Resource allocation and disturbance rejection in web servers using SLAs and virtualized servers

Abstract—Resource management in IT–enterprises gain more and more attention due to high operation costs. For instance, web sites are subject to very changing traffic–loads over the year, over the day, or even over the minute. Online adaption to the changing environment is one way to reduce losses in the operation. Control systems based on feedback provide methods for such adaption, but is in nature slow, since changes in the environment has to propagate through the system before being compensated. Therefore, feed–forward systems can be introduced that has shown to improve the transient performance. However, earlier proposed feed–forward systems have been based on offline estimation. In this article we show that off–line estimations can be problematic in online applications. Therefore, we propose a method where parameters are estimated online, and thus also adapts to the changing environment. We compare our solution to two other control strategies proposed in the literature, which are based on off-line estimation of certain parameters. We evaluate the controllers with both discrete-event simulations and experiments in our testbed. The investigations show the strength of our proposed control system

Multicriteria Resource Brokering in Cloud Computing for Streaming Service

By leveraging cloud computing such as Infrastructure as a Service (IaaS), the outsourcing of computing resources used to support operations, including servers, storage, and networking components, is quite beneficial for various providers of Internet application. With this increasing trend, resource allocation that both assures QoS via Service Level Agreement (SLA) and avoids overprovisioning in order to reduce cost becomes a crucial priority and challenge in the design and operation of complex service-based platforms such as streaming service. On the other hand, providers of IaaS also concern their profit performance and energy consumption while offering these virtualized resources. In this paper, considering both service-oriented and infrastructure-oriented criteria, we regard this resource allocation problem as Multicriteria Decision Making problem and propose an effective trade-off approach based on goal programming model. To validate its effectiveness, a cloud architecture for streaming application is addressed and extensive analysis is performed for related criteria. The results of numerical simulations show that the proposed approach strikes a balance between these conflicting criteria commendably and achieves high cost efficiency

Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters

Resource management of virtualized servers in data-centres has become a critical task, since it enables costeffective consolidation of server applications. Resource management is an important and challenging task, especially for multi-tier applications with unpredictable time-varying workloads. Work in resource management using control theory has shown clear benefits of dynamically adjusting resource allocations to match fluctuating workloads. However, little work has been done towards adaptive controllers for unknown workload types. This work presents a new resource management scheme that incorporates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. We present a set of controllers that continuously detect and self-adapt to unforeseen workload changes. Furthermore, our most advanced controller also self-configures itself without any a priori information and with a small 4.8% performance penalty in the case of high intensity workload changes. In addition, our controllers are enhanced to deal with multi-tier server applications: by using the pair-wise resource coupling between tiers, they improve server response to large workload increases as compared to controllers with no such resource-coupling mechanism. Our approaches are evaluated and their performance is illustrated on a 3-tier Rubis benchmark web-site deployed on a prototype Xen-virtualized cluster

Effective Resource and Workload Management in Data Centers

The increasing demand for storage, computation, and business continuity has driven the growth of data centers. Managing data centers efficiently is a difficult task because of the wide variety of datacenter applications, their ever-changing intensities, and the fact that application performance targets may differ widely. Server virtualization has been a game-changing technology for IT, providing the possibility to support multiple virtual machines (VMs) simultaneously. This dissertation focuses on how virtualization technologies can be utilized to develop new tools for maintaining high resource utilization, for achieving high application performance, and for reducing the cost of data center management.;For multi-tiered applications, bursty workload traffic can significantly deteriorate performance. This dissertation proposes an admission control algorithm AWAIT, for handling overloading conditions in multi-tier web services. AWAIT places on hold requests of accepted sessions and refuses to admit new sessions when the system is in a sudden workload surge. to meet the service-level objective, AWAIT serves the requests in the blocking queue with high priority. The size of the queue is dynamically determined according to the workload burstiness.;Many admission control policies are triggered by instantaneous measurements of system resource usage, e.g., CPU utilization. This dissertation first demonstrates that directly measuring virtual machine resource utilizations with standard tools cannot always lead to accurate estimates. A directed factor graph (DFG) model is defined to model the dependencies among multiple types of resources across physical and virtual layers.;Virtualized data centers always enable sharing of resources among hosted applications for achieving high resource utilization. However, it is difficult to satisfy application SLOs on a shared infrastructure, as application workloads patterns change over time. AppRM, an automated management system not only allocates right amount of resources to applications for their performance target but also adjusts to dynamic workloads using an adaptive model.;Server consolidation is one of the key applications of server virtualization. This dissertation proposes a VM consolidation mechanism, first by extending the fair load balancing scheme for multi-dimensional vector scheduling, and then by using a queueing network model to capture the service contentions for a particular virtual machine placement

Disturbance Rejection and Control in Web Servers

An important factor for a user of web sites on the Internet is the duration of time between the request of a web page until an answer has been returned. If this response time is too long, the user is likely to abandon the web site and search for other providers of the service. To avoid this loss of users, it is important for the web site operator to assure that users are treated sufficiently fast. On the other hand, it is also important to minimize the effort to optimize profit. As these objectives often are contradictory, an acceptable target response-time that can be formulated. The resources are allocated in a manner that ensures that long response times do not occur, while, at the same time, using as little resources as possible to not overprovision. The work presented in this doctoral thesis takes a control-theoretic perspective to solve this problem. The resources are considered as the control input, and the response time as the main output. Several disturbances affect the system, such as the arrival rate of requests to the web site. A testbed was designed to allow repeatable experiments with different controller implementations. A server was instrumented with sensors and actuators to handle requests from 12 client computers with capability for changing work loads. On the theoretical side, a model of a web server is presented in this thesis. It explicitly models a specific sensor implementation where buffering occurs in the computer prior to the sensor. As a result, the measurement of the arrival rate becomes state dependent under high load. This property turns out to have some undesirable effects on the controlled system. The model was capable of predicting the behavior of the testbed quite well. Based on the presented model, analysis shows that feed-forward controllers suggested in the literature can lead to instability under certain circumstances at high load. This has not been reported earlier, but is in this doctoral thesis demonstrated by both simulations and experiments. The analysis explains why and when the instability arises. In the attempt to predict future response-times this thesis also presents a feedback based prediction scheme. Comparisons between earlier predictions to the real response-times are used to correct a model based response time prediction. The prediction scheme is applied to a controller to compensate for disturbances before the effect propagates to the response time. The method improves the transient response in the case of sudden changes in the arrival rate of requests. This doctoral thesis also presents work on a control solution for reserving CPU capacity for a given process or a given group of processes on a computer system. The method uses only existing operating-system infrastructure, and achieves the desired CPU capacity in a soft real-time manner

A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment

We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function

Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey

The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including hardware and software vulnerabilities, communication issues, and personnel errors, etc. Therefore, enhancing the cyber-resiliency of DER-based smart grid - the ability to survive successful cyber intrusions - is becoming increasingly vital and has garnered significant attention from both industry and academia. In this survey, we aim to provide a systematical and comprehensive review regarding the cyber-resiliency enhancement (CRE) of DER-based smart grid. Firstly, an integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasis on vulnerability identification and impact analysis. Then, the defense-in-depth strategies encompassing prevention, detection, mitigation, and recovery are comprehensively surveyed, systematically classified, and rigorously compared. A CRE framework is subsequently proposed to incorporate the five key resiliency enablers. Finally, challenges and future directions are discussed in details. The overall aim of this survey is to demonstrate the development trend of CRE methods and motivate further efforts to improve the cyber-resiliency of DER-based smart grid.Comment: Submitted to IEEE Transactions on Smart Grid for Publication Consideratio