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Latency-Constrained Cost-Minimized Request Allocation for Geo-Distributed Cloud Services

Latency to end-users and regulatory requirements push cloud providers to operate many datacenters all around the globe to host their cloud services. An emerging problem under such geo-distributed architecture is to assign each user request to an appropriate datacenter to benefit both cloud providers (e.g., low bandwidth cost) and end-users (e.g., low latency)—known as request allocation. However, prior request allocation solutions have significant limitations: they either focus only on optimizing the benefits for one entity (e.g., providers or users), or ignore some practical yet indispensable factors (e.g., heterogeneous latency requirements of different users and diverse per unit bandwidth cost among different datacenters) when optimizing benefits for both entities. In this paper, we study the problem of minimizing the total bandwidth cost for cloud service providers while guaranteeing the latency requirement for end-users. Specifically, we formulate an integer programming with consideration of the diversities in both the delay of requests and per unit bandwidth cost of datacenters. To efficiently and practically solve this problem, we first relax the integer programming into a continuous convex optimization and then take the advantages of random sampling to enforce the solution to be a feasible one for the original integer programming. We have conducted rigorous theoretical analysis to prove that our algorithm can provide a considerable good competitive ratio. Extensive simulations demonstrate that our proposed algorithm can reduce the total bandwidth cost by 30% while guaranteeing the latency requirements of all requests, as compared to conventional methods.

BAT algorithm used for load balancing purpose in cloud computing: an overview

Cloud computing is modern technology that has led to significant change in different fields of life by providing different kinds of servicer like hardware and software on user demands based on pay and gain rule. Due to the rapid growth of cloud computing it faces different kinds of issues, and resource allocation is one of them. For the improvement of resource allocation system in cloud computing different kinds of technique are used, and load balancing technique is one of them. In this paper we discuss resource allocation system in virtual machines (VM) because when a user sends data in VM then a situation may occur that some VM are overloaded and some become underloaded, which may lead to failure of system or request delay. For improvement of this situation different researchers used different algorithms in load balancing technique for cloud computing. This paper only focuses on BAT algorithms which are used for load balancing technique in improved resource allocation system for VM and also defines those rules which are used for improvement in load balancing technique in cloud data centre.

Hierarchical Load Balancing and Clustering Technique for Home Edge Computing

The edge computing system attracts much more attention and is expected to satisfy ultra-low response time required by emerging IoT applications. Nevertheless, as there were problems on latency such as the emerging traffic requiring very sensitive delay, a new Edge Computing system architecture, namely Home Edge Computing (HEC) supporting these real-time applications has been proposed. HEC is a three-layer architecture made up of HEC servers, which are very close to users, Multi-access Edge Computing (MEC) servers and the central cloud. This paper proposes a solution to solve the problems of latency on HEC servers caused by their limited resources. The increase in the traffic rate creates a long queue on these servers, i.e., a raise in the processing time (delay) for requests. By leveraging, based on clustering and load balancing techniques, we propose a new technique called HEC-Clustering Balance. It allows us to distribute the requests hierarchically on the HEC clusters and another focus of the architecture to avoid congestion on a HEC server to reduce the latency. The results show that HEC-Clustering Balance is more efficient than baseline clustering and load balancing techniques. Thus, compared to the HEC architecture, we reduce the processing time on the HEC servers to 19% and 73% respectively on two experimental scenarios.

Dynamic Request Scheduling Optimization in Mobile Edge Computing for IoT Applications

In the era of 5G, with the increasing demands on computation and massive data traffic of the Internet of Things (IoT), mobile edge computing (MEC) and ultradense network (UDN) are considered to be two enabling and promising technologies, which result in the so-called ultradense edge computing (UDEC). Task offloading as an effective solution offers low latency and flexible computation for mobile users in the UDEC network. However, the limited computing resources at the edge clouds and the dynamic demands of mobile users make it challenging to schedule computing requests to appropriate edge clouds. To this end, we first formulate the transmitting power allocation (PA) problem for mobile users to minimize energy consumption. Using the quasiconvex technique, we address the PA problem and present a noncooperative game model based on subgradient (NCGG). Then, we model the problem of joint request offloading and resource scheduling (JRORS) as a mixed-integer nonlinear program to minimize the response delay of requests. The JRORS problem can be divided into two problems, namely, the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a double decision-making problem and propose a multiple-objective optimization algorithm based on i-NSGA-II, referred to as MO-NSGA. The simulation results show that NCGG can save the transmitting energy consumption and has a good convergence property, and MO-NSGA outperforms the existing approaches in terms of response rate and can maintain a good performance in a dynamic UDEC network.

BEAcM‐DP: A broadcast encryption anti‐censorship mechanism based on directory proxy

AbstractAs a typical representative of the next generation Internet, named data networking (NDN) solves many problems in IP network by adopting content‐oriented architecture. However, NDN also faces with severe challenges in the aspect of name and content privacy. One important privacy threat is the name censorship. By maintaining a blacklist at the hijacked router, an attacker can filter the received interest packets with sensitive content names. To solve this problem, we propose a broadcast encryption anticensorship mechanism, which is based on directory proxy. In our design, a directory proxy is deployed in the network, which provides a periodic updated directory file to all authorized users. In the directory file, a one‐to‐one mapping list of fake names with the censored names is given. By obtaining the directory file, the authorized user can request the censored content with its fake name. In addition, the directory proxy plays the role of translating the received fake name and then retrieving the target with real name. To guarantee the reusability of retrieved contents, the directory proxy returns them to the authorized users through broadcast encryption. The users within one broadcast group can share the encrypted censored contents at nearby routers. Simulation results show that, compared with ANDaNA, this mechanism can effectively avoid censorship in the network, while ensuring the utilization of in‐network caching and reducing the request delay.

An adaptive‐aware energy and queue improvement of AOMDV

SummaryBecause the node energy and network resources in the wireless sensor network (WSN) are very finite, it is necessary to distribute data traffic reasonably and achieve network load balancing. Ad hoc on‐demand multipath distance vector (AOMDV) is a widely used routing protocol in WSN, but it has some deficiencies: establishes the route by only using hop counts as the routing criterion without considering other factors such as energy consumption and network load; forwards route request in fixed delay resulting in building the nonoptimal path; and cannot update the path status after built paths. For the deficiency of AOMDV, this paper proposes a multipath routing protocol adaptive energy and queue AOMDV (AEQAOMDV) based on adaptively sensing node residual energy and buffer queue length. When sending a routing request, the forwarding delay of the routing request is adaptively adjusted by both the residual energy and the queue length of the intermediate node; when establishing routes, a fitness is defined as a routing criterion according to the link energy and the queue load, predicting the available duration of the node based on the energy consumption rate and adjusting the weight of the routing criterion by the available duration of the node; after the routes are established, the path information status are updated via periodically broadcasting Hello that carries the path information with the minimum fitness, making the source node update the path information periodically. By using NS‐2, simulations demonstrate that compared with AOMDV, AEQAOMDV has obvious improvements in increasing packet delivery ratio, reducing network routing overhead, reducing route discovery frequency, and decreasing the network delay. And AEQAOMDV is more suitable for WSN.

Energy-Efficient Joint Pushing and Caching Based on Markov Decision Process

Content caching provides a rare trade-off opportunity between low-cost memory and energy consumption, yet finding the optimal causal policy with low computational complexity remains a challenge. In this paper, we formulate the problem of joint pushing and caching at end users in a Markov decision process (MDP) framework with the aim of minimizing energy cost under causal knowledge of the user request delay information and strict delay constraint. We put forth a novel approach to decouple the influence of buffer occupancy and user request, allowing one to equivalently run value iteration in a much smaller state space. Furthermore, by exploiting a structural property we discovered as generalized monotonicity , we design a computationally efficient non-iterative algorithm, fast assignment of state transition, to map the optimal policy from the smaller state space back to the complete state space. This, in turn, significantly simplifies the search for optimal policy. The results attain close performance in comparison with theoretical bounds from non-causal policies, while benefiting from better time efficiency than the unadapted MDP solutions.

Request Delay-Based Pricing for Proactive Caching: A Stackelberg Game Approach

Proactively pushing content to users has emerged as a promising approach to improve the spectrum usage in off-peak times for fifth-generation mobile networks. However, owing to the uncertainty of future user demands, base stations (BSs) may not receive payments for the pushed files. To motivate content pushing, providing economic incentives to BSs becomes essential. Based on request delay information (RDI) that characterizes the users’ request time for content files, this paper studies the profit maximization for a BS and a spectrum provider (SP) by developing a Stackelberg game. Specifically, the SP sets different selling prices of bandwidth for pushing and on-demand services, while the BS responds with the optimal quantity to purchase. In the game with non-causal RDI, a sub-gradient algorithm is presented to achieve a Stackelberg equilibrium (SE). For the game with statistical RDI, a closed-form expression is derived for an SE in the single-user scenario and a simulated annealing-based algorithm is designed to obtain an SE in the multi-user scenario. It is shown that the proposed games achieve greater profit for both the SP and the BS, compared with the on-demand scheme. Furthermore, pricing with statistical RDI attains performance closely approaching that with non-causal RDI, while being more practical.

Small Dose-Clonidine Prolongs Postoperative Analgesia after Sciatic-Femoral Nerve Block With 0.75% Rovpivacaine for Foot Surgery

Background: Foot surgery is associated with severe pain that can extend significantly up to 48 hours and often requires large amounts of parenteral opioids. . The benefit of adding clonidine to LAs for peripheral nerve blocks is less clear, although it is widely believed that clonidine improves quality and duration of a LA block. The aim of this study was to evaluate the effects of adding 1 mg/kg clonidine to 0.75% ropivacaine during combined sciatic-femoral nerve block for hallux valgus repair. Subjects and Methods: Thirty six ASA physical status I and II patients, scheduled for elective hallux  valgus repair under combined sciatic-femoral nerve block, were enrolled in the study. By using a sealed envelope technique, patients were randomly allocated to receive sciatic-femoral nerve block with 30 mL of either 0.75% ropivacaine alone (group Ropivacaine, n 5 15) or 0.75% ropivacaine plus 1 mg/kg clonidine (group Ropivacaine- Clonidine, n 5 15). Standard monitoring was used throughout the study, including noninvasive arterial blood pressure, heart rate, and pulse oximetry. The time from the end of anesthetic injection to resolution of motor block at the ankle of the operated foot and first request for postoperative analgesic was recorded. At discharge from the orthopedic ward and 3 wk after hospital discharge, patients were also evaluated regarding the occurrence of neurological complications. Results: No differences in the time required to achieve surgical anesthesia were observed between patients receiving only 0.75% ropivacaine (10 [5–20] min) and those receiving the ropivacaine-clonidine mixture (10 [5–30] min).  The mean time from block placement to first request for pain medication was shorter in group Ropivacaine than in group Ropivacaine- Clonidine (P = 0.03,. No differences in postoperative consumption of ketoprofen were observed between patients. Conclusion: Adding 1mg/kg clonidine to 0.75% ropivacaine provided a 3-h delay in first request for pain medication after hallux valgus repair, with no clinically relevant side effects.

Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing.

Cloud computing technology is widely used at present. However, cloud computing servers are far from terminal users, which may lead to high service request delays and low user satisfaction. As a new computing architecture, fog computing is an extension of cloud computing that can effectively solve the aforementioned problems. Resource scheduling is one of the key technologies in fog computing. We propose a resource scheduling method for fog computing in this paper. First, we standardize and normalize the resource attributes. Second, we combine the methods of fuzzy clustering with particle swarm optimization to divide the resources, and the scale of the resource search is reduced. Finally, we propose a new resource scheduling algorithm based on optimized fuzzy clustering. The experimental results show that our method can improve user satisfaction and the efficiency of resource scheduling.

Joint Switch Upgrade and Controller Deployment in Hybrid Software-Defined Networks

To improve traffic management ability, Internet Service Providers (ISPs) are gradually upgrading legacy network devices to programmable devices that support Software-Defined Networking (SDN). The coexistence of legacy and SDN devices gives rise to a hybrid SDN. Existing hybrid SDNs do not consider the potential performance issues introduced by a centralized SDN controller: flow requests processed by a highly loaded controller may experience long tail processing delay; inappropriate multi-controller deployment could increase the propagation delay of flow requests. In this paper, we propose to jointly consider the deployment of SDN switches and their controllers for hybrid SDNs. We formulate the joint problem as an optimization problem that maximizes the number of flows that can be controlled and managed by the SDN and minimizes the propagation delay of flow requests between SDN controllers and switches under a given upgrade budget constraint. We show this problem is NP-hard. To efficiently solve the problem, we propose some techniques (e.g., strengthening the constraints and adding additional valid inequalities) to accelerate the global optimization solver for solving the problem for small networks and an efficient heuristic algorithm for solving it for large networks. The simulation results from real network topologies illustrate the effectiveness of the proposed techniques and show that our proposed heuristic algorithm uses a small number of controllers to manage a high amount of flows with good performance.

Scheduling the Operation of a Connected Vehicular Network Using Deep Reinforcement Learning

Driven by the expeditious evolution of the Internet of Things, the conventional vehicular ad hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. In an effort to satisfy the driver’s well-being and demand for continuous connectivity in the IoV era, this paper addresses both safety and quality-of-service (QoS) concerns in a green, balanced, connected, and efficient vehicular network. Using the recent advances in training deep neural networks, we exploit the deep reinforcement learning model, namely deep Q-network, which learns a scheduling policy from high-dimensional inputs corresponding to the current characteristics of the underlying model. The realized policy serves to extend the lifetime of the battery-powered vehicular network while promoting a safe environment that meets acceptable QoS levels. Our presented deep reinforcement learning model is found to outperform several scheduling benchmarks in terms of completed request percentage (10–25%), mean request delay (10–15%), and total network lifetime (5–65%).

Lightweight solutions to counter DDoS attacks in software defined networking

A distributed denial of service (DDoS) attack on any of the major components (e.g., controller, switches, and southbound channel) of software defined networking (SDN) architecture is a critical security threat. For example, the breakdown of controller could disrupt the data communication in the whole SDN network. A possible way to perform DoS is to generate a large number of new, but short length traffic flows. These flows will trigger malicious flooding requests to overload the controller and causes overflow in flow tables at SDN switches. In this paper, we propose two lightweight and practically feasible countermeasures against two different types of DDoS attacks called Route Spoofing and Resource Exhaustion in SDN networks. For Route Spoofing attack, we introduce a technique called “selective blocking”, which stops an adversary node from maliciously using other users active communication routes. To countermeasure Resource Exhaustion attack, we propose a solution called “periodic monitoring”, which detects adversary nodes based on the traffic analysis statistics that are gathered within a time window. We implement and perform result analysis of the attacks and their proposed countermeasures. When using our proposed countermeasures in the target SDN scenarios, the simulation results indicate an adequate reduction in bandwidth consumption and processing delay of new request, and it also depicts substantial gain in packet delivery rate. Additionally, we present the receiver operating characteristic curve, which shows the sensitivity and specificity of our countermeasures along with their detection accuracy.

Delay-Minimization Routing for Heterogeneous VANETs With Machine Learning Based Mobility Prediction

Establishing and maintaining end-to-end connections in a vehicular ad hoc network (VANET) is challenging due to the high vehicle mobility, dynamic inter-vehicle spacing, and variable vehicle density. Mobility prediction of vehicles can address the aforementioned challenge, since it can provide a better routing planning and improve overall VANET performance in terms of continuous service availability. In this paper, a centralized routing scheme with mobility prediction is proposed for VANET assisted by an artificial intelligence powered software-defined network (SDN) controller. Specifically, the SDN controller can perform accurate mobility prediction through an advanced artificial neural network technique. Then, based on the mobility prediction, the successful transmission probability and average delay of each vehicle's request under frequent network topology changes can be estimated by the roadside units (RSUs) or the base station (BS). The estimation is performed based on a stochastic urban traffic model in which the vehicle arrival follows a non-homogeneous Poisson process. The SDN controller gathers network information from RSUs and BS that are considered as the switches. Based on the global network information, the SDN controller computes optimal routing paths for switches (i.e., BS and RSU). While the source vehicle and destination vehicle are located in the coverage area of the same switch, further routing decision will be made by the RSUs or the BS independently to minimize the overall vehicular service delay. The RSUs or the BS schedule the requests of vehicles by either vehicle-to-vehicle or vehicle-to-infrastructure communication, from the source vehicle to the destination vehicle. Simulation results demonstrate that our proposed centralized routing scheme outperforms others in terms of transmission delay, and the transmission performance of our proposed routing scheme is more robust with varying vehicle velocity.

Caching With Time Domain Buffer Sharing

In this paper, storage efficient caching based on time domain buffer sharing is considered. The caching policy allows a user to determine whether and how long it should cache a content item according to the prediction of its random request time, also referred to as the request delay information (RDI). In particular, the aim is to maximize the caching gain for communications while limiting its storage cost. To achieve this goal, a queueing theoretic model for caching with infinite buffers is first formulated, in which Little's law is adopted to obtain the tradeoff between the hit ratio and the average buffer consumption. When there exist multiple content classes with different RDIs, the storage efficiency is further optimized by carefully allocating the storage cost. For more practical finite-buffer caching, a $G/GI/L/0$ queue model is formulated, in which a diffusion approximation and Erlang-B formula are adopted to determine the buffer overflow probability and the corresponding hit ratio. The optimal hit ratio is shown to be limited by the demand probability and buffer size for large and small buffers respectively. In practice, a user may exploit probabilistic caching with random maximum caching time and arithmetic caching without any need for content arrival statistics to efficiently harvest content files in air.

Routing algorithm design of satellite network architecture based on SDN and ICN

SummaryIn view of the problems of low routing efficiency, complex control process, and difficult network management in big data environment in the traditional integrated space‐terrestrial network, in the paper, we propose a satellite network architecture called software‐defined information centric satellite networking (SDICSN) based on software‐defined networking (SDN) and information‐centric networking (ICN), and we design a virtual node matrix routing algorithm (VNMR) under the SDICSN architecture. The SDICSN architecture realizes the flexibility of network management and business deployment through the features of the separation of forwarding and controlling by the SDN architecture and improves the response speed of requests in the network by the centric of “content” as the ICN idea. According to the periodicity and predictability of the satellite network, the VNMR algorithm obtains the routing matrix through the relative orientation of the source and destination nodes, thus reducing the spatial complexity of the input matrix of the Dijkstra algorithm and then reducing the time complexity of the routing algorithm. For forwarding information base (FIB), the mechanism of combination of event driven and polling can be quickly updated in real time. Finally, the advantages of the SDICSN architecture in routing efficiency, request delay, and request aggregation are verified by simulation.

A Virtual Network Resource Allocation Framework Based on SR-IOV

The workload of a data center has the characteristics of complexity and requirement variability. However, in reality, the attributes of network workloads are rarely used by resource schedulers. Failure to dynamically schedule network resources according to workload changes inevitably leads to the inability to achieve optimal throughput and performance when allocating network resources. Therefore, there is an urgent need to design a scheduling framework that can be workload-aware and allocate network resources on demand based on network I/O virtualization. However, in the current mainstream I/O virtualization methods, there is no way to provide workload-aware functions while meeting the performance requirements of virtual machines (VMs). Therefore, we propose a method that can dynamically sense the VM workload to allocate network resources on demand, and can ensure the scalability of the VM while improving the performance of the system. We combine the advantages of I/O para-virtualization and SR-IOV technology, and use a limited number of virtual functions (VFs) to ensure the performance of network-intensive VMs, thereby improving the overall network performance of the system. For non-network-intensive VMs, the scalability of the system is guaranteed by using para-virtualized Network Interface Cards (NICs) which are not limited in number. Furthermore, to be able to allocate the corresponding bandwidth according to the VM’s network workload, we hierarchically divide the VF’s network bandwidth, and dynamically switch between VF and para-virtualized NICs through the active backup strategy of Bonding Drive and ACPI Hotplug technology to ensure the dynamic allocation of VF. Experiments show that the allocation framework can effectively improve system network performance, in which the average request delay can be reduced by more than 26%, and the system bandwidth throughput rate can be improved by about 5%.

Multi-Controller Deployment Algorithm in Hierarchical Architecture for SDWAN

A software-defined network (SDN) is a new paradigm that separates control and forwarding. With the growth of SDN deployment scale, the centralized control plane becomes a bottleneck restricting the expansion of SDN networks. To avoid the problem of limited processing power in the wide area network (WAN), we focus on the multi-controller deployment in hierarchical architecture for software-defined WAN (SDWAN). The hierarchical architecture divides the control plane into multiple levels. The root controller has a global view. The domain controller is only responsible for the control in local networks. In this paper, we use the improved Louvain algorithm to discover the hierarchical community structure. Considering the load balancing and request delay in each partition and reliability of the control plane, we formulate the modeling and propose a hierarchical multi-controller deployment algorithm for the scalability of the SDWAN. The simulation results show that compared with the traditional algorithms, the proposed algorithm not only reduces the average request delay to a certain extent and effectively improves the performance of load balancing in each partition, but also efficiently enhances the reliability of the control plane in the SDWAN and achieves the good effect on the execution efficiency.

WED 260 Time to scan: barriers to imaging outside normal working hours

SSNAP measures quality and organisation of stroke care. Domain 1.1 looks at the number of patients scanned within 1 hour of arrival at hospital; local trust performance is 57.4% (national average 51.3%), with the specialist stroke unit at 79.2%. This is in part related to ‘direct to scan protocols’ in extended working hours, but we currently lack equivalent medical staffing and radiographer support during this time. We aimed to explore which factors impacted on scanning time out of hours. An initial consecutive 14 day sample identified delays in approval of scan request by the duty radiologist. A new Standard Operating Procedure allowed specialist stroke nurse practitioners (SNPs) to request CT head scans directly with the duty radiographer, eliminating need for liaison with the radiologist. A repeat 14 day analysis identified additional factors resulting in delays, including delays in scan request and in-hospital competing emergency clinical scanning requirements, meaning no significant improvement in percentage of patients scanned within 1 hour was observed. The mean time from arrival to scan performance was 52 min, but 21.8% of patients did not undergo a CT head within 1 hour of arrival. Further strategies are required to maximise patients meeting this target.

The Effect of Adding Ketamine or Midazolam to Bupivacaine for Ultrasound-Guided Supraclavicular Brachial Plexus Block for Upper Extremity Surgeries

AbstractBackground: The use of supraclavicular brachial plexus block is one of the most effective anesthetic techniques in operations for the upper extremity. The use of ultrasound guidance for regional anesthesia became popular owing to detection of anatomical variants, painless performance and correct needle placement. Aim of the study is to evaluate the value of adding ketamine or midazolam to bupivacaine when used for ultrasound guided supraclavicular brachial plexus block in upper extremity surgical procedures as regard the quality of surgical anesthesia and post-operative analgesia.Patients and Methods: Seventy adult patients of both sexes aged (18-60) years with ASA physical status I/II sched-uled for elective surgical procedure of the elbow, forearm, wrist and hand. Patients divided into two groups thirty-five patients were given 30ml total volume of 0.5% bupivacaine with midazolam 50μg/kg injected around brachial plexus cluster (Group A) and thirty-five patients were given 30ml general volume contained 0.5% bupivacaine with ketamine 2mg/kg injected around brachial plexus cluster (Group B).Results: There was no significant difference among both groups according to demographic data, hemodynamic changes, onset of motor and sensory block, sedation score, total doses of rescue analgesia and incidence of complications. There was significant prolongation in duration of sensory and motor block, significant decrease in VAS and significant delay in first request of rescue analgesia in Group A.Conclusion: The addition of midazolam (50μg/kg) when used as adjuvants to bupivacaine in ultrasound guided brachial plexus block produced prolongation of sensory and motor block, providing desirable sedation, improved quality of postoperative analgesia and decreased necessities of rescue analgesics in post-operative period.