time, branch mispredictions per kilo-instruction are 20x higher for short functions, memory bandwidth increases by 6x due to the invocation pattern, and IPC decreases by as much as 35% due to inter-function interference. We open-source FaaSProfiler, the FaaS testing and profiling platform that we developed for this work.

Abstract: With the growth in computing technologies, cloud computing has added a new paradigm to user services that allows accessing Information Technology services on the basis of pay-per-use at any time and any location. Owing to flexibility in cloud services, numerous organizations are shifting their business to the cloud and service providers are establishing more data centers to provide services to users. However, it is essential to provide cost-effective execution of tasks and proper utilization of resources. Several techniques have been reported in the literature to improve performance and resource use based on load balancing, task scheduling, resource management, quality of service, and workload management. Load balancing in the cloud allows data centers to avoid overloading/underloading in virtual machines, which itself is a challenge in the field of cloud computing. Therefore, it becomes a necessity for developers and researchers to design and implement a suitable load balancer for parallel and distributed cloud environments. This survey presents a state-of-the-art review of issues and challenges associated with existing load-balancing techniques for researchers to develop more effective algorithms.

Abstract: With the development of virtual reality (VR) and human-computer interaction technology, how to use natural and efficient interaction methods in the virtual environment has become a hot topic of research. Gesture is one of the most important communication methods of human beings, which can effectively express users’ demands. In the past few decades, gesture-based interaction has made significant progress. This article focuses on the gesture interaction technology and discusses the definition and classification of gestures, input devices for gesture interaction, and gesture interaction recognition technology. The application of gesture interaction technology in virtual reality is studied, the existing problems in the current gesture interaction are summarized, and the future development is prospected.

Abstract: The need to scale up machine learning, in the presence of a rapid growth of data both in volume and in variety, has sparked broad interests to develop distributed machine learning systems, typically based on parameter servers. However, since these systems are based on a dedicated cluster of physical or virtual machines, they have posed non-trivial cluster management overhead to machine learning practitioners and data scientists. In addition, there exists an inherent mismatch between the dynamically varying resource demands during a model training job and the inflexible resource provisioning model of current cluster-based systems.In this paper, we propose SIREN, an asynchronous distributed machine learning framework based on the emerging serverless architecture, with which stateless functions can be executed in the cloud without the complexity of building and maintaining virtual machine infrastructures. With SIREN, we are able to achieve a higher level of parallelism and elasticity by using a swarm of stateless functions, each working on a different batch of data, while greatly reducing system configuration overhead. Furthermore, we propose a scheduler based on Deep Reinforcement Learning to dynamically control the number and memory size of the stateless functions that should be used in each training epoch. The scheduler learns from the training process itself, in pursuit for the minimum possible training time given a cost. With our real-world prototype implementation on AWS Lambda, extensive experimental results have shown that SIREN can reduce model training time by up to 44%, as compared to traditional machine learning training benchmarks on AWS EC2 at the same cost.

Abstract: Large-scale applications of Internet of things (IoT), which require considerable computing tasks and storage resources, are increasingly deployed in cloud environments. Compared with the traditional computing model, characteristics of the cloud such as pay-as-you-go, unlimited expansion, and dynamic acquisition represent different conveniences for these applications using the IoT architecture. One of the major challenges is to satisfy the quality of service requirements while assigning resources to tasks. In this paper, we propose a deadline and cost-aware scheduling algorithm that minimizes the execution cost of a workflow under deadline constraints in the infrastructure as a service (IaaS) model. Considering the virtual machine (VM) performance variation and acquisition delay, we first divide tasks into different levels according to the topological structure so that no dependency exists between tasks at the same level. Three strings are used to code the genes in the proposed algorithm to better reflect the heterogeneous and resilient characteristics of cloud environments. Then, HEFT is used to generate individuals with the minimum completion time and cost. Novel schemes are developed for crossover and mutation to increase the diversity of the solutions. Based on this process, a task scheduling method that considers cost and deadlines is proposed. Experiments on workflows that simulate the structured tasks of the IoT demonstrate that our algorithm achieves a high success rate and performs well compared to state-of-the-art algorithms.

Abstract: Cloud computing is an Internet based computing environment, where storage and computing resources are assigned dynamically among users according to their needs, using the virtualization technology. Virtualization is an underlying infrastructure of cloud computing, and has led to certain security problems during the development of cloud computing. One essential but formidable task in cloud computing is to detect malicious attacks and their types. Due to increasing incidents of cyber-attacks, design and implementation of effective intrusion detection systems to protect the security of information systems is crucial. In this paper, a host-based intrusion detection system (H-IDS) for protecting virtual machines in the cloud environment is proposed. To this end, first, important features of each class are selected using logistic regression and next, these values are improved using the regularization technique. Then, various attacks are classified using a combination of three different classifiers: neural network, decision tree and linear discriminate analysis with the bagging algorithm for each class. The proposed model has been trained and tested using the NSL-KDD data set with an implementation in the Cloudsim software. Simulation results compared to other methods shows acceptable accuracy of about 97.51 for detecting attacks against normal states.

Abstract: In this paper, network function virtualization (NVF) is identified as a promising key technology that can contribute to energy-efficiency improvement in 5G networks. An optical network supported architecture is proposed and investigated in this work to provide the wired infrastructure needed in 5G networks and to support NFV towards an energy efficient 5G network. In this architecture the mobile core network functions as well as baseband function are virtualized and provided as VMs. The impact of the total number of active users in the network, backhaul/fronthaul configurations and VM inter-traffic are investigated. A mixed integer linear programming (MILP) optimization model is developed with the objective of minimizing the total power consumption by optimizing the VMs location and VMs servers’ utilization. The MILP model results show that virtualization can result in up to 38% (average 34%) energy saving. The results also reveal how the total number of active users affects the baseband virtual machines (BBUVMs) optimal distribution whilst the core network virtual machines (CNVMs) distribution is affected mainly by the inter-traffic between the VMs. For real-time implementation, two heuristics are developed, an Energy Efficient NFV without CNVMs inter-traffic (EENFVnoITr) heuristic and an Energy Efficient NFV with CNVMs inter-traffic (EENFVwithITr) heuristic, both produce comparable results to the optimal MILP results. Finally, a Genetic algorithm is developed for further verification of the results.

Abstract: Today's virtual reality (VR) systems offer chaperone rendering techniques that prevent the user from colliding with physical objects. Without a detailed geometric model of the physical world, these techniques offer limited possibility for more advanced compositing between the real world and the virtual. We explore this using a realtime 3D reconstruction of the real world that can be combined with a virtual environment. RealityCheck allows users to freely move, manipulate, observe, and communicate with people and objects situated in their physical space without losing the sense of immersion or presence inside their virtual world. We demonstrate RealityCheck with seven existing VR titles, and describe compositing approaches that address the potential conflicts when rendering the real world and a virtual environment together. A study with frequent VR users demonstrate the affordances provided by our system and how it can be used to enhance current VR experiences.

Abstract: Software-defined networking (SDN) and network function virtualization (NFV) processed in multi-access edge computing (MEC) cloud systems have been proposed as critical paradigms for achieving the low latency requirements of the tactile Internet. While virtual network functions (VNFs) allow greater flexibility compared to hardware-based solutions, the VNF abstraction also introduces additional packet processing delays. In this paper, we investigate the practical feasibility of NFV with respect to the tactile Internet latency requirements. We develop, implement, and evaluate Chain-based Low latency VNF ImplemeNtation (CALVIN), a low-latency management framework for distributed Service Function Chains (SFCs). CALVIN classifies VNFs into elementary, basic, and advanced VNFs; moreover, CALVIN implements elementary and basic VNFs in the kernel space, while the advanced VNFs are implemented in the user space. Throughout, CALVIN employs a distributed mapping with one VNF per Virtual Machine (VM) in a MEC system. Furthermore, CALVIN avoids the metadata structure processing and batch processing of packets in the conventional Linux networking stack so as to achieve short per-packet latencies. Our rigorous measurements on off-the-shelf conventional networking and computing hardware demonstrate that CALVIN achieves round-trip times from a MEC ingress point via two elementary forwarding VNFs (one in kernel space and one in user space) and a MEC server to a MEC egress point on the order of 0.32 ms. Our measurements also indicate that MEC network coding and encryption are feasible for small 256 byte packets with an MEC latency budget of 0.35 ms; whereas, large 1400 byte packets can complete the network coding, but not the encryption within the 0.35 ms.

Abstract: Mobile-edge computing (MEC) is an emerging technology for enhancing the computational capabilities of the mobile devices and reducing their energy consumption via offloading complex computation tasks to the nearby servers. Multiuser MEC at servers is widely realized via parallel computing based on virtualization. Due to finite shared I/O resources, interference between virtual machines (VMs), called I/O interference, degrades the computation performance. In this paper, we study the problem of joint radio-and-computation resource allocation (RCRA) in multiuser MEC systems in the presence of I/O interference. Specifically, offloading scheduling algorithms is designed targeting two system performance metrics: sum offloading rate maximization and sum mobile energy consumption minimization. Their designs are formulated as non-convex mixed-integer programming problems, which account for latency due to offloading, result downloading, and parallel computing. A set of low-complexity algorithms are designed based on a decomposition approach and leveraging classic techniques from combinatorial optimization. The resultant algorithms jointly schedule offloading users, control their offloading sizes, and divide time for communication (offloading and downloading) and computation. They are either optimal or can achieve close-to-optimality as shown by simulation. The comprehensive simulation results demonstrate that considering of I/O interference can endow on an offloading controller robustness against the performance-degradation factor.

Abstract: Unlike a traditional virtual machine (VM), a container is an emerging lightweight virtualization technology that operates at the operating system level to encapsulate a task and its library dependencies for execution. The Container as a Service (CaaS) strategy is gaining in popularity and is likely to become a prominent type of cloud service model. Placing container instances on virtual machine instances is a classical scheduling problem. Previous research has focused separately on either virtual machine placement on physical machines (PMs) or container, or only tasks without containerization, placement on virtual machines. However, this approach leads to underutilized or overutilized PMs as well as underutilized or overutilized VMs. Thus, there is a growing research interest in developing a container placement algorithm that considers the utilization of both instantiated VMs and used PMs simultaneously. The goal of this study is to improve resource utilization, in terms of number of CPU cores and memory size for both VMs and PMs, and to minimize the number of instantiated VMs and active PMs in a cloud environment. The proposed placement architecture employs scheduling heuristics, namely, Best Fit (BF) and Max Fit (MF), based on a fitness function that simultaneously evaluates the remaining resource waste of both PMs and VMs. In addition, another meta-heuristic placement algorithm is proposed that uses Ant Colony Optimization based on Best Fit (ACO-BF) with the proposed fitness function. Experimental results show that the proposed ACO-BF placement algorithm outperforms the BF and MF heuristics and maintains significant improvement of the resource utilization of both VMs and PMs.

Abstract: Effective simulation and testing environment is a vital part in the research of self-driving vehicles. It is capable of testing self-driving software and hardware quickly in a variety of virtual environments at low cost. However, as for the current mainstream simulation platforms, a considerable gap exists between the constructed virtual environment and the actual self-driving platform, which decreases the efficiency of development and makes it difficult to complete the migration from the virtual scenario to the real environment. Therefore, in this paper, we proposed a novel hardware-in-the-loop (HiL) simulation system. It takes the real hardware electronic control unit (ECU) of the self-driving vehicle as a part of the simulation platform, which improves the efficiency of development and testing, and also, the verified algorithms can be implemented into self-driving cars faster than ever before. The proposed HiL simulation system mainly consists of four parts: the vehicle kinematic model simulation, the multi-sensor simulation, the environment simulation, and the ECU hardware. Simulation experiments on applying the HiL system are used to verify the validity of self-driving algorithms in virtual scenes, including perception, planning, decision making, and control. Furthermore, algorithms that are tested in the simulation environment can be rapidly deployed into the real self-driving vehicles. In this paper, we also presented the verification processes of various algorithms, such as planning and control. These algorithms are implemented in the HiL system, and the experimental results show the validity of our proposed platform.

Abstract: A reflective analysis on the experience of virtual environment (VE) design is presented focusing on the human–computer interaction (HCI) challenges presented by virtual reality (VR). HCI design gui...

Abstract: Load balancing is the significant task in the cloud computing because the cloud servers need to store avast amount of information which increases the load on the servers. The objective of the load balancing technique is that it maintains a trade-off on servers by distributing equal load with less power. Accordingly, this paper presents the load balancing technique based on the constraint measure. Initially, the capacity and load of each virtual machine are calculated. If the load of the virtual machine is greater than the balanced threshold value then,the load balancing algorithm is used for allocating the tasks. The load balancing algorithm calculates the deciding factor of each virtual machine and checks the load of the virtual machine. Then, it calculates the selection factor of each task. Then, the task which has better selection factor is allocated to the virtual machine. The performance of the proposed load balancing method is evaluated with the existing load balancing methods, such as HBB-LB, DLB, and HDLB for the evaluation metrics load and capacity. The experimental results show that the proposed method migrate only three tasks while the existing method HDLB migrates seven tasks.

Abstract: This paper introduces virtual reality as an experimental method for the language sciences and provides a review of recent studies using the method to answer fundamental, psycholinguistic research questions. It is argued that virtual reality demonstrates that ecological validity and experimental control should not be conceived of as two extremes on a continuum, but rather as two orthogonal factors. Benefits of using virtual reality as an experimental method include that in a virtual environment, as in the real world, there is no artificial spatial divide between participant and stimulus. Moreover, virtual reality experiments do not necessarily have to include a repetitive trial structure or an unnatural experimental task. Virtual agents outperform experimental confederates in terms of the consistency and replicability of their behavior, allowing for reproducible science across participants and research labs. The main promise of virtual reality as a tool for the experimental language sciences, however, is that it shifts theoretical focus towards the interplay between different modalities (e.g., speech, gesture, eye gaze, facial expressions) in dynamic and communicative real-world environments, complementing studies that focus on one modality (e.g., speech) in isolation.

Abstract: Fog-aided Internet of Things (IoT) addresses the resource limitations of IoT devices in terms of computing and energy capacities, and enables computational intensive and delay-sensitive tasks to be offloaded to the fog nodes attached to the IoT gateways. A fog node, utilizing the cloud technologies, can lease and release virtual machines (VMs) in an on-demand fashion. For the power-limited mobile IoT devices (e.g., wearable devices and smart phones), their quality of service may be degraded owing to the varying wireless channel conditions. Power control helps maintain the wireless transmission rate and hence the quality of service (QoS). The QoS (i.e., task completion time) is affected by both the fog processing and wireless transmission; it is thus important to jointly optimize fog resource provisioning (i.e., decisions on the number of VMs to rent) and power control. This paper addresses this joint optimization problem to minimize the system cost (VM rentals) while guaranteeing QoS requirements, formulated as a mixed integer nonlinear programming problem. An approximation algorithm is then proposed to solve the problem. Simulation results demonstrate the performance of our proposed algorithm.

Abstract: Data centers consume an enormous amount of energy to meet the ever‐increasing demand for cloud resources. Computing and Cooling are the two main subsystems that largely contribute to energy consumption in a data center. Dynamic Virtual Machine (VM) consolidation is a widely adopted technique to reduce the energy consumption of computing systems. However, aggressive consolidation leads to the creation of local hotspots that has adverse effects on energy consumption and reliability of the system. These issues can be addressed through efficient and thermal‐aware consolidation methods. We propose an Energy and Thermal‐Aware Scheduling (ETAS) algorithm that dynamically consolidates VMs to minimize the overall energy consumption while proactively preventing hotspots. ETAS is designed to address the trade‐off between time and the cost savings and it can be tuned based on the requirement. We perform extensive experiments by using the real‐world traces with precise power and thermal models. The experimental results and empirical studies demonstrate that ETAS outperforms other state‐of‐the‐art algorithms by reducing overall energy without any hotspot creation.

Abstract: Network Function Virtualization (NFV) turns a sequence of network functions on hardwares into a service chain of virtual network functions (VNFs) provisioned on virtual machines or containers. However, the chain of VNFs may suffer from interruption as long as one VNF fails due to software faults or hardware malfunctions. A common approach to ensuring high availability is to provide backup nodes for primary VNFs. However, existing work on allocating backup nodes have not considered the heterogeneous resource demands of different VNFs. In this paper, we formalize the resource-aware backup allocation problem, which aims to minimize the backup resource consumption while meeting the overall availability demand. To this end, we prove the NP-hardness of this problem and propose the RABA-CDDE algorithm based on differential evolution to solve it. Besides, to reduce the computation overhead of RABA-CDDE, a greedy algorithm is proposed. Our extensive evaluation shows that the proposed algorithms can reduce the resource consumption by about 15% and 35% respectively compared to the state-of-art solutions in dedicated and shared protection scenarios.

Abstract: In recent years, the revolution of cloud computing has taken the IT business to greater heights with the rapid sharing of vast web resources over the internet Proficient task scheduling and balanced task distribution is still exists as a major challenging issue in cloud computing system due to dynamic heterogeneous nature of resources and tasks It is a NP-hard problem where the scheduler needs to find the best optimal virtual machines with minimum makespan and proper resource utilization The major part of this problem is to design an efficient intelligent searching pattern to schedule the tasks in best virtual available machines In this paper we propose a meta heuristic algorithm called chaotic social spider algorithm inspired by social spider to tackle the problem of task scheduling in various heterogeneous virtual machines This paper focus on minimizing overall makespan with effective load balancing by modelling the swarm intelligence of social spider with chaotic inertia weight based random selection The proposed algorithm prevents the local convergence and explores the global intelligent searching in finding the best optimized virtual machine for the user task among the set of virtual machines with minimum makespan and balanced resource utilization We have made the simulation and performance evaluation using cloudsim toolkit and compared the results with other swarm intelligent based algorithms such as GA, PSO and ABC The evaluation results show that there is a major improvement in minimizing the makespan with balanced task distribution

Abstract: Cloud environment is an assembly of resources for furnishing on-demand services to cloud customers. Here access to cloud environment is via internet services in which data stored on cloud environment are easier to both internal and external intruders. To detect intruders, various intrusion detection systems and authentication systems was proposed in earlier researches which are primarily ineffective. Many existing researchers were concentrated on machine learning approaches for detecting intrusions using fuzzy clustering, artificial neural network, support vector machine, fuzzy with neural network and etc., which are not furnishing predominant results based on detection rate and false negative rates. Our proposed system directed on intrusion detection system and it uses cloudlet controller, trust authority and virtual machine management in cloud environment. We propose two novel algorithms such as (i) packet scrutinization algorithm which examines the packets from the users and (ii) hybrid classification model called “NK-RNN” which is a combination of normalized K-means clustering algorithm with recurrent neural network. For preventing the user from intruders, we propose a one time signature for cloud user in order to access the data on cloud environment. Our proposed classifier effectively detects the intruders which are experimentally proved by comparing with existing classification models. Thus our proposed results are expressed by packet loss ratio, average packet delay, throughput, detection rate, false positive rate and false negative rate.

Abstract: Virtual network embedding (VNE) is the process of embedding the set of interconnected virtual machines onto the set of interconnected physical servers (PSs) in the cloud computing environment. The level of complexity of VNE problem increases when a large number of virtual machines with a set of resource demand need to be embedded onto a network of thousands of PSs. The key challenge of VNE is the efficient mapping of virtual networks (VNs), which may have dynamic resource demands. Existing solutions mainly emphasize on the embedding of static VN resulting in poor resource utilization and very low acceptance rate. To tackle such level of complexity in VNE, a fitness-based dynamic virtual network embedding (DYVINE) algorithm is proposed with the goal to maximize the resource utilization by maximizing the acceptance rate. Local and global fitness values of the virtual machines and VN, respectively, are used to utilize the maximum amount of physical resources. The proposed VNE algorithm allows the VN to be dynamic, which indicates that the structure and resource demand can be changed during its execution time. Furthermore, in order to reduce the embedding time in each time slot, a set of PSs is selected to host the VN instead of considering thousands of PSs, which may significantly increase the embedding time. The proposed embedding mechanism is evaluated through extensive simulation and is compared with similar existing embedding algorithms, which outperforms over others.

Abstract: Elasticity is considered as a fundamental feature of cloud computing where the system capacity can adjust to the current application workloads by provisioning or de-provisioning computing resources automatically and timely. Many studies have been already conducted to elasticity management systems, however, almost all lack to offer a complete modular solution. In this article, we propose MoDEMO, a new elasticity management system powering both vertical and horizontal elasticities, both VM and Container virtualization technologies, multiple cloud providers simultaneously, and various elasticity policies. MoDEMO is characterized by the following features: it represents (i) the first system that manages elasticity using Open Cloud Computing Interface (OCCI) model with respect to the OCCI standard specifications, (ii) the first unified system which combines the functionalities of the worldwide cloud providers: Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP), and (iii) allows a dynamic configuration at runtime during the execution of the application. MoDEMO permits to timely adapt resource capacity according to the workload intensity and increase application performance without introducing a significant overhead.

Abstract: The use of virtual laboratories is growing as companies and educational institutions try to expand their reach, cut costs, increase student understanding, and provide more accessible hands on training for future scientists. Many new higher education initiatives outsource lab activities so students now perform them online in a virtual environment rather than in a classroom setting, thereby saving time and money while increasing accessibility. In this paper we explored whether the learning and motivational outcomes of interacting with a desktop virtual reality (VR) science lab simulation on the internet at home are equivalent to interacting with the same simulation in class with teacher supervision. A sample of 112 (76 female) university biology students participated in a between-subjects experimental design, in which participants learned at home or in class from the same virtual laboratory simulation on the topic of microbiology. The home and classroom groups did not differ significantly on post-test learning outcome scores, or on self-report measures of intrinsic motivation or self-efficacy. Furthermore, these conclusions remained after accounting for prior knowledge or goal orientation. In conclusion, the results indicate that virtual simulations are learning activities that students can engage in just as effectively outside of the classroom environment.

Abstract: Although the main market for Virtual Reality (VR) is currently the gaming industry, advantages of using virtual environments in research and development have been already demonstrated e.g. for car industry or urban planning. Especially when no prototype is feasible or available, VR constitutes an advantageous alternative since it allows tests in laboratory conditions with high flexibility and ensured safety for test participants. In the presented study, it is investigated how VR can be used as a tool for Usability Tests to evaluate Human Machine Interfaces (HMI) for communication between autonomous vehicles and pedestrians. Singapore with its regulations and requirements has been selected as reference. Beyond the findings that explicit HMI concepts improve the communication between autonomous vehicles and pedestrians, VR was validated as suitable tool to conduct Usability Tests. Further studies plan to integrate additional case studies as well as improved immersion of test participants within the virtual environment.