CtrlCloud: Performance-Aware Adaptive Control for Shared Resources in Clouds

With the rapid development of cloud computing, the demand for infrastructure resources in cloud data centers has further increased, which has already led to enormous amounts of energy costs. Virtual machine (VM) consolidation as one of the important techniques in Infrastructure as a Service clouds (IaaS) can help resolve energy consumption by reducing the number of active physical machines (PMs). However, the necessity of considering energy-efficiency and the obligation of providing high quality of service (QoS) to customers is a trade-off, as aggressive consolidation may lead to performance degradation. Moreover, most of the existing works of threshold-based VM consolidation strategy are mainly focused on single CPU utilization, although the resource request on different VMs are very diverse. This paper proposes a novel self-adaptive VM consolidation strategy based on dynamic multi-thresholds (DMT) for PM selection, which can be dynamically adjusted by considering future utilization on multi-dimensional resources of CPU, RAM and Bandwidth. Besides, the VM selection and placement algorithm of VM consolidation are also improved by utilizing each multi-dimensional parameter in DMT. The experiments show that our proposed strategy has a better performance than other strategies, not only in high QoS but also in less energy consumption. In addition, the advantage of its reduction on the number of active hosts is much more obvious, especially when it is under extreme workloads.

Latency-critical workloads such as web search engines, social networks and finance market applications are sensitive to tail latencies for meeting Service Level Objectives (SLOs). Since unexpected tail latencies are caused by sharing hardware resources with other co-executing workloads, a service provider executes the latency-critical workload alone. Thus, the data center for the latency-critical workloads has exceedingly low hardware resource utilization. For improving hardware resource utilization, the service provider has to co-locate the latency-critical workloads and other batch processing workloads. However, because memory bandwidth cannot be provided in isolation unlike cores and cache memory, the latency-critical workloads experience poor performance isolation even though the core and the cache memory are allocated in isolation to the workloads. To solve this problem, we propose an optimized memory bandwidth management approach for ensuring Quality of Service (QoS) and high server utilization. By providing isolated shared resources including memory bandwidth to the latencycritical workload and co-executing batch processing ones, our proposed approach guarantees SLOs and improves hardware resource utilization. Firstly, we predict the size of the memory bandwidth to meet the SLO for all Queries Per Seconds (QPSs) while executing the latency-critical workload with minimal preprofiling. Then, our approach allocates the amount of the isolated memory bandwidth that guarantees the SLO to the latencycritical workload and the rest of the memory bandwidth to coexecuting batch processing workloads. As a result, our proposed approach can achieve up to 99% SLO assurance and improve server utilization up to 6.5x.

Many Cloud Service Providers (CSPs) provide data storage services with datacenters distributed worldwide. These datacenters provide different Get/Put latencies and unit prices for resource utilization and reservation. Thus, when selecting different CSPs' datacenters, cloud customers of globally distributed applications (e.g., online social networks) face two challenges: i) how to allocate data to worldwide datacenters to satisfy application SLO (service level objective) requirements including both data retrieval latency and availability, and ii) how to allocate data and reserve resources in datacenters belonging to different CSPs to minimize the payment cost. To handle these challenges, we first model the cost minimization problem under SLO constraints using integer programming. Due to its NP-hardness, we then introduce our heuristic solution, including a dominant-cost based data allocation algorithm and an optimal resource reservation algorithm. We finally introduce an infrastructure to enable the conduction of the algorithms. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage and Google Cloud Storage) show the effectiveness of our algorithms for SLO guaranteed services and customer cost minimization.

The article examines the importance of container migration within the context of container placement tasks in cloud data centers. Cloud computing offers organizations significant benefits such as flexibility, scalability, and reduced costs for equipment, energy, and maintenance. Cloud providers face the challenge of not only efficiently placing containers initially but also dynamically reallocating them, along with virtual machines, in response to changing demands and workloads. This necessitates the development and application of migration algorithms. The study analyzes how migration is a critical component for ensuring optimal resource use, maintaining high service availability, and reducing service level agreement (SLA) violations. The research aims to enhance the productivity of cloud data centers in the task of container placement by utilizing container mi-gration algorithms. This objective is driven by the need for cloud providers to use their resources effectively to provide higher quality service and maintain competitive pricing. The use of migrations prevents overloading or underloading of servers and virtual machines, which is especially impor-tant in the highly dynamic environment of cloud computing. The research developed a hybrid mi-gration algorithm that includes the migration of both containers and virtual machines. Mathemati-cal aspects of the container placement task are described, and the migration algorithm is detailed. Experiments showed that implementing this algorithm reduces operational costs by 24% compared to traditional methods that do not use migration, and by 9% compared to less flexible migration strategies. There was also a demonstrated reduction in SLA violation times by 40%, indicating im-provements in service quality and reliability of cloud services. The conclusions of this study confirm that container and virtual machine migration is necessary for effective management of dynamic re-sources in cloud data centers in the context of container placement tasks.

A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications

As there is a huge requirement for IT resources in business world, cloud computing is used by various businesses to satisfy their requirements. Many businesses outsource their IT needs to cloud and get benefited from cloud services. Cloud represents datacenter which has full infrastructure that provides virtualized services. The main challenge in cloud datacenter is energy consumption by server and computer and cooling equipment in cloud datacenter. Cloud datacenter emits huge amount of carbon dioxide which affect the environment acts against green atmosphere. This paper attempts to reduce the energy consumption of computing resources in cloud datacenter. Architecture has different entities which are described in the concerned sections. The proposed architecture saves energy by clustering similar request from the clients and also switches off ideal resource in cloud datacenter. The proposed architecture is an efficient energy consumption resource management model for cloud datacenter.

The recent advances in virtualization technology have enabled the sharing of computing and networking resources of cloud data centers among multiple users. Virtual Network Embedding (VNE) is highly important and is an integral part of the cloud resource management. The lack of historical knowledge on cloud functioning and inability to foresee the future resource demand are two fundamental shortcomings of the traditional VNE approaches. The consequence of those shortcomings is the inefficient embedding of virtual resources on Substrate Nodes (SNs). On the contrary, application of Artificial Intelligence (AI) in VNE is still in the premature stage and needs further investigation. Considering the underlying complexity of VNE that includes numerous parameters, intelligent solutions are required to utilize the cloud resources efficiently via careful selection of appropriate SNs for the VNE. In this paper, Reinforcement Learning based prediction model is designed for the efficient Multi-stage Virtual Network Embedding (MUVINE) among the cloud data centers. The proposed MUVINE scheme is extensively simulated and evaluated against the recent state-of-the-art schemes. The simulation outcomes show that the proposed MUVINE scheme consistently outperforms over the existing schemes and provides the promising results.

Defending against fake VIP in scant-transparency information systems with QoS differentiation

Recent advancements in the domain of Network Function Virtualization (NFV), and rollout of next-generation networks have necessitated the requirement for the upkeep of latency-critical application architectures in future networks and communications. While Cloud service providers recognize the evolving mission-critical requirements in latency sensitive verticals such as autonomous driving, multimedia, gaming, telecommunications, and virtual reality, there is a wide gap to bridge the Quality of Service (QoS) constraints for the end-user experience. Most latency-critical services are over-provisioned on all fronts to offer reliability, which is inefficient towards scalability in the long run. To address this, we propose a strategy to model frequent violations on the application level as a multi-output target to enable more complex decision-making in the management of virtualised communication networks. In this work, we utilize data from a real-world deployment to configure and draft a realistic set of Service Level Objectives (SLOs) for a voice based NFV application, and develop a deep neural network based multi-label classification methodology to identify and predict multiple categories of SLO breaches associated with an application state. With this, we aim to gain granular SLA and SLO violation insights, enabling us to study and mitigate their impact and inform precision in drafting proactive scaling policies. We further compare the performance against a set of multi-label compatible machine learning classifiers, and address class imbalance in a multi-label setup. We perform a comprehensive evaluation to assess the performance on example-based, label-based and ranking-based measures, and demonstrate the suitability of deep learning in such a use-case.

Cloud platforms are increasingly using containers for lightweight virtualization. However, the mainstream operating systems are currently limited in their capabilities in customizing containers’ resource management. There remains two main challenges in resource allocations. First, the application workloads can be time-varying, leading to a problem of resource over- or under-allocations. Second, it becomes difficult to minimize the resource provisioning cost while guaranteeing the SLO (service level objective). To address these challenges, we propose ConLAR, a learning-to-allocate approach that predicts and allocates resources to Docker containers under time-varying workloads. ConLAR efficiently reduces over-provisioning cost with the SLO guarantees by taking an Observing-Predicting-Allocating-Executing paradigm: given a container, it observes the running of the online application and its environment, leverages the LSTM (long short term memory) model to predict its future workload, adaptively learns to construct resource allocation strategies with two objectives through RL (reinforcement learning), and executes them to scale container resources dynamically. We have evaluated ConLAR on two real-world workloads of ClarkNet and GoogleClusterData. The results clearly show the effectiveness of ConLAR. In particular, ConLAR achieves a resource over-provisioning cost of less than 16.5% and an SLO violations rate of 8.9%; it also shows good flexibility to learn different adaption policies.

Failures are normal rather than exceptional in cloud computing environments, high fault tolerance issue is one of the major obstacles for opening up a new era of high serviceability cloud computing as fault tolerance plays a key role in ensuring cloud serviceability. Fault tolerant service is an essential part of Service Level Objectives (SLOs) in clouds. To achieve high level of cloud serviceability and to meet high level of cloud SLOs, a foolproof fault tolerance strategy is needed. In this paper, the definitions of fault, error, and failure in a cloud are given, and the principles for high fault tolerance objectives are systematically analyzed by referring to the fault tolerance theories suitable for large-scale distributed computing environments. Based on the principles and semantics of cloud fault tolerance, a dynamic adaptive fault tolerance strategy DAFT is put forward. It includes: (i) analyzing the mathematical relationship between different failure rates and two different fault tolerance strategies, which are checkpointing fault tolerance strategy and data replication fault tolerance strategy; (ii) building a dynamic adaptive checkpointing fault tolerance model and a dynamic adaptive replication fault tolerance model by combining the two fault tolerance models together to maximize the serviceability and meet the SLOs; and (iii) evaluating the dynamic adaptive fault tolerance strategy under various conditions in large-scale cloud data centers and consider different system centric parameters, such as fault tolerance degree, fault tolerance overhead, response time, etc. Theoretical as well as experimental results conclusively demonstrate that the dynamic adaptive fault tolerance strategy DAFT has high potential as it provides efficient fault tolerance enhancements, significant cloud serviceability improvement, and great SLOs satisfaction. It efficiently and effectively achieves a trade-off for fault tolerance objectives in cloud computing environments.

A Service Level Agreement (SLA) is a legal contract between parties to ensure the Quality of Service (QoS). It specifies one or more service level objectives (SLO), to ensure that the QoS delivered has met customer expectations. However, It becomes hard to guarantee QoS levels and detect SLA violations. Therefore, we propose to use MDE (Model Driven Engineering) to express the SLA contract requirements. This latter, created with a specific modeling language (DSML), will be used harmonically with a Multi-agent systems (MASs) in order to monitor SLA violations in real-time. Indeed, MASs are suitable tools for self-detection of failures and self-monitoring of cloud operations and services, QoS negotiation and SLA management. They are designed to operate in a dynamically changing environment. Our main motivation is firstly to use MDE technology for the creation of the SLA contract and then to integrate MASs in order to control the quality of service contract and guarantee transparency and symmetry with respect to the SLA contract between prospective signatories.

To provide Quality of Service (QoS) and data rate differentiation, we investigate in this paper a 2 dimensional optical code division multiple access (OCDMA) technique. A 2D multi-weight coding scheme with parallel mapping technique is developed. The system enhancement is assessed thanks to the use of Forward Error Correction (FEC) based on Low Density Parity Check (LDPC) codes. In order to have an efficient correction power, we develop an adaptation of the LDPC decoding algorithm to the specific multiple access interference distribution in a multimedia OCDMA network. The proposed network permits a high number of users to communicate with high data rates adapted to a multimedia transmission (voice, data and video) with corresponding QoS (10-3, 10-6, 10-9).

Elastic resource scaling lets cloud systems meet application service level objectives (SLOs) with minimum resource provisioning costs. In this paper, we present CloudScale, a system that automates fine-grained elastic resource scaling for multi-tenant cloud computing infrastructures. CloudScale employs online resource demand prediction and prediction error handling to achieve adaptive resource allocation without assuming any prior knowledge about the applications running inside the cloud. CloudScale can resolve scaling conflicts between applications using migration, and integrates dynamic CPU voltage/frequency scaling to achieve energy savings with minimal effect on application SLOs. We have implemented CloudScale on top of Xen and conducted extensive experiments using a set of CPU and memory intensive applications (RUBiS, Hadoop, IBM System S). The results show that CloudScale can achieve significantly higher SLO conformance than other alternatives with low resource and energy cost. CloudScale is non-intrusive and light-weight, and imposes negligible overhead (

This paper presents the experimental evaluation of an SDN-based orchestration system enabling the automated and coordinated arrangement of both computing and network resources in cloud Data Centers. The testbed has been set-up in the Fed4FIRE virtual experimental environment that allows for extensive, large-scale and cross-functional tests to be carried out. Results show that the selection of servers and paths that are driven by the estimated trends in their usage allows for resources to be better exploited at the cost of a limited risk of degradations.