Response Time Of Requests Research Articles

Load Balancing Algorithm for Efficient VM Allocation in Heterogeneous Cloud

Cloud computing is a growing service computing trend that offers users a range of on-demand variety of services from applications, processing capability, and storage based on the concept of the “Pay-As-PerUse” model. Organizations from every sector are now realizing the benefits offered by cloud computing technology and moving towards the cloud. Cloud computing offers numerous advantages over conventional computing. However, it still faces a few challenges such as resource utilization in a cloud data centre and quality of service to the end-users due to improper workload balance among available resources. Heterogeneous cloud resources also impact cloud systems overall performance. We proposed an enhanced load balancing algorithm in this research paper for efficient VM allocation in a heterogeneous cloud. Our proposed algorithm allocates independent user tasks or requests to available virtual machines in cloud datacentre efficiently to manage proper load balancing. This algorithm is aimed at minimizing user request response time and the time required for data centre processing. The results obtained showed a significant reduction in user request response time and data centre processing time as compared to “Throttled” and “Round Robin (RR)” algorithms.

Flat and hierarchical system deployment for edge computing systems

In this paper, we consider the server allocation problem for edge computing system deployment where each edge cloud is modeled as an M/M/c queue. Our goal is to minimize the overall average system response time of application requests generated by all mobile devices/users. We consider two approaches for edge cloud deployment: the flat deployment, where all edge clouds are co-located with the base stations, and the hierarchical deployment, where edge clouds can be co-located with other system components besides the base stations. In flat deployment, we demonstrate that the allocation of edge cloud servers should be balanced across all the base stations, if the application request arrival rates at the base stations are equal to each other; if the application request arrival rates are not the same, we propose a Largest Weighted Reduction Time First (LWRTF) algorithm to assign servers to edge clouds. Numerical comparisons of the proposed algorithm against several other reasonably designed heuristics verify that algorithm LWRTF has very good performances in terms of minimizing the average system response time. By theoretical analysis and numerical evaluations, we also show that, the hierarchical deployment approach has great potentials in minimizing the overall average system response time compared to the flat deployment approach. We also investigate the server allocation problem in hierarchical deployment and derive important insights to guide practical edge cloud server allocation in real-world systems.

A job scheduling algorithm for delay and performance optimization in fog computing

SummaryDue to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In this paper, we propose a novel Fog computing scheduler that supports service‐provisioning for Internet of Everything, which optimizes delay and network usage. We present a case study to optimally schedule the requests of Internet of Everything devices on Fog devices and efficiently address their demands on available resources on every Fog device. We consider delay and energy consumption as performance metrics and evaluate the proposed scheduling algorithm using iFogSim in comparison with existing approaches. The results show that the delay and network usage of the proposed scheduler improve by 32% and 16%, respectively, in comparison with FCFS approach.

Adaptive Replica Creation and Selection Strategies for Latency-Aware Application in Collaborative Edge-Cloud System

Abstract There are many research problems in cloud replica management such as low data reliability, unbalanced node load and large resource consumption. The strategy and status of replica creation, replica placement and replica selection are analyzed. The replica creation based on access tendency (DRC-AT), the replica placement based on user request response time and storage capacity (DRP-RS) and the replica selection based on response time (DRS-RT) are proposed. The DRC-AT algorithm introduces the two parameters of file popularity and period value of file popularity, calculates the file access tendency periodically and decides the creation and deletion of the replica of the file according to the size of the file access tendency. The DRP-RS algorithm evaluates the user’s request response time and storage capacity to select the best node set to place the replica. The DRS-RT algorithm returns to the user the node with the strongest service capability that contains the user’s requested data. Experiments show that the algorithm can improve the speed of data reading by the client, improve the resource utilization, balance the load of the node and improve the overall performance of the system.

A Delay and Energy Efficient Multicast Routing Protocol using IWO and MOLO Algorithm for Vehicular Networks

Vehicular networks are significantly improving wireless communication network which provides an intelligent transportation system services among faster moving vehicles with internet and brings safety and comfort drive. From a single source vehicle the multicast routing protocols delivers multicast messages to all members of the multicast group by means of multi-hop communication. Increasing the density of vehicles results in channel overload, which increases the probability of data collision; hence reduction in successful received data will increase the delay. Therefore, delay and energy consumption are the major constraints that should affect the performance of routing. In this paper we suggest a delay and energy efficient multicast routing (DEMR) protocol for vehicular network using a hybrid machine learning algorithm. The DEMR protocol consists of four layers; are vehicle layer, fog layer, OpenFlow switch layer, and SDN controller layer. Moreover, to partition the vehicle layer, and select the optimal multicast path based on multiple constraints improved weed optimization (IWO) algorithm is proposed. IWO algorithm separates the multicast request into emergency, common and police requests. We design a multi-objective lion optimization (MOLO) algorithm among fog nodes for resource management, which increases the utilization of resources in fog layer and decrease the response time of multicast session request. MOLO algorithm removes the unnecessary flow table and session table entries in the controller. The DEMR protocol is implemented in Network Simulator (NS3) tool and simulation results are compared with the protocols as EEMSFV, MABC, and CVLMS. From the simulation results we conclude that the DEMR algorithm is better than EEMSFV, MABC and CVLMS in terms of transmission ratio, overhead load, average end to end delay, packet loss ratio and, energy consumption.

Towards Yo-Yo attack mitigation in cloud auto-scaling mechanism

Cloud platforms could automatically scale underlying network resources up and down in response to changes in the traffic load. Such an auto-scaling mechanism can largely enhance the elasticity and scalability of cloud platforms. However, it may introduce new security threats. For example, the Yo-Yo attack is a newly disclosed attack against the cloud auto-scaling mechanism. Attackers periodically send bursts of traffic to cause the auto-scaling mechanism to oscillate between the scale-up process and the scale-down process, which may result in significant performance degradation and economic loss. None of the prior work addressed the problem of mitigating such an attack. In this paper, we propose a Trust-based Adversarial Scanner Delaying (TASD) approach to effectively and proactively mitigate the Yo-Yo attack on the cloud auto-scaling mechanism. In TASD, we first propose to use the trust-based scheme to establish trust values for users, which is leveraged to identify adversarial requests. Trust values are updated by jointly considering the request mode and the auto-scaling status. Then, we aim to disable the condition under which the Yo-Yo attack takes effect by injecting certain delay, under the QoS constraints, to manipulate the response time of suspicious requests and deceive the attackers. Our extensive evaluation demonstrates that our approach achieves promising results, e.g., it can detect at least 80% Yo-Yo adversarial users and reduce more than 41% malicious scale-ups.

Managing renewable energy and carbon footprint in multi-cloud computing environments

Cloud computing offers attractive features for both service providers and customers. Users benefit from the pay-as-you-go model by saving expenditures and service providers are deploying their services to cloud data centers to reduce their maintenance efforts. However, due to the fast growth of cloud data centers, the energy consumed by the data centers can lead to a huge amount of carbon emission with environmental impacts, and the carbon intensity of different locations are varied among different power plants according to the sources of energy. Thus, in this paper, to address the carbon emission problem of data centers, we consider shifting the workloads among multi-cloud located in different time zones. We also formulate the energy usage and carbon emission of data centers and model the solar power corresponding to the locations. This helps to reduce the usage of brown energy and maximize the utilization of renewable energy at different locations. We propose an approach for managing carbon footprint and renewable energy for multiple data centers at California, Virginia, and Dublin, which are in different time zones. The results show that our proposed approaches that apply workload shifting can reduce around 40% carbon emission in comparison to the baseline while ensuring the average response time of user requests.

Load Balancing of Fog Computing Centers: Minimizing Response Time of High Priority Requests

Fog computing is one of the enabling computing technology which primarily aims to fulfill the requirements of the Internet of Things (IoT). IoT is fast-growing networking and computing sector. The scalability of users, devices, and application is crucial for the success of IoT systems. The load balancing is an approach to distribute the load among computing nodes so that the computing nodes are not overloaded. In this paper, we propose the priority-based request servicing at fog computing centers. We particularly address the situation when the fog node in fog computing center (FCC) receives more workload than their capacity to handle it. The increased workload is shifted to nearby fog nodes rather than to the remote cloud. The proposed approach is able to minimize the offloading the high priority request to other nodes by 11% which proves the novelty of our proposed.

Lazy scheduling based disk energy optimization method

Reducing the energy consumption of the storage systems disk read/write requests plays an important role in improving the overall energy efficiency of high-performance computing systems. We propose a method to reduce disk energy consumption by delaying the dispatch of disk requests to the end of a time window, which we call time window-based lazy scheduling. We prove that sorting requests within a single time window can reduce the disk energy consumption, and we discuss the relationship between the size of the time window and the disk energy consumption, proving that the energy consumption is highly likely to decrease with increasing window size. To exploit this opportunity, we propose the Lazy Scheduling based Disk Energy Optimization (LSDEO) algorithm, which adopts a feedback method to periodically adjust the size of the time window, and minimizes the local disk energy consumption by sorting disk requests within each time window. We implement the LSDEO algorithm in an OS kernel and conduct both simulations and actual measurements on the algorithm, confirming that increasing the time window increases disk energy savings. When the average request arrival rate is 300 and the threshold of average request response time is 50 ms, LSDEO can yield disk energy savings of 21.5%.

The evaluation of performance cost for network based moving target defense

Network based moving target defense prevents attackers from reliably contacting a system by re-configuring network factors. Although limited researching has demonstrated it to be practical and feasible, little studies have been conducted to evaluate the performance cost appended to the original system. Without quantitative results, it is challenging to implement moving target techniques to typical information system broadly. This paper introduces a Queueing Petri Net model that can provide insight into the performance of network re-configurations. This model quantifies the system throughput, queue utilization and response time of requests regarding configurations of the information system, type of strategies for mutation and the frequency of networking transformation. Through logical deductions and simulations using Queueing Petri net Modelling Environment, results show that the network shifting method is an affordable defense technology compared to the performance loss in the processing of servers. Moreover, event-based strategies can get the trade-off between security and performance. These results guide the research deep into developing a more optimized method in moving target defense.

Popularity-Based Online Scaling for RAID Systems Under General Settings

The ever-increasing demand of storage capacity and system performance leads to the scaling requirement in redundant arrays of independent disks (RAID)-structured storage systems. Existing approaches mainly focus on minimizing data migration in offline scenario, but not consider the user accesses issued by applications. However, in online scenario, the scaling I/Os and user I/Os usually interfere with each other, and result in significant performance degradation. Thus, it is of big significance to develop an efficient online scaling scheme to mitigate the impact of I/O interference. In this paper, we propose popularity-based online scaling (POS), which exploits workload locality by scaling storage zones with high popularity in a higher priority. POS can efficiently alleviate the interference between scaling I/Os and user I/Os, and it is also general enough to scale various RAID systems like RAID-0, RAID-5, and RAID-6. It can also be readily deployed atop various conventional RAID scaling approaches to improve their performance. To demonstrate the effectiveness of POS, we implement various conventional RAID scaling approaches, and also implement POS on top of these approaches for comparison. Extensive simulations with real-world workloads show that POS can efficiently reduce the response time of user requests and scaling I/Os, and also improves the sequentiality of data accesses.

Improving Performance in Component Based Distributed Systems

Assuring high performance is one of the most important factors when building computerized systems. In distributed component-based systems different configurations and workloads are common. Over the years, many approaches were proposed to analyse, predict, measure and evaluate the performance of component-based distributed systems in an attempt to improve its performance. Higher performance results in better utilization of system resources, high throughput and quick response time of user requests. In this paper, we extend the work of the E-Avala approach to improve the overall performance of the proposed approach by adding power processing capabilities. The proposed approach has been implemented and compared with previous approach using Arena simulation with varying configuration parameters. Comparative results show that the proposed approach has given better performance with different levels of processing powers.

An Research on the Effectiveness of the Different Algorithms and the Scheduling in Improving the Performance of Cloud Computing by using Cloud Analyst Simulator

Cloud computing can be defined as a computing paradigm, where the various systems and large pool are connected to each other in private or public networks. The aim for that is to provide a dynamically scalable infrastructure, where it is used for applications, data and file storage. Cloud computing reduced the cost of computation and application hosting so that content storage and delivering services are handled faster and more flexibility. Load balancing is one of the challenges that affect the performance of cloud computing and the overcome it leads to better resource utilization and response time. The service broker policy plays an important role in accelerating the response time of customer requests by locating data centers or optimize the pattern of access to them. The contribution of this paper investigates the effectiveness of using the different algorithms and the approaches to improve the performance of cloud computing as it has been shown that there is a possibility to increase the performance of cloud computing by relying on certain criteria described in this paper. The results, which are presented in this paper were obtained using the cloud analyst simulator, where this simulator contains (Time duration, Load balancing algorithms, and Service Broker Algorithms, etc).

Research on Resource Prediction Model Based on Kubernetes Container Auto-scaling Technology

Cloud computing provides a new way for computing resource acquisition and brings about changes in software development deployment. Docker is a lightweight alternative technology of virtualization, which is simple, convenient and practical when developing and deploying applications. Kubernetes is an open source container management system based on Docker container technology. This paper studies the existing auto-scaling strategy of Kubernetes and proposes an auto-scaling optimization strategy which can solve the response delay problem in the expansion phase. This strategy uses a combination of empirical modal decomposition and ARIMA models to predict the load of Pods and adjust the number of Pods in advance according to the prediction result. Our experiment proves that the strategy can achieve the purpose of capacity expansion before the peak load and reduce the application request response time.

Scalable replica selection based on node service capability for improving data access performance in edge computing environment

The replica strategies in traditional cloud computing often result in excessive resource consumption and long response time. In the edge cloud environment, if the replica node cannot be managed efficiently, it will cause problems such as low user’s access speed and low system fault tolerance. Therefore, this paper proposed replica creation and selection strategy based on the edge cloud architecture. The dynamic replica creation algorithm based on access heat (DRC-AH) and replica selection algorithms based on node service capability (DRS-NSC) were proposed. The DRC-AH uses data block as replication granularity and Grey Markov chain to dynamically adjust the number of replicas. After the replica is created, when client receives the user’s request, the DRS-NSC selects the best replica node to respond to the user. The experiments show that the proposed algorithms have significant advantages in prediction accuracy, user’s request response time, resource utilization, etc., and improve the performance of the system to a certain extent.

IBTune

Tuning the buffer size appropriately is critical to the performance of a cloud database, since memory is usually the resource bottleneck. For large-scale databases supporting heterogeneous applications, configuring the individual buffer sizes for a significant number of database instances presents a scalability challenge. Manual optimization is neither efficient nor effective, and even not feasible for large cloud clusters, especially when the workload may dynamically change on each instance. The difficulty lies in the fact that each database instance requires a different buffer size that is highly individualized, subject to the constraint of the total buffer memory space. It is imperative to resort to algorithms that automatically orchestrate the buffer pool tuning for the entire database instances. To this end, we design iBTune that has been deployed for more than 10, 000 OLTP cloud database instances in our production system. Specifically, it leverages the information from similar workloads to find out the tolerable miss ratio of each instance. Then, it utilizes the relationship between miss ratios and allocated memory sizes to individually optimize the target buffer pool sizes. To provide a guaranteed level of service level agreement (SLA), we design a pairwise deep neural network that uses features from measurements on pairs of instances to predict the upper bounds of the request response times. A target buffer pool size can be adjusted only when the predicted response time upper bound is in a safe limit. The successful deployment on a production environment, which safely reduces the memory footprint by more than 17% compared to the original system that relies on manual configurations, demonstrates the effectiveness of our solution.

Towards Traffic Load Balancing in Drone-Assisted Communications for IoT

Edge computing enables data collected by Internet of Things (IoT) devices to be stored in and processed by local fog nodes as well as allows IoT users to access IoT applications via these nodes at the same time. In this case, the communications latency critically affects the response time of IoT user requests. Owing to the dynamic distribution of IoT users [i.e., user equipments (UEs)], drone base station (DBS), which can be flexibly deployed for hotspot areas, can potentially improve the wireless latency of IoT users by mitigating the heavy traffic loads of macro BSs. Drone-based communications poses two major challenges: 1) the DBS should be deployed in suitable areas with heavy traffic demands to serve more UEs and 2) the traffic loads in the network should be allocated among macro BSs and DBSs to avoid instigating traffic congestions. Therefore, we propose a traffic load balancing scheme in such drone-assisted fog network to minimize the wireless latency of IoT users. In the scheme, we divide the problem into two subproblems and design two algorithms to optimize the DBS placement and user association, respectively. Extensive simulations have been set up to validate the performance of the proposed scheme.

Euphoria: A Scalable, event-driven architecture for designing interactions across heterogeneous devices in smart environments

Context: From personal mobile and wearable devices to public ambient displays, our digital ecosystem has been growing with a large variety of smart sensors and devices that can capture and deliver insightful data to connected applications, creating thus the need for new software architectures to enable fluent and flexible interactions in such smart environments. Objective: We introduce Euphoria, a new software architecture design and implementation that enables easy prototyping, deployment, and evaluation of adaptable and flexible interactions across heterogeneous devices in smart environments. Method: We designed Euphoria by following the requirements of the ISO/IEC 25010:2011 standard on Software Quality Requirements and Evaluation applied to the specific context of smart environments. Results: To demonstrate the adaptability and flexibility of Euphoria, we describe three application scenarios for contexts of use involving multiple users, multiple input/output devices, and various types of smart environments, as follows: (1) wearable user interfaces and whole-body gesture input for interacting with public ambient displays, (2) multi-device interactions in physical-digital spaces, and (3) interactions on smartwatches for a connected car application scenario. We also perform a technical evaluation of Euphoria regarding the main factors responsible for the magnitudes of the request-response times for producing, broadcasting, and consuming messages inside the architecture. We deliver the source code of Euphoria free to download and use for research purposes. Conclusion: By introducing Euphoria and discussing its applicability, we hope to foster advances and developments in new software architecture initiatives for our increasingly complex smart environments, but also to readily support implementations of novel interactive systems and applications for smart environments of all kinds.

Cost-Optimized Video Dissemination Over Heterogeneous Cloud With SLAs Support

A growing number of social video distribution services are turning toward to the cloud-based architecture for lower cost and better scalability. Although appealing, such a cloud content delivery network (cloud-based CDN) involves two key tasks: to dynamically migrate the contents across multiple data centers on diverse locations and to place user requests in the proper sites such that the monetary cost and service latency are targeted appropriately. In this paper, we formulate these two objectives into a combinational optimization problem and develop a context-aware community-based computing model to discover the contextual information of video propagation. In particular, we explore the social graph that people created to estimate the potential demands of each video and to propose a basic scheme for video migration and request placement under the heterogeneous cloud paradigm. Since the basic scheme adopts a greedy algorithm per step, it only obtains the short-term optimality. To solve this problem, we design an advanced scheme that can align to the long-term optimum solution and can ensure that the total monetary cost is minimized while meeting the predefined service level agreements (SLAs). Compared with present scheduling techniques, we found that the designed algorithm achieves lower service cost yet guarantees that the response time of requests is all within the specified quality of service (QoS) requirements.

Gobierno electrónico y accesibilidad: Disponibilidad de servicio en las plataformas online estatales de la Argentina

Este artículo presenta los resultados de un relevamiento anual de calidad de servicio de servidores estatales de la Argentina. Por medio de ello, se busca dar cuenta de la importancia de la evaluación sistemática de la infraestructura pública digital de servicios de gestión e información gubernamental. En el caso de la infraestructura estatal, la importancia de su estabilidad técnica tiene una relevancia especial, en tanto la oferta de servicios estatales representa en muchos casos el acceso o la denegación de derechos tan básicos como la educación, la salud o el trabajo. El monitoreo se realizó durante 12 meses sobre 59 sitios dependientes del estado nacional, pudiendo observarse en ellos un rendimiento deficiente en comparación a los grupos de servicios seleccionados como grupos de referencia. Este peor rendimiento se registró tanto en su nivel de disponibilidad como en el tiempo total de respuesta a las solicitudes. Los sitios dependientes del Estado nacional mostraron a su vez patrones de falla diferenciados según grupo de pertenencia (ministerios nacionales, organismos estatales y Universidades nacionales) sugiriendo la existencia de infraestructuras y formas de trabajo heterogéneas que confluyen en todos los casos en altos niveles de no disponibilidad de las plataformas ofrecidas.