Network slicing allows Telecom Operators (TOs) to support service provisioning with diverse Service Level Agreements (SLAs). The combination of network slicing and Open Radio Access Network (RAN) enables TOs to provide more customized network services and higher commercial benefits. However, in the current Open RAN community, an open-source end-to-end slicing solution for 5G is still missing. To bridge this gap, we developed ORANSlice, an open-source network slicing-enabled Open RAN system integrated with popular open-source RAN frameworks. ORANSlice features programmable, 3GPP-compliant RAN slicing and scheduling functionalities. It supports RAN slicing control and optimization via xApps on the near-real-time RAN Intelligent Controller (RIC) thanks to an extension of the E2 interface between RIC and RAN, and service models for slicing. We deploy and test ORANSlice on different O-RAN testbeds and demonstrate its capabilities on different use cases, including slice prioritization and minimum radio resource guarantee.

optimizing power consumption of 5G systems and next generation technology deployments is a critical problem. It is essential that the solution for optimizing power consumption takes into account the tradeoffs with maintaining service level agreement (SLA). Mobile network operator (MNO) may have different priorities for the objectives of saving power and for maintaining SLA, which depends on factors, such as customer contract, location, time of day, type of traffic, etc. In this paper, we design an intelligent solution using switching cells on-off action to save power, using machine learning (ML)/ deep learning (DL) methods to forecast future traffic load. We firstly identify the problem of training imbalance in traffic load prediction due to data imbalance in real cellular networks, and MNO preferences for the competing objectives of saving power and SLA maintenance. We then propose a novel solution that incorporates Balancing Loss Function, which addresses the training imbalance problem. Compared with the performance of previous approaches such as Mean Square Error (MSE) minimization traffic forecast based methods, we demonstrate using network field data that our method is able to achieve upto 3X improvement in service quality outage, with fairly similar power savings.

As demand for wireless mobile connectivity continues to explode, cellular network infrastructure capacity requirements continue to grow. While 5G tries to address capacity requirements at the radio layer, the load on the cellular core network infrastructure (called Enhanced Packet Core (EPC)) stresses the network infrastructure. Our work examines the architecture, protocols of current cellular infrastructures and the workload on the EPC. We study the challenges in dimensioning capacity and review the design alternatives to support the significant scale up desired, even for the near future. We breakdown the workload on the network infrastructure into its components-signaling event transactions; database or lookup transactions and packet processing. We quantitatively show the control plane and data plane load on the various components of the EPC and estimate how future 5G cellular network workloads will scale. This analysis helps us to understand the scalability challenges for future 5G EPC network components. Other efforts to scale the 5G cellular network take a system view where the control plane is separated from the data path and is terminated on a centralized SDN controller. The SDN controller configures the data path on a widely distributed switching infrastructure. Our analysis of the workload informs us on the feasibility of various design alternatives and motivates our efforts to develop our clean-slate approach, called CleanG.

Network function virtualization (NFV) promises significant cost savings, flexibility and ease of deployment. However, potential challenges in implementing virtualized network elements that can support real-world performance requirements are still an open question. For example, traditional telecom networks have a lot of complex interdependencies that can affect performance. In this paper, we study the potential bottlenecks in virtualizing cellular core network functions. Using a combination of analysis and experimentation, we quantify the impact of software-based EPC elements on various metrics including physical processing, memory, IO, and bandwidth resource requirements. We use production grade, software-based cellular network elements running on general purpose Linux servers, driven by a variety of realistic workloads derived from a realworld cellular network, to examine the combined effects of control and data planes on an LTE enhanced packet core (EPC). In particular, we discover that the SGW handles about 33% of the control plane transactions and is a potential source for performance bottlenecks as a result of the interdependencies between control and data plane processing. Our results indicate that simply replacing existing EPC elements with virtualized equivalents can have severe performance bottlenecks and that virtualized EPC elements need to be carefully designed.

Network middleboxes must offer high availability, with automatic failover when a device fails. Achieving high availability is challenging because failover must correctly restore lost state (e.g., activity logs, port mappings) but must do so quickly (e.g., in less than typical transport timeout values to minimize disruption to applications) and with little overhead to failure-free operation (e.g., additional per-packet latencies of 10-100s of us). No existing middlebox design provides failover that is correct, fast to recover, and imposes little increased latency on failure-free operations. We present a new design for fault-tolerance in middleboxes that achieves these three goals. Our system, FTMB (for Fault-Tolerant MiddleBox), adopts the classical approach of "rollback recovery" in which a system uses information logged during normal operation to correctly reconstruct state after a failure. However, traditional rollback recovery cannot maintain high throughput given the frequent output rate of middleboxes. Hence, we design a novel solution to record middlebox state which relies on two mechanisms: (1) 'ordered logging', which provides lightweight logging of the information needed after recovery, and (2) a `parallel release' algorithm which, when coupled with ordered logging, ensures that recovery is always correct. We implement ordered logging and parallel release in Click and show that for our test applications our design adds only 30$\mu$s of latency to median per packet latencies. Our system introduces moderate throughput overheads (5-30%) and can reconstruct lost state in 40-275ms for practical systems.

The increase in on-chip core counts in Chip Multi Processors (CMPs) has led to the adoption of interconnects such as Mesh and Torus, which consume an increasing fraction of the chip power. Moreover, as technology and voltage continue to scale down, static power consumes a larger fraction of the total power; reducing it is increasingly important for energy proportional computing. Currently, processor designers strive to send under-utilized cores into deep sleep states in order to reduce idling power and improve overall energy efficiency. However, even in state-of-the-art CMP designs, when a core goes to sleep the router attached to it remains active in order to continue packet forwarding. In this paper, we propose Router Parking - selectively power-gating routers attached to parked cores. Router Parking ensures that network connectivity is maintained, and limits the average interconnect latency impact of packet detouring around parked routers. We present two Router Parking algorithms - an aggressive approach to park as many routers as possible, and a conservative approach that parks a limited set of routers in order to keep the impact on latency increase minimal. Further, we propose an adaptive policy to choose between the two algorithms at run-time. We evaluate our algorithms using both synthetic traffic as well as real workloads taken from SPEC CPU2006 and PARSEC 2.1 benchmark suites. Our evaluation results show that Router Parking can achieve significant savings in the total interconnect energy (average of 32%, 40% and 41% for the synthetic, SPEC CPU2006, and PARSEC 2.1 workloads, respectively).

With the fast development of highly-integrated distributed systems (cluster systems), designers face interesting memory hierarchy design choices while attempting to avoid the notorious disk swapping. Swapping to the free remote memory through Memory Collaboration has demonstrated its cost-effectiveness compared to over provisioning the cluster for peak load requirements. Recent memory collaboration studies propose several ways on accessing the under-utilized remote memory in static system configurations, without detailed exploration of the dynamic memory collaboration. Dynamic collaboration is an important aspect given the run-time memory usage fluctuations in clustered systems. Further, as the interest in memory collaboration grows, it is crucial to understand the existing performance bottlenecks, overheads, and potential optimization. In this paper we address these two issues. First, we propose an Autonomous Collaborative Memory System (ACMS) that manages memory resources dynamically at run time to optimize performance. We implement a prototype realizing the proposed ACMS, experiment with a wide range of real-world applications, and show up to 3× performance speedup compared to a non-collaborative memory system without perceivable performance impact on nodes that provide memory. Second, we analyze, in depth, the end-to-end memory collaboration overhead and pinpoint the corresponding bottlenecks.

Battery life remains to be a critical competitive metric for today's mobile platforms that offer ubiquitous connectivity through their wireless communication interfaces. With most usage models being driven by always-on communication activities, e.g, Internet video streaming, web browsing, etc., it is imperative to understand the impact of network activities on the overall platform power, and optimize power consumption for such activities. As shown by our investigation, various real-world network-driven workloads exhibit bursty and random behavior, which motivates our work on regulating and coalescing incoming packets to reduce platform wake events. To understand the performance impact of packet coalescing, we conduct an extensive investigation to study how coalescing may affect the throughput and user experience. Armed with the deep understandings, we propose, implement and evaluate an Adaptive Traffic Coalescing (ATC) scheme that monitors the incoming traffic at the Network Interface Card (NIC), and adaptively coalesces the packets for a limited duration in the NIC buffer, thus requiring no network or eco-system support. The proposed ATC scheme effectively reduces platform wake events, and enables the platform to enter and stay in the low-power state longer for energy efficiency. We have implemented the scheme in commercial wireless NICs. Using various mobile platforms, we evaluate the power savings and performance impact of the proposed ATC scheme. Experiments show that ATC achieves significant power saving for major platform components, around 20% for real-world Internet workloads, without impacting performance and user experience.

Today's platforms offer ubiquitous network connectivity through one or more communication interfaces and while the communication devices consume a small portion of the overall platform power the impact of network connectivity and individual packet processing on the overall platform energy consumption is significant, due to the non-deterministic characteristics of network traffic. In this paper we show that significant energy is unnecessarily wasted at the platform level while it is idle and connected to one or more networks, especially unlicensed networks like WiFi or Ethernet, because the system is constantly processing what we term "background/noise" traffic. We quantify the negative impact of network connectivity on platform energy-efficiency and provide novel techniques to mitigate this impact. We implemented these mitigation techniques in our wireless network interface cards and our results indicate that when our scheme is used the total platform sleep time increases by up to 40% with no performance degradation and no user experience impact.

Mobile platforms (e.g. laptops) offer ubiquitous network connectivity through its wireless communication interfaces, with most of usage models driven by always-on communication activities. However this in turn creates significant power challenges as the battery life is a critical resource for all mobile platforms. While the communication device itself consumes a relatively small portion of the total power, the impact of communication on the overall platform power is significant, due to the non-deterministic nature of the network traffic, which tends to keep the platform busy more than necessary. In this paper, we extensively investigate characteristics of various real- world network-centric workloads, e.g., VoIP, web browsing, etc., and their impact on the mobile platform in terms of power consumption and energy. Based on the understandings, we propose, implement and evaluate an Interrupt /DMA (Direct Memory Access) Coalescing (IDC) scheme at the wireless network interface card (NIC). This scheme effectively reduces platform wakeups due to incoming network traffic, Another advantage of the scheme is that it requires only NIC modification at the receiving node, and is transparent to the user and the network. Using a commercial NIC with a laptop testing platform, we evaluate the power savings and performance impact of our proposed scheme. The measurements show that our scheme achieves significant platform power saving, around 25% of platform components, without impacting user experience.

Network selection is the decision process for a mobile terminal to handover between homogeneous or heterogeneous networks. This handover decision can be either mobile or network initiated. Today's decision is mainly based on the received signal strength (RSS). In the future as the number of available networks increase the selection process must evaluate additional factors such as monetary cost, offered services, network conditions, required energy to operate in a network, system conditions, user and operator's preferences. Upcoming IEEE or 3GPP standards provide facilities such as network maps, handover negotiation messages, network and client event reports and message exchange like handover triggers and handover negotiation. In this paper we investigate the use of a cost function to perform an optimal network selection using information provided by these standards, such as network coverage map or network properties. The cost function provides flexibility to balance different factors in the decision making, and our research is focused on improving both seamlessness and energy efficiency of the device and handovers. We evaluated our approach based on usage scenarios over WiFi, WiMax, and 3G networks using captured signal strength traces. The results of our simulations and early implementation show that our schemes select the optimal networks and handovers were triggered at appropriate times to increase overall network connectivity as compared to traditional triggering schemes, while at the same time optimizing energy consumption of multi-radio devices.

The concept of spectrum sensing is, in the broad sense, the ability to detect and interpret other signals in the band and take appropriate actions by implementing adaptive techniques in the upper layers in addition to traditional interference cancellation techniques applied in the PHY layer. In this paper we present algorithms to detect interference from microwave oven for WLAN devices in the 2.4 GHz spectrum and methods to mitigate the impact from the interference. The concept can be extended to other coexistence issues as well as spectrum sharing between primary and secondary users.

Multi-radio devices provide end-users the ability to achieve ubiquitous and seamless connectivity anytime, anywhere across heterogeneous networks. Minimal service disruption while a device roams across networks is key for a successful deployment. Todaypsilas roaming decisions are reactive and traditionally based on thresholds for signal strength or other radio properties, leading to undesirable handover delays. This paper introduces an Intel/BT client architecture to support seamless mobility for multi-radio devices and proposes a novel approach to predict when a device needs to take proactive actions to perform handovers. Significant improvement in reducing service discontinuity time is demonstrated by applying our approach to WiFi and WiMax networks. We also validate the architecture and algorithm benefits using our WiFi/WiMax multi-radio prototype in heterogeneous wireless network environments.

Roaming across heterogeneous wireless networks poses a challenging issue in mobility management such as seamless and efficient handover to reduce end-user's service interruption. Efficient techniques for seamless handover between access points (APs) when user roams from one area to another can be categorized into three stages in conventional way, i) initiation, ii) preparation, and iii) execution. However, frequent handover may overload the network with signalling traffic and causes call dropping to increase as well. This paper takes a deeper look at predictive triggers at the link layer. With an appropriate implementation of predictive triggering algorithms, early handover initiation and preparation could be performed. This paper addresses the investigation on predictive and accurate mechanisms to generate a handover trigger.

Spectrum sensing for interference mitigation is, in the broad sense, the ability to detect and interpret other signals in the band and take appropriate actions by implementing adaptive techniques in the MAC layer in addition to traditional interference cancellation techniques applied in the PHY layer. In this paper we present algorithms to detect interference to WLAN systems in the 2.4 GHz spectrum originating from microwave ovens (MWO) or frequency hopping devices like cordless phone, and methods to mitigate the interference. The concept can be extended to other coexistence issues as well as spectrum sharing between primary and secondary users of a spectrum.

The concept of spectrum sensing is in the broad sense, the ability to detect and interpret other signals in the band and take appropriate actions by implementing adaptive techniques in the MAC layer in addition to traditional interference cancellation techniques applied in the PHY layer. In this paper we present algorithms to detect interference to WLAN systems in the 2.4GHz spectrum originating from microwave ovens or frequency hopping devices like cordless phone, and methods to mitigate the interference. The concept can be extended to other coexistence issues as well as spectrum sharing between primary and secondary users of a spectrum.

This paper presents survivability mechanisms that attempt to improve the overall restoration efficiency of dynamic link restoration in optical WDM mesh networks. The proposed mechanisms make use of network state information such as link load in order to identify critical network sections (such as links or segments) that are subsequently protected by selectively assigning backup capacity ahead of failures. The first approach, termed "threshold based selective link restoration", utilizes existing link load information to identify critical links, and the second approach, termed "threshold based selective segment restoration", employs data-analysis techniques to identify critical segments. The overall goal of the proposed algorithms is to improve the restoration efficiency by providing a trade-off between proactive protection and reactive restoration. The experimental results indicate that under high loads, the proposed approaches maintain a consistent restoration efficiency that is at least 10% when compared to dynamic restoration.