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As a key technology in the 5G era, Mobile Edge Computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an extension of cloud computing on the user side, which can deploy edge servers and bring computing resources closer to mobile users, and provide more efficient interactions. However, due to the user's dynamic mobility, the distance between the user and the edge server will change dynamically, which may cause fluctuations in Quality of Service (QoS). Therefore, when a mobile user moves in the MEC environment, certain approaches are needed to schedule services deployed on the edge server to ensure the user experience. In this paper, we model service scheduling in MEC scenarios and propose a delay-aware and mobility-aware service management approach based on concise probabilistic methods. This approach has low computational complexity and can effectively reduce service delay and migration costs. Furthermore, we conduct experiments by utilizing multiple realistic datasets and use iFogSim to evaluate the performance of the algorithm. The results show that our proposed approach can optimize the performance on service delay, with 8% to 20% improvement and reduce the migration cost by more than 75% compared with baselines during the rush hours.

Service migration in mobile edge computing is a promising approach to improving the quality of service (QoS) for mobile users and reducing the network operational cost for service providers as well. However, these benefits are not free, coming at costs of bulk-data transfer, and likely service disruption, which could consequently increase the overall service costs. To gain the benefits of service migration while minimizing its cost across the edge nodes, in this paper, we leverage reinforcement learning (RL) method to design a cost-effective framework, called Mig-RL, for the service migration with a reduction of total service costs as a goal in a mobile edge environment. The Mig-RL leverages the infrastructure of edge network and deploys a migration agent through Q-learning to learn the optimal policy with respect to the service migration status. We distinguish the Mig-RL from other existing works in several major aspects. First, we fully exploit the nature of this problem in a modest migration space, which allows us to constrain the number of service replicas whereby a defined state–action space could be effectively handled, as opposed to those methods that need to always approximate a huge state–action space for policy optimality. Second, we advocate a migration policy-base as a cache to save the learning process by retrieving the most effective policy whenever a similar migration pattern is encountered as time goes on. Finally, by exploiting the idea of software defined network, we also investigate the efficient implementation of Mig-RL in mobile edge network. Experimental results based on some real and synthesized access sequences show that Mig-RL, compared with the selected existing algorithms, can substantially minimize the service costs, and in the meantime, efficiently improve the QoS by adapting to the changes of mobile access patterns.

Service migration between datacenters can reduce the network overhead within a cloud infrastructure; thereby, also improving the quality of service for the clients. Most of the algorithms in the literature assume that the client access pattern remains stable for a sufficiently long period so as to amortize such migrations. However, if such an assumption does not hold, these algorithms can take arbitrarily poor migration decisions that can substantially degrade system performance. In this paper, we approach the issue of performing service migrations for an unknown and dynamically changing client access pattern. We propose an online algorithm that minimizes the inter-datacenter network, taking into account the network load of migrating a service between two datacenters, as well as the fact that the client request pattern may change “quickly”, before such a migration is amortized. We provide a rigorous mathematical proof showing that the algorithm is 3.8-competitive for a cloud network structured as a tree of multiple datacenters. We briefly discuss how the algorithm can be modified to work on general graph networks with an $O{\mathrm{(log\vert V\vert)}}$ O ( log | V | ) probabilistic approximation of the optimal algorithm. Finally, we present an experimental evaluation of the algorithm based on extensive simulations.

Moving data warehouses to the cloud is what today's companies consider a trend towards cost-effective data management. To fully achieve the economic goal, the cloud data warehouse system is supposed to be able to adjust its resource provisioning to adapt to changing workload requirements. However, traditional data warehousing architecture is not flexible enough to allow on demand resource control, which severely restrains the cloud provider as well as the users to optimize total cost and maintain desired quality of service. To build the data warehouses for the cloud, new architectures should be studied. In this paper we explore such an architecture that decouples data management and data computation to facilitate the on demand resource control. By separating the two parts, the resulting system gains more elasticity and more adaptivity. For the poof of concept, we build a prototype system, called DuoSQL, based on PostgreSQL and Spark. We validate the system using the TPC-H benchmark. The experiment results show the decoupling approach is not only flexible but also has great performance potentials.

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidation-based energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided., Comment: In the Proceedings of the 20th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID 2020)

We study the problem of dynamically migrating a service in the cloud to satisfy an online sequence of mobile batch-request demands in a cost-effective way. The service may have single or multiple replicas, each running on a virtual machine. As the origin of mobile accesses frequently changes over time, this problem is particularly important for time-bounded services to achieve enhanced Quality of Service and cost effectiveness. Moving the service closer to the client locations not only reduces the service access latency but also minimizes the network costs for service providers. However, these benefits are not free. The migration comes at a cost of bulk-data transfer and service disruption, and hence, increasing the overall service costs. To gain the benefits of service migration while minimizing the caused monetary costs, we propose an efficient search-based algorithm Dmig to migrate a single server, and then extend it as a scalable algorithm, called mDmig , to the multi-server situation, a more general case in the cloud. Both algorithms are fully distributed, symmetric, and characterized by the effective use of historical access information to conduct virtual migration so that the limitations of local search in the cost reduction can be overcome. To evaluate the algorithms, we compared them with some existing algorithms and an off-line algorithm. Our simulation results showed that the proposed algorithms exhibit better performance in service migration by adapting to the changes of mobile access patterns in a cost-effective way.

In this paper, we propose a simulation model to study real-world replication workflows for cloud storage systems. With this model, we present three new methods to maximize the storage space usage during replica creation, and two novel QoS aware greedy algorithms for replica placement optimization. By using a simulation method, our algorithms are evaluated, through a comparison with the existing placement algorithms, to show that i a more evenly distributed replicas for a data set can be achieved by using round-robin methods in replica creation phase and ii the two proposed greedy algorithms, named GS_QoS and GS_QoS_C1, not only have more economical results than those from Chen et al., but also guarantee the QoS for clients. Copyright © 2016 John Wiley & Sons, Ltd.

Network is an essential infrastructure for mobile edge clouds. The communication between mobile devices and the communication between mobile device and the cloud both rely on the network infrastructure. The reliability of network infrastructure is important to ensure the QoS (Quality of Services) of mobile edge clouds. However, the current network suffers from many attacks. Early detect network attacks is very important. Traditional anomaly network detection methods have some weaknesses, such as slow detection speed, fail to recognize new anomalies, and insufficient accuracy. In order to solve these problems, in this paper we design and implement a new anomaly detection platform - NetDetector for networked systems. We first present the architecture design and then show the implementation details. NetDetector consists of five important modules, and its detection algorithm is based on LSTM (Long Short Term Memory), a special kind of recurrent neural network. A case study is proposed to show the detection performance of NetDetector. Experimental results show NetDetector achieves about 8% performance improvement as compared with the systems that use traditional machine learning methods.

In virtualized environments, virtual machine (VM) scheduling strategies incorporated with energy efficient techniques are needed to reduce the operational cost of the system while delivering high Quality of Service (QoS). It is widely accepted that the cost of the energy consumption in the environment is a dominant part of the owner’s budget. However, when considering energy efficiency, VM scheduling decisions become more constrained, leading in the violation of job deadlines and hence compromising QoS. This paper studies energy efficient VM scheduling strategies in virtualized environments to minimize the queue time and makespan under the fulfillment of SLA requirements (i.e., deadline). Specifically, six energy efficient VM scheduling strategies are investigated incorporated with the dynamic voltage and frequency scaling (DVFS) power management technique. They consist of user-oriented and system-oriented policies. The strategies are extensively simulated and compared with three power management governing methods provided at hypervisor level (i.e., userspec, ondemand, and performance). To conduct simulation experiments, we employ real-world high performance computing (HPC) workloads collected from a production data center. For comparison and evaluation, we analyze the: (a) energy consumption, (b) runtime, (c) queue time, (d) makespan, and (e) slowdown ratio. Lastly, we highlight the strengths and weaknesses of the VM scheduling strategies that can help to choose the most appropriate VM scheduling strategy for a given scenario.

To improve resource efficiency and design intelligent scheduler for clouds, it is necessary to understand the workload characteristics and machine utilization in large-scale cloud data centers. In this paper, we perform a deep analysis on a newly released trace dataset by Alibaba in September 2017, consists of detail statistics of 11089 online service jobs and 12951 batch jobs co-locating on 1300 machines over 12 hours. To the best of our knowledge, this is one of the first work to analyze the Alibaba public trace. Our analysis reveals several important insights about different types of imbalance in the Alibaba cloud. Such imbalances exacerbate the complexity and challenge of cloud resource management, which might incur severe wastes of resources and low cluster utilization. 1) Spatial Imbalance: heterogeneous resource utilization across machines and workloads. 2) Temporal Imbalance: greatly time-varying resource usages per workload and machine. 3) Imbalanced proportion of multi-dimensional resources (CPU and memory) utilization per workload. 4) Imbalanced resource demands and runtime statistics (duration and task number) between online service and offline batch jobs. We argue accommodating such imbalances during resource allocation is critical to improve cluster efficiency, and will motivate the emergence of new resource managers and schedulers.

As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such ‘Data Explosions’ has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service Provider must assure that data is efficiently processed and distributed in a way that does not compromise end-users’ Quality of Service (QoS) in terms of data availability, data search delay, data analysis delay, and the like. In the aforementioned perspective, data replication is used in the cloud for improving the performance (e.g., read and write delay) of applications that access data. Through replication a data intensive application or system can achieve high availability, better fault tolerance, and data recovery. In this paper, we survey data management and replication approaches (from 2007 to 2011) that are developed by both industrial and research communities. The focus of the survey is to discuss and characterize the existing approaches of data replication and management that tackle the resource usage and QoS provisioning with different levels of efficiencies. Moreover, the breakdown of both influential expressions (data replication and management) to provide different QoS attributes is deliberated. Furthermore, the performance advantages and disadvantages of data replication and management approaches in the cloud computing environments are analyzed. Open issues and future challenges related to data consistency, scalability, load balancing, processing and placement are also reported. Scopus

Migrating service to certain vantage locations that are close to its clients can not only reduce the service access latency,but also minimize the network costs for its service provider. As such, this problem is particularly important for time-bounded services to achieve both enhanced QoS and cost effectiveness as well. However, the service migration is not free, coming at costs of bulk-data transfer and likely service disruption, as a result, increasing the overall service costs. To gain the benefits of service migration while minimizing service costs, in this paper, we leverage reinforecement learning (RL) methods to propose an efficient algorithm, called Mig- RL, for the service migration in a cloud environment. The Mig-RL utilizes an agent to learn the optimal policy that determines service migration status by using a typical RL algorithm, called Q-learning. Specifically, the agent learns from the historical access information to decide when and to where the service should be migrated, without requiring any prior information regarding the service accesses. Therefore, the agent can dynamically adapt to the environment and achieve online migration in real time. Experimental results on the real and synthesized access sequences from cloud networks show that Mig-RL can minimize the service costs, and in the meantime, improve the quality of service (QoS) by adapting to the changes of mobile access patterns.

In the large-scale parallel computing environment, resource allocation and energy efficient techniques are required to deliver the quality of services (QoS) and to reduce the operational cost of the system. Because the cost of the energy consumption in the environment is a dominant part of the owner's and user's budget. However, when considering energy efficiency, resource allocation strategies become more difficult, and QoS (i.e., queue time and response time) may violate. This paper therefore is a comparative study on job scheduling in large-scale parallel systems to: (a) minimize the queue time, response time, and energy consumption and (b) maximize the overall system utilization. We compare thirteen job scheduling policies to analyze their behavior. A set of job scheduling policies includes (a) priority-based, (b) first fit, (c) backfilling, and (d) window-based policies. All of the policies are extensively simulated and compared. For the simulation, a real data center workload comprised of 22385 jobs is used. Based on results of their performance, we incorporate energy efficiency in three policies i.e., (1) best result producer, (2) average result producer, and (3) worst result producer. We analyze the (a) queue time, (b) response time, (c) slowdown ratio, and (d) energy consumption to evaluate the policies. Moreover, we present a comprehensive workload characterization for optimizing system's performance and for scheduler design. Major workload characteristics including (a) Narrow, (b) Wide, (c) Short, and (d) Long jobs are characterized for detailed analysis of the schedulers' performance. This study highlights the strengths and weakness of various job scheduling polices and helps to choose an appropriate job scheduling policy in a given scenario.

We study the problem of autonomous service migration in the cloud to satisfy an online sequence of mobile batch-request demands in a cost-effective way. As the origins of the mobile accesses frequently change over time, this problem is particularly important for time-bounded services to achieve enhanced QoS and cost effectiveness. Moving the service closer to its client locations not only reduces the service access latency but also minimizes the network costs for service providers. However, the migration comes at costs of bulk-data transfer and service disruption, as a result, increasing the overall service costs. To gain the benefits of service migration while minimizing the service costs, we propose an efficient search-based algorithm Dmig the service migration in an autonomous way. Compared with existing algorithms, the proposed algorithm is fully distributed, symmetric, and characterized by the effective use of historical access information to perform virtual migration that overcomes the limitation of traditional local search in cost reduction. To evaluate the algorithm, we compared it with some existing algorithms, and show that the proposed algorithm exhibits better performance by adapting to the changes of mobile access patterns in a cost effective way.

OTN technology has been widely used in smart grid, which improves transmission speed, transmission efficiency and transmission capacity of the network. In order to supervise the operation of the network in real time and improve quality of service (QoS), we need to make a quantitative assessment of operation quality for OTN in smart grid. In this paper, we propose LM-BP evaluation method to assess the operation quality of OTN in smart grid. At first, we give the evaluation indicator system. Secondly, we introduce LM-BP algorithm. At last we make simulation experiments based on MATLAB with last 10 years network operation data of a province electric power OTN company in smart grid. The simulation results show that BP neutral network optimized by Levenberg-Marquardt (LM) algorithm has shorter training time, faster convergence rate and better prediction accuracy than that standard BP neutral network. Demonstrating a new method of LM-BP network in operation quality evaluation of OTN in Smart grid is feasible, effective and reasonable.

Cloud elasticity allows dynamic resource provisioning in concert with actual application demands. Feedback control approaches have been applied with success to resource allocation in physical servers. However, cloud dynamics make the design of an accurate and stable resource controller challenging, especially when application-level performance is considered as the measured output. Application-level performance is highly dependent on the characteristics of workload and sensitive to cloud dynamics. To address these challenges, we extend a self-tuning fuzzy control (STFC) approach, originally developed for response time assurance in web servers to resource allocation in virtualized environments. We introduce mechanisms for adaptive output amplification and flexible rule selection in the STFC approach for better adaptability and stability. Based on the STFC, we further design a two-layer QoS provisioning framework, DynaQoS, that supports adaptive multi-objective resource allocation and service differentiation. We implement a prototype of DynaQoS on a Xen-based cloud testbed. Experimental results on representative server workloads show that STFC outperforms popular controllers such as Kalman filter, ARMA and, Adaptive PI in the control of CPU, memory, and disk bandwidth resources under both static and dynamic workloads. Further results with multiple control objectives and service classes demonstrate the effectiveness of DynaQoS in performance-power control and service differentiation.

As e-commerce services are exponentially growing, businesses need quantitative estimates of client-perceived response times to continuously improve the quality of their services. Current server-side nonintrusive measurement techniques are limited to nonsecured HTTP traffic. In this paper, we present the design and evaluation a monitor, namely sMonitor, which is able to measure client-perceived response times for both HTTP and HTTPS traffic. At the heart of sMonitor is a novel size-based analysis method that parses live packets to delimit different webpages and to infer their response times. The method is based on the observation that most HTTP(S)-compatible browsers send significantly larger requests for container objects than those for embedded objects. sMonitor is designed to operate accurately in the presence of complicated browser behaviors, such as parallel downloading of multiple webpages and HTTP pipelining, as well as packet losses and delays. It requires only to passively collect network traffic in and out of the monitored secured services. We conduct comprehensive experiments across a wide range of operating conditions using live secured Internet services, on the PlanetLab, and on controlled networks. The experimental results demonstrate that sMonitor is able to control the estimation error within 6.7 percent, in comparison with the actual measured time at the client side.

Understanding server capacity is crucial to system capacity planning, configuration, and QoS-aware resource management. Conventional stress testing approaches measure server capacity offline in terms of application-level performance metrics like response time and throughput. They are limited in measurement accuracy and timeliness. In a multitier website, resource bottleneck often shifts between tiers as client access pattern changes. This makes the problem of online capacity measurement even more challenge. This paper presents an online measurement approach based on low-level hardware performance metrics such as instructions execution rate and cache access behavior. Such metrics together define a system internal running state. The measurement approach uses machine learning techniques to infer application-level performance at each tier from a set of selected hardware performance counters. A coordinated predictor is induced over individual tier-wide models to make global system performance prediction and identify the bottleneck when the system becomes overloaded. Experiments were conducted on a two-tier Tomcat/MySQL-configured website using TPC-W benchmarks. Experimental results demonstrated that this approach was able to achieve an overload prediction accuracy of higher than 90 percent for a priori known input traffic mix and over 85 percent accuracy even for traffic causing frequent bottleneck shifting. It costs less than 0.5 percent runtime overhead for data collection and no more than 50 ms for each online decision making.

The eQoS framework represents a first step in a systematic approach to provisioning user- perceived page-view response time differentiation and assurance. It deploys a model predictive feedback controller approach to adjust the processing rate allocated to each class of requests dynamically in response to measured performance deviation.

In this paper, we study the problem of packet scheduling in a wireless environment with the objective of minimizing the average transmission energy expenditure under individual packet delay constraints. Most past studies assumed that the input arrivals followed a Poisson process or were statistically independent. However, traffic from a real source typically has strong time correlation. We model a packet scheduling and queuing system for a general input process in linear time-invariant systems. We propose an energy-efficient packet scheduling policy that takes the correlation into account. Meanwhile, a slower transmission rate implies that packets stay in the transmitter for a longer time, which may result in unexpected transmitter overload and buffer overflow. We derive the upper bounds of the maximum transmission rate under an overload probability and the upper bounds of the required buffer size under a packet drop rate. Simulation results show that the proposed scheduler improves up to 15 percent in energy savings compared with the policies that assume statistically independent input. Evaluation of the bounds in providing QoS control shows that both deadline misses and packet drops can be effectively bounded by a predefined constraint.

Internet applications and clients have very diverse service expectations, demanding for provisioning of different levels of quality of service (QoS) to multiple traffic classes on the Internet. To meet this demand, many different approaches and performance metrics have been proposed in an attempt to achieve per-class QoS differentiation in the network core, network edges, proxy and end servers. However, due to the wide variety of approaches to the problem, it is difficult to meaningfully compare different approaches since there is no uniform means for quantitatively or qualitatively evaluating them. Thus, it is hard to build upon existing work or identify areas worthy of additional efforts without understanding of the relationships between existing efforts. In this paper, a taxonomy of approaches to per-class QoS differentiation is presented in an attempt to provide a common terminology and classification mechanism necessary in addressing this problem. The taxonomy categorizes state-of-the-art QoS differentiation approaches in three dimensions. The first categorization dimension is the locations where the approaches are deployed, i.e., server, proxy and network sides. The QoS differentiation approaches are secondly categorized according to their policies, i.e., admission control, resource management, and content adaptation. The third categorization dimension is their implementation layer, i.e., application level, and kernel level. Representative QoS differentiation approaches in each category are reviewed.

It is important to guarantee client-perceived end-to-end quality of service (QoS) under heavy load conditions. Existing work focuses on network transfer time or server-side request processing time. In this paper, we propose a novel framework, eQoS, to monitor and control client-perceived response time in heavy loaded Web servers. The response time is measured with respect to Web pages that contain multiple embedded objects. Within the framework, we propose an adaptive fuzzy controller, STFC, to allocate server resources. The controller assumes no knowledge of the pageview traffic model. It deals with the effect of process delay in resource allocation by its two-level self-tuning capabilities. We also prove the stability of the STFC. We implement a prototype of eQoS in Linux and conduct comprehensive experiments across wide-range server workload conditions on PlanetLab and simulated networks. Experimental results demonstrate the effectiveness of the framework: it controls the deviation of client-perceived pageview response time to be within 20 percent of a predefined target with both synthetic and real Web traffics. We also compare the STFC with other controllers, including static fuzzy, linear proportional integral (PI), and adaptive PI controllers. Experimental results show that, although the STFC works slightly worse than the static fuzzy controller in the environment where the static fuzzy controller is best tuned, because of its self-tuning capabilities, it has better performance in all other test cases by around 25 percent on average in terms of the deviation from the target response time. In addition, due to its model independence, the STFC outperforms the linear PI and adaptive PI controllers by 50 percent and 75 percent on average, respectively

Proportional delay differentiation (PDD) model is an important approach to relative differentiated services provisioning on the Internet. It aims to maintain pre-specified packet queueing-delay ratios between different classes of traffic at each hop. Existing PDD packet scheduling algorithms are able to achieve the goal in long time-scales when the system is highly utilized. This paper presents a new PDD scheduling algorithm, called Little's average delay (LAD), based on a proof of Little's Law. It monitors the arrival rate of the packets in each traffic class and the cumulative delays of the packets and schedules the packet according to their transient queueing properties in order to achieve the desired class delay ratios in both short and long time-scales. Simulation results show that LAD is able to provide predictable and controllable services in various system conditions and that such services, whenever feasible, can be guaranteed, independent of the distributions of packet arrivals and sizes. In comparison with other PDD scheduling algorithms, LAD can provide the same level of service quality in long time-scales and more accurate and robust control over the delay ratio in short time-scales. In particular, LAD outperforms its main competitors significantly when the desired delay ratio is large.

A scalable e-commerce server should be able to provide different levels of quality of service (QoS) to different types of requests based on clients' navigation patterns and the server capacity. E-commerce workloads are composed of sessions. In this paper, we propose a session-based two-dimensional (2D) service differentiation model for online transactions: intersession and intrasession. The intersession model aims to provide different levels of QoS to sessions from different customer classes, and the intrasession model aims to provide different levels of QoS to requests in different states of a session. A primary performance metric of online transactions is slowdown. It measures the waiting time of a request relative to its service time. We present a processing rate allocation scheme for 2D proportional slowdown differentiation. We then introduce service slowdown as a systemwide QoS metric of an e-commerce server. It is defined as the weighted sum of request slowdown in different sessions and in different session states. We formulate the problem of 2D service differentiation as an optimization of processing rate allocation with the objective of minimizing the service slowdown of the server. We prove that the derived rate allocation scheme based on the optimization guarantees client requests' slowdown to be square-root proportional to their prespecified differentiation weights in both intersession and intrasession dimensions. We evaluate this square-root proportional rate allocation scheme and a proportional rate allocation scheme via extensive simulations. Results validate that both schemes can achieve predictable, controllable, and fair 2D service differentiation on e-commerce servers. The square-root proportional rate allocation scheme provides 2D service differentiation at a minimum cost of service slowdown

A desirable behavior of an Internet server is that a request's queuing delay depends on its service time in a linear fashion. Measuring the quality of service in terms of slowdown, the ratio of a request's queuing delay to its service time, provides a simple way to attain the objective. Moreover, it treats client requests equally regardless of their service time, whereas response time favors requests that need more processing resources. In this paper, we propose a proportional slowdown differentiation (PSD) service model on Internet servers. It aims to maintain prespecified slowdown ratios between different classes of client requests. To provide PSD services, we first derive a closed-form expression of the expected slowdown in an M/G/1 FCFS queuing system with a typical heavy-tailed service time distribution, the bounded Pareto distribution. Based on the closed-form expression, we design a queuing-theoretic strategy of processing-rate allocation. The rate allocation is realized by deploying a virtual server for each class. Simulation results show that the strategy can provide controllable PSD services on Internet servers. It, however, comes along with large variance and weak predictability due to the dynamics of Internet traffic. To address these issues, we design an integral feedback controller and integrate it into the queuing-theoretic strategy. Simulation results demonstrate that the integrated strategy is robust and can deliver predictable PSD services at a superior fine-grained level. We modified the Apache Web server with an implementation of the integrated processing-rate allocation strategy. Experimental results further demonstrate its effectiveness and feasibility in practice.

To provide ubiquitous access to the proliferating rich media on the Internet, scalable streaming servers must be able to provide differentiated services to various client requests. Recent advances of transcoding technology make network-I/O bandwidth usages at the server communication ports controllable by request schedulers on the fly. In this article, we propose a transcoding-enabled bandwidth allocation scheme for service differentiation on streaming servers. It aims to deliver high bit rate streams to high priority request classes without overcompromising low priority request classes. We investigate the problem of providing differentiated streaming services at application level in two aspects: stream bandwidth allocation and request scheduling. We formulate the bandwidth allocation problem as an optimization of a harmonic utility function of the stream quality factors and derive the optimal streaming bit rates for requests of different classes under various server load conditions. We prove that the optimal allocation, referred to as harmonic proportional allocation, not only maximizes the system utility function, but also guarantees proportional fair sharing between classes with different prespecified differentiation weights. We evaluate the allocation scheme, in combination with two popular request scheduling approaches, via extensive simulations and compare it with an absolute differentiation strategy and a proportional-share strategy tailored from relative differentiation in networking. Simulation results show that the harmonic proportional allocation scheme can meet the objective of relative differentiation in both short and long timescales and greatly enhance the service availability and maintain low queueing delay when the streaming system is highly loaded.

Recently, power consumption of radio access networks (RAN) has attracted a lot of attention since energy cost takes up a vast portion of operational expenditure (OPEX). Specifically, more than half of the energy is consumed by base stations (BS). Baseband Unit (BBU), which is responsible for baseband signal processing, is an important part of BS, and BBU consolidation in the form of a cluster is an promising solution to enhance energy efficiency. Although there are several existing work addressing similar ideas from theoretical perspective, there is still a gap between theoretical and practical solutions. Hence, by leveraging cloud computing and software-defined radio technologies, we develop a BBU cluster testbed, eBase, to consolidates multiple isolated BBUs into a virtualized BBU and then provide unified baseband resource pool for RAN. We present the architecture and implementation of eBase in detail. We also design a distributed resource management middleware to realize comprehensive resource management strategy for BBU cluster in order to improve energy efficiency. Through experiments, we find eBase can reduce total energy consumption by about 20% while satisfying about 95% QoS. These results not only demonstrate BBU cluster's significant capability to reduce energy cost and improve resource utilization, but also show eBase is a capable prototyping testbed for cloud radio access network research.

Recently, power consumption of radio access networks (RAN) has attracted a lot of attention since energy cost takes up a vast portion of operational expenditure (OPEX). Specifically, more than half of the energy is consumed by base stations (BS). Baseband Unit (BBU), which is responsible for baseband signal processing, is an important part of BS but still lacks effective power management solutions to improve energy efficiency. We present a power management middleware, GreenBase, to improve energy efficiency for BBUs. GreenBase consists of a software-based power metering tool, power and performance monitors as well as managers, an extensible power management policy library, and an energy efficiency analyzer. To evaluate this middleware, we construct a BBU testbed running video streaming applications based on software-defined radio platform GNU Radio/USRP. The experimental results validate GreenBase can achieve power and performance target with integrated power management policies. In particular, we find GreenBase can reduce total energy consumption by about 13% while satisfying target statistic QoS using a stochastic QoS controller.

Understanding server capacity is crucial for system capacity planning, configuration, and QoS-aware resource management. Conventional stress testing approaches measure the server capacity in terms of application-level performance metrics like response time and throughput. They are limited in measurement accuracy and timeliness. In a multitier website, resource bottleneck often shifts between tiers as client access pattern changes. This makes the capacity measurement even more challenging. This paper presents a measurement approach based on hardware performance counter metrics. The approach uses machine learning techniques to infer application-level performance at each tier. A coordinated predictor is induced over individual tier models to estimate system-wide performance and identify the bottleneck when the system becomes overloaded. Experimental results demonstrate that this approach is able to achieve an overload prediction accuracy of higher than 90% for a priori known input traffic patterns and over 85% accuracy even for traffic causing frequent bottleneck shifting. It costs less than 0.5% runtime overhead for data collection and no more than 50 ms for each on-line decision.

Resource management is critical to the usability and accessibility of grid computing systems. Conventional approaches to grid resource discovery are either centralized or hierarchical, and these prove to be inefficient as the size of the grid system increases. The peer-to-peer (P2P) paradigm has been applied to grid systems as a mechanism for providing scalable range- query and multi-attribute resource management. However, most current P2P-based resource management approaches support multi-attribute range queries at a high cost. They either depend on multiple P2P networks with each P2P network responsible for a single attribute, or they keep the resource information of all attributes in a single node. This paper presents a low-overhead range-query multi-attribute P2P-based resource management approach, LORM. Unlike other P2P-based approaches, it relies on a single P2P network and allocates resource information to different nodes based on resource attributes and values. Moreover, it has high capability to handle the large-scale and dynamic characteristics of resources in grids. Simulation results demonstrate the efficiency of LORM in comparison with other resource management approaches in terms of resource management overhead and resource discovery efficiency.

On Internet servers, there is an increasing demand for provisioning of fine-grained quality of service (QoS). Feedback control provides a sound way for such QoS provisioning by amortizing the effect of disturbances from unpredictable and dynamic server workloads. The performance of a feedback control approach is dependent on its agility to the workload changes. Queueing-model based prediction is effective in improving the agility. In this paper, we present a feedback control approach to manage the processing rate on Internet servers for QoS provisioning with respect to slowdown. Slowdown is the ratio of a request's queueing delay to its service time and reflects the requirement that all requests should be treated equally regardless of their resource requirements. The approach adjusts the processing rate of a class using an integral feedback controller according to measured deviations from the target slowdown ratios. For agility, a queueing-model based predictor is integrated to periodically estimates the processing rate of the class. The experimental results under various environment settings demonstrate the approach's effectiveness, robustness, and agility in providing proportional slowdown differentiation services.

Due to the open and dynamics nature of ubiquitous computing environments and services, quantitative service differentiation is needed to provide controllable quality of service (QoS) levels to meet changing system configuration and resource availability and to satisfy different requirements of applications and users. A proportional differentiation model was proposed in the DiffServ context, which states that QoS factors of certain classes of aggregated traffic be proportional to their differentiation weights. While it provides compelling proportionality fairness to clients, it lacks of the support of a server-side QoS optimization with respect to the resource allocation. In this paper, we propose and promote a square-root proportional differentiation model for delay-sensitive Internet services. Interestingly, both popular QoS factors with respect to delay, queueing delay and slowdown, are reciprocally proportional to the allocated resource usages. We formulate the problem of quantitative service differentiation as a resource allocation optimization towards the minimization of system delay, defined as the sum of weighted responsiveness of client request classes. We prove that the optimization-based resource allocation scheme essentially provides square-root proportional service differentiation to clients. We then propose a generalized rate-based resource allocation approach. Simulation results demonstrate that the approach provides quantitative service differentiation at a minimum cost of system delay

It is important to guarantee quality of service (QoS) in heavy-loaded Web servers. In this paper, we present a framework eQoS that monitors and controls the service quality delivered to clients in Web servers. Because existing approaches have weak control over delivered service quality, we present two control approaches to manage server resources: a model-based approach based on queuing theory and feedback control theory; and a model-free control approach that based on fuzzy control theory within the framework. The model-based approach is desirable when Web servers can be characterized as an M/G/1 queuing system. Otherwise, the model-free approach is appropriate. Experiments have been conducted to demonstrate the effectiveness of these two control approaches. We compared the model-free approach with queuing-theoretic approach. We also carried out experiments to compare the model-free approach with static and adaptive feedback control approaches and static fuzzy control approach. Experimental results show that the model-based and model-free approaches outperform their competitors with smaller variance.

The increasing popularity of high-volume performance-critical Internet applications is a challenge for servers to provide individual response-time guarantees. Considering the fact that most Internet applications can tolerate a small percentage of deadline misses, we define delay constraint as a statistical guarantee to relax server resource requirements. A recent decay function model characterizes the relationship between the request delay constraint, deadline misses, and server capacity in a transfer function based filter system. A time-invariant scheduler was proposed to minimize system load variances in support of requests with the same delay constraints. This paper extends the model to support requests with different deadlines and describes an optimal time-variant scheduling policy that minimizes load variances and capacity requirement. The resultant capacity bound is further tightened by utilizing the information of request arrival distribution. Simulation results validate the extended decay function model and show the superiority of the scheduler in comparison with other scheduling algorithms.

Proportional slowdown differentiation (PSD) aims to maintain slowdown ratios between different classes of clients according to their pre-specified differentiation parameters. In this paper, we design a feedback controller to allocate processing rate on Internet servers for PSD. In this approach, the processing rate of a class is adjusted by an integral feedback controller according to the difference between the target slowdown ratio and the achieved one. The initial rate class is estimated based on predicted workload using queueing theory. We implement the feedback controller in an Apache Web server. The experimental results under various environments demonstrate the controller's effectiveness and robustness.

This paper considers packet scheduling with delay constraint that minimizes the average transmission energy expenditure in a wireless environment. There were recent studies that modeled transmission rate as a linear combination of input packet arrivals in the derivation of optimal scheduling policies for independent input arrivals. In this paper, we extend the scheduling policies in two aspects. We first consider the impact of input time-correlation and derive an optimal scheduling policy for energy minimization. It is observed that existing energy optimized scheduling policies save energy by transmitting packets slowly. They may result in unexpected deadline misses. We establish a relationship between the maximum reliable transmission rate and a QoS constraint, characterized by a deadline miss rate. The relationship facilitates the trade-off study of power consumption and delay constraint. Simulation results verify the relationship and show the superiority of the scheduler in comparison with the existing approaches

Proportional delay differentiation (PDD) is an important service model for providing relative differentiated services on the Internet. It aims to maintain pre-specified packet queueing-delay ratios between different classes of traffic at each hop. Existing rate-allocation approaches for PDD services assume the average queueing delay of a class is inversely proportional to its service rate. This assumption is not necessarily valid when the system is not heavily loaded. To provide consistent PDD services under various load conditions, in this paper, we propose a novel rate-allocation approach that applies fuzzy control theory to capture the nonlinear relationship between the queueing delay and the service rate. In the approach, a class's service rate is adjusted according to a set of fuzzy control rules defined over its error (the difference between the target delay ratio and the achieved one), the change of error, and the change of service rate. We prove that the fuzzy control system is stable and the service rate of a class converges to its equilibrium point at steady state. Simulation results demonstrate that, in comparison with other rate-allocation approaches, the fuzzy control approach is able to provide consistent PDD services under wide range load conditions. It is also shown robust under various system conditions, including with multiple classes, changing target delay ratios, changing load conditions, and different traffic patterns.

Server-side resource configuration and allocation for QoS guarantee is a challenge in performance-critical Internet applications. To overcome the difficulties caused by the high-variability and burstiness of Internet traffic, this paper presents a decay function model of request scheduling algorithms for the resource configuration and allocation problem. Under the decay function model, request scheduling is modelled as a transfer-function based filter system that has an input process of requests and an output process of server load. Unlike conventional queueing network models that rely on mean-value analysis for input renewal or Markovian processes, this decay function model works for general time-series based or measurement based processes and hence facilitates the study of statistical correlations between the request traffic, server load, and QoS of requests. Based on the model, we apply filter design theories in signal processing in the optimality analysis of various scheduling algorithms. We reveal a relationship between the server capacity, scheduling policy, service deadline, and other request properties in a formalism and present the optimality condition with respect to the second moments of request properties for an important class of fixed-time scheduling policies. Simulation results verify the relationship and show that optimal fixed-time scheduling can effectively reduce the server workload variance and guarantee service deadlines with high robustness on the Internet.

One of the important issues in building differentiated services is how to support relative differentiated queueing delay services for packets from different classes. This paper presents a new packet scheduling algorithm, called virtual-length, to address this issue. It provides effective control over the relative average queueing delay ratio between two classes of traffic by monitoring their average arrival rates and cumulative queue lengths. The packets of different classes are forwarded according to their weighted queue lengths with respect to predefined proportional delay parameters. The simulation results show that virtualLength is capable of providing controllable and predictable services for classes with different load distributions under various load conditions. In particular, it can provide more accurate control over classes of traffic in short timescales than other packet scheduling algorithms, and comparable service in long timescales. The results also show that the performance of virtualLength is independent of the distributions of packet arrivals and sizes.