A Shapley-value Mechanism for Bandwidth On Demand between Datacenters

postprin

Scheduling of data-intensive workloads in a brokered virtualized environment

Providing performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource management solutions that consider the brokered nature of these workloads, as well as the special demands of their intra-dependent components. In this paper, we present an offline mechanism for scheduling batches of brokered data-intensive workloads, which can be extended to an online setting. The objective of the mechanism is to decide on a packing of the workloads in a batch that minimizes the broker's incurred costs, Moreover, considering the brokered nature of such workloads, we define a payment model that provides incentives to these workloads to be scheduled as part of a batch, which we analyze theoretically. Finally, we evaluate the proposed scheduling algorithm, and exemplify the fairness of the payment model in practical settings via trace-based experiments

Resource management for cost-effective cloud and edge systems

With the booming of Internet-based and cloud/edge computing applications and services,datacenters hosting these services have become ubiquitous in every sector of our economy which leads to tremendous research opportunities. Specifically, in cloud computing, all data are gathered and processed in centralized cloud datacenters whereas in edge computing, the frontier of data and services is pushed away from the centralized cloud to the edge of the network. By fusing edge computing with cloud computing, the Internet companies and end users can benefit from their respective merits, abundant computation and storage resources from cloud computing, and the data-gathering potential of edge computing. However, resource management in cloud and edge systems is complicated and challenging due to the large scale of cloud datacenters, diverse interconnected resource types, unpredictable generated workloads, and a range of performance objectives. It necessitates the systematic modeling of cloud and edge systems to achieve desired performance objectives.This dissertation presents a holistic system modeling and novel solution methodology to effectivelysolve the optimization problems formulated in three cloud and edge architectures: 1) cloud computing in colocation datacenters; 2) cloud computing in geographically distributed datacenters; 3) UAV-enabled mobile edge computing. First, we study resource management with the goal of overall cost minimization in the context of cloud computing systems. A cooperative game is formulated to model the scenario where a multi-tenant colocation datacenter collectively procures electricity in the wholesale electricity market. Then, a two-stage stochastic programming is formulated to model the scenario where geographically distributed datacenters dispatch workload and procure electricity in the multi-timescale electricity markets. Last, we extend our focus on joint task offloading and resource management with the goal of overall cost minimization in the context of edge computing systems, where edge nodes with computing capabilities are deployed in proximity to end users. A nonconvex optimization problem is formulated in the UAV-enabled mobile edge computing system with the goal of minimizing both energy consumption for computation and task offloading and system response delay. Furthermore, a novel hybrid algorithm that unifies differential evolution and successive convex approximation is proposed to efficiently solve the problem with improved performance.This dissertation addresses several fundamental issues related to resource management incloud and edge computing systems that will further in-depth investigations to improve costeffective performance. The advanced modeling and efficient algorithms developed in this research enable the system operator to make optimal and strategic decisions in resource allocation and task offloading for cost savings

Strategic and operational services for workload management in the cloud

In hosting environments such as Infrastructure as a Service (IaaS) clouds, desirable application performance is typically guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated by a service provider for unencumbered use by customers to ensure proper operation of their workloads. Most IaaS offerings are presented to customers as fixed-size and fixed-price SLAs, that do not match well the needs of specific applications. Furthermore, arbitrary colocation of applications with different SLAs may result in inefficient utilization of hosts' resources, resulting in economically undesirable customer behavior. In this thesis, we propose the design and architecture of a Colocation as a Service (CaaS) framework: a set of strategic and operational services that allow the efficient colocation of customer workloads. CaaS strategic services provide customers the means to specify their application workload using an SLA language that provides them the opportunity and incentive to take advantage of any tolerances they may have regarding the scheduling of their workloads. CaaS operational services provide the information necessary for, and carry out the reconfigurations mandated by strategic services. We recognize that it could be the case that there are multiple, yet functionally equivalent ways to express an SLA. Thus, towards that end, we present a service that allows the provably-safe transformation of SLAs from one form to another for the purpose of achieving more efficient colocation. Our CaaS framework could be incorporated into an IaaS offering by providers or it could be implemented as a value added proposition by IaaS resellers. To establish the practicality of such offerings, we present a prototype implementation of our proposed CaaS framework

On the dynamics of valley times and its application to bulk-transfer scheduling

Periods of low load have been used for the scheduling of non-interactive tasks since the early stages of computing. Nowadays, the scheduling of bulk transfers—i.e., large-volume transfers without precise timing, such as database distribution, resources replication or backups—stands out among such tasks, given its direct effect on both the performance and billing of networks. Through visual inspection of traffic-demand curves of diverse points of presence (PoP), either a network, link, Internet service provider or Internet exchange point, it becomes apparent that low-use periods of bandwidth demands occur at early morning, showing a noticeable convex shape. Such observation led us to study and model the time when such demands reach their minimum, on what we have named valley time of a PoP, as an approximation to the ideal moment to carry out bulk transfers. After studying and modeling single-PoP scenarios both temporally and spatially seeking homogeneity in the phenomenon, as well as its extension to multi-PoP scenarios or paths—a meta-PoP constructed as the aggregation of several single PoPs—, we propose a final predictor system for the valley time. This tool works as an oracle for scheduling bulk transfers, with different versions according to time scales and the desired trade-off between precision and complexity. The evaluation of the system, named VTP, has proven its usefulness with errors below an hour on estimating the occurrence of valley times, as well as errors around 10% in terms of bandwidth between the prediction and actual valley trafficThis work has been partially supported by the European Commission under the project H2020 METRO-HAUL (Project ID: 761727