Network slicing is emerging as a promising method to provide sought after versatility and flexibility to cope with ever-increasing demands. To realize such potential advantages and to meet the challenging requirements of various network slices in an on-demand fashion, we need to develop an agile and distributed mechanism for resource provisioning to different network slices in a heterogeneous multi-resource multi-domain mobile network environment. We formulate inter-domain resource provisioning to network slices in such an environment as an optimization problem which maximizes social welfare among network slice tenants (so that maximizing tenants’ satisfaction), while minimizing operational expenditures for infrastructure service providers at the same time. To solve the envisioned problem, we implement an iterative auction game among network slice tenants, on one hand, and a plurality of price-taking subnet service providers, on the other hand. We show that the proposed solution method results in a distributed privacy-saving mechanism which converges to theoptimal solutionof the described optimization problem. In addition to providing analytical results to characterize the performance of the proposed mechanism, we also employ numerical evaluations to validate the results, demonstrate convergence of the presented algorithm, and show the enhanced performance of the proposed approach (in terms of resource utilization, fairness and operational costs) against the existing solutions.

We propose a new approach for proportionally fair sharing of radio access resources (i.e., frequency carriers) in a 5G mobile environment. In particular, we formulate the multi-carrier scheduling problem as a game (played by different carriers) which results in proportionally fair user service rates at its equilibrium. We subsequently present a globally convergent algorithm to find the equilibrium point of the devised game, based on which we develop a frame-by-frame scheduling mechanism. We further discuss how to extend the proposed scheduling scheme to a multi-tenant setting in order to efficiently implement radio access network slicing in a 5G mobile system.

In this paper, we study cost-efficient and fair allocation of multiple types of resources in an environment of heterogeneous and self-organizing servers. To address this problem, we formulate an optimization problem which aims at minimizing the operational costs for all servers, while providing fairness across different users. We propose a fully distributed implementation to solve this problem. The proposed mechanism is shown to achieve envy-freeness among different users. Furthermore, we show how it captures the trade-off between cost-efficiency and fairness. We employ numerical experiments to show the effectiveness of our proposed mechanism in reducing operational costs for a geo-distributed data-center.

We first consider the static problem of allocating resources to (i.e., scheduling) multiple distributed application frameworks, possibly with different priorities and server preferences, in a private cloud with heterogeneous servers. Several fair scheduling mechanisms have been proposed for this purpose. We extend prior results on max-min fair (MMF) and proportional fair (PF) scheduling to this constrained multiresource and multiserver case for generic fair scheduling criteria. The task efficiencies (a metric related to proportional fairness) of max-min fair allocations found by progressive filling are compared by illustrative examples. In the second part of this paper, we consider the online problem (with framework churn) by implementing variants of these schedulers in Apache Mesos using progressive filling to dynamically approximate max-min fair allocations. We evaluate the implemented schedulers in terms of overall execution time of realistic distributed Spark workloads. Our experiments show that resource efficiency is improved and execution times are reduced when the scheduler is "server specific" or when it leverages characterized required resources of the workloads (when known).

Efficient and fair allocation of multiple types of resources is a crucial objective in a cloud/distributed computing cluster. Users may have diverse resource needs. Furthermore, diversity in server properties/capabilities may mean that only a subset of servers may be usable by a given user. In platforms with such heterogeneity, we identify important limitations in existing multi-resource fair allocation mechanisms, notably Dominant Resource Fairness and its follow-up work. To overcome such limitations, we propose a new server-based approach; each server allocates resources by maximizing a per-server utility function. We propose a specific class of utility functions which, when appropriately parameterized, adjusts the trade-off between efficiency and fairness, and captures a variety of fairness measures (such as our recently proposed Per-Server Dominant Share Fairness ). We establish conditions for the proposed mechanism to satisfy certain properties that are generally deemed desirable, e.g., envy-freeness, sharing incentive, bottleneck fairness, and Pareto optimality. To implement our resource allocation mechanism, we develop an iterative algorithm which is shown to be globally convergent. Subsequently, we show how the proposed mechanism could be implemented in a distributed fashion. Finally, we carry out extensive trace-driven simulations to show the enhanced performance of our proposed mechanism over the existing ones.

Users of cloud computing platforms pose different types of demands for multiple resources on servers (physical or virtual machines). Besides differences in their resource capacities, servers may be additionally heterogeneous in their ability to service users - certain users' tasks may only be serviced by a subset of the servers. We identify important shortcomings in existing multi-resource fair allocation mechanisms - Dominant Resource Fairness (DRF) and its follow up work - when used in such environments. We develop a new fair allocation mechanism called Per-Server Dominant-Share Fairness (PS-DSF) which we show offers all desirable sharing properties that DRF is able to offer in the case of a single “resource pool” (i.e., if the resources of all servers were pooled together into one hypothetical server). We evaluate the performance of PS-DSF through simulations. Our evaluation shows the enhanced efficiency of PS-DSF compared to the existing allocation mechanisms. We argue how our proposed allocation mechanism is applicable in cloud computing networks and especially large scale data-centers.

We describe a scheduler for multiple servers shared among different packet-flows, where each packet-flow may be served by only a subset of available (preferred) servers. The scheduler allocates tokens to flows in a round-by-round manner, where token allocation to flows at the beginning of each round is weighted max-min fair. We present a packet scheduling scheme where when a server becomes free, it is allocated to serve the HOL packet of an eligible flow with the maximum remaining tokens. The scheduling algorithm is applicable even when the capacity of servers are not known a priori and may vary over duration of a round. Numerical examples are given to illustrate that the scheduler itself is weighted max-min fair.

In Cognitive Radio (CR), secondary radios are allowed to use the spectrum allocated to primary radios (PR) only if the spectrum is sensed to be unused by the PR users. Cooperative spectrum sensing is a sensing methodology in which secondary users send their detection results to a central fusion node for global decision. It has been shown that for single node detection, SNR wall provides a hard threshold on the sensed SNR for robust signal detection. In this paper, asymptotic performance of cooperative spectrum sensing with Hard Decision Combining (HDC) will be studied under noise uncertainty. It is first shown that under noise uncertainty, SNR wall is yet a substantial constraint on the performance of the cooperative spectrum sensing when applying HDC. We then show that k-out-of-n fusion rule with finite k (e.g., “OR” rule) does not result in robust detection of the PR signal, even for SNR values greater than SNR wall. Finally, it is shown that for SNR values greater than the SNR wall, the PR signal can be robustly detected by the k-out-of-n fusion rule with finite n - k (e.g., “AND” rule).