As the adoption of Virtualized Radio Access Networks (vRAN) is gaining momentum in 5 G networks, Mobility Network Operators are considering a Centralized RAN (CRAN) architecture that moves the baseband functions to a far-edge cloud in order to gain dimensioning flexibility, resiliency and improved RAN performance. However, there have been limited studies on the benefits of centralization in improving RAN compute utilization, especially in the context of pooling the compute-intensive Distributed Unit (DU) resources. In this paper, we present the first study on the benefits of pooling in improving DU server utilization. Using longitudinal traces from a real-world 5G network, we show that significant Capex and Opex gains of $\mathbf{8 4 \%}$ and $\mathbf{9 4 \%}$, respectively, can be obtained through fine-grained pooling at a granularity of 1 second. We also present an affinitybased and dynamic pooling algorithm that can reduce the pooling overheads while still achieving significant pooling gains.

We study the Traffic Steering (TS) problem in Open Radio Access Network (O-RAN), leveraging its RAN Intelligent Controller (RIC), in which RAN configuration parameters of cells can be jointly and dynamically optimized in near-real-time. To address the TS problem, we propose a novel Cascade Reinforcement Learning (CaRL) framework, where we propose state space factorization and policy decomposition to mitigate the need for large complex models and well-labeled datasets. For each sub-state space, an RL sub-policy is trained to optimize the Quality of Service (QoS). To apply CaRL to new network areas, we propose a knowledge transfer approach to initialize a new sub-policy based on knowledge learned by the trained policies. To evaluate CaRL, we build a data-driven and scalable RIC Digital Twin (DT) that is modeled using real-world data, including network setup, user geo-distribution, and traffic demand, among others, from a tier-1 RAN operator. We evaluated CaRL in two DT scenarios representing two different US cities and compared its performance with business-as-usual policy as a baseline and other competing optimization approaches (i.e., heuristic and Q-table algorithms). Furthermore, we have conducted a field trial with the RAN operator to evaluate the performance of CaRL in two areas in the Northeast US regions.

This paper explores network-side cellular user localization using fingerprints created from the angle measurements enabled by 5G. Our key idea is a binning-based fingerprinting technique that leverages multipath propagation to create fingerprint vectors based on angles of arrival of signals along multiple paths at each user. In network simulations that recreate urban environments with 3D building geometry and base station locations for a major city, our binning-based fingerprinting for 5G achieves significantly lower localization errors with a single base station than signal strength-based fingerprinting for LTE.

Ensuring high availability for applications despite unpredictable cloud component failure events is a well-known problem in managing cloud infrastructure. One proposed solution uses a VM redundancy approach, reserving cloud resources for backup VMs that can substitute for primary ones in case of a failure event. However, this solution decreases the cloud resource utilization, since the backup resources usually remain idle. In this paper, we propose ECHO, a cloud resource management system that overbooks these backup VMs by optimizing the overbooking rate tradeoff between maximizing the cloud resource utilization, and thus maximizing the cloud provider's revenue; and improving application availability, thus satisfying users. Specifically, ECHO first obtains the optimal overbooking rate required to achieve a cloud provider's desired resource utilization level. It then computes the optimal (required) number of backup VMs that are required to maintain a given application availability level. Our extensive experimental and simulation results show that using ECHO can increase the number of accepted applications with satisfied availability by about 30%, while increasing the defined resource utilization at the same time.

VM redundancy is the foundation of resilient cloud applications. While active-active approaches combined with load balancing and autoscaling are usually resource efficient, the stateful nature of many cloud applications often necessitates 1+1 (or 1+n) active-standby approaches. Keeping the standbys, however, could result in inefficient utilization of cloud resources. We explore an intriguing cloud-based solution, where standby VMs from active-standby applications are selectively overbooked to reduce resources reserved for failures. The approach requires careful VM placement to avoid a situation where multiple standby VMs activate simultaneously on the same host and thus cannot get the full resource entitlement. Indeed today's clouds do not have this visibility to the applications. We rectify this situation through ShadowBox, a novel redundancy-aware VM scheduler that optimizes the placement and activation of standby VMs, while assuring applications' resource entitlements. Evaluation on a large-scale cloud shows that ShadowBox can significantly improve resource utilization (i.e., more than 2.5 times than traditional approaches) while minimizing the impact on applications' entitlements.

Network clouds are typically private clouds owned by the network provider, consisting of a large number of geo-distributed sites with heterogeneous capabilities and small capacities. Each of these small clouds often run specialized service chains of Virtual Network Functions (VNFs), which need to meet strict Service Level Objectives (SLOs), especially along the lines of availability (e.g., First responder services). Hence, VNFs in such thinly provisioned clouds may need to be moved (rehomed), both within and across sites, much more frequently than in traditional public clouds (like Amazon's EC2 cloud), in order to meet the performance SLOs, when reacting to various cloud events like hotspots, interference from co-located VMs, failures and upgrades. Rehoming is also required by the infrastructure (platform) providers for various other reasons such as consolidation of resources for saving energy and improving the platform utilization. In this paper, we propose a model-based approach to show that naive strategies for rehoming, applied uniformly across all VNFs of the service chain, are often sub-optimal when considering different metrics like user-perceived service disruption time and the time taken to complete the rehoming action. Our model leverages the transparency between the services and platforms on private clouds (grayness), and provides appropriate rehoming recommendations based on various factors including service characteristics and runtime platform dynamics. We validate our models using a simple, yet ubiquitously deployed service chain, and using out-of-the-box rehoming options provided by Openstack, the most commonly used open-source cloud. Our results show that our graybox approach is able to achieve significant reductions in service disruption times and time taken for the rehoming action.

The performance of n-tier web-facing applications often suffer from response time long-tail problem. With relatively low resource utilization (less than 50%) and the majority of requests returning within a few milliseconds, a non-negligible num-ber of normally short requests may take seconds to return. We propose the millibottleneck theory of performance bugs (that lead to long-tail problems). Several case studies have confirmed the millibottlenecks (that last a few tens to hundreds of milliseconds) as causal agents of long requests. A concrete example (garbage collection) illustrates the experimental verification of millibottlenecks. An open source fine-grain monitoring toolkit is being devel-oped to facilitate the experimental research on millibottlenecks.

A complex cloud application consists of virtual machines (VMs) running software such as web servers and load balancers, storage in the form of disk volumes, and network connections that enable communication between VMs and between VMs and disk volumes. The application is also associated with various requirements, including not only quantities such as the sizes of the VMs and disk volumes, but also quality of service (QoS) attributes such as throughput, latency, and reliability. This paper presents Ostro, an Open Stack-based scheduler that optimizes the utilization of data center resources, while satisfying the requirements of the cloud applications. The novelty of the approach realized by Ostro is that it makes holistic placement decisions, in which all the requirements of an application -- described using an application topology abstraction -- are considered jointly. Specific placement algorithms for application topologies are described including an estimate-based greedy algorithm and a time-bounded A algorithm. These algorithms can deal with complex topologies that have heterogeneous resource requirements, while still being scalable enough to handle the placement of hundreds of VMs and volumes across several thousands of host servers. The approach is evaluated using both extensive simulations and realistic experiments. These results show that Ostro significantly improves resource utilization when compared with naive approaches.

As cloud computing has become popular and the number of cloud providers has proliferated over time, the first barrier to cloud users will be how to accurately estimate performance capabilities of many different clouds and then, select a right one for given complex workload based on estimates. Such cloud capability estimation and selection can be a big challenge since most clouds can be considered as black-boxes to cloud users by abstracting underlying infrastructures and technologies. This paper describes a cloud recommender system to recommend an optimal cloud configuration to users based on accurate estimates. To achieve this, our system generates the capability vector that consists of relative performance scores of resource types (e.g., CPU, memory, and disk) estimated for given user workload using benchmarks. Then, a search algorithm has been developed to identify an optimal cloud configuration based on these collected capability vectors. Experiments show our approach accurately estimate the performance capability (less than 10% error) while scalable in large search space.

Federated cloud enables a workflow to be deployed in multiple private and public clouds. By facilitating external cloud-based services to execute sub-tasks of the workflow, service workflow owners can reduce the cost of executing the workflow, while meeting a performance requirement, since those cloud-based services can be more cost efficient and have better performance than internal ones. However, due to inter-dependencies between sub-tasks, the complexity of the workflow, and the heterogeneity of clouds, it is a challenge to achieve the optimal tradeoff between cost and performance. This paper presents a novel workflow scheduler designed to achieve the optimal end-to-end execution time and cost when deploying such complex workflows in heterogeneous computing nodes in clouds. Specifically, our scheduling algorithm addresses the tradeoff between the execution cost, the computing time, and the data transfer delay between sub-tasks. Our scheduler can handle complex workflows that contain recursively paralleled sub-flows caused by branch and merging sub-tasks. Experiments indicate that our scheduler can efficiently compute the near optimal deployment compared with greedy and evolutionary algorithms for both end-to-end execution time and corresponding cost.

The proliferation of cloud computing can imply a barrier to cloud users. When deploying their complex workloads into clouds, cloud users are typically overwhelmed by too many technical choices. Moreover, underlying technologies and pricing mechanisms of clouds vary and are not transparent to them. Consequently, it is hard for cloud users to capture the monetary and performance implications of their workload deployments. This paper introduces a cloud recommendation platform, referred to as Cloud Advisor. It allows cloud users to explore various cloud configurations recommended based on user preferences such as budget, performance expectation, and energy saving for given workload. Then, it allows cloud users to compare offered price and performance with other clouds' offerings for the workload. By providing transparent comparisons, it can also support cloud provider to develop a competitive pricing strategy such as price reduction driven by energy efficiency. We have applied the proposed platform for recommendation from a real data center and some external clouds.

Parallelization of big-data analytics services over a federation of heterogeneous clouds has been considered to improve performance. However, contrary to common intuition, there is an inherent tradeoff between the level of parallelism and the performance for big-data analytics principally because of a significant delay for big-data to get transferred over the network. The data transfer delay can be comparable or even higher than the time required to compute data. To address the aforementioned tradeoff, this paper determines: (a) how many and which computing nodes in federated clouds should be used for parallel execution of big-data analytics; (b) opportunistic apportioning of big-data to these computing nodes in a way to enable synchronized completion at best-effort performance; and (c) sequence of apportioned, different sizes of big-data chunks to be computed in each node so that transfer of a chunk is overlapped as much as possible with the computation of the previous chunk in the node. In this regard, Maximally Overlapped Bin-packing driven Bursting (MOBB) algorithm is proposed, which improve the performance by up to 60% against existing approaches.

Business Process Management (BPM) software provides visibility into business processes in organizations of all sizes and helps increase process efficiency continuously. However, the time and effort involved in modeling, deploying and executing a business process is tremendous and as a result organizations struggle to agilely adapt business processes to dynamic business requirements. On the other hand, the growing popularity of cloud computing poses opportunities and challenges on how business processes can leverage resource outsourcing and elasticity. In light of the above, this paper presents a business process management platform that assists business analysts lacking necessary programming expertise by automating manual steps and providing guidance and recommendations to quickly and efficiently design, implement, deploy and execute business processes in a hybrid cloud environment.

Virtual machine technology enables agile system deployments in which software components can be cheaply moved, replicated, and allocated hardware resources in a controlled fashion. This paper examines how these facilities can be used to provide enhanced solutions to the classic problem of ensuring high availability while maintaining performance. By regenerating software components to restore the redundancy of a system whenever failures occur, we achieve improved availability compared to a system with a fixed redundancy level. Moreover, by smartly controlling component placement and resource allocation using information about application control flow and performance predictions from queuing models, we ensure that the resulting performance degradation is minimized. We consider an environment in which a collection of multitier enterprise applications operates across multiple hosts, racks, clusters, and data centers to maximize failure independence. Simulation results show that our proposed approach provides better availability and significantly lower degradation of system response times compared to traditional approaches.

Server consolidation based on virtualization is an important technique for improving power efficiency and resource utilization in cloud infrastructures. However, to ensure satisfactory performance on shared resources under changing application workloads, dynamic management of the resource pool via online adaptation is critical. The inherent tradeoffs between power and performance as well as between the cost of an adaptation and its benefits make such management challenging. In this paper, we present Mistral, a holistic controller framework that optimizes power consumption, performance benefits, and the transient costs incurred by various adaptations and the controller itself to maximize overall utility. Mistral can handle multiple distributed applications and large-scale infrastructures through a multi-level adaptation hierarchy and scalable optimization algorithm. We show that our approach outstrips other strategies that address the tradeoff between only two of the objectives (power, performance, and transient costs).

Control theory has been utilized in recent years to manage the resources in virtualized environment for applications with time-varying resource demand. The systems under control, including the servers and the applications, are taken as black-boxes, and the controllers are generally expected to be adaptive to the underline systems. However, little attention has been paid to the behaviors of the applications themselves, and most of time, single performance target such as the mean response time threshold has been tracked. In this paper, we experimentally show that more than one performance metrics have to be considered to characterize the quality of service that the end users receive when the performance is managed through dynamic resource allocation. Moreover, the behavior of the applications, especially that of the workload generators has significant effect on the quality of the service. Our study provides insights and guidance for end-to-end performance management problem in virtualized environment.

BPEL is emerging as an open-standards language for Web service composition. However, its procedural style can lead to inflexible and tangled code for managing a crosscutting aspect - synchronization constraints that define permissible sequences of execution for activities in a process. In this paper, we present DSCWeaver, a tool that enables a synchronization-aspect extension to BPEL. It uses DSCL, a synchronization expression language, to specify constraints. DSCL has the desirable features of declarative syntax, fine granularity, and validation support. A designer can use DSCL to describe and validate the synchronization behavior and rely on DSCWeaver to generate BPEL code. We demonstrate the advantages of our approach in a service deployment process and evaluate its performance using two metrics: lines of code (LoC) and places to visit (PtV). Evaluation results show that our approach can effectively reduce development effort of process designers while providing performance competitive to un-woven BPEL code

In this work, the performance of various machine learning classifiers with regard to bottleneck detection in enterprise, multi-tier applications governed by service level objectives is described. Specifically, in this paper, it demonstrates the effectiveness of three classifiers, a tree-augmented Naive Bayesian network, a J48 decision tree, and LogitBoost, using our bottleneck detection process, which delves into a new area of performance analysis based on the trends of metrics (first order derivative) rather than the metric value itself. Furthermore, the efficiency of each classifier by measuring the convergence speed, or the number of staging trials required in order to provide positive results is illustrated. Finally, the effectiveness of the classifiers used in the bottleneck detection process as each classifier strongly identifies the enterprise system bottleneck

The increasing complexity of enterprise and distributed systems demands automated design, testing, deployment, and monitoring of applications. Testing, or staging, in particular poses unique challenges. In this paper, we present the Elba project and Mulini generator. The goal of Elba is creating automated staging and testing of complex enterprise systems before deployment to production. Automating the staging process lowers the cost of testing applications. Feedback from staging, especially when coupled with appropriate resource costs, can be used to ensure correct functionality and provisioning for the application. The Elba project extracts test parameters from production specifications (such as SLAs) and deployment specifications, and via the Mulini generator, creates staging plans for the application. We then demonstrate Mulini on an example application, TPC-W, and show how information from automated staging and monitoring allows us to refine application deployments easily based on performance and cost.

IT today is driven by the trend of increasing scale and complexity. Utility and Grid computing models, PlanetLab, and traditional data centers, are reaching the scale of thousands of computers. Installed software consists of dozens of interdependent applications and services. As the complexity and scale of these systems continues to grow, it becomes increasingly difficult to administer and manage them. At the same time, the service deployment technologies are still based on scripts and configuration files with minimal ability to express dependencies, to document and to verify configurations. This results in hard-to-use and erroneous system configurations. Language- and model-based tools, such as SmartFrog and Radia, are proposed for addressing these deployment challenges, but it is unclear whether they are beneficial over traditional solutions. In this paper, we quantitatively compare manual, script-, language-, and model-based deployment solutions as a function of scale, complexity, and susceptibility to change. We also qualitatively compare them in terms of expressiveness and barrier to first use. We demonstrate that script-based solutions are well matched for large scale deployments, language-based for services of large complexity, and model-based for dynamic changes to the design. Finally, we offer a table summarizing rules of thumb regarding which solution to use in which case, subject to deployment needs.

Distributed applications typically interact with a number of heterogeneous and autonomous components that evolve independently. Methodical development of such applications can benefit from approaches based on domain-specific languages (DSLs). However, the evolution and customization of heterogeneous components introduces significant challenges to accommodating the syntax and semantics of a DSL in addition to the heterogeneous platforms on which they must run. In this paper, we address the challenge of implementing code generators for two such DSLs that are flexible (resilient to changes in generators or input formats), extensible (able to support multiple output targets and multiple input variants), and modular (generated code can be re-written). Our approach, Clearwater, leverages XML and XSLT standards: XML supports extensibility and mutability for in-progress specification formats, and XSLT provides flexibility and extensibility for multiple target languages. Modularity arises from using XML meta-tags in the code generator itself, which supports controlled addition, subtraction, or replacement to the generated code via XML-weaving. We discuss the use of our approach and show its advantages in two non-trivial code generators: the Infopipe Stub Generator (ISG) to support distributed flow applications, and the Automated Composable Code Translator to support automated distributed application deployment. As an example, the ISG accepts as input an XML description and generates output for C, C++, or Java using a number of communications platforms such as sockets and publish-subscribe.