An Economic Analysis of Cloud Computing Service Using Reclaimed Resources

Capacity planning for IaaS cloud providers offering multiple service classes

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.

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.

Business demands for powerful computing resources are increasing the workload in data centers, hence raising the complexity in managing the data center. The impact of data centers on the environment is now a particular concern. Although there are now efforts to realize Green data centers through harnessing Green technologies, their sustainability still remains an issue. Data center management includes planning its capacity. However, capacity planning for data center efficiency has only focused on measurements of power and cooling (part of infrastructure operation), which are insufficient to ensure the sustainability of the data center. In fact, a more thorough set of components must be considered. Therefore, this study proposes a capacity planning framework to derive practical solutions for Green data center management and thus ensure the sustainability of the Green data center. To this end, the components for Green data center sustainability and capacity planning were determined using Content Analysis and the findings verified by Green data center experts.

This paper addresses the problem of electricity cost management for Internet service providers with a collection of spatially distributed data centers. As the demand on Internet services drastically increases in recent years, the electricity consumed by Internet data centers (IDCs) has been skyrocketing. While most existing research focuses on reducing electric energy consumption of IDCs at one specific location, the problem of reducing the total electricity cost has been overlooked. This is an important problem faced by service providers, especially in the present multi-electricity-market environment, where the price of electricity may exhibit temporal and spatial diversities. Further, for these service providers, guaranteeing the quality of service (i.e., service level objectives) such as service delay guarantees to the end users is of critical importance. This paper studies the problem of minimizing the total electricity cost under multiple electricity markets environment while guaranteeing the quality of service geared to the location diversity and time diversity of electricity price. The problem is modeled as a constrained mixed-integer programming and an efficient solution algorithm is proposed. Extensive evaluations based on real-world electricity price data for multiple IDC locations illustrate the efficiency and efficacy of our approach.

Service level agreements (SLAs) gain more and more importance in the area of cloud computing. An SLA is a contract between a customer and a cloud service provider (CSP) in which the CSP guarantees functional and non-functional quality of service parameters for cloud services. Since CSPs have to pay for the hardware used as well as penalties for violating SLAs, they are eager to fulfill these agreements while at the same time optimizing the utilization of their resources.In this paper we examine SLA-aware VM scheduling strategies for cloud data centers. The service level objectives considered are resource usage and availability. The sample resources are CPU and RAM. They can be overprovisioned by the CSPs which is the main leverage to increase their revenue. The availability of a VM is affected by migrating it within and between data centers. To get realistic results, we simulate the effect of the strategies using the FederatedCloudSim framework and real-world workload traces of business-critical VMs.Our evaluation shows that there are considerable differences between the scheduling strategies in terms of SLA violations and the number of migrations. From all strategies considered, the combination of the Minimization of Migrations strategy for VM selection and the Worst Fit strategy for host selection achieves the best results.

Dynamic Voltage and Frequency Scaling (DVFS) is a method to save energy consumption of electronic devices and to protect them against overheating by automatic sensing and adaptation of their energy consumption. This can be accomplished either on the program instruction level for electronic devices or on the task or job level for server clusters. This paper models DVFS on the job level and through which Service Levels Objectives can be guaranteed with respect to prescribed mean or quantiles of service delays according to given Service Level Agreements (SLA) between user and service provider. The two parameters V (voltage) and f (frequency) cannot be changed independently of each other; typically only several combinations of V and f values are implemented in hardware for several power states. In this paper a novel analysis of operating DVFS is suggested for Server Clusters of Cloud Data Centers (CDC) under prescribed bounds of service level objectives which are defined by SLAs. The method is based on the theory of queuing models of the type GI/G/n for a server cluster to establish a relationship between SLA parameters and the power consumption and is performed for the example of the Intel Pentium M Processor with Enhanced SpeedStep Power Management. As result of this method precise bounds are provided for the load ranges of service request rates $\lambda$ for each power mode which guarantee minimum power consumption dependent on given SLA values and job arrival and service statistics. As the instantaneous load in a CDC can be highly volatile the current load level is usually monitored by periodic sensing which may result in a rather high frequency of DVFS range changes and corresponding overhead. For that reason an automated smoothing method is suggested which reduces the frequency of DVFS range changes significantly. This method is based on a Finite State Machine (FSM) with hysteresis levels.

SUMMARYAs modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining an importance as a foundation for online capacity planning and resource management. Time series analysis offers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting QoS metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self‐adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real‐world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run‐time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared with statically applied forecasting methods, for example, in an exemplary scenario on average by 37%. In a case study, between 55 and 75% of the violations of a given service level objective can be prevented by applying proactive resource provisioning based on the forecast results of our implementation. Copyright © 2014 John Wiley & Sons, Ltd.

Analyzing Hadoop power consumption and impact on application QoS

Network usage accountability is critical in helping operators and customers of multi-tenant data centers deal with concerns such as capacity planning, resource allocation, hotspot detection, link failure detection, and troubleshooting. However, the cost of measurements and instrumentation to achieve flow-level accountability is non-trivial. We propose Polygravity to determine tenant traffic usage via lightweight measurements in multi-tenant data centers. We adopt a tomogravity model widely used in ISP networks, and adapt it to a multi-tenant data center environment. By integrating datacenter-specific domain knowledge, sampling-based partial estimation and gravity-based internal sinks/sources estimation, Polygravity addresses two key challenges for adapting tomogravity to a data center environment: sparse traffic matrices and internal traffic sinks/sources. We conducted extensive evaluation of our approach using realistic data center workloads. Our results show that Polygravity can determine tenant IP flow usage with less than 1% average relative error for tenants with fine-grained domain knowledge. In addition, for tenants with coarse-grained domain knowledge and with partial host-based sampling, Polygravity reduces the relative error of sampling-based estimation by 1/3.

CHAPTER 6 - SUMMARY AND RECOMMENDATIONS

We investigate optimal pricing and capacity planning decisions for product-line settings such as introducing a new product or dropping an existing one. We consider a two-product, two-period model with stochastic demands, where price and capacity decisions are made at the outset. Investment in capacity must be traded-off against the possibility of buying at higher spot market prices due to shortage in capacity or charging a higher price to manage the demand. Prior studies argue that introducing an additional product to the product-line strains capacity, resulting in an increase in the price of an existing product. In contrast, we find that introducing a new product can also result in a drop in price of an existing product, enabling strategic pricing by firms. The necessary condition for this to occur is that the demand uncertainties for the products are of similar magnitude and negatively correlated. Similar insights are obtained for the setting where an existing product is dropped from the product-line. Hence, product-market decisions and contextual factors play a role in capacity planning, capacity cost allocation and pricing.

Optimization models for assessing the peak capacity utilization of intelligent transportation systems

Data centers have significant capital investments in large underutilized energy resources in the form of backup generators and batteries. In this paper, we propose to use these energy resources in demand response (DR) by operating a data center as a virtual power plant (VPP). An optimization model is designed which schedules the energy resources on a day-ahead basis using forecasted values of both renewable energy sources and the data center load for participation of the data center in DR. This optimization model also incorporates realtime dispatch of units to handle variations during operation. An improved battery cost model is designed and implemented as a soft constraint in the VPP energy management system (EMS) model, ensuring that the batteries' use in DR does not affect their float life. A case study is conducted in which the data center is connected to bus 8 of the modified IEEE 30-bus system. The results show that real-time operation of a data center participating in DR significantly reduces the operational cost, especially during network congestion, without causing battery degradation.

Data center systems must adopt end-to-end resource management, covering both cyber and physical components. Modeling data center components can help to estimate and predict key performance indicators under various assumptions. This paper describes a data center modeling approach using a cyber-physical systems lens. The approach is aimed at facilitating the modeling of quality of service and energy consumption parameters to evaluate data centers under different workload and IT resources conditions, and to then compare them to one another, or compare different scenarios under which data centers operate. Results of the model are also a way to communicate and better understand data center behavior given the data center workload, and to help evaluate areas of improvement.