Lightweight QoS-Aware Workflow Scheduling for Efficient Task Execution in Cloud Environments

Cloud computing serves as a platform for remote users to utilize the heterogeneous resources in data-centers to compute High-Performance Computing jobs. The physical resources are virtualized in Cloud to entertain user services employing Virtual Machines (VMs). Job scheduling is deemed as a quintessential part of Cloud and efficient utilization of VMs by Cloud Service Providers demands an optimal job scheduling heuristic. An ideal scheduling heuristic should be efficient, fair, and starvation-free to produce a reduced makespan with improved resource utilization. However, static heuristics often lead to inefficient and poor resource utilization in the Cloud. An idle and underutilized host machine in Cloud still consumes up to 70% of the energy required by an active machine (Ray, in Indian J Comput Sci Eng 1(4):333–339, 2012). Consequently, it demands a load-balanced distribution of workload to achieve optimal resource utilization in Cloud. Existing Cloud scheduling heuristics such as Min–Min, Max–Min, and Sufferage distribute workloads among VMs based on minimum job completion time that ultimately causes a load imbalance. In this paper, a novel Resource-Aware Load Balancing Algorithm (RALBA) is presented to ensure a balanced distribution of workload based on computation capabilities of VMs. The RABLA framework comprises of two phases: (1) scheduling based on computing capabilities of VMs, and (2) the VM with earliest finish time is selected for jobs mapping. The outcomes of the RALBA have revealed that it provides substantial improvement against traditional heuristics regarding makespan, resource utilization, and throughput.

Cloud systems are transforming the Information Technology trade by facultative the companies to provide admission to their structure and also software products to the membership foundation.Because of the vast range within the delivered Cloud solutions, from the customer's perspective of an aspect, it's emerged as troublesome to decide whose providers they need to utilize and then what's the thought of his or her option.Especially, employing suitable metrics is vital in assessing practices.Nevertheless, to the most popular of our knowledge, there's no methodical explanation relating to metrics for estimating Cloud products and services.QoS (Quality of Service) metrics playing an important role in selecting Cloud providers and also optimizing resource utilization efficiency.While many reports have got to devote to exploitation QoS metrics, relatively not much equipment supports the remark and investigation of QoS metrics of Cloud programs.To guarantee a specialized product is published, describing metrics for assessing the QoS might be an essential necessity.So, this text suggests various QoS metrics for service vendors, especially thinking about the consumer's worry.This article provides the metrics list may stand to help the future study and also assessment within the field of Cloud service's evaluation.

Admission control based on multiple criteria has always been a desirable feature for computer networks. In the past, we presented such a system, but it was based on a centralised decision mechanism, which by itself constitutes a significant limitation. Here, we describe a decentralised version of the initial admission control algorithm, while still keeping the multiple-criteria aspect, with each user being able to specify their Quality of Service (QoS) metrics at the level of the individual. Our scheme decides whether a new call should be allowed to enter the network based on measurements of the QoS metrics on each link of the network before and after the transmission of probe packets. The decision is based on a novel algebra of QoS metrics, inspired by Warshall's algorithm that searches whether there is a feasible path to accommodate the new flow without affecting the existing users. The decision is made at each source node individually, based on either only personal information or also on information exchange among the nodes that are involved. The performance of the algorithm is evaluated in terms of QoS throughout the lifetime of all connections. The experiments presented in this paper were conducted in an actual laboratory test-bed of realistic topology and under highly congested circumstances. The results are particularly encouraging.

Web services orchestrations conventionally employ exhaustive comparison of runtime quality of service (QoS) metrics for decision making. The ability to incorporate more complex mathematical packages are needed, especially in case of workflows for resource allocation and queuing systems. By modeling such optimization routines as service calls within orchestration specifications, techniques such as linear programming can be conveniently invoked by non-specialist workflow designers. Leveraging on previously developed QoS theory, we propose the use of a high-level flexible query procedure for embedding optimizations in languages such as Orc. The Optima site provides an extension to the sorting and pruning operations currently employed in Orc. Further, the lack of an objective technique for consolidating QoS metrics is a problem in identifying suitable cost functions. We employ the analytical hierarchy process (AHP) to generate a total ordering of QoS metrics across various domains. With constructs for ensuring consistency over subjective judgements, the AHP provides a suitable technique for producing objective cost functions. Using the Dell Supply Chain example, we demonstrate the feasibility of decision making through optimization routines, specially when the control flow is QoS dependent.KeywordsWeb ServicesQoSOptimizationOrcAHP

Cloud record facilities provide flexible and scalable surroundings for executing loads of workloads. However, efficient useful resource allocation is crucial for the effective transport of cloud offerings, making sure they're fee-efficient and keep Quality of Service (QoS). Multi-Objective Scheduling Algorithms (MOSAs) have emerged as a promising solution to balance performance, value, and QoS metrics in cloud environments. This study investigates the effectiveness of numerous MOSAs for optimizing useful resource allocation in cloud workloads. We behavior a comparative has a look at of several algorithms, assessing their overall performance, fee implications, and QoS metrics across various cloud situations. Our method includes a quantitative analysis of execution time and fee, along with a qualitative assessment of QoS attributes inclusive of availability, reliability, and response time. Additionally, we discover how wonderful workload traits along with batch processing, real-time processing, and fact-intensive analytics impact the performance of those algorithms. The findings will provide valuable insights into the strengths and weaknesses of various scheduling algorithms and inform the future layout of greater effective cloud scheduling structures

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.

MANET is a mobile ad hoc network with many mobile nodes communicating without a centralized module. Infrastructure-less networks make it desirable for many researchers to publish and bind multimedia services. Each node in this infrastructure-less network acts as self-organizing and re-configurable. It allows services to deploy and attain from another node over the ad hoc network. The service composition aims to provide a user’s requirement by combining different atomic services based on non-functional QoS parameters such as reliability, availability, scalability, etc. To provide service composition in MANET is challenging because of the node mobility, link failure, and topology changes, so a traditional protocol will be sufficient to obtain real-time services from mobile nodes. In this paper, the ad hoc on-demand distance vector protocol (AODV) is used and analyzed based on MANET’s QoS (Quality of Service) metrics. The QoS metrics for MANET depends on delay, bandwidth, memory capacity, network load, and packet drop. The requester node and provider node broker acts as a composer for this MANET network. The authors propose a QoS-based Dynamic Secured Broker Selection architecture (QoSDSBS) for service composition in MANET, which uses a dynamic broker and provides a secure path selection based on QoS metrics. The proposed algorithm is simulated using Network Simulator (NS2) with 53 intermediate nodes and 35 mobile nodes of area 1000 m × 1000 m. The comparative results show that the proposed architecture outperforms, with standards, the AODV protocol and affords higher scalability and a reduced network load.

A Mobile Ad Hoc Network represents a system of wireless mobile nodes that can freely and dynamically selforganize into arbitrary and temporary network topologies, allowing people and devices to seamlessly communicate without any pre-existing communication architecture. The growth of real time multimedia application in such environments, has drawn a lot of attention to wireless networks that support quality of service (QoS). In this work, the QoS metrics, delay and link life time (LLT) of the path are dynamically calculated. Based on these values the routing decisions are made. Here the multipath routing protocol is taken as the base protocol. The proposed algorithm finds the multiple QoS routes only, based on the QoS metrics and stores only those paths in the routing table. The route is selected based on the integrated QoS metric and is used to transmit the data. The base protocol used is multipath AODV(Ad Hoc On demand Distance Vector ) routing protocol. The proposed protocol may take extra time during route discovery process to compute the additional QoS parameters, but this time is compensated by having only QoS paths for data transmission. A few patents on the QoS Routing for MANETS have been reviewed.The simulator NS-2.34 is used to simulate and compare the performance of the protocols in terms of the QoS metrics such as end-to-end delay, PDR in the MANET. Keywords: Mobile Ad Hoc Network (MANET), Quality of Service (QoS), Destination Sequence Distance Vector (DSDV), Dynamic Source Routing (DSR), Ad Hoc On demand Distance Vector (AODV), LLT (Link Life Time), RLT (Route Life Time), PLTR (Percentage Life Time Ratio), TCP (Transmission Control Protocol), CBR (Constant Bit Rate), PDR (Packet Delivery Ratio).

Evolution of virtualization in data center is fast and is being widely used in the world. Usage of virtual machine (VM) in data center (DC) cannot be aside from problems such as: Operating System (OS) problem, virtual network, memory and CPU utilizations. Besides of those problems, utilization of VM can be found in cloud computing technology to make more efficient and its performance should be good entirely. Problem in VM is very complex. It can be found in OS, application and physical server. To keep it in good performance, operational engineer should operate VM monitoring system in order to keep VM run well. This research will use several methods, such as: fuzzy Mamdani, holdout validation and naive Bayes. These methods will then create decision making for VM performance condition.

Wireless communication is one of most active areas in modern communication researches, QoS (Quality of Service) assurance is very important for wireless communication systems design, especially for applications such as streaming video etc., which requires strict QoS assurance. The modern wireless networks multi-layer protocol stack structure results in QoS metrics layered and acting in cascade and QoS metrics vertical mapping between protocol layers. Based on virtual buffer between protocol layers and queuing technology, a unified layered QoS mapping framework is proposed in this paper, in which we first propose virtual queue concept, give a novelty united neighboring protocol layers QoS metric mapping framework, and analysis method based on dicerete-time Markov chain, and numerical results show that our proposed framework represents a significant improvement over previous model.

Cognition is of paramount importance in modern communication systems for this brings the potential for adaptiveness and self-fine-tuning for dynamic reconfigurability. To achieve this feat, two primary tasks are to identify the influential configurable parameters and availability of comprehensive datasets representative of the real-world scenarios rather than simulated ones. For this article, an extensive dataset covering diverse settings of wireless sensor networks (WSNs) driven internet of things (IoT) is collected. It covers broad variations of 10 pre-configured communication parameters as well as some runtime information. In addition to legacy parameters (e.g., transmission power, and packet size, etc.), we also used two different medium access control protocols (i.e., carrier sense multiple access (CSMA) and time-slotted channel hopping (TSCH)), and routing metrics (i.e., objective function 0 (OF0), minimum rank with hysteresis (MRH), MRH with expected transmission count (ETX2)). Important quality of service (QoS) metrics like packet delivery ratio, throughput, and energy consumption against all combinations of the communication parameters are measured and recorded. A thorough statistical analysis is carried out to identify the correlations among the communication parameters and QoS metrics. The results lay the foundation for the design of a data-driven framework for predictive QoS control in the IoT.

Vehicular ad-hoc networks (VANETs) are a significant field in the intelligent transportation system (ITS) for improving road security. The interaction among the vehicles is enclosed under VANETs. Many experiments have been performed in the region of VANET improvement. A familiar challenge that occurs is obtaining various constrained quality of service (QoS) metrics. For resolving this issue, this study obtains a cost design for the vehicle routing issue by focusing on the QoS metrics such as collision, travel cost, awareness, and congestion. The awareness of QoS is fuzzified into a price design that comprises the entire cost of routing. As the genetic algorithm (GA) endures from the most significant challenges such as complexity, unassisted issues in mutation, detecting slow convergence, global maxima, multifaceted features under genetic coding, and better fitting, the currently established lion algorithm (LA) is employed. The computation is analyzed by deploying three well-known studies such as cost analysis, convergence analysis, and complexity investigations. A numerical analysis with quantitative outcome has also been studied based on the obtained correlation analysis among various cost functions. It is found that LA performs better than GA with a reduction in complexity and routing cost.

The extremely large number of devices available to the modern day user, with the increase in device intercommunication, is fuelling the latest Internet of Things (IoT) development. IoT needs to enable exchange of various types of data, from sensor data to multimedia, between numerous diverse devices differing in power, connectivity, mobility and energy, while also maintaining high levels of Quality of Service (QoS). This paper performs statistical analysis of the innovative NETworking Scheme for sMart IoT gatewayS (NETSMITS) in terms of several QoS network-related metrics with most significant impact on devices' performance. NETSMITS introduces an innovative algorithm which uses QoS and service relevance metrics in order to efficiently cluster intercommunicating IoT objects. Statistical analysis is performed on the QoS data collected in a highly relevant multi-device scenario in order to understand NETSMITS' behaviour. Interesting results were obtained, describing the relationship between the QoS metrics and different types of IoT devices.

Cloud providers generally run one or more Virtual Machine (VM) instances on the same physical machine. Though it increases data center utilization, it exposes VM to a co-location attack. VM placement and migration are the two strategies adopted for mitigating co-locations. Current methods for VM placement or VM migration consider only security as decision criteria and do not consider other factors like Quality-of-Service degradation, data center utilization, etc. This work proposes a placement and migration strategy for mitigation of co-location attacks with joint consideration of multi objectives like QoS, data center utilization, energy consumption, and security risks. A security-driven multi-criteria optimization -based VM placement policy is proposed. A joint consideration of multiobjective performance optimization along with co-location security risk minimization is done to design a novel VM placement policy based on user categorization. The policy can reduce the likelihood of co-location target VM with attacker VM without much degradation to the performance of VM and data center utilization. The solution mitigates co-location risks without much compromise to the performance of VM and data center resource utilization. The co-residence risk is mitigated by the categorization of users into three levels i.e. unlabeled, risky, and safe, and physical machines into two groups as safe and unsafe. The PMs available in data center is grouped into three different VM placement policies, they are undecided pool, safe pool and unsafe pool.

A new localized quality of service (QoS) routing protocol for wireless sensor networks (WSN) is proposed in this paper. The proposed protocol targets WSN's applications having different types of data traffic. It is based on differentiating QoS requirements according to the data type, which enables to provide several and customized QoS metrics for each traffic category. With each packet, the protocol attempts to fulfill the required data-related QoS metric(s) while considering power efficiency. It is modular and uses geographical information, which eliminates the need of propagating routing information. For link quality estimation, the protocol employs distributed, memory and computation efficient mechanisms. It uses a multisink single-path approach to increase reliability. To our knowledge, this protocol is the first that makes use of the diversity in data traffic while considering latency, reliability, residual energy in sensor nodes, and transmission power between nodes to cast QoS metrics as a multiobjective problem. The proposed protocol can operate with any medium access control (MAC) protocol, provided that it employs an acknowledgment (ACK) mechanism. Extensive simulation study with scenarios of 900 nodes shows the proposed protocol outperforms all comparable state-of-the-art QoS and localized routing protocols. Moreover, the protocol has been implemented on sensor motes and tested in a sensor network testbed.