Terms Of Service Level Agreements Research Articles

Redundancy Planning for Cost Efficient Resilience to Cyber Attacks

We investigate the extent to which redundancy (including with diversity) can help mitigate the impact of cyber attacks that aim to reduce system performance. Using analytical techniques, we estimate impacts, in terms of monetary costs, of penalties from breaching Service Level Agreements (SLAs), and find optimal resource allocations to minimize the overall costs arising from attacks. Our approach combines attack impact analysis, based on performance modeling using queueing networks, with an attack model based on attack graphs. We evaluate our approach using a case study of a website, and show how resource redundancy and diversity can improve the resilience of a system by reducing the likelihood of a fully disruptive attack. We find that the cost-effectiveness of redundancy depends on the SLA terms, the probability of attack detection, the time to recover, and the cost of maintenance. In our case study, redundancy with diversity achieved a saving of up to around 50 percent in expected attack costs relative to no redundancy. The overall benefit over time depends on how the saving during attacks compares to the added maintenance costs due to redundancy.

BEAT: Blockchain-Enabled Accountable and Transparent Infrastructure Sharing in 6G and Beyond

It is widely expected that future networks of 6G and beyond will substantially improve on 5G. Technologies such as Internet of Skills and Industry 4.0 will become stable and viable, as a direct consequence of networks that offer sustained and reliable mobile performance levels. The primary challenges for future technologies are not just low-latency and high-bandwidth. The more critical problem Mobile Service Providers (MSPs) will face will be in balancing the inflated demands of network connections and customers’ trust in the network service, that is, being able to interconnect billions of unique devices while adhering to the agreed terms of Service Level Agreements (SLAs). To meet these targets, it is self-evident that MSPs cannot operate in a solitary environment. They must enable cooperation among themselves in a manner that ensures trust, both between themselves as well as with customers. In this study, we present the BEAT (Blockchain-Enabled Accountable and Transparent) Infrastructure Sharing architecture. BEAT exploits the inherent properties of permissioned type of distributed ledger technology (i.e., permissioned distributed ledgers) to deliver on accountability and transparency metrics whenever infrastructure needs to be shared between providers. We also propose a lightweight method that enables device-level accountability. BEAT has been designed to be deployable directly as only minor software upgrades to network devices such as routers. Our simulations on a resource-limited device show that BEAT adds only a few seconds of overhead processing time – with the latest state-of-the-art network devices, we can reasonably anticipate much lower overheads.

Optimizing Flying Base Station Connectivity by RAN Slicing and Reinforcement Learning

The application of flying base stations (FBS) in wireless communication is becoming a key enabler to improve cellular wireless connectivity. Following this tendency, this research work aims to enhance the spectral efficiency of FBSs using the radio access network (RAN) slicing framework; this optimization considers that FBSs’ location was already defined previously. This framework splits the physical radio resources into three RAN slices. These RAN slices schedule resources by optimizing individual slice spectral efficiency by using a deep reinforcement learning approach. The simulation indicates that the proposed framework generally outperforms the spectral efficiency of the network that only considers the heuristic predefined FBS location, although the gains are not always significant in some specific cases. Finally, spectral efficiency is analyzed for each RAN slice resource and evaluated in terms of service-level agreement (SLA) to indicate the performance of the framework.

Blockchain-enabled real-time SLA monitoring for cloud-hosted services

Cloud computing is an important technology for businesses and individual users to obtain computing resources over the Internet on-demand and flexibly. Although cloud computing has been adopted across diverse applications, the owners of time-and-performance critical applications require cloud service providers’ guarantees about their services, such as availability and response times. Service Level Agreements (SLAs) are a mechanism to communicate and enforce such guarantees typically represented as service level objectives (SLOs), and financial penalties are imposed on SLO violations. Due to delays and inaccuracies caused by manual processing, an automatic method to periodically verify SLA terms in a transparent and trustworthy manner is fundamental to effective SLA monitoring, leading to the acceptance and credibility of such service to the customers of cloud services. This paper presents a blockchain-based distributed infrastructure that leverages fundamental blockchain properties to achieve immutable and trustworthy SLA monitoring within cloud services. The paper carries out an in-depth empirical investigation for the scalability of the proposed system in order to address the challenge of transparently enforcing real-time monitoring of cloud-hosted services leveraging blockchain technology. This will enable all the stakeholders to enforce accurate execution of SLA without any imprecisions and delays by maintaining an immutable ledger publicly across blockchain network. The experimentation takes into consideration several attributes of blockchain which are critical in achieving optimum performance. The paper also investigates key characteristics of these factors and their impact to the behaviour of the system for further scaling it up under various cases for increased service utilization.

MODELING OF SYSTEMS UNDER CLOUD ENVIRONMENT

The emergence of large amount of data requires an efficient means of processing and storage facilities. Cloud computing provides an effective solution; MapReduce programming paradigm has the ability to handle such data by implementing Hadoop, but came up with some conflicting challenges in terms of Service Level Agreement (SLA) between major stakeholders. This paper focuses on coming up with a MapReduce model through system identification in order to address the requirement of the service time to meet-up the SLA within the limit of defined threshold in the presence of uncertainties in the system. A second order nonlinear model was obtained, which shows a good representation of the real system and could be used to develop control laws on the real system.

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.

Adapted smart home services based on smart contracts and service level agreements

SummaryIn smart home systems (SHS), devices intercommunicate to create an intelligent environment that can monitor the inhabitant's health state and provide adapted services. However, existing health monitoring systems (HMS) do not provide adapted services to the patient autonomy degree. Furthermore, in these critical systems, the Quality of Service (QoS) aspect is very important; therefore, service level agreements (SLA) are the best solution to define and guarantee the expected QoS levels, nevertheless, existing SLA specification languages lack expressiveness in terms of user preference specification, dynamic service management and pricing. In this work, we propose an elderly health state‐aware SHS to offer adapted services according to the elderly health state, it is based on three main parts: (a) the functional autonomy measurement system (SMAF) for calculating the inhabitant's profile or autonomy degree, (b) domain‐specific language (DSL) for SLA that aims to increase patient‐awareness through the use of preferences, facilitate the SLA management using microservices, and manage the QoS dynamically using multilevel SLA (ML‐SLA) concept, and (c) smart contracts for the provision of adapted services and the verification of SLA terms.

Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

Energy management of the cloud datacentre is a challenging task, especially when the cloud server receives a number of the user’s request simultaneously. This requires an efficient method to optimally allocate the resources to the users. Resource allocation in cloud data centers need to be done in optimized manner for conserving energy keeping in view of Service Level Agreement (SLA). We propose, Eagle Strategy (ES) based Modified Particle Swarm Optimization (ES-MPSO) to minimize the energy consumption and SLA violation. The Eagle Strategy method is applied due to its efficient local optimization technique. The Cauchy Mutation method which schedules the task effectively and minimize energy consumption, is applied to the proposed ES-MPSO method for improving the convergence performance. The simulation result shows that the energy consumption of ES-MPSO is 42J and Particle Swarm Optimization (PSO) is 51J. The proposed method ES-MPSO achieves higher efficiency compared to the PSO method in terms of energy management and SLA.

Allocation Policies to Fulfil Heterogeneous Service Requirements under Resource Pooling

ABSTRACTDesigning effective settings for performance measures (e.g., fill rate) of a service‐level agreement (SLA) is challenging. This challenge is intensified when a firm adopts the pooling inventory model to allocate inventory/capacity to multiple buyers. Each buyer has its own service‐level contract outlining the required service level, the penalty structures, and the performance review period (PRP) length, which might not be the same as other buyers. This means the supplier requires an effective resource allocation policy whereby the different requirements of multiple buyers are integrated into a pooling model and capacities/inventories are allocated in the most effective way. Given a base‐stock replenishment policy and finite time horizon PRP, in this study we propose two new (anticipative) allocation policies—foresight linear programming (FLP) and two‐stage stochastic (TS)—and compare them with existing allocation policies. These allocation policies are developed for different penalty structures of linear, lump‐sum, hybrid, and no‐penalty settings. Results show that suppliers benefit from longer PRPs if linear or hybrid penalty structures are employed. We also find that when the length of PRP of buyers is not identical, TS is the recommending policy. Further, results provide a guideline for selecting the best resource allocation policy under various SLA terms, in particular, where buyers' PRP lengths are not identical.

SLA-Driven modeling and verifying cloud systems: A Bigraphical reactive systems-based approach

We propose a formal approach based on Bigraphical Reactive Systems (BRSs) and model checking techniques for modeling and verifying the interaction behaviours of SLA-based cloud computing systems. In the first phase of this approach, we address the modeling of the static structure and the dynamic behavior of cloud systems using BRSs. We show how bigraphs enable the description of the different cloud entities, including cloud actors, cloud services, Service Level Agreements (SLAs), the diversity of their properties, and the complex interactions and dependencies among them. Furthermore, we propose a four-stages SLA lifecycle, and define a set of bigraphical reaction rules to abstract these stages and model the dynamic nature of the cloud. The second phase of this approach verifies that the behavior of services and cloud actors will cope with the agreed SLA terms during the lifecycle of the SLA. We map the proposed bigraphical models into SMV descriptions. Then, we express the interaction behaviors as a set of liveness and safety properties using Linear Temporal Logic (LTL) and Computation Tree Logic (CTL) formulas, and we use the NuSMV model checker to verify them. Finally, we define a case study on which we illustrate the application of our proposed approach.

Disruption risk management in service-level agreements

This paper studies the impact of capacity/inventory disruption on a supplier's cost, where the supplier has heterogeneous service level agreements (SLAs) in place with multiple customers (retailers). We compare three capacity allocation policies in the presence of supply disruption, with both full and partial disruption cases investigated. The results show that the two-stage policy outperforms other capacity allocation policies when a disruption is anticipated and that partial disruption is more manageable than full disruption in terms of meeting the SLAs. Furthermore, the supplier is better off to negotiate a longer performance review period assuming the penalty per time unit is fixed. This result is contrary to findings in the literature for SLAs when there is no consideration of disruption. The insights from this study can assist suppliers in determining their capacity level, making capacity-related decisions such as locations of warehouses or production sites, in allocating capacity to customers and in negotiating SLA terms such as the performance review period length and penalty rates.

Trusted UAV Network Coverage Using Blockchain, Machine Learning, and Auction Mechanisms

The UAV is emerging as one of the greatest technology developments for rapid network coverage provisioning at affordable cost. The aim of this paper is to outsource network coverage of a specific area according to a desired quality of service requirement and to enable various entities in the network to have intelligence to make autonomous decisions using blockchain and auction mechanisms. In this regard, by considering a multiple-UAV network where each UAV is associated to its own controlling operator, this paper addresses two major challenges: the selection of the UAV for the desired quality of network coverage and the development of a distributed and autonomous real-time monitoring framework for the enforcement of service level agreement (SLA). For a suitable UAV selection, we employ a reputation-based auction mechanism to model the interaction between the business agent who is interested in outsourcing the network coverage and the UAV operators serving in closeby areas. In addition, theoretical analysis is performed to show that the proposed auction mechanism attains a dominant strategy equilibrium. For the SLA enforcement and trust model, we propose a permissioned blockchain architecture considering Support Vector Machine (SVM) for real-time autonomous and distributed monitoring of UAV service. In particular, smart contract features of the blockchain are invoked for enforcing the SLA terms of payment and penalty, and for quantifying the UAV service reputation. Simulation results confirm the accuracy of theoretical analysis and efficacy of the proposed model.

Scheduling Algorithms for Heterogeneous Cloud Environment: Main Resource Load Balancing Algorithm and Time Balancing Algorithm

Cloud computing and Internet of Things (IoT) are two of the most important technologies that have significantly changed human’s life. However, with the growing prevalence of Cloud-IoT paradigm, the load imbalance and higher SLA lead to more resource wastage and energy consumption. Although there are many researches that study Cloud-IoT from the perspective of offloading side, few of them have focused on how the offloaded workload are dealt with in Cloud. This paper proposes two IoT-aware multi-resource task scheduling algorithms for heterogeneous cloud environment namely main resource load balancing and time balancing. The algorithms aim to obtain better result of load balance, Service-Level Agreement (SLA) and IoT task response time and meanwhile to reduce the energy consumption as much as possible. They both are devised to assign single task to a properly selected Virtual Machine (VM) each time. The task placed in a pre-processed queue is assigned sequentially each time. And the VM selection rule is carried out based on the newly inventive ideas called relative load or relative time cost. Besides, two customized parameters that influence the result of pre-process tasks are provided for users or administrators to flexibly control the behavior of the algorithms. According to the experiments, the main resource load balancing performs well in terms of SLA and load balance, while time balancing is good at saving time and energy. Besides, both of them perform well in IoT task response time.

Opportunistic live migration of virtual machines

SummaryLive migrations of virtual machines are required in cloud data centers in various contexts such as evacuating a host machine for maintenance, balancing the workloads on host machines, optimizing physical resource utilization, and meeting the custom demands of the user applications. Virtual machine migrations can, however, be costly in terms of both resources consumed for the migration as well as in terms of service level agreement (SLA) violations during the migration window. The cost is determined by many factors and they, in turn, are impacted by WHEN the migration happens during the lifetime of the virtual machine. Empirical studies show that if we have a window in which the migration is to be done (proactive rather than reactive where no such window exists), we can do it by carefully choosing the starting point within the window so that the SLA violation is minimum. In this paper, we propose a model to migrate virtual machines opportunistically to minimize the SLA violations. The idea is to find the time instants, called opportunities, at which, if the migration starts, the number and extent of SLA violations are minimal. Our experiments show that the migrations time savings with opportunistic migrations are up to 25%.

Workload-driven coordination between virtual machine allocation and task scheduling

The current task scheduling is separated from the virtual machine (VM) allocation, which, to some extent, wastes resources or degrades application performance. The scheduling algorithm influences the demand of VMs in terms of service-level agreement, while the number of VMs determines the performance of task scheduling. Workload plays an indispensable role in both dynamic VM allocation and task scheduling. To address this problem, we coordinate task scheduling and VM allocation based on workload characteristics. Workload is empirically time-varying and stochastic. We demonstrate that the acquired workload data set has Markov property which can be modeled as a Markov chain. Then, three workload characteristic operators are extracted: persistence, recurrence and entropy, which quantify the relative stability, burstiness, and unpredictability of the workload, respectively. Experiments indicate that the persistence and recurrence of workloads has a direct bearing on the average response time and resource utilization of the system. A nonlinear model between the load characteristic operators and the number of VMs is established. In order to test the performance of the collaborative framework, we design a scheduling algorithm based on genetic algorithm (GA), which takes the estimated number of VMs as input and the task completion time as the optimization target. Simulation experiments have been performed on the CloudSim platform, testifying that the estimated average absolute VMs error is only 2.6%. The GA-based task scheduling algorithm could improve resource utilization and reduce task completion time compared with the first come first serve and greedy algorithm. The proposed coordination mechanism in this paper has proved able to find the optimal match and reduce the resource cost by utilizing the interaction between VM allocation and task scheduling.

A Comprehensive Survey of Load Balancing Strategies Using Hadoop Queue Scheduling and Virtual Machine Migration

The recent growth in the demand for scalable applications from the consumers of the services has motivated the application development community to build and deploy the applications on cloud in the form of services. The deployed applications have significant dependency on the infrastructure available with the application providers. Bounded by the limitations of available resource pools on-premises, many application development companies have migrated the applications to third party cloud environments called data centers. The data center owners or the cloud service providers are entitled to ensure high performance and high availability of the applications and at the same time the desired scalability for the applications. Also, the cloud service providers are also challenging in terms of cost reduction and energy consumption reductions for better manageability of the data center without degrading the performance of the deployed applications. It is to be noted that the performance of the application does not only depend on the responsiveness of the applications rather also must be measured in terms of service level agreements. The violation of the service level agreements or SLA can easily disprove the purpose of application deployments on cloud-based data centers. Thus, the data center owners apply multiple load balancing strategies for maintaining the desired outcomes from the application owners at the minimized cost of data center maintainability. Hence, the demand of the research is to thoroughly study and identify the scopes for improvements in the parallel research outcomes. As the number of applications ranging from small data-centric applications coming with the demand of frequent updates with higher computational capabilities to the big data-centric application as big data analytics applications coming with efficient algorithms for data and computation load managements, the data center owners are forced to think for efficient algorithms for load managements. The algorithms presented by various research attempts have engrossed on application specific demands for load balancing using virtual machine migrations and the solution as the proposed algorithms have become application problem specific. Henceforth, the further demand of the research is a guideline for selecting the appropriate load balancing algorithm via virtual machine migration for characteristics-based specific applications. Hence, this paper presents a comprehensive survey on existing virtual machine migration and selection processes to understand the specific application-oriented capabilities of these strategies with the advantages and bottlenecks. Also, with the understanding of the existing measures for load balancing, it is also important to furnish the further improvement strategies, which can be made possible with a detailed understanding of the parallel research outcomes. Henceforth, this paper also equips the study with guidelines for improvements and for further study. Nonetheless, the study cannot be completed without the mathematical analysis for better understanding and experimental analysis on different standards of datasets for better conclusive decisions. Hence, this paper also presents the discussion on mathematical models and experimental result analysis for the conclusive decision on the improvement factors and the usability of the migration methods for various purposes. Finally, this paper is a comprehensive survey on the background of the research, recent research outcomes using mathematical modeling and experimental studies on various available datasets, and finally identify the scopes of improvements considering various aspects such as execution time, mean time before a VM migration, mean time before a host shutdown, number of node shutdowns, SLA performance degradation, VM migrations, and energy consumption.

Multi Criteria based Resource Score Heuristic for Cloud Workflow Scheduling

Abstract Scheduling scientific workflows modelled by Directed Acyclic Graphs (DAG) is an NP complete problem. Cloud computing provides reliable Quality of Service defined in terms of Service Level Agreements (SLA). To schedule scientific workflows in cloud environment, where resources are shared, it is important to manage the cloud resources efficiently by maximizing utilization. The dynamic nature of cloud resources, due to sharing, heterogeneity, virtualization and workload variations offer a host of challenges in terms of resource availability and performance. This may have a significant impact on task execution times and data transfer times, thus introducing delay in overall execution time called makespan, In this paper, Multi Criteria based Resource Score Heuristic for Cloud Workflow Scheduling is proposed, with an objective of minimizing makespan considering the probability of temporal availability of resources in a cloud computing environment. To choose the best virtual machine with optimal value of maximum resource availability and minimum task execution time, a method of compensatory aggregation of conflicting criteria, is used for scoring each resource. The simulation results demonstrate that the proposed heuristic, can generate schedules with better makespan minimization and is found to be more reliable since resource availability factor is considered while mapping tasks to virtual machines.

SLA-Aware Best Fit Decreasing Techniques for Workload Consolidation in Clouds

Cloud computing emerged as one of the leading computational paradigms due to elastic resource provisioning and pay-as-you-go model. Large data centers are used by the service providers to host the various services. These data centers consume enormous energy, which leads to increase in operating costs and carbon footprints. Therefore, green cloud computing is a necessity, which not only reduces energy consumption, but also affects the environment positively. In order to reduce the energy consumption, workload consolidation approach is used that consolidates the tasks in minimum possible servers. However, workload consolidation may lead to service level agreement (SLA) violations due to non-availability of resources on the server. Therefore, workload consolidation techniques should consider the aforementioned problem. In this paper, we present two consolidation based energy-efficient techniques that reduce energy consumption along with resultant SLA violations. In addition to that, we also enhanced the existing Enhanced-Conscious Task Consolidation (ECTC) and Maximum Utilization (MaxUtil) techniques that attempt to reduce energy consumption and SLA violations. Experimental results show that the proposed techniques perform better than the selected heuristic based techniques in terms of energy, SLA, and migrations.

Dynamic changes of QoS parameters in cloud computing service level agreement

Service level agreement (SLA) in cloud computing can be defined as a contractual agreement signed between cloud user and cloud provider in order to manage the quality of service (QoS) parameters which are the fundamental part of SLAs. The current dynamic SLA monitoring approaches have considered the cloud provider's perspective only. To take into consideration the user's perspective; this research proposes a dynamic SLA monitoring approach by generating new SLA terms based on analysing the SLA penalties. A new proposed trade-off mechanism is applied between monthly cost and QoS guarantee values. Thus; we propose a dynamic model for updating QoS parameters of SLA in cloud environment. Java language is used with a QoS measurement dataset to experiment the proposed approach. The current Static SLA (SSLA) approaches are compared with the proposed Dynamic SLA (DSLA) approach. The results of this experimental study show that the number of SLA violations in DSLA are less than the number of SLA violations in SSLA.

Dynamic changes of QoS parameters in cloud computing service level agreement

Service level agreement (SLA) in cloud computing can be defined as a contractual agreement signed between cloud user and cloud provider in order to manage the quality of service (QoS) parameters which are the fundamental part of SLAs. The current dynamic SLA monitoring approaches have considered the cloud provider's perspective only. To take into consideration the user's perspective; this research proposes a dynamic SLA monitoring approach by generating new SLA terms based on analysing the SLA penalties. A new proposed trade-off mechanism is applied between monthly cost and QoS guarantee values. Thus; we propose a dynamic model for updating QoS parameters of SLA in cloud environment. Java language is used with a QoS measurement dataset to experiment the proposed approach. The current Static SLA (SSLA) approaches are compared with the proposed Dynamic SLA (DSLA) approach. The results of this experimental study show that the number of SLA violations in DSLA are less than the number of SLA violations in SSLA.