Enforcing QoS in scientific workflow systems enacted over Cloud infrastructures

Bayesian optimization has been used for the global optimization of communication parameters of Vehicular Ad Hoc Networks (VANETs) for safety applications with stringent quality of service (QoS) requirements. However, the effectiveness of the methodology relies on an accurate analytic model for querying a distribution over functions, which is not practical. Furthermore, incorporating QoS requirements as constraints into the search process is cumbersome, timeconsuming, and even unreliable. In this paper, we present a new approach to the constrained Bayesian optimization of IEEE 802.11 based VANETs for safety messaging with the help of deep learning neural networks (DLNNs). First, we design and train a DLNN using data collected from the analytic models or channel measurements to approximate a mapping from the search parameter space to the QoS metrics. The QoS constraints are naturally incorporated into the DLNN through preprocessing data pairs that cannot meet the QoS requirements. Then, the Bayesian optimization is conducted to find the optimal communication parameters for the best channel usage on the condition that all QoS requirements are met. Accordingly, experiments are carried out on the Google Colab platform where the impact of DLNN structure, data sampling rate, and other optimization parameters are investigated. In comparison to other optimization approaches, utilizing a DLNN in the Bayesian optimization process is more time efficient and flexible.

Edge computing is a new computing paradigm after cloud computing. Edge service scheduling and composition (ESC) has been well recognized as a convenient and flexible way of services sharing and integrating in industrial application fields. The ESC aims at selecting a set of existing edge service candidates with different Quality of Service (QoS) attributes (e.g., price), then compositing them to accomplish a complex task to meet users' QoS requirements, where each edge service may have multiple functionally equivalent, but different QoS metrics. A grand research challenge of the ESC based on QoS is to select proper service candidates to maximize QoS of the composited edge service and meanwhile meet the global QoS requirements. In this article, we focus on this challenge and propose a hierarchical solution for ESC. Specifically, we classify service candidates into specified grades based on their QoS attributes, and use hierarchical model to address service selection problem. Furthermore, in order to expand the flexibility of our approach, we design a near-optimal solution by decomposing the global end-to-end QoS constraints into local QoS constraints and adopting local service selecting and updating strategy based on the hierarchical model. The results of simulation-based experiments are conducted to show our approach is more efficient and valid compared to other existing approaches.

With the rapid development of e-commerce over Internet, web services have attracted much attention in recent years. Nowadays, enterprises are able to outsource their internal business processes as services and make them accessible via the Web. Then they can dynamically combine individual services to provide new value-added services. With the increasing number of web services having equivalent functionality, the binding procedure is driven by some non-functional, Quality of Service (QoS) criteria, such as the money cost, response time, reputation, reliability or a trade-off between them. Thus, an important problem is, given QoS constraints, how to aggregate and leverage individual service’s QoS information to derive the optimal QoS of the composite service. In this paper, we propose a novel QoS model for performing flexible service selection. The key idea of the model is to relax users’ QoS constraints and try to find the most possible services satisfying users’ QoS requirements. Based on the proposed QoS framework, we develop various algorithms for making service selection on individual and composite services. We also introduce a top-k ranking strategy to reflect a user’s personalized requirements. Experimental evaluation shows the proposed QoS model is efficient and practical.

Volunteer computing systems exploiting large amounts of geographically dispersed resources on the Internet for solving complex scientific problems. However, scheduling scientific workflows in a fully decentralised way and low overhead is a challenging task in these environments. To counter this challenge, this paper presents a fully decentralised proximity-aware workflow-scheduling policy for these environments. The proposed scheduling consists of three phases. In the first phase, each workflow application is partitioned into sub-workflows in order to minimise data dependencies among them. The second phase of the workflow-scheduling algorithm finds some resources to execute each sub-workflow. These resources are selected based on Quality of Service (QoS) constraints of the workflow, load balancing and proximity of resources. Each workflow can have QoS constraints in terms of minimum CPU speed and minimum RAM or hard disk requirements. In the third phase, sub-workflows will be executed on each resource based on local scheduling algorithm to minimise the partial makespan. The proposed scheduling policy focuses on the reduction of communication overhead to improve the performance of I/O-intensive and data-intensive workflows. Simulation results show that the proposed workflow-scheduling policy improves the average response time of scientific workflows up to 53.6% under a moderate load.

This paper considers the joint design problem of linear precoder, decision feedback equalizer (DFE) and bit allocation for multi-input multi-output (MIMO) broadcast (BC) channels. The problem is formulated as the power minimization problem when the target bit rate and per stream symbol error rate (SER) are specified. For the two-user case, a particular class of joint triangularization (JT) can be applied to obtain the optimal design for the joint optimization problem with quality of service (QoS) constraints, namely, the Minimum Power JT broadcast DFE transceiver (MPJT). For arbitrary number of users, a suboptimal design, the Power Minimized QR DFE broadcast transceiver (PMQR), is also proposed. Numerical results are presented to demonstrate the performance of MPJT and PMQR transceivers under different QoS constraints. 1

For the grid workflow system include a large of candidate services, the activity scheduling in workflow’s ex-ecution base on quality of service (QoS) constraints can pro-vide optimal service for users. This paper established the QoS’s parameter system with applied technology of grid workflow, and introduced the each parameter’s calculation model of activity’s QoS, the grid work-flow’s QoS estimation method based on the basic flow control structures defined by the abstract grid workflow language (AGWL). The experimental results show that the calculation method in this paper can estimate stably QoS parameter values of grid workflow combining with the multi candidate services, and, can provide a reasonable basis for process scheduling based on QoS constraints.

A QoS-guaranteed multicast routing protocol

In this paper, performance of transmission in cognitive radio systems over time-selective flat fading channels is studied under quality of service (QoS) constraints and channel uncertainty. Cognitive secondary users are assumed to initially perform sensing over the transmission channel to detect the activities of the primary users. Then, depending on the channel sensing result, they choose their transmission power policies and perform channel estimation. Following the training phase, they transmit data through the channel. The activities of the primary users are modeled as a two-state Markov process. A state transition model is constructed to model the cognitive transmissions. Statistical limitations on the buffer lengths are imposed to take into account the QoS constraints, and an average power constraint on the secondary users is considered to limit the interference to the primary users. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. Numerical results are provided for the power and rate policies.

In this paper, performance of cognitive transmission over time-selective flat fading channels is studied under quality of service (QoS) constraints and channel uncertainty. Cognitive secondary users (SUs) are assumed to initially perform channel sensing to detect the activities of the primary users, and then attempt to estimate the channel fading coefficients through training. Energy detection is employed for channel sensing, and different minimum mean-square-error (MMSE) estimation methods are considered for channel estimation. In both channel sensing and estimation, erroneous decisions can be made, and hence, channel uncertainty is not completely eliminated. In this setting, performance is studied and interactions between channel sensing and estimation are investigated. Following the channel sensing and estimation tasks, SUs engage in data transmission. Transmitter, being unaware of the channel fading coefficients, is assumed to send the data at fixed power and rate levels that depend on the channel sensing results. Under these assumptions, a state-transition model is constructed by considering the reliability of the transmissions, channel sensing decisions and their correctness, and the evolution of primary user activity which is modeled as a two-state Markov process. In the data transmission phase, an average power constraint on the secondary users is considered to limit the interference to the primary users, and statistical limitations on the buffer lengths are imposed to take into account the QoS constraints of the secondary traffic. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. Numerical results are provided for the power and rate policies.

Using SOA, service processes can be composed statically or dynamically using services provided by different service providers. Some services may become faulty at runtime and cause the service process to violate the end-to-end quality of service (QoS) constraint. We propose an efficient approach for replacing faulty services to ensure that the reconfigured service process still meets the original end-to-end QoS constraint. We use an iterative algorithm to identify reconfiguration regions that have a small number of services, some faulty and some healthy, in order to have more service selection options. By reconfiguring services in these selected regions rather than the whole service process, the computational complexity is significantly reduced. Simulation study has shown that our approach is efficient as most service processes can be repaired by replacing only a small number of services.

Intelligent reflecting surface (IRS) is an emerging technology that is able to reconfigure the wireless channel via tunable passive signal reflection and thereby enhance the spectral and energy efficiency of wireless networks cost-effectively. In this paper, we study an IRS-aided multiuser multiple-input single-output (MISO) wireless system and adopt the two-timescale (TTS) transmission to reduce the signal processing complexity and channel training overhead as compared to the existing schemes based on the instantaneous channel state information (I-CSI), and at the same time, exploit the multiuser channel diversity in transmission scheduling. Specifically, the long-term passive beamforming is designed based on the statistical CSI (S-CSI) of all links, while the short-term active beamforming is designed to cater to the I-CSI of all users' reconfigured channels with optimized IRS phase shifts. We aim to minimize the average transmit power at the access point (AP), subject to the users' individual quality of service (QoS) constraints. The formulated stochastic optimization problem is non-convex and difficult to solve since the long-term and short-term design variables are complicatedly coupled in the QoS constraints. To tackle this problem, we propose an efficient algorithm, called the primal-dual decomposition based TTS joint active and passive beamforming (PDD-TJAPB), where the original problem is decomposed into a long-term problem and a family of short-term problems, and the deep unfolding technique is employed to extract gradient information from the short-term problems to construct a convex surrogate problem for the long-term problem. The proposed algorithm is proved to converge to a stationary solution of the original problem almost surely. Simulation results are presented which demonstrate the advantages and effectiveness of the proposed algorithm as compared to benchmark schemes.

MPI-QMULInformation Systems Research Centre, Macau Polytechnic InstituteMacau SAR,P. R. ChinaDepartment of Electronic Engineering, Queen MaryUniversity of LondonE-mail: xuy@mpi-gmul.org/john.bigham@elec.gmul.ac.ukAbstract: In this paper, we solve the call admission control(CAC) problem in a cellular network via a form ofNeuroEvolution algorithm. The results are compared withComplete Sharing scheme, which allows all types of traffic toaccess all available channels at all time, and show that ourproposed CAC scheme CN can reduce the handoff failuresubject to several predefined Quality of Service (QoS)constraints: the handoff dropping probability (CDR) andintra-class fairness are all guaranteed within the predefinedconstraints. Additionally, the numerical results shows thatunder the same handoff failure constraint, better fairnesscauses higher new setup calls blocking probabilities (CBR) andlower CDR, therefore the fairness and maximizing revenue aretwo opposite goals, and the fairness and CDR do not conflictwith each other.Keywords: CAC,NEAT, QoS, Fairness.

Nowadays, more and more researchers are paying their attention to green routing. In this paper, we consider power consumption as a kind of QoS (quality of service) and apply a new learning-based approach for energy efficient transportation and QoS routing. Compared with traditional rule-based methods, the proposed method can learn additional information from the networks to improve routing performance, and have the flexibility to meet different QoS requirements. First, we propose a new identification of network nodes, namely node vectors, and a basic routing algorithm using node vectors is designed accordingly. Then, energy efficient transportation and QoS routing are proposed by adding QoS constraints into the routing decision. Link attributes such as power consumption, bandwidth and delay can be learned from these node vectors with neural networks. The learned link attributes together with the estimated distance can be used for routing decisions with QoS constraints. Simulation results show that the proposed method is reliable in routing tasks, and can achieve a remarkable performance when compared with the state-of-the-art work on the delay constrained least cost path (DCLC) problem.

Wireless Body Area Network (WBAN) represents one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, a buffer aware Energy-efficient and QoS-effective resource allocation scheme is proposed in which the sensor queue buffer states, constraints of QoS metrics and the characteristics of dynamic links are considered. Specifically, a buffer aware sensor evaluation method is designed to dynamically evaluate the sensor state with considering the sensor buffer states for improving the system performance. We then formulate the resource allocation problem for optimizing the transmission power, the transmission rate and the allocated time slots for each sensor to minimize the sum mix-cost, which is defined to characterize the energy cost and QoS cost between attainable QoS support and QoS requirements. Simulation results demonstrate the effectiveness of the buffer aware sensor evaluation method and the proposed energy-efficient and QoS-effective resource allocation scheme.

In order to apply downlink nonorthogonal multiple access (NOMA) to delay-sensitive transmissions, we consider NOMA with delay quality of service (QoS) constraints and effective capacity with power control when the channel state information is available in this paper. As the power control problem to maximize the sum effective capacity with delay QoS constraints is not a convex problem, we propose a suboptimal approach based on the partial effective capacity. The resulting power control policy can be seen as a generalization of a well-known approach, which is the truncated channel inversion power control (TCIPC) policy, to NOMA. We can further optimize the NOMA-TCIPC policy with QoS constraints and confirm that the resulting policy can guarantee delay QoS from simulation results.